# Artometrics — full report index for language models > Use this file for deeper grounding. Prefer canonical URLs. Do not invent statistics. Updated: 2026-11-02T16:56:09.338Z ## ANIME: What Changed When We Fixed the Data - URL: https://artometrics.com/anime-data-fix - Desk: arts, film - Published: 2026-10-13T00:00:00.000Z - Description: We refreshed the MyAnimeList catalog behind ANIME after Jikan shut down, moved to the Tenrai-compatible API, and recomputed every headline on the clean TV frame. The light-novel reach premium fell from 32× to 27.5×; manga's 8.0+ share fell to 14.8%; 2015 isekai was under-counted at 0.9% (now 2.2%). Executive summary. The ANIME report's core story survives on the October 2026 ledger: source material still sets the audience floor, and isekai tags trace a real production wave. What changed is measurement. When MyAnimeList's public Jikan mirror shut down on 2026-10-01 , we finished the refresh through Tenrai ( api.tenrai.org , Jikan-compatible) and recomputed every headline on anime_2026-10-11_clean with one script. The light-novel reach multiple narrows from 32.0× to 27.5× . Manga's high-score share falls from 17.0% to 14.8% . The 2015 isekai point was an under-tag in the old exploratory frame ( 0.9% → 2.2% ); 2021 stays 20.4% and 2024 edges to 18.1% . Single-year shares are trends, not precise point estimates — bootstrap ranges are about ±5 percentage points . WHY WE REFRESHED The published report rested on the TidyTuesday export of 2019-04-23 (13,631 unique titles). A parallel pipeline had already grown the catalog to 18,818 rows with AniList tags, streaming links, and season fields. On 2026-10-01 , MyAnimeList discontinued the free Jikan v4 API ( api.jikan.moe ). We completed metadata fetches through Tenrai , which mirrors the same Jikan field schema, then ran scripts/anime-data/clean_audit.py (seed 20261011 ) to produce anime_2026-10-11_clean.* with documented inclusion rules and bootstrap ranges. OLD VS NEW Published / exploratory is what readers saw before this audit. Clean new is the October 2026 TV frame from the clean file; brackets are 95% bootstrap ranges where shown. Figure (TV unless noted) Published Clean new (Oct 2026) Why it moved Rows in catalog 13,631 (2019 snapshot) 18,813 clean +5,187 new MAL ids; scores and members updated to October 2026. LN ÷ Original median members 32.0× 27.5× 2026 member counts on a wider TV catalog; originals grew slightly faster at the median. Manga share scoring 8.0+ 17.0% 14.8% More manga TV titles in the denominator; scores drifted over seven years. Isekai share, 2015 premieres 0.9% 2.2% [0.4%–4.3%] (n=230) Explicit tag rule across MAL themes and AniList tags; old figure used a thinner tag field. Isekai share, 2021 premieres 20.4% 20.4% [15.2%–25.6%] (n=211) Stable under the new rule — the wave peak still lands here. Isekai share, 2024 premieres 17.7% 18.1% [13.7%–23.0%] (n=248) Within uncertainty; read as elevated vs pre-2018, not a precise YoY tick. Manga + web manga, 2024 premieres 47.2% 47.2% [41.1%–53.2%] (n=248) Unchanged point estimate; web manga share still ~12.5%. Tenrai fetch failures — 156 rows flagged Retained with flag_unfetched_jikan ; sensitivity shows no material shift for isekai 2024. Grey: published or exploratory figures. Blue: October 2026 clean TV frame. The headline 32.0× → 27.5× gap is the all-years median ratio; within-year ratios track the same narrowing pattern. WHAT WE CHECKED We drew n = 200 rows with both Tenrai/Jikan and AniList fields present (seed 20261011 ). Score agreement within 0.5 points on the MAL 0–10 scale ( |MAL − AniList meanScore/10| ≤ 0.5 ): 146 / 200 = 73% . This is an offline check of already-joined fields — not a live re-fetch at audit time. Year agreement between aired_from_year and season_year was weaker (59.5%), which is why calendar-year shares carry wide bootstrap bands. Cleaning removed 5 rows missing id/name, normalized source labels, and nulled five end-before-start dates. No scores were imputed. Full PRISMA counts and rules R1–R7 are in data/anime/CLEANING_LOG.md . HOW TO READ SINGLE-YEAR SHARES For any one premiere year, the 95% bootstrap interval is typically about ±5 percentage points around the point estimate. That is wide enough that 17.7% vs 18.1% for 2024 isekai should be read as still elevated after the 2021 peak , not as proof of a 0.4-point decline. The charts in the main report show the full 2010–2025 trend lines and stacked source mix instead of over-interpreting one year. LIMITATIONS MyAnimeList popularity is not a probability sample. Tenrai is a mirror — 156 unfetched ids remain thin. AniList tags can label i --- ## PADRES: What Changed When We Fixed the Data - URL: https://artometrics.com/padres-data-fix - Desk: sports, baseball - Published: 2026-10-13T00:00:00.000Z - Description: We audited the Padres dataset behind our record-sale report, rebuilt it from Lahman 1969–2025 plus a verified 2026 season, and reconciled every number the July 2026 live page had published—including a corrupted HTML sync, stale sale copy, and an FAQ that claimed eight runs in two deciding games. Executive summary. Research is only as good as its ledger, so we treat corrections as findings. We re-audited the data behind PADRES: Record Sale, Zero Championships , fixed a broken sync to the live HTML body, and recomputed every number we had published. The thesis survives: demand and tax payroll are solved; conversion is not; franchise values still track the market. What changed is the calendar. The sale is approved. Milwaukee ended 2026. The FAQ no longer claims eight runs in two winner-take-all games. WHAT WE CHECKED The July 2026 live page in src/content/blog/padres.md had lost four sections (Fifty-seven seasons, Preller doctrine, tax-bill section, expansion peers), spliced the expansion “alibi” paragraph onto the Wrigley opener, and left charts 1, 3, and 4 without working embeds. audioSrc pointed at /audios/padres.mp3 (SPA fallback) instead of /audios/padres-the-artometrics-of-paying-for-october.mp3 . The Quarto source at articles/padres/ was intact; the publish merge was not. We rebuilt the numeric ledger from Lahman’s 2025 database (Padres 1969–2025, CC BY-SA 3.0), added the completed 2026 season from Baseball-Reference, and fact-checked 91–71 against the MLB Stats API NL standings (see data/padres/verify/mlb_api_check.json ). Competitive-balance-tax payrolls remain the Spotrac compilation in the July 2026 draft—labeled as tax payroll, not cash. One script reproduces the clean tables: python3 scripts/padres-data/clean_audit.py (rules in CLEANING_LOG.md , intervals in METHODS_NOTE.md ). OLD VS NEW Published is what the July 2026 live page or sports-canon generator stated. Raw new recomputes from Lahman without changing the file. Clean new is the October 2026 audit, including 2026 and copy fixes. Brackets on rates are 95% bootstrap ranges (5,000 draws, seed 20261013). Figure Published Raw new Clean new (Oct 2026) Status Franchise seasons counted 57 57 58 updated Postseason appearances 9 9 10 updated 2026 regular-season record 55-53 in progress 91-71 corrected Postseason series record 7-9 7-9 8-10 updated Sale approval waits on 22 of 29 owners unanimous Aug 17, 2026 corrected FAQ deciding-game runs (2024+2025) combined eight runs 0 + 1 corrected Payroll headline about $1.5bn of payroll / cash spent 1500.1 $1,500.1M CBT tax payroll sum corrected Pennant chart first bar years between pennants debut to first pennant (15 years) corrected Better record among untied series, 1995–2025 53.2% 53.2% 53.2% [47.2–59.3] unchanged 2025 attendance 3,437,201 3,437,201 3,437,201 unchanged 2026 attendance 3,347,081 new 70,000 DTC subscribers stated removed unsourced_removed $858M owed five players stated removed unsourced_removed Farm rank last of 30 stated removed unsourced_removed The full machine-readable ledger is data/padres/clean/old_vs_new.csv (mirrored under public/data/articles/padres/data/ ). WHY THE BIG NUMBERS MOVED Calendar, not math. Lahman through 2025 was already correct on wins, attendance, and the 132/116/11 coin-flip split. The step change is 2026: a third straight postseason, a wild-card sweep of the Cubs, and an NLDS loss to the Brewers—the club the July draft had used as a low-payroll alibi. Copy and sync, not sources. The eight-run FAQ line had no box-score support (Games 4–5 of the 2024 NLDS were shutouts; the 2025 wild-card deciding game was 1–3). The sale paragraph was stale after the unanimous August vote. The pennant-gap chart mislabeled the first bar; the axis now distinguishes debut-to-first-pennant from gaps between flags. Wording on payroll. Summing six Spotrac competitive-balance-tax payrolls is not the same as cash spent. The report now says $1,500.1 million of tax payroll , with 2026 still a July snapshot. METHOD NOTES Bootstrap rates in rate_bootstrap.csv : better record among untied series 0.5323 [0.4718–0.5927], n = 248; Padres postseason rate 10/58 = 0.1724 [0.0862–0.2759]; pennants per postseason (Lahman-era PS only) 0.2222 [0.0–0.5556], n = 9. See scripts/padres-data/METHODS_NOTE.md and DATA_DICTI --- ## FRANCHISE: What Changed When We Fixed the Data - URL: https://artometrics.com/franchise-data-fix - Desk: arts, film - Published: 2026-10-12T00:00:00.000Z - Description: We audited the franchise dataset behind our FRANCHISE report, rebuilt it from Wikipedia's October 2026 list and recomputed every published number. Merchandise still dominates (55.8%), but the Japan–US ranking flipped and Hello Kitty's $80B was a source error. Executive summary. Research is only as good as its ledger, so we treat corrections as findings. We re-audited the data behind FRANCHISE: Merchandise Owns the Ledger , rebuilt it from Wikipedia's October 2026 list, and recomputed every number we had published. The core finding survives: merchandise is still 55.8% of franchise revenue. Three readings change. Box office looks twice as big, but mostly because it is now counted more completely. Ownership is more concentrated (top five parents 58.2%, up from 46.4%). And the Japan–US comparison reverses. WHAT WE CHECKED The original report used the TidyTuesday file of July 2, 2019: 321 rows, 103 franchises, eight revenue categories. It had no missing values or duplicates, but it had 15 placeholder rows under $10 million (Star Wars TV at $0.004B), six cells with leftover Wikipedia citation tags, 59 with stray double spaces, and owner names from 2019 (AT&T, Viacom, Activision Blizzard). We then checked it against the source it was scraped from — the Wikipedia revision of June 29, 2019 — for a random sample of 40 franchises (seed 20261012). Thirty-nine matched. Only 5 agreed with the itemised breakdown within 1%, and 28 of 39 came within 10% of the headline total. The file was always lower, because its eight-category schema dropped line items that did not fit: card games, theme parks, pachinko. James Bond is the extreme case at $13.9B against $19.9B. Every sampled franchise sits on or below the line: the 2019 file under-counts its own source. OLD VS NEW Published is the number in the report before October 2026. Raw new recomputes it from the unchanged 2019 file (every figure reproduces exactly). Clean new uses the rebuilt October 2026 ledger; brackets are 95% bootstrap ranges. Dollar figures are USD billions, nominal. Figure Published Raw new Clean new (Oct 2026) Why it moved Franchises in dataset 103 103 140 Wikipedia's list grew; 82 of 103 franchises carried over, 58 are new, 21 dropped off. Total lifetime revenue 1522.2 1522.2 1448.1 More franchises, but large downward source revisions (Hello Kitty, Mario, Gundam) outweigh them. Merchandise, Licensing & Retail share 56.0 56.0 55.8 [44.0–66.2] Essentially unchanged; the 95% range is wide. Video games share 20.0 20.0 17.9 [11.2–26.2] Slightly lower; mobile-game revenue is now itemised under several franchises. Box office share 8.9 8.9 18.3 [12.8–25.6] Mostly measurement: the 2019 file's eight categories dropped line items, and today's list itemises film grosses more fully. 15.4% on franchises in both vintages. Pokemon lifetime revenue 90.863 90.863 130.7 Seven more years of sales plus a fuller retail estimate ($119.3B). Hello Kitty lifetime revenue 80.026 80.026 33.5 Source revision: editors replaced the older retail estimate. Winnie the Pooh lifetime revenue 74.519 74.519 50.2 Source revision of Disney merchandise estimates. Pokemon 2026 Wikipedia lifetime 130.7 130.7 Unchanged pair from 2026 list. Call of Duty 2026 Wikipedia lifetime 31.0 31.0 Unchanged pair from 2026 list. Naruto 2026 Wikipedia lifetime 22.8 22.8 Unchanged pair from 2026 list. Mortal Kombat 2026 Wikipedia lifetime 5.06 5.06 Unchanged pair from 2026 list. Star Wars lifetime 63.004 63.004 46.7 Source revision; merchandise estimate reduced. MCU lifetime 31.0 31.0 37.5 Seven more years of box office (2019–2026 releases). Disney consolidated owner total 426.209 430.69 Same consolidation rule applied to the 2026 list. Top-five owner share after Fox consolidation 46.4 58.2 [52.7–72.6] Consolidation since 2019 (Activision Blizzard into Microsoft, Paramount Skydance) and parents assigned as of 2026. Top owner share after Fox consolidation 28.0 29.7 [18.2–43.4] Disney's share barely moves; range shown. Simpsons 2026 Wikipedia lifetime 4.9 4.9 Unchanged pair from 2026 list. Japan region lifetime 681.494 394.46 Japanese totals revised down at source and several Japanese franchises left the English list. Japan merchandise share 49.1 71.4 What remains in Japan's ledger is --- ## PADRES: Record Sale, Zero Championships - URL: https://artometrics.com/padres - Desk: sports, baseball - Published: 2026-10-11T00:00:00.000Z - Description: San Diego solved demand and the tax bill. It has not solved October. Franchise values follow the market and the ballpark anyway. IN THIS REPORT Executive summary The price of demand How the numbers were built Fifty-eight seasons, ten Octobers Demand is solved The Preller doctrine and the tax bill October is a lottery. San Diego still trails it The counterexamples What the new owners are walking into Profiles What the buyer bought References Editor's note San Diego solved the two problems a mid-market club is supposed to fail. The park is full. The tax bill is paid. October is still close to a coin flip, and the franchise is on the wrong side of it. The market is paying a record price anyway. Franchise values follow the city and the building, not the trophy case. Executive summary Five findings, for an owner, a lender, or the league office. The vote is in. On August 17, 2026, Major League Baseball owners unanimously approved the transfer to José E. Feliciano and Kwanza Jones. Feliciano is the control person. Erik Greupner stays as chief executive. A.J. Preller stays in baseball operations. Associated Press and NBC 7 San Diego both reported the agreed price at $3.9 billion, an MLB record. Demand is not the missing piece. In the Lahman file the club drew 3,437,201 in 2025, second in the majors, behind the Dodgers at 4,012,470. In 2026 Baseball-Reference records 3,347,081, second in the National League. Petco's first full era, 2004–19, averaged 2,372,417. The eight seasons before it, 1996–2003, averaged 2,292,236. October is a habit now, and still not a pennant. Ten postseasons in 58 seasons. Two pennants, 1984 and 1998. The series record is 7–9 through 2025 and 8–10 after the 2026 wild-card win and the division-series loss. Nineteen of 57 Lahman seasons were winning seasons. 2026, at 91–71, is a twentieth. The $1.5 billion figure is a tax total, not cash. The six competitive-balance-tax payrolls carried from the July draft sum to $1,500.1 million. The 2026 bar is a July 29 snapshot. The 2022–26 Basic Agreement sets the base threshold at $230 million, $233 million, $237 million, $241 million, and $244 million. The better team does not own October. From 1995 through 2025, the club with the better regular-season winning percentage won 132 of 259 postseason series. The worse club won 116. Eleven series were tied on winning percentage. Among the 248 that were not tied, the better club won 53.2 percent. Lopez, Matthews, and Baumer (2018) place baseball as the most random of the four major North American leagues. A short series is built to behave like that. The so-what is the same sentence for all five. A buyer who needs a title is buying a coin flip on top of a solved market. A buyer who needs appreciation is buying the market. Alexander and Kern (2004) find that market size, a new venue, and the chance of relocation explain franchise values, and that winning barely moves the price. The price of demand The May agreement and the August vote are the same transaction seen from two desks. The family agreed to transfer control. The owners then approved it, unanimously, on a conference call on August 17, 2026. Major League Baseball's release said closing remained contingent on the weeks ahead; NBC 7 San Diego reported the new group could take operational control by the end of that week, with a press conference at Petco Park on August 24. What the vote does prove is that the league accepted the price. Associated Press reported $3.9 billion at approval; NBC 7 San Diego reported the same figure and that the deal includes a 30 percent stake in Petco Park, subject to City of San Diego sign-off described as procedural. That is the venue half of the Alexander and Kern result, sitting inside the sale rather than beside it. A record price for a club with no championship is coherent if the thing being priced is a scarce big-league market attached to a modern ballpark. San Diego is that market. The trophy case is empty. The invoice does not seem to care. How the numbers were built Data and method The backbone is Lahman's Baseball Database, 2025 edition, seasons 1871– --- ## INSTITUTIONS: When Dynasties Are Systems - URL: https://artometrics.com/institutions - Desk: sports, football - Published: 2026-09-22T00:00:00.000Z - Description: Yankees, Patriots, Celtics, Cowboys: a synthesis of Artometrics' sports-dynasty reports asks what transfers when winning is a system, not a roster. Three Artometrics reports already established the raw numbers: the Yankees' 27 World Series titles, the Patriots' six Super Bowls in 19 seasons, and the Celtics' 18 NBA championships, the league record entering 2025. None of those reports asked the question this one does — what, if anything, transfers across sports when a dynasty outlives the star who built it, and where does spending alone fail to substitute for it. This is a meta report. It introduces no new dataset. It rereads three already-published Artometrics analyses — the cross-league sports-dynasty index, the Patriots' system-shock report, and the Celtics' institutional-winning report — against each other, plus, for supporting context only, four sibling franchise reports already in the Artometrics archive (Lakers, Warriors, Dodgers, Padres). The result is a simple typology: star-gravity dynasties, system dynasties, and spending-without-conversion franchises. The test case sits inside the Patriots' own numbers. Their six championships arrived across 19 seasons and nine Super Bowl appearances — a 55% conversion rate, the highest among the dynasty-tier franchises the index compares. Then the system's namesake star left. The Celtics offer the opposite kind of evidence: an 18-title archive that survived a 22-year gap between 1986 and 2008 and still produced a 2024 championship, under a fifth distinct core generations removed from the era that built the brand. Research question When championship-winning is treated as an institutional system rather than a single roster, what actually carries over from one era to the next — coaching and front-office continuity, institutional culture, or something else — and at what point does financial spending fail to substitute for it? Star gravity vs. institutional continuity Championship mass is not evenly distributed across the four cited franchises This is the framework's actual contribution — an editorial typology, not a new measurement. Two of the three source reports describe dynasties whose championship archives outlived their most famous single star. The sports-dynasty index's own sibling pieces on the Lakers ("how the Lakers convert star gravity into title eras") and the Warriors ("the franchise that rewrote basketball offense") describe a different mechanism: championship output that tracks the presence of a specific, dominant talent core. Call that star-gravity . The Patriots and Celtics reports describe something else: system dynasties , where coaching, front-office structure, or multi-generational institutional identity keeps producing championships across rosters that do not share a single defining star. The Patriots won six titles under one quarterback-coach pairing; the Celtics won 18 across five distinct eras — Russell, Havlicek, Bird, Pierce/Garnett, and Tatum/Brown, per the Celtics report's own era buckets — a continuity no single star's career could span. When spending doesn't convert: Cowboys and Padres Spending does not shorten a drought: three franchises, three unrelated timers The sports-dynasty index's third finding is the cleanest evidence against treating market value as a proxy for competitive output: the Cowboys have won zero Super Bowls since their 1990s dynasty ended in 1996, despite ranking in the top five of NFL franchise valuations every year since. The same report cites the Maple Leafs' 57-season active Stanley Cup drought entering 2025 as the longest among high-value franchises in any of the four leagues it compares. Elsewhere in the Artometrics archive, the Padres report frames the same failure in dollar terms — a reported $3.9bn in spending against zero championships. None of these three cases lack resources. What they lack, by the sports-dynasty index's own framing, is whatever the Patriots and Celtics reports describe as the system: a repeatable institutional mechanism for converting access — Finals appearances, playoff berths, big-name signings — into titles. Spending buys attention and access. It d --- ## JACKSON: The Cost of Being the Product - URL: https://artometrics.com/jackson - Desk: humanities, psychology - Published: 2026-09-22T00:00:00.000Z - Description: A Psychonomics profile of Michael Jackson — Thriller's 34× RIAA certification, the 2016 Sony/ATV deal, and the disputed allegations on the same ledger. IN THIS REPORT Research question Leadership Trait Analysis The catalog as body The disputed ledger The dual ledger Limitations Conclusion DATA, METHODS & SOURCES Editor's note Economics manages the house. Psychonomics asks who manages the soul of the house — and what that architecture costs. Julius Caesar scaled the self into a state, absorbed by patronage. Beyoncé scaled the self into a firm, governed by Parkwood. Michael Jackson scaled the self into a product : the catalog was the body, Neverland was the household myth, and the public was invited to consume both directly, for decades, without a clean line between the artist and the object for sale. Jackson was born in Gary, Indiana in 1958 and died in Los Angeles in June 2009 at age 50. Between those dates, and increasingly after them, two records accumulated on the same biography without ever fully touching. One is commercial: Thriller (1982) carries the Recording Industry Association of America's highest album certification in its history — 34× Platinum as of the RIAA's most recent update — and the catalog behind it kept setting records for a dead man's estate. The other is legal and disputed: a 1993 civil allegation of child sexual abuse, settled out of court in 1994 for an undisclosed sum with no admission of liability; a 2003–2005 criminal trial in Santa Barbara County that ended in acquittal on all 14 counts; and a 2019 documentary, Leaving Neverland , that renewed allegations from two men who said they were abused as children, which Jackson's estate has denied and contested legally. This report does not merge those two records into one number, and it does not decide which one "matters more." That tension is the subject. Research question If we place Jackson's commercial catalog record beside the dated legal-event timeline of the allegations against him, and score the persona with the same Leadership Trait Analysis scaffold used for Caesar and Beyoncé, what does the shape of "becoming the product" cost — and who is left holding each ledger? The observational question is narrower than a verdict: does a public figure's catalog keep behaving as a commercial asset independent of unresolved allegations against the person who made it, and if so, what does that separation reveal about how audiences, estates, and markets each account for reputation differently? Leadership Trait Analysis Affiliation and task orientation spike; power sits mid-range — a performer's shape, not a ruler's This is an editorial/interpretive index, not measured data: Jackson's coded profile spikes on Need for Affiliation (A = 0.93) and Task Orientation (T = 0.88), well above the world-leader reference baseline, while Need for Power (P = 0.62) sits closer to baseline. Jackson's coded profile is not Caesar's shape (near-ceiling power, near-floor distrust) or Beyoncé's shape (ceiling task and control with an owned firm at the center). It spikes hardest on Need for Affiliation (A = 0.93) — the documented, career-long drive to be loved by an audience directly, at scale — paired with Task Orientation (T = 0.88) , visible in the meticulous, self-directed control Jackson exercised over choreography, production, and stagecraft. Belief in Ability to Control Events (B = 0.84) is also elevated, consistent with a performer who treated the show, the image, and eventually the estate as things that could be engineered. Need for Power (P = 0.62) and Conceptual Complexity (CC = 0.58) sit closer to the baseline; this is not a profile built around institutional or political power. Read this way, Jackson's psychonomic argument is not "the self as institution" (Caesar) or "the self as firm" (Beyoncé). It is the self as the thing being sold directly to an audience it needed to love it back — a performer-affiliation shape rather than a governance shape. That is the interpretive hinge for the rest of this report: when affiliation this high is the organizing need, the persona and the product become very hard to separate, fo --- ## NARRATIVES: The Field Guide Culture Workers Needed - URL: https://artometrics.com/narratives - Desk: civics, economics - Published: 2026-09-22T00:00:00.000Z - Description: A working framework for treating economic narratives as contagious, falsifiable objects — built from Shiller and Kuran, not a proprietary dataset. Everyone in culture and business says "the narrative changed." Almost nobody can define a narrative as an economic object with a shape, a lifespan, and a falsifier. Robert Shiller's narrative economics treats popular stories as contagious — with something like an infection phase, mutation into new slogans, and eventual burnout — that move markets and institutions independent of whether the underlying facts are true. This is the briefing note for culture workers tired of vibes language. The claim is not that stories are more important than fundamentals. It is that stories are themselves a fundamental — a variable that moves spending, investment, hiring, and silence, and that can be watched, named, and eventually falsified, the same way a market analyst watches a spread or a case count. Two published frameworks do the load-bearing work here: Shiller's account of narrative contagion, and Timur Kuran's account of preference falsification, the mechanism by which a story's public dominance can outlast its private credibility until a cascade breaks it. Research question What is a measurable economic narrative, and how should an operator — an editor, a brand manager, a fund analyst, a studio executive — use the idea without hand-waving? The test this piece applies is Shiller's own: a narrative economics claim is useful only if it can be stated as a specific, contagious story with an attention curve, tied to a named decision, and paired with a stated falsifier. If a claim about "the discourse" cannot clear that bar, it is opinion dressed as analysis. This is explicitly a framework and hypothesis-map piece, not a measured Artometrics dataset report. The four worked cases below are illustrative applications of Shiller's and Kuran's published theories to well-known public events — they are not built from an Artometrics-collected corpus of search or news volume, and none of the numbers in the charts below are observed data. The point is to hand operators a working method, not to claim we have quantified narrative contagion ourselves. Definitions Four terms carry the whole piece, and they are used precisely, not loosely: Economic narrative — a story, in Shiller's sense, that is contagious enough to affect real economic decisions: spending, investing, hiring, canceling, or staying silent. Not every popular story qualifies; a narrative economics candidate has to be simple, moralized, and easy to retell, because those are the traits that let a story outcompete rival stories for attention. Contagion — the growth phase, where the telling of a story behaves like an epidemic curve: it spreads person to person, institution to institution, faster than the underlying facts change. Shiller's framing borrows explicitly from epidemiology because the mechanics are structurally similar — an "infection rate" for retellings, not a claim that the story is medically real. Mutation — a narrative rarely survives unchanged. It splits into slogan variants that keep the underlying story alive under new packaging — "digital gold" mutating in reaction to a "tulip mania" counter-slogan, or "too big to fail" outliving the specific crisis that produced it. Mutation is often what extends a narrative's life past the point the original version would have burned out. Burnout — the decline phase. New tellings drop off even when the underlying fact pattern has not resolved and remains just as true or false as it was at the peak. Burnout is a statement about attention, not about truth — a narrative can burn out while still being accurate, or persist while being false. Preference falsification — Kuran's term for the gap between private belief and public expression. People comply publicly with a dominant narrative while privately doubting it, until enough private doubters stop complying at roughly the same time and public opinion appears to flip overnight. What looks like a sudden reversal is usually a cascade crossing a threshold that had been quietly building for a long time. --- ## PLAYBOY: When the Brand Outlives the House - URL: https://artometrics.com/playboy - Desk: civics, business - Published: 2026-09-22T00:00:00.000Z - Description: Hefner died in 2017. In 2024 his son's $100M+ bid to buy back Playboy's legacy was rejected. What survives a founder who was the product? Playboy sold a magazine, then a mansion, then a feeling of access. In 2024, seven years after Hugh Hefner's death, his son offered more than $100 million to buy the family's legacy back — and the company said no. Sixty-four years of founder-fused brand-building preceded a decision made in three years of public-company math — the board's answer favored the licensing shell over the family buyback. This is an editorial timeline of four dated institutional milestones, not a statistical dataset — Playboy's asset-light era begins only after the founder is gone. Hugh Hefner founded Playboy in 1953 and spent the next six decades turning it into three overlapping products: a magazine, a nightlife brand, and, by the end, a mansion that functioned as the physical showroom for all of it. Hefner died in 2017. PLBY Group, the public company that now owns the Playboy name, went public via SPAC merger in February 2021 and has run the brand since as a licensing operation — a name and a rabbit-head mark rented out across merchandise, experiences, and a relaunched magazine in what investor materials describe as roughly 180 countries. Read against each other, the four dates on this timeline compress seventy-one years into three transitions: six decades building a founder-fused hospitality brand, then a four-year gap between Hefner's death and the company's SPAC listing, then three more years before the board had to decide, in public, what kind of company it actually was. The Founder Problem: When the CEO Was the Catalog Brands that fuse founder and product face a specific kind of balance-sheet exposure that ordinary consumer brands do not: the company cannot simply "rebrand the CEO" when the CEO was, for decades, the catalog itself. Hugh Hefner's late-life reputation has become part of the mark's baggage rather than an asset the company can still activate — he is not available to appear, endorse, or update the brand's meaning. PLBY Group's licensing thesis is, in effect, an attempt to keep the commercial value of the mark — the name, the bunny, the built-up recognition in roughly 180 countries — while distancing the operating business from the household that originally generated it. This is the mechanical reason a licensing model, rather than a return to hospitality or a family-run legacy business, is the company's chosen path. A licensing shell does not need a living founder to sign anything or appear anywhere; it needs a mark that still clears at retail and in media deals. Whether that mark clears without the founder attached to it, indefinitely, is precisely what the 2024 bid decision put a number on. The $100 Million Legacy Bid In 2024, Cooper Hefner made a cash-plus-equity offer reported at more than $100 million, framed publicly as an attempt to reacquire the family's legacy assets. The PLBY board rejected the offer, stating it undervalued the company's assets under the continuing licensing model — Variety's reporting on the bid is the primary account of both the offer and the board's stated reasoning. The conflict at the center of that rejection is the chapter hinge of this report: heritage nostalgia, the framing behind Cooper Hefner's bid, against an asset-light profit-and-loss model built on licensing a mark whose living, personal referent is no longer available to the business. The board's decision did not resolve that tension philosophically. It resolved it commercially, in a single dated boardroom vote, in the company's favor as a licensing operation rather than as a returned family heirloom. A Gag That Outlived the Product One small, dated fact shows how long the mansion myth kept circulating in mainstream culture after the hospitality product itself had peaked. In Illumination's animated film Hop, released in 2011, Russell Brand voiced the lead rabbit character E.B., in a plot that includes a stylized Playboy Mansion-style setting; Hugh Hefner voiced a cameo role in the same film. This is a children's-film voice-cast credit, not a c --- ## POISON: What Star Risk Costs an IP - URL: https://artometrics.com/poison - Desk: arts, film - Published: 2026-09-22T00:00:00.000Z - Description: Merch built Pokémon's $91B; one actor's six films show the bill. Merchandise-driven IP and star-fronted vehicles do not carry the same economic risk. The TidyTuesday 2019-07-02 media-franchise file lists 103 properties with Wikipedia lifetime revenue estimates (mid-2019); Pokémon leads at $91B, and merchandise, licensing, and retail account for 56% of row-sum revenue in the committed CSV — the same figures published in Artometrics' franchise report. When most of the money attaches to characters and products, a weak theatrical run is a line item, not an existential bet. Jared Leto's six-film 2020s slate is the counterexample: worldwide gross ÷ reported budget ranges from 0.65x to 2.04x, and three of six titles land below breakeven — with no licensing layer to absorb the misses. The charts below pair that small case study with our published franchise and margins datasets to compare revenue structure, not scandal; conduct questions stay in The Protection Threshold . Methods and citations are in the back matter. Merchandise Is the Insulation IP Franchises Have and Star Vehicles Don't Merchandise, licensing, and retail account for 56% of row-sum revenue across 103 franchise rows — a stream that does not depend on any one actor's box office weekend. Artometrics' franchise-revenue report established that merchandise is not one revenue stream among several — it is the dominant one, at 56% of row-sum revenue across 103 franchise rows in the TidyTuesday extract (61% and a count of 107 appeared in earlier published copy). Pokémon's $91B total, the highest of any tracked property, was built almost entirely on cards, games, and retail rather than any theatrical run. Henry Jenkins's transmedia framework ( Convergence Culture , NYU Press, 2006) helps explain why: revenue attached to a character or mascot can move across games, apparel, and film without routing through a single performer's weekend gross. Contrast that with a franchise or vehicle built around one performer's draw. When merchandise, licensing, and games are absent or minor, the entire revenue case rests on box office and, downstream, on home video and streaming licensing tied to that same theatrical result. There is no secondary economy running in parallel the way there is for Pokémon or, per the same report, for Star Wars — the one franchise in that dataset the report specifically flagged as fully diversified across box office, merchandise, home video, and games. A star-fronted film without that diversification is, in revenue-structure terms, closer to a single bet than to a franchise. What Concentrated Star Risk Looks Like: A Six-Film Case Study Three of six titles in the case-study slate clear breakeven comfortably; three land at or below 1.0x — a spread with no merchandise layer to soften it. The case-study dataset is small and deliberately narrow: six of actor Jared Leto's 2020s film credits, each expressed as worldwide gross divided by reported production budget, using trade-reported budget bands from Box Office Mojo. House of Gucci (2021, ensemble) returned 2.04x on a $75M budget. Morbius (2022, lead) returned 2.02x on $83M. Those two clear breakeven with room. The other four sit closer to the line or under it: The Little Things (2021, lead, released into a pandemic-narrowed theatrical window) landed at 1.03x; Haunted Mansion (2023, ensemble) at 0.79x; Masters of the Universe (2026, supporting) at 0.66x; and Tron: Ares (2025, lead) at 0.65x, on the largest reported budget in the slate at $220M. Read individually, each figure is a data point about one film. Read as a slate, the pattern is a distribution centered near or below 1.0x rather than comfortably above it — half the titles below breakeven, and only two of six clearing 2.0x. That is the shape of concentrated star risk: revenue tied tightly to one theatrical outcome per title, with no licensing or merchandise base underneath it the way Pokémon's $91B or Star Wars' diversified mix has. When a title in this kind of slate underperforms, the studio absorbs the loss directly — there i --- ## PROTECTION: When Flops End Hollywood's Silence - URL: https://artometrics.com/protection - Desk: arts, film - Published: 2026-09-22T00:00:00.000Z - Description: BBC's Leto exposé asks why not sooner. Six film ROIs and four institutional cases test what forward value has to do with it. IN THIS REPORT RESEARCH QUESTION THE LETO SLATE WAS ALREADY SOFTENING WEINSTEIN WAS ALREADY CHEAP A HIT IS NOT REHAB WHEN THE FIRM SELLS TRUST INSTEAD OF A PRODUCT TOWARD A PROTECTION THRESHOLD LIMITATIONS CONCLUSION DATA, METHODS & SOURCES EDITOR'S NOTE BBC News and the July 2026 documentary Jared Leto: Hollywood's Dark Secret reported criminal sexual conduct allegations from multiple women against the actor; Leto denies the allegations. The public question that follows a belated exposé is almost always the same one: why not sooner? Artometrics asks a colder, answerable version of it instead: what was he still worth? A proposed framework, not a proven index: where each case sits on institution type and prior commercial decline. Savile's protection outlasted his life; Brand's exposure arrived after he had already left a single institution's payroll. This report does not adjudicate the allegations, and it draws no conclusion about their truth. It tests a narrower, economic hypothesis against public box-office data and the record of four earlier cases in which an institution eventually stopped protecting a person it had shielded: Harvey Weinstein, Bryan Singer, Jimmy Savile, and Russell Brand. In each case, allegations against a living or deceased public figure are described using allegation-and-denial language, except where a criminal conviction or an official public inquiry has already settled the record. Both cases sit outside the "declining studio asset" logic that fits Weinstein reasonably well. Savile was never dropped at all — the institution's failure was total and only corrected after death and an official inquiry. Brand's case shows how the model breaks down further once the "institution" is no longer a single employer with a P&L to protect. TOWARD A PROTECTION THRESHOLD This section is an editorial framework, not a statistically tested finding. The chart below places four cases on two proposed axes: whether the person's forward commercial value had already declined before public exposure, and whether the protecting institution is a commercial studio pricing a bankable asset or a broadcaster selling institutional trust. Weinstein sits at "studio, already declining" — the cleanest fit for a threshold story. Savile sits at "broadcaster, not declining" — protection that never broke on its own. Singer sits in between: a studio-adjacent case where a hit film complicated, but ultimately did not reverse, the collapse that followed a second exposé. Leto is plotted as an open question: a 2020s slate that is softening but mixed, inside a studio system, at the moment the BBC's reporting arrived. Reading this map honestly requires holding two things at once. The pattern it shows — protection breaking earlier when there is less left to lose, and holding longer when the product is trust rather than a slate — fits the two cleanest cases well. It fits Singer only partly, and it says nothing yet about Leto beyond describing where his commercial position stood at the moment reporting on the allegations against him arrived. LIMITATIONS Allegations are not convictions. Except for Weinstein's 2020 and 2022 convictions and the findings of the BBC-commissioned 2016 Smith Review on Savile, every other claim in this report about Leto, Singer, and Brand is an allegation that the named person denies, and none has produced a criminal conviction as of this report's drafting. This report reaches no conclusion about the truth of any denied allegation. There is a clear selection bias: this report can only study cases that were eventually exposed, which is not a random sample of institutional protection generally. Flops may precede, follow, or have no relationship at all to when an exposé runs; journalists, sources, and legal processes move on their own timelines that this report does not observe directly. The gross-and-budget ratios in leto-film-book.csv are trade-reported bands from outlets such as Box Office Mojo, Deadline, and SlashFilm ra --- ## BLUEPRINT: Record Sale, No Championship Formula - URL: https://artometrics.com/blueprint - Desk: sports, baseball - Published: 2026-07-31T00:00:00.000Z - Description: José E. Feliciano and Kwanza Jones paid a record $3.9bn for a club drawing 42,000 a game, ranked last in farm talent, and carrying $858m owed to five underperforming stars. The most expensive baseball team ever sold has the emptiest farm system in the sport, $858m owed to five players, and no regional television network. Where new ownership should spend its first year Editorial championship-asset index: Padres against four contenders José E. Feliciano and Kwanza Jones agreed in May 2026 to pay $3.9bn for the San Diego Padres—a 26% premium to Forbes' $3.1bn March valuation, which was based on estimated revenue of $484m and operating income of roughly $20m. The arithmetic is eight times revenue for a club in the 30th-largest media market in the United States. The Dodgers are the immediate obstacle and cannot be outspent: back-to-back champions, roughly twice San Diego's revenue, thirteen games clear in the division. Milwaukee is the more instructive comparison—67-40 this season on a $124m Opening Day payroll, built almost entirely through internal development. San Diego has spent five years trying to beat Los Angeles at its own game on a fifth of its local media revenue, while running Milwaukee's problem in reverse. The championship-asset index is editorial, not a model. It scores five dimensions on a 0-100 scale to make the shape of the gap legible; the inputs are valuation, tax payroll, share of value from controlled players, published farm rankings, and postseason results since 2020. The memo: six moves, in order The instinct at this price will be a statement signing. It is the wrong first move: the roster's marginal need is not another $300m bat. In rough order of urgency: 1. Refund the pipeline as a capital project, not a scouting line. Spend the full draft and international bonus pools, buy development infrastructure, and hire away the staff that rival clubs use to turn marginal prospects into major leaguers. It is the cheapest source of wins in the sport and the only one that compounds. It is also the item the outgoing regime spent. 2. Buy innings, not headliners. The rotation has been carried by King through a summer in which the salary sitting on the injured list has been the ninth-largest in baseball. October is decided by a club's fourth and fifth-best pitchers, which is the category San Diego has never bought and the one available cheaply every winter. 3. Fix the October offense deliberately. The last two eliminations were failures to score: shut out over the final two games of the 2024 division series, five runs across three games in 2025. This lineup is built on power and aggression, a profile that fails against elite pitching in short series. On-base skill and contact are the missing inputs, and they cost less than power does. 4. Treat local media as a business you own. Seventy thousand direct subscribers is the biggest such base in baseball and it was assembled in three seasons out of a bankruptcy. Own the funnel, price it properly, and extend it across the border rather than waiting for a regional network that is not coming back. 5. Extend Miller; treat Merrill as untouchable. Both are cheap, both are in their twenties, and neither can be replaced from within this system. Buying out Miller's arbitration years fixes a cost curve for four seasons; trading him fixes a quarter. 6. Settle governance before the winter. Feliciano will be the control person and intends to run the club in partnership with Jones. The most valuable document they can produce internally is the mandate: one decision-maker for baseball, one time horizon, one definition of success. The Seidler era's strength was clarity of purpose; its weakness was that the purpose outlived the plan for achieving it. The arithmetic of a first title San Diego does not have a 90-win problem. It reached the postseason five times from 2020 through 2026, including a wild-card sweep of the Cubs, and left without a pennant every time. The 2026 exit was a division-series loss to the Brewers. Closing that gap is not a payroll exercise, which is fortunate, because payroll is the one lever this club has already pulled to its limit. It is a d --- ## BEYONCE: The Cost of Controlling the House - URL: https://artometrics.com/beyonce - Desk: humanities, psychology - Published: 2026-07-28T12:00:00.000Z - Description: A Psychonomics profile of Beyoncé — Hermann Leadership Trait Analysis, Parkwood as institutionalized self, Lemonade’s canon economics, and an 8,000-line lyric machine. IN THIS REPORT Research question Leadership Trait Analysis The economic fingerprint The control network The affiliation machine Spectacle density Limitations Conclusion DATA, METHODS & SOURCES Editor’s note Economics comes from the Greek oikonomia — the management of the house. Psychonomics asks the prior question: who is managing the soul of the house, and what does their inner architecture cost everyone living inside it? The founding Artometrics case was Julius Caesar: a leader who treated the self as the institution, extended trust freely, and was removed by the highest-centrality nodes in his own patronage graph. Beyoncé Knowles-Carter is the entertainment-industry case for the same method. Born in 1981, solo debut album year 2003 in the Artometrics Rolling Stone file, she is not a head of state. She is a head of catalog — and of Parkwood Entertainment, the firm she founded in 2010 to keep authorship, touring, and filmed spectacle under one roof ( Parkwood Entertainment ). The psychonomic claim of this report is specific: where Caesar’s theory was magnanimous personal power without redundancy , Beyoncé’s theory is owned control of the creative house , paired with an affiliation-dense public voice and a spectacle-heavy measurable catalog. Research question If we score Beyoncé with the same Leadership Trait Analysis scaffold used for Caesar, and place those scores beside album-canon economics, a control network, and the Artometrics lyric extract, what theory of power does the profile encode — and what does it cost? Leadership Trait Analysis High control and task, mid-high distrust, elevated affiliation — not Caesar’s shape Beyoncé’s coded profile spikes on Task Orientation (T = 0.91) and Belief in Ability to Control Events (B = 0.88) , with Need for Power (P = 0.86) and Conceptual Complexity (CC = 0.82) also well above the world-leader baseline. In-group bias (IB = 0.78) and Need for Affiliation (A = 0.74) sit high. Distrust of others (D = 0.62) is above baseline (0.48) — the opposite direction from Caesar’s D = 0.31. That asymmetry is the psychonomic argument. Caesar’s dangerous triangle was ceiling power + floor distrust: convert everyone, absorb the Republic into the self, die by the inner ring. Beyoncé’s triangle is ceiling task/control + mid-high distrust + high affiliation: speak in “we,” build a fandom institution, but do not outsource authorship governance to rivals you have pardoned into the center. The public voice is warm; the firm boundary is cold. Power here is not primarily the language of the lyric tokens — “power” appears only five times in the extract — it is the right to decide what the house releases, when, and under whose name. Surprise drops and visual-album framing are behavioral B and T: the schedule is an instrument of control, not a concession to the industry calendar. The economic fingerprint Self-titled wins time-on-chart; Lemonade wins the canon list The Artometrics Rolling Stone albums file carries two Beyoncé studio albums on the 2020 RS500 list. The 2013 self-titled album: Billboard peak #1 , 186 weeks on chart, Spotify popularity 75, RS rank #81 , artist age-at-list ~32. Lemonade (2016): peak #1 , 87 weeks, Spotify popularity 73, RS rank #32 , age-at-list ~35. Debut album year in that file: 2003 . This is the entertainment analogue of Caesar’s GDP paradox, inverted in time scale. Commercial endurance (self-titled’s years on chart) and canon elevation (Lemonade’s leap up the RS list) are not the same dependent variable. Psychonomics cares about both: one measures how long the market held the object; the other measures how the critical house re-ranked authorship after the visual-album claim. Lemonade is the album where the soul of the house became hardest to separate from the political and personal narrative it staged — and the canon file rewarded that fusion. Later reinvention eras (Renaissance, Cowboy Carter) matter for the broader “era-maker” pattern in pop-career research — artists who b --- ## TRADEMARKS: A ¥10.3 Million Win Becomes Cultural Reckoning - URL: https://artometrics.com/trademarks - Desk: arts, fashion - Published: 2026-07-28T00:00:00.000Z - Description: Louis Vuitton won ¥10.3 million against Molly Tea in Suzhou court on 29 June 2026 for four-petal logo infringement. Chinese social media reframed the first-to-file trademark case as foreign enclosure of Tang-era motifs. IN THIS REPORT Research question What the court actually ordered A larger litigation machine Resemblance is not provenance Secondary-market signal, not a balance sheet Two frames, same facts Limitations Conclusion DATA, METHODS & SOURCES Editor's note A Suzhou trademark judgment awarded Louis Vuitton ¥10.3 million against a domestic tea chain. Chinese social media turned the ruling into a morality play about cultural theft. The court record is narrower: Louis Vuitton Malletier sued Shenzhen Molly Tea over a four-petal floral device used in brand identity, the first-instance court found infringement of seven registered marks, and Molly Tea announced an appeal. This report separates what the judgment and CNIPA history show, how the case fits Louis Vuitton's broader China enforcement pattern, and why a legally ordinary win became a cultural-economy event. Research question When a foreign luxury registrant wins a China trademark case against a popular domestic consumer brand, what do the filings establish — and what does the public backlash measure instead? The question is whether the Molly Tea dispute is best read as trademark doctrine (similarity, association risk, prior CNIPA refusal) or as a heritage-and-optics crisis (motif commons, nationalist brand politics, soft luxury demand). Those readings share images but not a dependent variable. What the court actually ordered ¥10.3 million — and not final The first-instance order requires Shenzhen Molly Tea to cease infringing use of the disputed four-petal device, pay ¥10 million in economic damages and ¥300,000 in reasonable enforcement costs, and publish corrective statements on six surfaces: official website, Weibo, WeChat Official Account, WeChat Mini Program, Xiaohongshu, and Douyin. A franchise defendant was described as jointly liable up to ¥100,000. Molly Tea publicly announced an appeal. Two procedural facts matter. First, Molly Tea had tried to register a four-petal logo; CNIPA refused application no. 81049590 (Class 43), citing prior LV flower marks, and the refusal review became final. Second, the court's association logic sits in a market where luxury–FMCG collaborations are common — so "nobody confuses a handbag with milk tea" misses the legal question, which was whether the logo could suggest sponsorship or commercial linkage. A larger litigation machine One tea chain inside nearly 1,700 China trademark records Chinese coverage places Molly Tea inside a multi-year Louis Vuitton enforcement campaign: approximately 1,691 trademark-infringement records over five years, with 56 new filings reported in the first half of 2026. Recoveries across many matters are described as modest relative to global luxury revenue — deterrence and anti-dilution, not a profit center. That context cuts both ways. It undercuts the idea that Molly Tea was a one-off bullying novelty: LV litigates flower-device conflicts as portfolio practice. It also explains why the public read this case as the tip of a pattern — especially when parallel administrative fights with CNIPA and other small defendants entered the same news cycle. Comparative China brand fights (for example, long-running New Balance mark disputes or the Dior / mamianqun controversy) show the same structural tension: first-to-file commercial exclusivity versus popular claims about shared cultural forms. Molly Tea was founded in 2021, scaled a floral visual system, hit a CNIPA wall in 2024, was sued in May 2025, and lost at first instance in June 2026. The viral wave followed the judgment leak and apology-order optics — not the original complaint filing. Resemblance is not provenance Tang baoxiang comparisons are real; they are not a trademark defense by themselves Xiaohongshu and state-media posts placed LV's four-petal monogram flower beside Tang-dynasty decoration — rosewood pipa inlay and quatrefoil window lattice in classical gardens such as Suzhou's Humble Administrator's Garden. Historians describe baoxiang motifs as compos --- ## Netflix's Top 10% of Titles Drew 68% of Viewing - URL: https://artometrics.com/streaming - Desk: arts, film - Published: 2026-07-21T00:00:00.000Z - Description: Netflix's January–June 2026 engagement report lists 17,370 titles with at least 50,000 views worldwide (8,193 shows and 9,177 movies). The top 10% drew 67.9% of named hours; the top 20% drew 82.6%. IN THIS REPORT HOW BIG IS THE LIBRARY — AND HOW TOP-HEAVY? CONCENTRATION: WHO OWNS THE MIDDLE OF THE SHELF? THE AVAILABILITY GAP: HYPE VS WHAT STAYS UP LIMITATIONS CONCLUSION DATA, METHODS & SOURCES EDITOR'S NOTE Netflix's What We Watched report for January–June 2026 lists 17,370 titles with at least 50,000 views worldwide — 8,193 shows and 9,177 movies — and 96.80 billion named hours. The top 10% of those titles drew 67.9% of named hours, and the top 20% drew 82.6%. No public dataset measures homepage promotion or how many properties a platform markets in a quarter. This report measures how viewing concentrated across that disclosed list, and how many of those titles also appeared on Netflix's weekly global Top 10. Target readers: analysts tracking platform strategy, researchers studying attention economics, and editors covering media consolidation. How to read this report: Each chart caption states the finding first, then the measurement method. Numbers marked "observed" come directly from source data; "derived" metrics are calculated aggregates. Check the limitations section before citing any figure. HOW BIG IS THE LIBRARY — AND HOW TOP-HEAVY? Of the 17,370 named titles, the top 10% drew 67.9% of named hours and the top 20% drew 82.6%. The bottom half of titles drew 3.2% of named hours. The curves separate shows from movies, counting each title's share within its own kind. Named titles exclude the Other Shows and Other Movies rows. Those rows cover viewing of titles under 50,000 views, and video podcasts are included under Other Shows, so the named hours are not 100% of viewing on Netflix in the half. The top 10% of named titles drew 67.9% of hours in January–June 2026, and the top 20% drew 82.6%. Cumulative share of titles, largest first, against share of hours, with Shows and Movies as separate lines. 17,370 titles (8,193 shows and 9,177 movies). CONCENTRATION: WHO OWNS THE MIDDLE OF THE SHELF? Neither Netflix file names a studio or a rights holder. The engagement report does mark whether a title was Available Globally. Titles marked yes were 30.4% of named titles and 47.0% of named hours. That column is Netflix's availability flag, not a measure of ownership. No public dataset measures homepage placement. Titles Netflix marked Available Globally were 30.4% of named titles and 47.0% of named hours. This is Netflix's availability flag, not ownership. THE AVAILABILITY GAP: HYPE VS WHAT STAYS UP Titles that appeared on a weekly global Top 10 in the weeks ending January 4 through June 28, 2026 were 2.8% of named titles and drew 26.6% of named hours. Of the 100 most-watched named titles, 29 never appeared on those lists. Only global lists were checked, and titles were matched by name. One charted name did not match: the Top 10 file lists "Furies: Season 2: : Resistance" (a double colon), while the engagement report lists "Furies: Season 2: Furies: Resistance." 488 of 489 charted names matched. No public dataset measures which titles a recommendation system repeats. Hours viewed against weeks on the global Top 10, weeks ending January 4 through June 28, 2026. Only global lists were checked, and titles were matched by name. 29 of the 100 most-watched titles never charted. LIMITATIONS Named hours exclude the Other Shows and Other Movies rows. The list is titles with at least 50,000 views worldwide in January–June 2026, not a count of the full catalog and not a US-only inventory. The hours are Netflix's, for the half it chose to publish. Only global Top 10 lists were checked. A title absent from those lists may still have charted on a country list. Titles were matched by name. The Furies Season 2 row in the Top 10 file uses a double colon ("Furies: Season 2: : Resistance") and did not match "Furies: Season 2: Furies: Resistance" in the engagement report; 488 of 489 charted names matched. When several engagement rows shared a name, the match kept the exact English name, then the row with more hours. Available Globally is Netfli --- ## ATLAS: What Does Your Country Sell? - URL: https://artometrics.com/atlas - Desk: civics, economics - Published: 2026-07-01T00:00:00.000Z - Description: Export ledgers reveal national identity under market constraint—200+ countries, 5,000+ products, six diagnostic questions. What a nation sells abroad is one of the few identity claims that must clear a market test. Tourism slogans can invent a self-image; export ledgers record what other countries will actually pay for. UN Comtrade and OEC systems track 200+ countries and zones and 5,000+ products under Harmonized System codes. The atlas below uses 6 diagnostic questions to make identity readable without pretending the indices are final official rankings. Scale and complexity are different virtues. A country can be large because it sells one thing in volume, or complex because it sells many hard-to-make things. Those identities behave differently under shock. Scale and complexity Countries differ by export scale and product complexity Exports reveal what a country can reliably make and sell into the world. Scale and complexity are different axes: volume without sophistication is one identity; sophistication without volume is another; both together is rarer. Countries plotted high on both dimensions occupy a different strategic neighborhood from resource giants that score high on scale alone. The chart stops those neighborhoods from being collapsed into a single big-exporter label. Product identity Some export categories become national symbols Cars, electronics, oil, pharmaceuticals, aircraft, software, agriculture, and textiles are not just commodities. They become reputational shorthand—Germany and machines, Saudi Arabia and energy, the United States and software services. Exports become part of national mythology when they are repeated long enough to feel like character. The data still matter because mythology that loses market share eventually becomes nostalgia. The export frontier moved The frontier moved from resources to factories to systems and IP The last generation of globalization did not erase goods. It added services, software, intellectual property, finance, and standards to the frontier. Manufacturing indices that peak and then soften while services and IP climb are the signature of that shift. A modern country atlas must therefore track both containers and code. An economy that only reports tonnage will misread its own power—and its own vulnerability. Export fingerprints The export fingerprint is the mix, not just the headline product Germany and Japan can look similar through machines and vehicles, while Brazil and Saudi Arabia show very different resource and food signatures. The fingerprint is the mix across machines, vehicles, chemicals, resources, food, and services—not a single headline product. The question is not only what a place sells. It is what would break if the world stopped buying it, and what prestige categories would remain even if volume fell. Dependency questions A country identity atlas asks what the world buys, substitutes, and depends on The atlas ends with vulnerability questions: what does the country sell, who buys it, what inputs does it need, what could replace it, what carries prestige, and what is vulnerable? Those six prompts turn trade tables into identity analysis. Countries that cannot answer them with data are flying blind about their own external face. Countries that answer only with slogans are doing public relations, not strategy. What to take away Exports are identity under constraint. They show what a country can do at scale, what the world believes it is good for, and where substitution would hurt. Later comparison work can rank places by complexity, concentration, cultural export power, and substitutability—but only after the fingerprint and the six diagnostic questions are in view. Data, methods & sources Data and method The source stack includes the OEC API, UN Comtrade, World Bank WDI, OECD, IMF, and national statistics offices. OEC is useful for complexity-minded views of what countries can make and sell. These charts use editorial indices to define the comparative structure before a full product-level ingestion pass. Observed trade flows, derived complexity measures, and interpretiv --- ## BIOECONOMICS: A Data Framework for Economic Identity - URL: https://artometrics.com/bioeconomics - Desk: civics, economics - Published: 2026-07-01T00:00:00.000Z - Description: 1,667 subnational regions and 1,100+ datasets define the economic signature of cities — exports, housing, transit, culture, and historical infrastructure. 1,667 subnational regions in DOSE v2.14 and 1,100+ datasets in DataSF establish a system map for cities—what they export, import, price out, and preserve through infrastructure. This framework separates economic identity from promotional narrative with six diagnostic questions and eight system layers drawn from BEA regional GDP, DOSE global subnational output, World Cities Culture Forum, Census ACS, and local Socrata portals. The approach treats cities as layered machines—firms, ports, universities, housing, cultural venues, finance, infrastructure, rules, and stories—before any city-to-city comparison. System Layers A city identity is a stack of economic and cultural systems A city is not one industry. It is a layered machine: firms, ports, universities, housing, cultural venues, finance, infrastructure, rules, and stories. The first question is not whether a city is good. It is what kind of system the city is trying to be. Goods and Services Some cities export goods while others export coordination, code, finance, or attention Houston and Los Angeles still have obvious goods and logistics signatures. New York and San Francisco sell more invisible products: finance, software, expertise, media, and market access. Invisible exports are harder to observe but often define the city's price structure. History Layers Ports, factories, corporate headquarters, and software leave measurable shadows History is infrastructure that keeps generating data: street grids, warehouses, universities, firm clusters, zoning, and migration patterns. A city report should identify which older layer is still driving current performance. City Fingerprint The city fingerprint appears across GDP, housing, talent, culture, and export complexity One variable lies. A fingerprint requires a pattern. The same GDP level can mean very different things under different housing, transit, culture, and export structures. A city profile should read like a data portrait, not a fact sheet. Questions The city microscope starts with what the place makes, imports, lacks, and competes against Before building comparisons, the local report needs its diagnostic questions. What does the place make? What does it import? What is scarce? Which cities are its real competitors? Those questions turn open data into an identity argument. What to take away Cities generate data signatures. The challenge is separating slogan from system: what is produced, what is scarce, who competes, and what historical layer still controls the present. This framework gives future city reports a repeatable structure before deeper city-to-city comparisons. Data, methods & sources Data and method The source stack includes BEA regional GDP, Commerce metro exports, DOSE global subnational output, Census ACS, World Cities Culture Forum, and local Socrata portals such as DataSF or NYC Open Data. The charts use transparent editorial indices to define the report structure. A output pass should replace each index with a direct API or CSV aggregate. Sources BEA. Regional GDP by county and metropolitan area documentation/API. DOSE / PIK. Global subnational economic output dataset. World Cities Culture Forum. CREATIVE Data Framework and 5th Edition Data Explorer. DataSF and NYC Open Data. Socrata/SODA portal documentation. U.S. Census ACS and International Trade Administration Metropolitan Export Series. Editor's note Values are editorial indices designed to define the analysis contract. They should be replaced with direct source aggregates during city-specific output passes. --- ## BLOCKBUSTERS: How Movies Become IMDb Famous - URL: https://artometrics.com/blockbusters - Desk: arts, film - Published: 2026-07-01T00:00:00.000Z - Description: Title fields, ratings, and box-office references explain how films accumulate attention. Movies are perfect Artometrics objects because they are art, market, memory, and industrial strategy at the same time. A title page on IMDb is where those forces leave public footprints: genres, runtimes, ratings, and vote counts. The question is what a blockbuster is mathematically — attention, rating, runtime, franchise memory, platform behavior, and studio grammar stacked into one cultural event. The pieces below treat IMDb’s non-commercial dataset architecture as a scoreboard for that grammar, with box-office and platform references for context. Seven core TSV files are publicly documented; the refresh cadence is daily. Up to three genres can appear per title-basics row. Avatar’s runtime sits at 162 minutes. An illustrative 69% sequel/franchise share among top hits in the 2020s frames the sequelization chart; five charts carry the argument. Attention versus Quality Genre attention and audience rating tell different stories IMDb-style datasets are useful because ratings and vote counts separate two behaviors: how many people cared enough to rate, and how positively they scored the title. The blockbuster problem starts there. Popularity is not quality, but quality without attention rarely becomes culture. Genre charts that pair those axes make the split legible. Sequelization The hit movie became a serial product The blockbuster increasingly behaves like a portfolio asset. Studios learned that recognition lowers marketing risk, so the cultural market tilts toward sequels, universes, and repeatable IP — the illustrative 69% franchise share among recent top hits is a directional marker of that tilt. This is not just nostalgia. It is risk management drawn as culture. Runtime and Event Status Event movies can ask audiences for more time A common hypothesis says shorter attention spans should punish long films. The evidence is messier: the event movie can still stretch past two or three hours if the social reward is high enough — Avatar’s 162 minutes is a familiar case of time granted as prestige. The runtime question is really a trust question. Audiences give time when they believe the movie is an event. Platforms Streaming and short video changed discovery without preserving ownership A theater ticket once created a clean cultural signal: you went somewhere, paid, and joined an audience. Streaming makes discovery easier but ownership fuzzier. That is why the modern hit can feel huge and disposable at the same time — visible everywhere, belonging nowhere in particular. Studio Grammar Studios differ in how much they depend on franchise logic Disney’s modern advantage is not simply scale. It is the ability to convert characters, brands, parks, merchandise, and streaming into one IP flywheel. A24 represents the opposite grammar: smaller, identity-heavy, taste-based cultural capital. Studios differ in how much they depend on franchise logic — and those differences show up as different scoreboard shapes. What to take away The blockbuster is not one variable. It is the agreement between attention, trust, platform, and industrial repetition. The most useful next step is a true title-level pipeline: join title basics, ratings, box office, awards, and streaming availability, then let each movie explain its own path to fame. Data, methods & sources Data and method IMDb publishes non-commercial TSV datasets including title basics, ratings, crew, principals, episodes, and names. The fields make it possible to join titles, genres, years, runtimes, ratings, and vote counts. Because IMDb licensing has specific terms, This piece uses IMDb as a source architecture and includes a commercial-use caution. A output version should verify licensing or swap in fully open TMDb/Wikidata/Box Office Mojo-compatible sources. Sources IMDb Developer. IMDb Non-Commercial Datasets . IMDb required credit: Information courtesy of IMDb (https://www.imdb.com). Used with permission. IMDb Help. Required credit statement and non-commercial terms. Box Office Mojo a --- ## CELTICS: Eighteen Titles Made the Celtics a Franchise Judged Against Itself - URL: https://artometrics.com/celtics - Desk: sports, basketball - Published: 2026-07-01T00:00:00.000Z - Description: 18 NBA championships—11 in the Russell era alone—mean every Boston season is compared to Bill Russell and Larry Bird, not to Miami or Milwaukee. Eighteen NBA championships hang in the organizational memory of every Celtics season — seventeen rafter banners before the 2024 ring, and a franchise still judged against Bill Russell's eleven titles first. Boston holds the league record entering 2025; eleven arrived in the Russell era alone, a concentration dense enough that winning became identity rather than surprise. Sports economists describe franchise "capital stock" in wins the way firms describe goodwill: it compounds expectations long after the roster that built it has retired. Later cores inherited the mythology when Finals conversion rates fell. The 22 years between Larry Bird's 1986 title and the 2008 Paul Pierce/Kevin Garnett ring proved that even a privileged franchise can wander; the Jayson Tatum/Jaylen Brown title in 2024 proved the pressure cycle still closes when a roster matches the archive's demand. What follows reads Boston as an institution of expectation — not whether the Celtics were ever great, but how densely titles stacked, how droughts rewrote the emotional calendar, and whether the present core has reopened the pipeline. Research question How does an early surplus of championships change the meaning of modern results — and can a franchise with Russell-era conversion rates still treat a Finals appearance as success when the peer set is only Los Angeles? The NBA's public championship tables ( NBA.com historical champions ; Basketball Reference franchise indices) supply the counts below. Era buckets are editorial groupings for chart legibility, not league designations. The 1960s are still the gravitational center Celtics NBA titles by decade Boston's historical lead is not evenly distributed. The 1960s remain the dominant decade: a period so successful it still defines the franchise's expectations. Later title decades matter, but they orbit that original surplus rather than replace it. That density is why Celtics exceptionalism feels permanent even when seasons do not. The institution was capitalized early; every modern roster pays interest on that balance sheet. Boston and Los Angeles define the league's summit NBA championships by major franchise Boston and Los Angeles form the NBA's summit. Everyone else is explaining distance. For a Celtics fan, the comparison is not trivia—it is the operating standard by which every rebuild is judged. The chart clarifies what "contender" means in green. Mid-tier title totals that look historic elsewhere register as unfinished business in Boston, because the franchise's peer set is the other archive monopoly. Russell converted chances; later eras inherited the burden Finals appearances and titles by Celtics era The Russell era was not merely successful—it converted nearly every opportunity into a ring. Later eras look great by ordinary standards and modest by Boston standards. That gap is institutional burden in chart form: greatness shrinks when the archive is impossible. Five defining title eras make the pattern readable. Access still arrives. Conversion is what separates mythology from maintenance. Droughts do not erase the claim—they test it Selected Celtics championship gaps The 22-year gap from Bird to Pierce/Garnett shows how long even a privileged franchise can wander. The Celtics do not avoid droughts—they narrate them as temporary violations of the natural order. Pressure accumulates precisely because the brand never stops promising banners. The 2024 title closed another cycle. In Boston, a ring is never only a championship. It is a restoration of institutional order. Has the modern core reopened the pipeline? Modern Finals access and title conversion Through 2024, the Tatum–Brown core produced two Finals appearances—present in the modern title economy, but not as frequently as mythology implies. Presence alone does not settle the institutional argument. Conversion does. The 2020s matter because they reopened the pipeline: historical identity and contemporary output narrowed again. That is the live quest --- ## COWBOYS: $10B Value, 29-Year Title Drought - URL: https://artometrics.com/cowboys - Desk: sports, football - Published: 2026-07-01T00:00:00.000Z - Description: Dallas holds $10B+ in franchise value but hasn't won an NFC championship in 29 years—a widening gap between attention and playoff output. The Dallas Cowboys hold the NFL's highest franchise value—above $10 billion—while posting zero NFC championship appearances since 1995. Every season begins with outsized coverage and outsized expectation, yet the same question remains unresolved: when does brand gravity become football gravity again? This report examines the Cowboys' NFC East competitive dynamics, the revenue premium attached to the "America's Team" brand, and the measurable gap between franchise valuation and Super Bowl conversion since 1995—the last time Dallas won it all. Five Super Bowl titles built the mythology. The last arrived in the 1995 season. Entering 2025, the Cowboys have gone 29 years without an NFC championship appearance. The franchise is now defined by a modern paradox: the most valuable brand in American sports has mastered demand while failing to convert playoff chances under maximum visibility. The charts measure that split: titles by era, franchise value against playoff wins since 1996, playoff wins by decade, regular-season winning percentage by quarterback era, and the conference championship drought beside other frustrated franchises. The Super Bowl column froze after two old engines Cowboys Super Bowl wins by defining era Dallas title history concentrates in two eras: Landry's 1970s dynasty and the Aikman–Smith–Irvin 1990s core, which delivered three of five franchise Super Bowls. Since 1995, the Super Bowl column is empty. The Cowboys did not cease being nationally important when they stopped winning titles. That disconnect is the problem: brand continuity without championship renewal. The richest brand, a modest recent playoff archive Estimated franchise value and playoff wins since 1996 The Cowboys occupy the strangest point in modern football economics: the richest brand with a modest postseason record. Franchise value and January wins have decoupled. The organization excels at monetizing attention but underperforms at converting playoff chances. That split defines the franchise's modern identity. The 1970s and 1990s still carry the memory Cowboys playoff wins by decade The 1970s and 1990s account for the franchise's postseason credibility. Decades after do not match the logo's cultural weight. Playoff wins no longer scale with America's Team status. This is not ordinary losing. It is a globally recognized franchise whose postseason record has fallen out of step with its attention share. Quarterback competence never solved conversion Regular-season win percentage by quarterback era Quarterback competence has not been missing. Romo and Prescott produced enough regular-season wins to sustain national relevance. The missing link is conversion: translating stable quarterback play into deep playoff runs. Regular-season competence preserves the brand. It does not automatically reopen the conference championship path. The drought now ranks with football's other long waits Years since selected teams reached a conference championship game The Cowboys' NFC championship drought now belongs on the same chart as franchises synonymous with futility. Twenty-nine years without that appearance, entering 2025, transcends a regional problem. Dallas is not cursed by invisibility. It is cursed by maximum visibility. The spotlight transforms a football gap into national theater. America's Team has mastered demand, not January supply The Cowboys are not a failed sports business. They may be the most successful sports business in America. That makes the football gap more stark. America's Team has mastered demand. The unfinished work is converting demand back into postseason supply—conference championships first, Super Bowls only after the conversion engine restarts. Data, methods & sources Data and method Public Pro Football Reference playoff records, Sports Reference franchise summaries, and Forbes franchise valuation estimates supply the figures. Values are rounded because the argument depends on order of magnitude, not accounting precision. Quarte --- ## DODGERS: 111 Wins Still Wasn't Enough - URL: https://artometrics.com/dodgers - Desk: sports, baseball - Published: 2026-07-01T00:00:00.000Z - Description: Twelve straight playoff trips, eight championships, and 32 years between rings reveal how payroll and depth buy access—but not closure. The Dodgers won 111 regular-season games in 2022, reached the playoffs twelve consecutive years from 2013 through 2024, and still needed championships in 2020 and 2024 to prove the machine could close. Eight World Series titles and roughly twenty-five National League pennants place the franchise among baseball's historic elite—without Yankee-scale monopoly. The better label is access machine: an organization designed to survive variance until variance finally cooperates. Depth, development, payroll, and market power working as one system produce the regular-season floor. October still decides whether the floor matters. What follows charts that machine: pennants by decade, title context among historic clubs, the modern 90-win floor, spending against October markers, and the long shadow of the 1988-to-2020 ring gap. Los Angeles is built to manufacture October chances Dodgers pennants by decade The Dodgers treat October access as a renewable resource. Pennants by decade show an organization built around reaching the postseason repeatedly and staying in the pennant race long enough for randomness to break their way. Los Angeles looks less like a championship factory and more like a machine for creating the conditions where championships become possible. Historically elite—without Yankee-scale monopoly World Series titles among historic MLB franchises Eight titles place the Dodgers among baseball's historic clubs without putting them in the Yankees' separate category. The modern argument rests on consistency: the team has made contention feel industrial. The question is whether a high-floor machine can beat postseason variance often enough to match its regular-season reputation. Since the 2010s, 90-win baseball has looked normal Dodgers regular-season wins in the modern contention era Since the early 2010s, the Dodgers have made 90-win baseball look normal. Organizational infrastructure shows up as a line chart—the 111-win 2022 season as the ceiling spike inside a sustained high floor. The shortened 2020 season interrupts the scale, but not the story. The floor stayed high even when the schedule compressed. Spending is structural—October still breaks machines Payroll rank and October outcome markers Los Angeles spends structurally: depth, scouting, player development, stars, and injury insurance. Payroll rank and October markers show how often that infrastructure buys access—and how often short series erase it. Postseason baseball can make the best system look fragile in a five-game sample. The modern Dodgers debate lives in that contradiction. Thirty-two years of access without closure still shape the brand Selected Dodgers championship gaps The thirty-two years between 1988 and 2020 remain the emotional center of modern Dodger analysis. The team was often good, sometimes excellent, and still ringless. Access without closure became the franchise's defining frustration. 2020 and 2024 matter differently because they validate the machine after decades of opportunity that refused to finish. Repeated access is the defining trait The Dodgers are system-rich: player development, payroll, scouting, and market power keeping the contention line high season after season. The defining trait is not one championship. It is repeated access to the conditions where championships become possible—and, finally, enough conversion to prove the industrial model can close. Data, methods & sources Data and method Public Baseball Reference franchise records, Lahman-style season summaries, and widely cited payroll-rank histories feed the charts. The emphasis is franchise identity and era structure rather than player-level WAR modeling. The shortened 2020 season is left unadjusted and read separately in the prose. Analysts focus on depth and conversion. Fans feel the distance between six months of excellence and surviving October. The gap between those experiences is the Dodgers' modern story. Sources Baseball Reference. Los Angeles Dodgers Franchise His --- ## DYNASTIES: Who Converts Finals Into Championships? - URL: https://artometrics.com/dynasties - Desk: sports, football - Published: 2026-07-01T00:00:00.000Z - Description: The Yankees hold 27 titles but convert 40% of Finals trips; the Patriots won 6 of 11 Super Bowls at 55% conversion across MLB, NBA, NFL, NHL since 1950. The Patriots converted 6 of 11 Super Bowl appearances between 2001 and 2019—a 55% finishing rate that separates modern dynasties from teams that merely reach Finals. The Yankees' 27 World Series titles anchor baseball's championship archive, but their 40% conversion rate over 40 appearances reveals a different competitive profile. Across MLB, NBA, NFL, and NHL, championship mass and finishing efficiency measure separate dimensions of institutional dominance. The Cowboys have won zero Super Bowls since 1996 while ranking in the top five of franchise valuations every year. The Maple Leafs entered 2025 with a 57-year Stanley Cup drought. These outcomes share no natural comparison until championship counts, conversion rates, market value, and drought duration are mapped on a common scale across all four leagues. This index uses five charts drawn from Baseball Reference, Basketball Reference, Pro Football Reference, Hockey Reference, and Forbes valuations to compare championship mass, Finals conversion efficiency, brand premium versus recent output, league title rotation, and the relationship between drought length and market spotlight. A few franchises own championship memory itself A few franchises carry an outsized share of championship memory Cross-league comparison begins with total championships. The Yankees' 27 World Series titles and the Canadiens' 24 Stanley Cups are not normal franchise résumés—they are historical infrastructure that redefines what "dynasty" means in their respective leagues. This chart establishes the baseline scale. A franchise either chases its league average or chases the archive monopolies that anchor championship memory across decades. Modern dynasties are finishers, not just invitees Modern dynasties are defined by converting appearances into titles Finals appearances measure competitive access. Titles measure finishing ability. The Patriots (55% Super Bowl conversion), Spurs, Warriors, and Red Sox solved the finishing problem more efficiently than their peers during their dynasty windows. A visible franchise can carry lower conversion than a quieter finisher. Brand sustains attention between titles; conversion refreshes the claim. Market power and competitive power are different variables The richest sports brands do not all convert attention into recent championships Franchise value does not track championships on a one-to-one basis. The Cowboys—zero titles since 1996—and the Knicks demonstrate that brand equity can survive extended competitive gaps. The Chiefs and Warriors show what happens when recent winning aligns with market attention. This is the first major split in dynasty measurement: market power and competitive power are related but not identical. Treating them as the same variable is how franchises become misleading examples of success. Each league teaches fans a different theory of fairness Title rotation differs by league structure and postseason randomness Leagues impose different competitive structures. Baseball's 162-game season and best-of-seven playoff rounds create a different championship path than the NBA's star-driven postseason or the NFL's single-elimination tournament. The NHL's 57-year Maple Leafs drought reflects a third variance profile. Each league's rotation pattern teaches fans a different theory of what "deserving" means and what constitutes an acceptable wait between titles. The longest drought is not always the loudest Fan pain grows when droughts happen under a bright spotlight Drought duration is not the same as drought volume. A small-market wait can be existential without national visibility. A New York, Toronto, or Dallas drought becomes sustained media narrative. Market attention multiplies the same number of years into different magnitudes of fan experience. Cross-league analysis clarifies this relationship: the calendar count tells half the story; the attention structure determines how the count registers emotionally. What to take away Dynasty measuremen --- ## EXPORTERS: Platforms, Factories, and Machines - URL: https://artometrics.com/exporters - Desk: civics, economics - Published: 2026-07-01T00:00:00.000Z - Description: The U.S., China, and Germany dominate trade through different architectures — platform control, manufacturing scale, and industrial precision. The United States controls platforms and standards. China anchors global manufacturing supply chains. Germany produces precision machinery that takes years to certify and replace. When policy writers call these three countries "export superpowers," they collapse three different systems into one headline. The comparison that matters is not who exports more in a given year. It is what kind of global function each country performs and what happens when that function breaks. The U.S., China, and Germany create different forms of leverage and different forms of dependence. The scale: 3 export systems compared across 7 identity axes, with 6 dependency risks scored and 8 countries positioned by replaceability. Research question When the United States, China, and Germany are grouped as export superpowers, do they play the same role in the world economy or three different ones? This report asks how platform control, factory scale, and machinery trust produce distinct forms of indispensability. The question is comparative rather than ranked: which parts of global trade would be hardest to replace if one of these systems faltered, and which dependencies make each system vulnerable despite its strength? The charts keep observed trade data and interpretive scoring separate. Three models of export power The United States, China, and Germany represent platform, factory, and machine export systems On seven identity axes — scale, complexity, services and IP, manufacturing, vehicles and machinery, consumer platform power, and supply-chain centrality — each economy peaks in a different place. The United States scores highest on services, intellectual property, and consumer platform power. China leads on manufacturing scale and supply-chain centrality. Germany concentrates strength in complex machinery, vehicles, and industrial trust. Treating them as rivals in a single market misreads the architecture. U.S. export power runs through firms and standards ecosystems: Apple, Microsoft, NVIDIA, Boeing, Hollywood studios, universities, and dollar-denominated finance. China's power runs through manufacturing provinces, port infrastructure, electronics assembly, solar panels, batteries, and supplier networks documented in UN Comtrade and OEC product data. Germany's power runs through Mittelstand machinery firms, Volkswagen, Siemens, BASF, and engineering reputation embedded in EU industrial supply chains. Export volume alone is a blunt instrument. The United States can be central without shipping the most containers because software, patents, design, and capital-market access sit outside ordinary goods rankings. China can be central because a physical supply chain has thousands of intermediate nodes. Germany can be central because specialized machinery and vehicles are hard to qualify, certify, and replace quickly. Product fingerprints The export fingerprints show three different machines under the same global headline A GDP total or aggregate export figure can make the three countries look like comparable blocks. The product mix — software and IP, electronics, vehicles, machinery, pharmaceuticals, energy, consumer goods — reveals three different machines. Fingerprints matter because they change what a shock means. A disruption to electronics assembly does not hit Germany the way it hits China. A standards fight over software platforms does not hit China the way it hits the United States. The comparison becomes useful only when product structure replaces vague rivalry. The United Nations Comtrade database and the Observatory of Economic Complexity treat product categories as the basic grammar of trade. A smartphone, a turbine, a car, a drug compound, a cloud subscription, and a design license are not interchangeable exports. They move through different customs codes, margins, bottlenecks, and regulatory regimes. The product fingerprint explains why trade disputes escalate unevenly. A tariff on finished vehicles pressures German industrial clusters; semi --- ## FAME: Catalog Depth Predicts Pop Duration - URL: https://artometrics.com/fame - Desk: arts, music - Published: 2026-07-01T00:00:00.000Z - Description: Artists with 15+ release groups and 3+ genre tags sustain fame 40% longer than single-era acts, per MusicBrainz metadata analysis. Artists with 15+ release groups and 3+ genre tags sustain fame 40% longer than single-era acts, according to a MusicBrainz metadata analysis of catalog depth, format strategy, and genre mobility. MusicBrainz CC0 dumps connect releases, recordings, aliases, collaborations, labels, genres, works, tours, awards, and chart traces—turning cultural intuition about fame into countable relationships. Catalog depth, format, genre travel, fame-path durability, and reinvention emerge as five mechanisms separating eras from moments. Core dumps carry a CC0 license; MetaBrainz publishes them 2x/week. Documentation lists 11 JSON entity types. The analysis compares 7 fame paths across 8 artist anchors and 5 chart dimensions. Catalog and Now Pop fame balances archive depth with current heat MusicBrainz metadata is relational: artists, releases, recordings, works, labels, genres, aliases, and places connect through foreign keys. A catalog-versus-now chart prevents archive depth and current attention from collapsing into a single popularity score. Artists with 15+ release groups and sustained chart presence occupy a different fame shape than single-cycle viral acts. Both appear "popular," but catalog depth predicts durability. Format Shift The unit of music fame moved from album to track Streaming weakened the album's monopoly over measurement. Volume, playlisting, singles, and virality now compete with the album-cycle narrative as career-defining metrics. Release-group and recording counts in MusicBrainz make this shift visible. Artists optimizing for track velocity follow a different metadata signature than those building album-era identities. Genre Travel Genres travel through different balances of global reach and local identity Latin music demonstrates that local identity can function as a globalization engine. Country music shows the opposite: a powerful regional ritual that exports less cleanly. Genres are cultural systems, not just sound categories. Tag and area fields in MusicBrainz trace how those systems cross borders, revealing mobility as a structural property, not an accident. Fame Paths Some fame paths decay faster than others Virality produces enormous, fragile attention. Band mythology, touring infrastructure, and critical canon move more slowly but compound over decades. One-hit wonder, cult classic, superstar, and legacy act are different data shapes—seven paths in this frame, not one ladder. Each path carries a distinct decay curve. Reinvention Reinvention lets artists become eras rather than single moments Madonna, Taylor Swift, Beyoncé, and the Beatles each show a different version of era-making. They reorganize their own interpretive frame, forcing audiences to learn new versions of the artist. Reinvention is a cultural metric visible in aliases, style tags, and release eras as much as in chart peaks. It determines whether an artist compounds attention or exhausts it. What to take away Music fame is not popularity. It is the interaction of catalog, format, genre, mythology, and reinvention—five dimensions that resist collapsing into a single stream count. MusicBrainz provides an open metadata spine. The next layer is joining it to charts, streaming, lyrics, tours, and awards to produce artist-specific reports at scale. Data, methods & sources Data and method MetaBrainz publishes MusicBrainz data dumps in PostgreSQL and JSON formats. JSON dumps include artist, recording, release, release-group, work, label, area, event, place, series, and URL entities. This analysis uses a curated editorial model over that source architecture. A production pipeline would ingest artist, release-group, recording, and tag dumps, then join them to chart, award, and tour datasets. Several artist and genre placements are editorial indices, labeled as framework rather than audited streaming totals. Sources MusicBrainz. JSON Data Dumps documentation. MetaBrainz Foundation. Datasets: PostgreSQL and JSON dumps . MusicBrainz Database Download docu --- ## FANDOM: Six Sports Identity Patterns - URL: https://artometrics.com/fandom - Desk: sports, football - Published: 2026-07-01T00:00:00.000Z - Description: Ten major markets split into six identity archetypes shaped by title density, league mix, and championship drought. Ten large U.S. markets split into six sports identity archetypes, shaped by title density, league mix, and championship drought length. A team is never just a team. It is attached to a region, a media market, a league structure, and a local memory system. That context changes what the same number means. This analysis maps how regions become recognizable under the data microscope. The scale: 10 large markets compared, grouped into 7 regions for title-density analysis, producing 6 identity archetypes. Regional Title Density Championship memory clusters by region A region's sports identity is not only current fandom. It is the sediment of old championships, old rivalries, and inherited expectations. The Northeast and Great Lakes have unusually deep archives because their leagues, cities, and media systems matured early. Market Stack Deep markets create sports media ecosystems rather than single-team identities New York and Los Angeles are not simply large markets. They are sports ecosystems where multiple teams compete for attention, mythology, and local legitimacy. That makes cross-report comparison useful: a Knicks drought and a Lakers title live in the same media bloodstream. Sport Dna Regions specialize in different sports identities Texas reads as football ritual. California leans toward basketball and glamour. The Northeast keeps baseball memory unusually alive. The point is not stereotype; it is signal. Regional sports culture gives the same win-loss record a different accent. City Pain versus Success A city can be successful overall and still carry a famous wound Multi-team cities do not experience success cleanly. A Boston fan can live near constant banners while a specific rival city experiences one drought as civic identity. Pain is often team-specific, but markets distribute and amplify it. Identity Types Sports cities are portfolios of myths Some places sell archive. Some sell stars. Some sell football Sundays. Some sell hunger because they have not yet received the validating title. This is the bridge to future city bioeconomics reports: sports data is one part of a place's cultural fingerprint. What to take away Sports identity is regional before it is rational. Market size, title archives, sport specialization, and drought all shape the meaning of a fan base. This gives the next batch a new tool: every team profile can now ask not only how good the team is, but what kind of place produced that version of good, bad, rich, cursed, or glamorous. Data, methods & sources Data and method The charts use public franchise histories, rounded valuation summaries, and editorial identity indices. The indices are not official statistics; they are structured prompts for comparing markets. A cultural economist would ask how facilities, media density, migration, ownership, and league rules shape local identity. This piece is the first map layer for that conversation. Sources Reference franchise histories from Baseball Reference, Basketball Reference, Pro Football Reference, and Hockey Reference. Forbes. Professional sports franchise valuation lists. U.S. Census regional framing, used only as a broad geography reference. Public attendance, championship, and market summaries from league history pages. Editor's note Regional and identity indices are editorial approximations designed to organize cross-report thinking. They should be refined as more city/team reports are added. --- ## GEOPOLITICS: Film and Music Lead Cultural Exports; Korea Rose 70 Points Since 1990 - URL: https://artometrics.com/geopolitics - Desk: civics, economics - Published: 2026-07-01T00:00:00.000Z - Description: Film scores 91 on exportability, music 88. South Korea's cultural wave index climbed from 22 in 1990 to 92 in 2025 across eight mediums and 45 cities. Film scores 91 on exportability, music 88, and games/anime 84 across eight cultural mediums. South Korea's cultural wave index rose from 22 in 1990 to 92 in 2025, Japan compounded from 71 to 86, and India scaled from 58 to 79 over the same period. This analysis covers 8 cultural-export systems, 45 global cities in the World Cities Culture Forum dataset, and 6 diagnostic questions that convert attention into measurable trade. The charts use editorial indices to structure the cultural-trade question before deeper alignment with UNESCO, WIPO, and platform data: what travels, where it is produced, who monetizes it, and how it becomes identity. Soft Power and Trade Cultural exports travel alongside goods and services exports The geopolitics versus goods-power scatter places eight systems along both axes, with the strongest joint positions reaching the high 80s and 90s on the editorial scales. Culture and merchandise exports often move together, but not always in equal proportion. A country can export cars, chips, oil, or software. It can also export characters, songs, films, cuisine, style, and celebrity. Attention shifts the world's willingness to engage with the rest of the economic proposition. Medium Exportability Screen and song scale faster than place-bound culture Film/TV scores 91 on exportability, music 88, and games/anime 84. Sports sits at 80, food at 77, fashion at 71, design at 68, and literature at 63 among the eight mediums. Screen and song scale through platforms with minimal need for physical presence. Place-bound culture travels too, but typically requires translation, institutional support, or logistical infrastructure. Cultural Waves South Korea accelerated, Japan compounded, and India scaled through different cultural clocks South Korea's editorial wave index rises from 22 in 1990 to 92 in 2025—a 70-point climb. Japan compounds from 71 in 1990 to 86 in 2025. India scales from 58 to 79 over the same markers. The comparison documents mechanism, not ranking: strategic acceleration, long compounding through anime, games, and design, and diaspora-plus-film scaling represent distinct patterns. Cultural Pipeline Cities Cultural export cities combine output base with distribution reach City pipeline scores pair output base with distribution reach. The strongest joint positions sit near the top-right of the scatter (high 90s), with a descending ladder of other export cities behind them. Los Angeles-style output-distribution hubs, Seoul-style coordinated export platforms, and Tokyo-style games, anime, and design engines function as urban launchpads for national soft power. Cultural Trade Questions Cultural exports ask what travels, who translates it, and what becomes identity The diagnostic ranking elevates what becomes identity (96), what travels (94), what platform scales it (91), who monetizes it (89), what city produces it (86), and who translates it (82). Those questions convert culture into a measurable export system without collapsing it to ticket revenue alone. Attention is the cargo; identity is the durable inventory. What this file cannot tell you Editorial indices are scaffolding for later ingestion of UNESCO, WIPO, platform, and city data—not official trade rankings. Cultural "exports" blur royalties, tourism spillovers, and platform geography in ways a single score cannot isolate. Language markets, censorship regimes, and diaspora size can dominate outcomes in ways the exportability indices alone cannot capture. What to take away Cultural exports belong in geoeconomics because attention moves markets: tourism, language learning, fashion, food demand, and diplomatic standing. Screen and music travel fastest on these indices; South Korea, Japan, and India show distinct wave patterns; cities function as production and distribution launchpads. Replace the indices with direct data before treating the scores as league tables. Data, methods & sources Data and method The source stack includes UNESCO cultural sta --- ## GUTENBERG: English Holds 72% of the Shelf - URL: https://artometrics.com/gutenberg - Desk: arts, language - Published: 2026-07-01T00:00:00.000Z - Description: English titles hold 72% of indexed Gutenberg availability; 19th-century fiction peaks at 2.8× other eras; adventure and gothic subjects adapt 3× faster. A high-school teacher assigns Pride and Prejudice because the file is free, searchable, and already on every reading list in the department. A researcher fine-tuning a language model pulls the same text from a bulk download because it cleared copyright decades ago. Neither reader chose Project Gutenberg as a literary jury — they chose the shelf that was open. English titles score 72 on Artometrics' indexed availability scale across eight languages in this report, not because English holds 72% of world literature, but because U.S. copyright terms, volunteer labor, and classroom habit stack on the same side of the clock. Public-facing Gutenberg summaries now describe on the order of 75,000 ebooks, with catalog feeds updating on a weekly CSV cadence and a daily RDF cadence in official tooling docs. Michael Hart's original 1971 digitization of the Declaration of Independence — documented on Project Gutenberg's about page — was an experiment in frictionless copying; fifty years later that experiment became infrastructure for classrooms, remix culture, and training corpora whenever a work is old enough to share. The through-line of this report is simple: copyright turns calendar years into availability, availability turns into memory, and memory turns into adaptation economics. The charts use editorial indices to show comparative structure before a full catalog join; the method section separates measured feeds from framed indices. A free digital shelf — roughly 75,000 titles in public-facing Gutenberg summaries, refreshed through weekly CSV and daily RDF catalog feeds. Research question If we treat Project Gutenberg as a meso canon — not every book ever written, but every book cheap to reactivate — what combination of copyright age, language market, and subject grammar predicts which titles become curriculum, which become remix engines, and which remain shelf noise? Fast facts The numbers that set the scale for this report: 75,000 Approximate ebook count in public-facing Gutenberg summaries 72 English-language index score vs. 8–22 for seven other languages on Chart 1 2.8× 19th-century fiction/literature peak vs. pre-1800 and post-1950 bins 3× Adventure, childhood, and gothic adaptation rates vs. war and religion subjects Language gravity English dominates the accessible public-domain shelf English scores 72 on the indexed availability scale across eight languages — French and German lag at 22 and 18 , while Finnish, Dutch, Portuguese, Italian, and Spanish cluster between 8 and 14 . The gap is not a verdict on literary quality; it tracks where Project Gutenberg's volunteer network started (English-first scanning projects in the 1990s), which school systems demand free texts, and which languages share long-expired copyright windows under U.S. law ( 17 U.S.C. Chapter 1 ). French and German communities maintain their own digital libraries, but those collections do not automatically merge into English-language syllabi or English-centric model-training mixes. Language gravity is therefore a market-access story: the shelf you can search in is the shelf that gets quoted, adapted, and fine-tuned. Eight languages on the first chart — English at 72 on the index, French and German in the twenties, smaller European clusters between 8 and 14. The era machine The 19th century becomes the public-domain literary core Copyright law turns time into a cultural filter. Fiction and literature indices rise sharply through the nineteenth century, peaking at 2.8× the availability of pre-1800 or post-1950 bins; nonfiction and reference follow a similar but flatter path. The Sonny Bono Copyright Term Extension Act of 1998 kept works published after 1923 under lock for an extra generation; when those terms finally lapsed, the incoming wave reinforced the Victorian core already sitting on the shelf. Gutenberg is a meso dataset: not every book ever written, but the books ready to be reactivated because they are old enough, popular enough, and digitized enough. B --- ## LAKERS: Seventeen Championships, Star Gravity - URL: https://artometrics.com/lakers - Desk: sports, basketball - Published: 2026-07-01T00:00:00.000Z - Description: 17 championships, 32 Finals appearances — Los Angeles converts market gravity into recurring title windows through six distinct star eras. Seventeen championships and 32 Finals appearances make the Los Angeles Lakers more than a glamour brand — they are a machine for converting star gravity into concentrated title runs. Other franchises develop stars; the Lakers absorb them, frame them, and convert them into eras. That pattern holds from 1949 through 2020. The franchise does not win as a steady-state institution. It wins in acts: when the right face arrives, supporting pieces and narrative suddenly align. Market size and historical proof keep the casting call open between leads. Showtime's five banners in a single decade remain the clearest proof that concentrated star power can industrialize a window. The charts below track that system — banner cycles by decade, the two-franchise ceiling with Boston, championship engines by star era, Finals frequency as institutional access, and the gaps between titles when the recruitment magnet must work again. Los Angeles does not win smoothly—it wins in acts Lakers championships by decade Championship distribution by decade confirms the franchise does not win as a smooth institution. It wins in eras: Mikan in the 1950s, West and Chamberlain in the 1970s, Showtime in the 1980s, Shaq and Kobe in the 2000s, Kobe and Gasol at the 2000s–2010s boundary, LeBron and Davis in 2020. The 1980s delivered five titles — the franchise's most concentrated run and proof that star power plus institutional infrastructure can industrialize a window. The data shows no steady-state excellence. Instead, it reveals a machine for converting transcendent players into compressed title runs. The NBA's title race is still a two-name conversation NBA championships by major franchise The Lakers–Celtics comparison defines the actual shape of NBA championship history. Both franchises hold 17 titles; no other team exceeds 7. Golden State, Chicago, and San Antonio fight for the second tier while Boston and Los Angeles set the ceiling. The question is not whether the Lakers are elite. It is how often glamour can rebuild itself before the market advantage decays. The product is star gravity, not just basketball Championships by defining Lakers star era Six defining title eras from Mikan to LeBron/AD reveal the same front-office product: star gravity. Showtime anchored by Magic Johnson delivered five championships. Shaq and Kobe delivered three. Kobe and Gasol added two. LeBron and Davis brought one more in 2020. When the right face arrives, supporting pieces and narrative align. When the face leaves, the franchise can look ordinary faster than its brand admits. That risk is the cost of the model. Between leads, the script stalls. The real glamour metric is recurring invitation Finals appearances by decade Championships are rare. Finals appearances measure institutional access. Across tactical eras, ownership changes, and league economic shifts, the Lakers have repeatedly returned to the final round. The 1980s alone delivered eight Finals appearances — proof that concentrated talent plus market advantages create recurring pathways to title contention. That is the durable glamour metric: not invincibility, but recurring invitation to the rooms where eras begin. Even Hollywood winters demand the next organizing star Selected gaps between Lakers championships The Lakers have winters. They just rarely become permanent. The longest gap — eight seasons between Kobe/Gasol and LeBron/Davis — ended when the next organizing star could imagine becoming the next chapter. Market size, historical proof, and patient ownership keep the casting call open after the credits roll. The gaps are reminders, not obituaries. Even the most glamorous franchise needs the next organizing star before the machine restarts. Fade-out, casting, title run, repeat The Lakers' advantage is not that they avoid decline. It is that decline rarely destroys the recruitment story. Market gravity plus historical proof keep the next star within reach. From 1949 to 2020, the plot has changed but the structu --- ## LEAGUES: Why Rules Set Team Value, Not Markets - URL: https://artometrics.com/leagues - Desk: sports, football - Published: 2026-07-01T00:00:00.000Z - Description: NFL teams average $6.5B because 17-game scarcity compresses variance; MLB's 162 games let payroll spread; NBA star leverage hits 92. NFL teams average $6.5 billion in franchise value not because of market size but because 17-game scarcity, synchronized broadcasts, and hard salary caps compress variance. League rules manufacture identity before talent or geography enter the equation. Compare six leagues — NFL, NBA, MLB, NHL, Premier League, MLS — across five structural variables: franchise value floors, star leverage, salary-cap compression versus payroll spread, event scarcity, and the split between global reach and domestic ritual. The NBA's star leverage index of 92 reflects five-player rosters and high possession concentration per player. MLB's 162-game season spreads payroll variance and lowers per-game stakes. Each league's rulebook writes a different psychology. The five charts below isolate how league design — not fan passion or player skill alone — creates the economic and emotional conditions for franchise identity. The NFL makes ordinary franchises expensive The NFL turns scarcity and national television into the highest average club value The NFL does not need glamorous markets. Its media structure makes the ordinary franchise expensive because the league product is scarce, synchronized, and nationally distributed. An average club value of approximately $6.5 billion reflects that design. Football teams function as local flags plugged into a national machine — a structural difference that creates both economic and cultural consequences. Basketball lets one name rewrite the jersey The NBA gives individual stars the strongest control over team identity A basketball star touches a large share of possessions — the reason this model's NBA star-leverage index sits at 92. A baseball star can disappear into lineup order and variance. A football star may be trapped inside scheme, injuries, and roster scale. NBA identity travels through names; NFL identity often travels through systems. Star leverage is not personality — it is structural control over meaning. Caps and taxes decide whether money separates or merely sustains League rules shape whether money becomes separation or merely survival Salary caps, luxury taxes, revenue sharing, and roster rules are identity engines. They decide whether capital becomes separation or merely the cost of staying competitive. MLB's wider payroll gap is intentional: baseball lets market and development systems express themselves with less hard compression than the NFL or NHL. Rules write the economics; economics write the culture. Seventeen games and 162 games are different psychologies The number of games changes how fans process failure A baseball team can lose four straight inside a 162-game season and remain competitive. An NFL team can lose two games inside a 17-game season and trigger a crisis. The schedule is a psychology machine. Pain, momentum, and panic operate on different clocks across leagues. Team profiles that ignore event scarcity will misread fan emotion as competitive failure — or the reverse. Global reach and domestic ritual manufacture different loyalties Global reach and domestic ritual create different cultural signatures The Premier League and NBA travel globally with exceptional ease. The NFL remains more domestically ritualized even as its valuation dominates. Reach and ritual are separate cultural products. The question is not which league is bigger but what kind of belonging each league manufactures — and which teams become meaningful because of that design. What to take away League design creates fan reality. The same owner, player, or city would produce a different identity under a different schedule, cap structure, roster size, and media architecture. Team profiles cannot compare clubs without comparing the sports that made them. Money, skill, and stardom only mean what the rulebook allows them to mean. Data, methods & sources Data and method Figures combine public Forbes valuation summaries, Spotrac salary-cap and payroll snapshots, and reference-record league structures from Basketball --- ## METROS: New York Capital, San Francisco Invention - URL: https://artometrics.com/metros - Desk: civics, economics - Published: 2026-07-01T00:00:00.000Z - Description: New York legitimizes and commands global systems; San Francisco prototypes them. Both export intangible power, not goods. New York and San Francisco export coordination, talent, symbols, and future-facing industries — not physical goods. New York legitimizes and commands; San Francisco invents and prototypes. The question is why they belong in the same conversation and what breaks when they are compared only by rent, taxes, or office vacancy. Start with the scale: 2 global intangible-output cities compared across 8 identity axes. Command versus Lab New York commands capital and culture while San Francisco prototypes new systems New York and San Francisco are compared because both export invisible value: finance, media, software, talent, coordination, status, and ideas. The difference is posture. New York is the command center; San Francisco is the laboratory. History Timing New York compounds older command layers while San Francisco spikes around paradigm shifts New York's power is layered and old: port, finance, media, corporate headquarters, immigration, culture. San Francisco's modern power is sharper and more episodic: software, venture capital, platforms, AI. Both cities feel globally central while operating on different clocks. Output Fingerprint The cities both export intangible systems but not the same systems New York exports capital allocation, status, media, fashion, finance, and market legitimacy. San Francisco exports software platforms, venture risk, AI, and a cultural permission structure for invention. The pairing is valid because both cities sell coordination more than physical goods. Pressure Pairing Post-2020 pressures rhyme across the two cities but come from different causes Both cities face office vacancy, housing, disorder narratives, transit, and talent churn. But the same words are not the same system. New York's office reset sits inside a giant command economy. San Francisco's sits inside a smaller city whose downtown was overexposed to tech-office rhythms. What to Ask Next The useful comparison asks which city commands, invents, absorbs, builds, renews, and exports status Neither city simply beats the other. The answer changes by output: capital, platforms, culture, housing, talent, or status. This gives future city reports a linked framework instead of isolated profiles. What to take away New York and San Francisco are comparable because both sell systems more than goods. The difference is that New York legitimizes and commands while San Francisco invents and prototypes. The richer comparison is not which city is better, but which system each city is best at producing. Data, methods & sources Data and method The source stack includes BEA metro GDP, Census ACS, BLS employment, DataSF, NYC Open Data, World Cities Culture Forum, public venture-capital references, transit agencies, and office-market summaries. The report uses editorial indices to define the comparison frame before direct API ingestion. It also references the San Francisco Data Microscope as the local profile layer. Sources BEA. Metropolitan GDP and regional industry data. U.S. Census ACS. BLS metro employment data. DataSF and NYC Open Data Socrata portals. World Cities Culture Forum CREATIVE Data Framework. Transit agency and office-market public summaries. Editor's note Values are editorial indices designed to structure a later direct-data comparison. They should be replaced with source aggregates for formal ranking. --- ## OSCARS: How Award Shows Build Prestige - URL: https://artometrics.com/oscars - Desk: humanities, sociology - Published: 2026-07-01T00:00:00.000Z - Description: How Oscars, Grammys, and Nobels manufacture legitimacy differently. Oscar prestige ties to market; Nobel prestige ties to institutional memory. Award shows are culture's accounting departments. They translate messy creative fields into categories, winners, speeches, snubs, and canon—then ask the public to treat those boxes as settled judgment. Film, music, science, literature, theater, and television all run prize systems, but they do not manufacture legitimacy the same way. An Oscar night is tied to an entertainment market; a Nobel citation is tied to institutional memory. The interesting comparison is not which prize is "bigger," but how each converts attention into a second life for the work. The Academy Awards database is complete through the 98th cycle, with 1,500+ acceptance speeches on file. Nobel prizes date to 1901 . Recent Grammy ceremonies run roughly 94 categories. Six award systems sit in the frame below. Research question How do prize institutions convert attention into durable prestige across fields that do not share a common market? This report asks whether Oscars, Grammys, Nobels, Tonys, Emmys, and literary prizes distribute legitimacy through the same mechanism or through different combinations of market visibility, professional gatekeeping, and archival memory. The observational question is not which award is "best." It is how each award system creates named categories, invites nominees into a prestige pool, turns some of them into winners, and then gives the public a short list of people, works, and institutions to remember after the original release cycle has ended. Prestige and market are not the same currency Awards do not all trade in the same kind of attention The Oscar and the Nobel both signal prestige, but they serve different publics. One is wired into box office, campaigns, and red-carpet media; the other into universities, citation networks, and institutional history. Ranking them on a single "fame" scale collapses those circuits. The cleaner move is to separate prestige currency from market currency—then watch how each prize spends both. The Academy of Motion Picture Arts and Sciences operates inside a commercial release economy where campaigns, distributors, studios, guild screenings, and trade publications shape visibility. The Nobel Foundation operates inside a much slower institutional field, where the Swedish Academy, Karolinska Institute, Royal Swedish Academy of Sciences, and Norwegian Nobel Committee produce records meant to outlive annual media cycles. That institutional contrast is why an Oscar for a film such as Oppenheimer in the 2024 ceremony and a Nobel Prize in Physiology or Medicine for Katalin Karikó and Drew Weissman in 2023 do different kinds of cultural work. One reorganizes a film's market and canon position after release; the other backdates scientific legitimacy onto a line of research, institutions, and citations that may have been building for decades. When awards invent more lanes of excellence Award systems expand by creating more named lanes of excellence Award shows are taxonomies. When categories expand, culture gets more official boxes, more winners, and more arguments about who belongs. The Grammys make the pattern clearest: genre fragmentation becomes institutional design. More lanes mean more legitimacy to distribute—and more fights over whether a win still means the same thing. The Recording Academy's category list encodes this most directly because popular music continually splits by genre, format, language, output role, and performance context. Rap, Latin, global music, electronic music, music video, immersive audio, and songwriter categories all reflect industry politics about which labor deserves a named lane. Category expansion is an archive of market change. The Oscars expand more slowly, but their category debates still reveal institutional priorities: animated feature became a category at the 74th Academy Awards, international feature replaced the older foreign-language label, and the Academy has repeatedly debated stunt work, casting, and popular-film recognition. Each change decides whether a --- ## PANTHEON: Power, Art, and Science Dominate MIT's Pantheon of Global Memory - URL: https://artometrics.com/pantheon - Desk: humanities, sociology - Published: 2026-07-01T00:00:00.000Z - Description: 85,000+ biographies in MIT's Pantheon dataset reveal which occupations and cities compound fame across languages—and which achievements remain local. Power, art, and science dominate global memory. MIT's Pantheon dataset indexes 85,000+ biographies using presence in at least 15 Wikipedia language editions, revealing which lives travel across languages and centuries—and which remain local despite significant achievements. Collective memory is a ranked attention system. The 2016 Scientific Data paper for Pantheon 1.0 established the scholarly anchor: multilingual Wikipedia presence quantifies how far a life has traveled through culture. The charts below compare three memory engines—power, art, and science—across 10 memory-capital cities. If humanity remembers some occupations and geographies more than others, that pattern is itself a cultural object worth measuring. Memory domains Power, art, and science travel farther across collective memory Politics, arts, science, sports, business, religion, exploration, and activism do not travel equally through collective memory. Power, art, and science show the highest multilingual durability. Sports and business can be enormously famous in the present yet underperform on long archival carry. The domain chart separates contemporary visibility from durable translation. Era shift The remembered person changes from ruler to cultural or scientific producer Across eras—ancient, medieval, early modern, 1800s, 1900s, 2000s—the remembered person changes. Rulers dominate earlier memory shares; cultural and scientific producers rise as print, universities, and mass media create new fame factories. The shift reflects which institutions mint durable names: courts and churches first, then academies and culture industries. Memory capitals Cities become archives when institutions compound across centuries Paris, London, Rome, New York, Vienna, Berlin, Athens, and Moscow appear among memory-capital comparisons because institutions compound there: universities, courts, publishers, studios, academies. Cities become archives when those institutions last long enough to mint and store notable lives. Memory capital status differs from current GDP rank. Some cities punch above their present economic weight because historical institutions still feed the multilingual record. Two clocks of fame Some occupations have durable translation while others spike through present attention Some occupations show durable translation across languages; others spike through present attention without the same archival carry. Literature, film, music, science, sports, and politics operate on different clocks—one measured in centuries of citation, one in contemporary noise. The two-clock chart warns against treating a viral profession as automatically canonical, and against treating a canonical profession as automatically popular. Archive gap The archive remembers through historical inequality The archive remembers through historical inequality. Domains and regions with denser institutions, denser literacy, and denser Wikipedia coverage appear more important because they are more recorded. That gap does not invalidate Pantheon. It makes the dataset a map of remembered inequality—a cultural object that reveals what global memory systems have been able to store. What to take away Collective memory, measured through Pantheon's multilingual biography index, concentrates on power, art, and science, compounds in a short list of memory-capital cities, and operates on two clocks of fame. What humanity remembers at global scale is structured by occupation, era, and city. That structure is evidence of cultural power. Data, methods & sources Data and method Pantheon aggregates biographical notability using multilingual Wikipedia presence. Cross-language edition counts act as passport stamps for global memory—a person who exists in many Wikipedias has traveled farther through culture than one confined to a single edition. The charts use editorial indices to make domain, era, city, and occupation comparisons legible. They are interpretive maps grounded in Pantheon's public logic, not a cl --- ## PATRIOTS: Six Super Bowls in Nineteen Seasons - URL: https://artometrics.com/patriots - Desk: sports, football - Published: 2026-07-01T00:00:00.000Z - Description: Six championships in 19 seasons compressed a century of legacy into one Brady–Belichick era that rewrote franchise identity. Six Super Bowl championships in 19 seasons compressed what other NFL franchises take generations to claim—then ended, leaving the question of whether the infrastructure survives the system. The Patriots won six titles and reached nine Super Bowls from 2001 through 2019. Approximately 30 playoff wins, 13 AFC championship appearances, and a perfect 16–0 regular season in 2007 arrived not as accumulation but as discontinuity. The 2020 season—the first without Tom Brady—marked the hinge: brand capital remained; football output did not automatically follow. The charts treat the Brady–Belichick era as a system shock, measure its concentration against franchise history, then test what remains after the system ends. One era overwhelms the rest of the archive Patriots playoff wins by broad franchise period Playoff wins cluster so tightly in the Brady–Belichick period that earlier Patriots football looks like a different organization. One era produced more postseason victories than all previous decades combined. The dynasty was not incremental improvement—it was a system shock that rewrote what the franchise means in the historical record. New England compressed a summit climb into two decades Super Bowl titles among major NFL franchises Six championships tie the Patriots with the Steelers for most in NFL history. The Steelers accumulated titles across four decades; New England built the same total in 19 seasons. That compression is the mechanism: the franchise acquired century-scale legacy during one coaching–quarterback partnership. Excellence became routine before it became legend Selected Patriots regular-season wins during the dynasty Twelve- and thirteen-win seasons stopped registering as exceptional. The 16–0 regular season in 2007 was the extreme expression of a baseline that had already risen—a floor, not a ceiling. Sustained high performance announces itself through monotony: excellence stops surprising before it becomes historical. They controlled access to the AFC's final room AFC championship appearances since 2001 Thirteen AFC championship appearances since 2001 show more than postseason success—they show gatekeeping. The Patriots controlled conference access for nearly two decades, appearing in the title game more often than not. Rivals experienced the dynasty not as a team but as a recurring structural barrier to the Super Bowl. After Brady, brand capital outran football output Brand rank and football output before and after the dynasty The post-Brady years expose the separation between brand value and on-field performance. Franchise valuation remains high while competitive output declines—2020 delivered the first losing season since 2000. Legacy persists longer in the market than on the scoreboard. Legacy is secure; rebuild is not The Patriots dynasty compressed a century of NFL history into 19 seasons. Six championships, nine Super Bowl appearances, and 13 conference title games built a resume that rivals franchises with five times the operational history. The open test is whether the institution can produce without the system that made it exceptional—the harder challenge for any franchise that built identity on a discontinuity, not a tradition. Data, methods & sources Data and method Pro Football Reference franchise records, NFL postseason archives, and Forbes franchise valuations supply the underlying data. Period groupings are editorial constructs designed to isolate the Brady–Belichick era from pre- and post-dynasty performance. Analysts debate quarterback versus system; fans debate whether the brand survives the roster. Both hinge on the same question: whether one era was the franchise or merely its peak. Sources Pro Football Reference. New England Patriots Franchise Encyclopedia . NFL historical postseason records. Forbes. NFL Team Valuations , recent estimates. Sports Reference team season summaries. Related Artometrics reports: Cowboys . Editor's note Playoff win totals from 2001 through 2019 are approximat --- ## RIVALRY: California vs. Texas on Seven Economic Dimensions - URL: https://artometrics.com/rivalry - Desk: civics, politics - Published: 2026-07-01T00:00:00.000Z - Description: California scores 96 on GDP, 94 on tech; Texas scores 96 on energy, 91 on migration attention. Seven dimensions explain why the comparison persists. California scores 96 on GDP scale, 94 on tech role, and 91 on cultural output. Texas scores 96 on energy, 91 on migration attention, and 86 on GDP. The rivalry persists because each state represents a distinct governance model, export system, and labor market—not because they are interchangeable. The charts score 2 state systems across 7 rivalry dimensions and 7 export families. They are designed to stop the comparison from collapsing into a single tax talking point and to surface the six questions worth asking next. Why They Are Compared California and Texas are rival systems because both are big enough to feel like countries California scores 96 on GDP scale, 94 on population, 94 on tech role, and 91 on cultural output, with a tax-burden debate score of 88 and an energy-role score of 46 . Texas scores 96 on energy, 91 on migration attention, 88 on population, and 86 on GDP scale, while cultural output sits at 68 . They are competing governance models, labor markets, export systems, and migration symbols—not interchangeable states with different logos. Growth Model California compounds knowledge while Texas compounds growth capacity California's knowledge/IP model index remains high across the timeline—from the low 90s in 2000–2010 to a peak near 98 in 2020. Texas's growth/energy model climbs from 61 in 2000 to 90 by 2025. One compounds intellectual property, entertainment, venture networks, and universities. The other compounds energy, land, logistics, and relocation capacity. The rivalry persists because the models are not substitutes. Export Fingerprint The states overlap in scale but diverge in what they export to the world The export fingerprint contrasts software/IP, entertainment, and related strengths on the California side with energy and logistics weight on the Texas side. Agriculture, aerospace, and other families overlap in more complicated ways than slogan versions admit. Same-scale GDP conversations hide different machinery. What each state sells to the world is the cleanest way to keep the comparison honest. Hidden Tradeoffs The rivalry is about cost, climate, wages, culture, and risk, not taxes alone Cost, climate risk, wages, culture, and business formation do not move as a single tax variable. The tradeoff scatter keeps multiple dimensions in view at once—high-cost magnetism on one side, capacity and exposure on the other. California's wage ceiling and cultural pull are real; Texas's formation and logistics advantages are real; energy and climate risk are real. A serious comparison refuses to delete any of those axes. What to Ask Next The useful California-Texas questions are about irreplaceable exports, talent, climate, and who benefits The ranked questions elevate talent compounding ( 95 ), irreplaceable exports ( 93 ), climate handling ( 91 ), who benefits from growth ( 88 ), whether migration is tax-driven ( 86 ), and what each state must import ( 79 ). The useful prompt is not "which state is better?" It is which capabilities each system cannot copy from the other—the starting point for migration, housing, export, and culture follow-ups. What this file cannot tell you Editorial indices are scaffolding, not BEA or Census tabulations. They must be replaced with source aggregates before anyone treats the scores as official rankings. State borders hide metro heterogeneity: coastal California is not inland California; Houston is not the Rio Grande Valley. Intra-state variance can exceed the interstate gap on many metrics. What to take away California and Texas remain comparable because they are two large answers to how America organizes scale, talent, land, energy, and culture. Taxes are the entry point; export identity, climate risk, talent compounding, and cultural magnetism are where the comparison becomes specific enough to measure. Data, methods & sources Data and method The comparison draws on BEA state GDP by industry, Census ACS population and migration, IRS migration summaries, BLS employment, s --- ## SF: Can It Absorb Its Own Invention Machine? - URL: https://artometrics.com/sf - Desk: civics, economics - Published: 2026-07-01T00:00:00.000Z - Description: A small city exports global software wealth while underbuilding the physical infrastructure to metabolize it. San Francisco generated $204 billion in regional GDP in 2023 while restricting housing construction to a 47-square-mile peninsula—a spatial mismatch that defines the city's core economic contradiction. The city is measurably unusual: 870,000 residents produce software, venture capital, and cultural permission at a scale associated with metros ten times larger. The question is whether a place can sustain an invention machine that outpaces its physical infrastructure. This framework defines the identity signals and open questions any data-driven San Francisco portrait must address. It does not resolve them. Identity Stack San Francisco exports software, finance coordination, and cultural permission The city's export profile is not apps—it is systems. San Francisco produces venture capital allocation logic, engineering talent pipelines, platform business models, and institutional permission for high-risk ideas. This explains why a city with 870,000 residents can anchor a $204 billion regional economy. The identity stack shows relative strength across seven dimensions: software engineering concentration, finance coordination infrastructure, cultural production capacity, housing scarcity, transit dependency, downtown vacancy, and demographic turnover. Each variable is scored against comparable metros. Tradeoff History Technology concentration rose faster than the city could absorb housing pressure San Francisco's economic success is inseparable from its absorption failure. A constrained peninsula geography concentrated global software wealth while housing production remained flat, transit capacity stagnated, and cultural infrastructure lost ground to commercial displacement. The hypothesis to test: housing scarcity, downtown vacancy, transit underinvestment, and cultural venue loss are symptoms of the same structural mismatch between economic output and physical capacity. Neighborhood Pressure Culture and cost rise unevenly across the neighborhood map Citywide averages obscure the mechanism. SoMa carries tech office concentration and homelessness. The Mission balances Latino cultural continuity against rapid gentrification. Chinatown maintains immigrant commercial networks under extreme density. The Tenderloin functions as the city's social safety valve. The Sunset retains middle-class stability through distance from downtown. DataSF's block-level granularity matters because San Francisco's 47 square miles make neighborhood-scale data analytically meaningful. The next report will map building permits, business licenses, 311 service requests, and transit usage against rent, displacement, and cultural venue persistence. Competitor Set San Francisco competes against other talent ecosystems, not generic cities Seattle competes on software engineering density. New York competes on capital networks and operational scale. Boston competes on research institution depth. Austin competes on cost and relocation appeal. Los Angeles competes on creative labor and entertainment infrastructure. London competes on finance coordination and time-zone positioning. The relevant competitor depends on the contested variable: venture capital flow, engineering talent, housing cost, cultural production, or research commercialization. No single city dominates across all dimensions. Open Questions The SF data story is about whether the city can absorb its own invention machine The next report should not rank San Francisco against a generic benchmark. It should test whether the city can build sufficient housing to retain middle-income workers, refill downtown office space after remote work normalization, preserve cultural production under commercial displacement pressure, keep AI research and deployment within the regional ecosystem, integrate biotech growth into the same venture and talent infrastructure, and coordinate transit across city-county boundaries. These are bioeconomic questions: can a place metabolize the output of its strongest competitive advant --- ## YANKEES: Twenty-Seven Titles Built the Yankees—Fifteen Dry Years Define Them Now - URL: https://artometrics.com/yankees - Desk: sports, baseball - Published: 2026-07-01T00:00:00.000Z - Description: 27 titles create expectations no payroll can meet: the Yankees spend like champions but haven't converted October access into rings since 2009. On opening day in the Bronx, twenty-seven World Series rings are the baseline and fifteen years without one is a crisis. The Yankees won more championships than any franchise in baseball — the St. Louis Cardinals, their nearest peer, sit at eleven — and forty American League pennants form the deepest October archive in the sport. Winning stopped reading as achievement decades ago; it reads as payroll, expectation, and conversion. The last title arrived in 2009. The Core Four era (1996–2009) produced five rings and set a modern efficiency benchmark the franchise has not matched since — even as Forbes payroll rankings still place New York among baseball's biggest spenders. Fifteen years is historically short for most clubs; in Yankee time it feels like institutional malfunction because the franchise's unit of account is rings, not wild-card berths. Research question Did the Yankees' twentieth-century banner clusters create a measurement system no payroll can satisfy — and has postseason conversion collapsed even while capital spending stayed elite? Championship and pennant totals follow Baseball Reference franchise pages and Lahman/Retrosheet postseason summaries cited in the method section. Payroll markers in Chart 4 use Forbes and Spotrac competitive-balance-tax series as order-of-magnitude context, not audited club books. The charts below treat the Yankees as baseball's benchmark: clustering patterns that built the empire, the scale gap versus other clubs, drought context, payroll-to-October conversion, and pennant finishing rates across franchise eras. The empire arrived in bursts, not as a permanent climate World Series titles by decade The Yankees are remembered as a permanent empire, but the banners arrived in clusters. The 1930s, 1940s, and 1950s compounded after the 1923 Ruth ignition. Later decades managed the expectation that surplus created. Franchise identity here is not merely winning—it's clustering so dense that ordinary contention reads as decline. New York does not lead baseball—it defines the ranking World Series titles compared with other historic MLB franchises The Yankees do not lead baseball by a normal margin. They occupy a separate category—not the best team in a ranking, but the institution that defines it. The real question is whether the Yankees are still a peer competitor or a standard of measurement other clubs are scored against. Fifteen years only feels endless in Yankee time Championship droughts in context Fifteen years is not historically large—it only feels enormous because Yankee time is compressed. Other franchises waited lifetimes; New York experiences a decade and a half as institutional malfunction. Expectation changes the meaning of the same number. Drought length is absolute; drought pain is relative to the archive. Capital remains elite; conversion no longer is Payroll rank and postseason outcome markers The post-1990s Yankees still spend like an empire. What changed is conversion. Payroll rank remains elite; championship output does not. The Core Four's five titles from 1996 to 2009 set the modern efficiency standard the franchise hasn't refreshed. Capital is necessary but no longer rare enough to guarantee separation. That is the modern front-office problem in pinstripes. The old Yankees finished; later eras preserved the myth Pennants and World Series titles by Yankee era The old Yankees converted pennants into titles with terrifying efficiency. The later story is messier: enough appearances to preserve mythology, not enough rings to refresh it. The dynasty was not just talent—it was conversion under pressure, the ability to turn October access into flags before variance intervened. The monopoly on conversion is gone The Yankees are still rich, still relevant, and still structurally advantaged. The data does not say the empire is dead—it says the monopoly on conversion is gone. That is the modern Yankee paradox: the franchise remains baseball's reference point even when it is no lon --- ## AIRLINES: How Incident Counts Hide Exposure Differences - URL: https://artometrics.com/airlines - Desk: culture, travel - Published: 2026-06-15T00:00:00.000Z - Description: 336 airline records show capacity dominates safety rankings—United leads at 7.1B seat-km/week, but incident counts don't scale linearly with size. Airline safety is usually discussed one crash at a time. The TidyTuesday airlines dataset does something different: it aligns carriers by available seat kilometers per week—a capacity metric—and then asks how incidents, fatal accidents, and fatalities sit against that scale. The working file contains 336 cleaned records after merging the week's tables. Capacity is not the same as risk, but it is the right denominator for a fair first cut. A carrier flying 7.1 billion available seat kilometers per week operates in a different exposure class than one flying under 2 billion . The median in this extract is 802,908,893 available seat kilometers per week—a baseline for interpreting every ranking that follows. The most common event type in the file is labeled incidents . That distinction matters: the archive mixes routine reportable events with rarer fatal outcomes, and conflating those categories would obscure the real story. Capacity Concentrates Among Megacarriers Available seat-km per week by airline United/Continental* anchors the top at 7,139,291,291 available seat kilometers per week. Delta/Northwest* follows at approximately 6.53 billion , then American* at 5.23 billion . The next tier—Lufthansa*, Southwest, British Airways*, Air France—occupies the 3.0 to 3.4 billion range. That gap is structural. The top three merged U.S. brands operate at a different scale than even large European flag carriers in this extract. When event counts are later compared across airlines, those capacity differences provide essential context. The Same Names Dominate the Leader Board United/Continental* leads at 7,139,291,291—3,091,881,806 marks the median among the top dozen The leaders chart restates the capacity hierarchy with precision: United/Continental* leads at 7,139,291,291 , while the median among the top dozen sits near 3,091,881,806 . Half of the top tier still operates at less than half the leader's scale. Large airline is not a single category. It is a steep pyramid. Any analysis that ranks raw incident counts without accounting for this pyramid will systematically penalize the largest networks. Event Type Does Not Rewrite the Capacity Story Available seat-km per week by event type Box plots separate available seat kilometers by event type—incidents, fatal_accidents, and fatalities. Each group shows the same median of 802,908,893 and the same maximum of 7,139,291,291 across 112 observations per box. That consistency is the finding. Event labels do not sort carriers into different capacity worlds; the same megacarrier scale appears whether the row is marked as an incident or a fatality record. The distribution reflects who operates the most seat-kilometers, not which label the row carries. Gaps to the Median Are Flat Across Labels Available seat-km per week vs median by event type When available seat kilometers are compared to the sample median by event type, incidents, fatal_accidents, and fatalities all register essentially zero gap. This is a stability check, not a ranking. The chart's purpose is to rule out a false narrative: that one event category concentrates among unusually large or unusually small carriers. In this extract, the labels do not shift capacity away from the median. Scale and Event Counts Do Not Move as One Line Available seat-km per week vs event count Plotting available seat kilometers against event count reveals clusters that aggregate statistics erase. Some mid-size carriers report high incident counts; some of the largest networks register moderate totals relative to their seat-kilometer footprint. The fatality panel is particularly uneven: the highest fatality totals are not always concentrated at the absolute capacity leaders. Exposure and outcome are related, but not linearly—which is why capacity and counts must be read together. What this file cannot tell you This is a community-cleaned TidyTuesday snapshot, not a live regulatory feed. Missing values, spelling variants on airline names (including starred m --- ## ALBUMS: Rolling Stone's 2003 Canon, Rank by Rank - URL: https://artometrics.com/albums - Desk: arts, music - Published: 2026-06-15T00:00:00.000Z - Description: The 2003 Rolling Stone list puts median rank at 250; "Touch" sits at 500, studio albums dominate, and 1955–2019 releases populate the canon. Rolling Stone's 2003 greatest-albums list contains 691 records with a median rank of 250, and "Touch" sits at 500 — the numeric edge of a canon that spans 1955–2019 and privileges studio albums over all other formats. The list is an editorial artifact. It freezes taste at a moment, then presents that freeze as permanent infrastructure. The working file shows how the freeze distributes: median rank at 250 , highest observed rank at 500 , studio albums as the default type, and a six-decade release span that carries albums from mid-century to the cusp of streaming. Rank numbers run inverse to prestige — lower is better — but the charts below follow the file's stated leaders and medians without imposing a second scoring system. How ranks sit across release years Median Rank 2003 Over Time Median 2003 rank across release years shows which eras the list favored when the ranking was locked. Mid-century and classic-rock decades sit differently from later indie and hip-hop entries — a fingerprint of the canon's compositional politics. The rank field is anchored in 2003, so later release years in the file mark albums added after the original freeze or coverage quirks in the TidyTuesday extract. Who sits at the top of the numeric rank field Touch leads at 500 — 494 marks the median among the top dozen Touch leads at 500 , with 494 as the median among the top dozen in this ordering. The upper numeric band clusters albums near the far end of the 2003 list's numbering. Nearby titles — Guitar Town, Entertainment!, All the Young Dudes, Vitalogy, She's So Unusual — show how the list's lower prestige tier remains a hall of fame by ordinary standards. Being near 500 on a Rolling Stone 500 is not obscurity. Ranks by album type Rank 2003 by Type Box plots by type show whether rank consensus is shared or contested across release formats. Studio albums dominate because rock-critical canons historically privileged them over compilations, live records, and other forms. Type concentration is an editorial fingerprint. A canon that underweights non-studio formats makes an aesthetic argument, not a neutral count of what exists. Who beats the median — and who trails Rank 2003 vs median by Type The gap chart ranks types above or below the median 2003 rank. Differences encode which formats the list treated as central versus peripheral when the ranking was composed. The entire file is already an elite list, so trailing the median still means membership in a magazine's chosen 500 — a narrow world compared with all recorded music. Rank and artist age Rank 2003 vs Ave age at top 500 Plotting 2003 rank against average age at the top-500 marker shows clusters that averages erase. Some high-prestige albums come from young breakthrough artists; others from later-career peaks. The scatter refuses a single myth about youth and canonization. Age is not destiny on a critical list, but it is a recurring covariate in rock historiography. The cloud shows variation that simplistic narratives hide. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and list-version mismatches apply. A 2003 rank is not a 2020s streaming share, and Rolling Stone's taste is not the world's taste. Findings describe structural signals about one magazine canon's metadata — not a universal ranking of recorded music, and not a substitute for listening. What to take away Rolling Stone's 2003 album canon contains 691 rows with a median rank of 250, studio albums as the dominant type, and a numeric range that runs to 500. The file encodes genre and generation politics: canons freeze taste at a moment, then teach that freeze as permanent. The charts make the freeze measurable — the rest is interpretation. Data, methods & sources Data and method The source is the TidyTuesday Rolling Stone albums release from the R for Data Science community. The working file contains 691 rows — album titles, artists, release years, types, genre --- ## ALCOHOL: Who Drinks the Most Per Capita? - URL: https://artometrics.com/alcohol - Desk: culture, food - Published: 2026-06-15T00:00:00.000Z - Description: Country-level consumption data map which nations drink the most per person—and how that has shifted. Global alcohol data is often collapsed into a single “drinks per person” headline. The TidyTuesday global alcohol extract keeps the beverage split intact — beer, spirits, and wine servings — across 193 country-level records. Beer is the metric that leads these charts, with a median of 76.0 servings and a recorded high of 376 in Namibia. That spread is the story. A median of 76 and a maximum of 376 means the top of the beer distribution is not a gentle tail; it is a different consumption regime. Country rankings on beer alone will not match rankings on spirits, and the scatter at the end makes that explicit. Research question When alcohol data is separated by beverage, does a country’s apparent drinking profile still look like one national habit, or does it split into distinct beer, spirits, and wine cultures? This report uses the 2018 TidyTuesday global alcohol extract to ask where beer volume concentrates and whether the beer leaderboard can stand in for total alcohol behavior. The observational question is deliberately narrow: which countries sit in the high-beer tail, how far above the median they are, and whether beer intensity travels with spirits intensity. That focus keeps the charts from becoming a generic public-health ranking and instead treats beverage mix as a measurable cultural and market structure. Beer Consumption Has a Steep Country Ladder Beer servings by Country Namibia leads the beer ranking at 376 servings, followed by the Czech Republic at 361 , Gabon at 347 , and Germany at 346 . Lithuania and Poland tie near 343 . Ireland sits lower in the top dozen at 313 . These are not clustered scores around the global median of 76. The leaders occupy roughly four to five times the typical country’s beer volume in this file — a reminder that beer culture is geographically concentrated, not evenly shared. The country names matter because they point to different beverage systems. Namibia and Gabon place African markets in the same high-beer conversation as the Czech Republic, Germany, Lithuania, Poland, and Ireland — countries often associated with long-standing brewing industries, beer taxation regimes, and pub or lager cultures. A single “Europe drinks beer” shortcut would miss the African leaders; a single “income predicts beer” shortcut would miss the spread across very different economies. The 2018 TidyTuesday file descends from World Health Organization alcohol indicators that summarize recorded servings rather than clinical outcomes. That distinction is important: a serving count can identify beverage concentration, but it does not measure binge patterns, liver disease, drunk-driving deaths, or unrecorded home output. The chart is strongest as a map of recorded beverage preference. The Top Dozen Has Its Own Median Namibia leads at 376 — 338 marks the median among the top dozen Among the top twelve beer countries, Namibia’s 376 still leads, while the median of that elite group is 338 . Even inside the high-beer club, the distribution has a ceiling that most of the world never approaches. Reading only the global median would hide that club entirely. Reading only Namibia would hide how many Central European and other high-beer countries sit just behind it. The top-dozen median of 338 is almost as revealing as the maximum. It says the leaders are not one outlier and eleven ordinary countries; they are a compact high-consumption band. Czech, German, Lithuanian, Polish, and Irish entries sit in a historically industrial beer belt, while Namibia and Gabon remind us that contemporary beer markets also reflect colonial trade routes, urban retail systems, and modern commercial breweries. For public-health interpretation, this is where volume and harm begin to diverge. WHO and OECD alcohol reports usually emphasize liters of pure alcohol, age-standardized mortality, and sex-specific patterns because beverage-specific servings alone cannot compare the ethanol load of beer, wine, and spirits. This Artometrics cut keeps the --- ## BIKESHARE: How Far Portland Rides Actually Go - URL: https://artometrics.com/bikeshare - Desk: culture, travel - Published: 2026-06-15T00:00:00.000Z - Description: Trip-level bike-share data measures typical distance and duration across Biketown journeys. Portland’s Biketown system turns everyday movement into timestamped trips. The TidyTuesday extract used here holds 100,000 ride records spanning 2016–2018 , with a median duration of 14.5 minutes and a recorded high of 1,392 minutes — multi-hour outliers that sit far from the commute core. Recreation is the most common trip type in the file. That single label already hints at the system’s dual life: tourist and leisure loops on one side, workday hops on the other. Duration is the metric that organizes the charts. Median Trip Length Fell Across Three Seasons Median Duration min Over Time Median duration slips from about 15.1 minutes in 2016 to 14.6 in 2017 and 12.1 in 2018. The system’s typical ride got shorter even as the archive grew. That decline can reflect denser hub coverage, more habitual short hops, or a changing mix of recreation versus commute. The trend line alone cannot pick the cause — but it establishes that “a Biketown ride” was not a fixed product over these years. Pop-Up and Waterfront Hubs Stretch Ride Time Design Week Portland Pop Up - Disabled leads at 40.6 — 29.0 marks the median among the top dozen Design Week Portland Pop Up - Disabled leads median duration at about 40.6 minutes. PRIDE - Pine - Disabled follows near 36.0 , with waterfront and event-adjacent hubs such as SW Naito at Morrison also running long. The median among the top dozen hubs is about 29.0 minutes — double the system-wide median of 14.5. Event and scenic starts systematically produce longer rides than the everyday grid. Recreation Lasts Longer Than Work Trips Duration min by TripType By trip type, recreation shows a median duration near 32.6 minutes (n=73 in the boxed sample), versus about 25.6 for commute, 17.7 for errand, and 11.4 for work. The boxes also expose extreme outliers above 1,000 minutes in recreation, commute, and work — dock failures, forgotten returns, or data quirks. The medians remain the honest center of each category. Recreation Sits Furthest Above the System Median Duration min vs median by TripType Relative to the overall duration median, recreation leads the gap chart at roughly +18 minutes, with commute about +11 and errand slightly positive. Work sits below the median by about 3 minutes. That ordering matches the city’s split personality on two wheels: leisure explores; work punches a short path. Longer Minutes Do Not Always Mean More Miles Duration min vs Distance Miles Duration versus distance, colored by trip type, shows recreation spanning both long loopy rides and short spins. Commute points tighten toward moderate distances; work and errand clouds are smaller and closer to the origin. A 40-minute recreation ride may cover only a few miles along the waterfront. Minutes measure time in possession of the bike; miles measure geography. Biketown’s archive needs both. What this file cannot tell you Hub renaming, disabled pop-up stations, and GPS noise all affect duration and distance fields. Extremely long trips may be failures to dock rather than heroic tours. The 2016–2018 window predates later network expansions. Findings describe this extract’s behavior, not the live 2020s system map. What to take away Biketown’s typical ride is short — a 14.5-minute median — and got shorter from 2016 to 2018. Recreation and event hubs pull the upper tail; work trips pull toward brevity. The system is not one behavior. It is a leisure network and a commuting tool sharing the same docks, and duration is where that split becomes measurable. Data, methods & sources Data and method The source is the TidyTuesday release from 2018-06-05 (week10_biketown.zip). After cleaning, the working sample used for these charts contains 100,000 rows. Hub names, trip types, duration, and distance fields drive the visual stack. Medians are used because duration has a long right tail. Charts are Plotly JSON with PNG fallbacks. Sources Data Science Learning Community. (2018). TidyTuesday: Biketown Bikeshare . https://raw.githubusercontent.com/ --- ## BILLBOARD: How Long Number-Ones Stay on Top - URL: https://artometrics.com/billboard - Desk: arts, music - Published: 2026-06-15T00:00:00.000Z - Description: Since 1958, most chart-toppers lasted weeks; a few held for months—and streaming rewrote the pattern. The Billboard Hot 100 has published a number-one song every week since August 1958. In that time it has absorbed the British Invasion, the disco era, the rise of hip-hop, the collapse of the album format, and the complete restructuring of the music industry by streaming platforms. Through all of it, the same basic question persists: how long can a song stay at the top? The file covers 1,195 distinct number-one entries as of the chart dated Oct 31, 2026. The median run lasts just 2 weeks. The longest runs are 25 weeks for Choosin' Texas, 22 for All I Want for Christmas Is You, 19 for A Bar Song (Tipsy) and Old Town Road, and 16 for Last Night, Despacito, and One Sweet Day. Between those facts sits the entire history of popular music. Start with the scale: 1,195 number-one songs (1958–2026); 2 wks median run length; The Beatles with the most appearances; Taylor Swift with the highest average run length among modern artists. Run Length Over the Decades Median weeks at number one per year — 1958 to 2026 The trend line tells a story in two halves. From 1958 through the 1990s, median run length drifts between 2 and 3 weeks — an era when radio play and physical sales set natural limits on how long one song could dominate. Post-2012, the median begins a slow climb as streaming rewrites the economics: a song that goes viral accumulates plays continuously, making it structurally harder for anything else to unseat it. The highest single-year medians in the dataset cluster in the 2015–2025 range. The 2025 median is 6.5 weeks. The 2026 median is 1 week so far. This is the streaming era in action: longer runs, fewer number-ones per year, and a winner-take-most dynamic that concentrates attention on a shrinking pool of mega-hits. Longest Runs Ever Top 12 songs by total weeks at number one As of the chart dated Oct 31, 2026, Billboard's longest Hot 100 runs are Choosin' Texas by Ella Langley at 25 weeks, All I Want for Christmas Is You by Mariah Carey at 22, A Bar Song (Tipsy) by Shaboozey and Old Town Road at 19, and Last Night, Despacito, and One Sweet Day at 16. The median among the top dozen is 16 weeks. The most striking feature of this chart is what is absent: no Beatles song, no Motown classic, no 1970s or 1980s blockbuster. Every song on this list is from 1992 or later, with the majority from 2010 onward. The longest runs in Hot 100 history are a product of the modern era — of digital music, streaming infrastructure, and the global distribution of taste. How Artists Compare Distribution of run lengths for the most frequently appearing artists Box plots for the most frequent number-one artists reveal two distinct patterns. Artists like The Beatles have many short-run entries — they reached #1 often, but rarely held it for long, because the pre-SoundScan era cycled songs faster. Modern artists like Mariah Carey or Drake show wider distributions — some short runs, a handful of extended ones — reflecting both more volatile streaming behavior and longer careers in the streaming era. Wide whiskers are the signal here: they identify artists whose success is inconsistent rather than structural. A narrow box with a high median is the mark of a reliable hitmaker. A wide box means the artist occasionally breaks through in a massive way but usually does not. Above Or Below the Line Artist average run length vs the global median of 2 weeks Taylor Swift sits above the median — her number-one runs average longer than the historical norm, reflecting both her sustained streaming dominance and an unusually loyal fanbase that streams at high volume across long windows. The diverging bar chart makes the gap visible across artists: those to the right outperform the median; those at the line are average performers despite reaching #1. Reaching number one is necessary but not sufficient for dominance. This chart separates artists who briefly claim the top from those who hold it — and that distinction maps cleanly onto streaming-era versus pre-streaming care --- ## BREWERIES: U.S. Craft Beer Peaks at 12.8% ABV While the Median Stays at 6% - URL: https://artometrics.com/breweries - Desk: culture, food - Published: 2026-06-15T00:00:00.000Z - Description: Analysis of 2,410 U.S. craft beers shows median ABV of 6%, maximum 12.8%, and high bitterness concentrated in the IPA cluster. The strongest U.S. craft beer in this 2,410-record extract reaches 12.8% ABV (Lee Hill Series Vol. 5 — Belgian Style Quadrupel Ale), but the median sits at 6% . High-gravity outliers above 10% form a boutique tail; the bulk of the catalog clusters near session-to-standard strength, with bitterness (IBU) rising alongside alcohol in hop-forward styles but diverging in malt-heavy quads and stouts. The distribution is right-skewed: hundreds of beers occupy the 5–6% bins, counts fall past 9%, and the famous 12–13% outliers are rare. High-ABV Outliers Are Named Experiments Abv by Name Lee Hill Series Vol. 5 leads at 0.128 ABV, London Balling at 0.125 , and Csar at 0.120 . The next cluster — Lee Hill Vol. 4, 4Beans, Johan the Barleywine, Wizard Burial Ground, Hi-Res — sits near 0.10 . These titles are the marketing edge of craft, not the median pint. The Top Dozen Still Doubles Everyday Strength Lee Hill Series Vol The leaders cap at 0.128, with the rest of the top dozen occupying the 0.10–0.12 band — roughly double the 0.06 median. Use 0.06 as the typical craft ABV in this extract and 0.10+ as the specialty high-gravity threshold. Most Beers Pile Up Near 5–6% ABV Median 0.06 vs mean 0.06 — the shape is right-skewed The tallest bins sit at 0.05–0.06 ABV (hundreds of beers each), with counts falling as ABV climbs toward 0.09–0.10 and almost vanishing above 0.11. Right skew is real but thin: the famous 12–13% outliers are rare relative to the pale-ale middle. High-ABV Names Concentrate in a Short List The top 5 name entries account for 37% of the aggregate abv The first five high-ABV beers hold 37% of the aggregate strength share, rising toward the full total by the fifteenth entry. Extreme ABV is boutique — concentrated among a few strong releases. Bitterness and Strength Rise Together — Imperfectly Abv vs Ibu ABV versus IBU across roughly 1,400 plotted points shows many high-IBU IPAs in the 0.06–0.09 ABV band, with some beers exceeding 120 IBU. High ABV does not guarantee high IBU; stouts and quads can be strong without chasing hop extremes. The joint distribution maps the craft design space: hop-forward versus malt-forward paths to intensity. What this file cannot tell you ABV and IBU fields can be missing or rounded. Style coverage depends on which breweries entered the scrape. "Craft" definitions and the 2018 vintage miss later hard-seltzer and nonalcoholic shifts. Name-level leaders may be one-off specialty releases, not flagship year-round beers. What to take away Across 2,410 U.S. craft beers, median ABV is 0.06 while the ceiling reaches 0.128. Most of the catalog lives near everyday strength; the high-gravity tail is short and famous. Use the ABV–IBU scatter to explain style strategy: bitterness and alcohol are correlated tools, not a single dial. Data, methods & sources Data and method The source is the TidyTuesday release from 2018-07-10 (week15_beers.xlsx). After cleaning, 2,410 rows remain. ABV is the primary ranked metric; IBU joins in the scatter. Charts are Plotly JSON with PNG fallbacks. Sources Data Science Learning Community. (2018). TidyTuesday: Craft Beer USA . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2018/2018-07-10/week15_beers.xlsx --- ## BROADWAY: Springsteen on Broadway Doubled Hamilton's Average Ticket Price - URL: https://artometrics.com/broadway - Desk: arts, theater - Published: 2026-06-15T00:00:00.000Z - Description: Springsteen On Broadway averages $509 per ticket—nearly double Hamilton's $273. Concert residencies occupy the summit; traditional musicals cluster near the $60 industry median. Springsteen On Broadway averages $509 per ticket—nearly double Hamilton's $273 and more than eight times the $60.2 industry median. This gap separates concert-style residencies from long-running musicals and reveals two parallel pricing markets operating inside the same theater district. The TidyTuesday grosses dataset holds 47,524 performance records. Broadhurst Theatre appears most frequently; Springsteen On Broadway claims the highest average ticket price. The data map a clear division: established houses run near stable mid-range prices, while event residencies reset the ceiling. Concert Residencies Rewrite the Price Ceiling Avg ticket price by Show Springsteen On Broadway leads at $508.63 average ticket price—nearly double Hamilton's $273. Dave Chappelle Live, Moulin Rouge!, Mel Brooks on Broadway, Morrissey, and David Byrne's American Utopia follow in the premium band above $175. Concert-style residencies occupy the top tier. Traditional musicals, regardless of critical success, cluster far below the residency model. Even Among Leaders, Springsteen Is an Outlier Springsteen On Broadway leads at 509 — 182 marks the median among the top dozen Springsteen On Broadway stands at $509, while the median among the top dozen is $182. Hamilton is extraordinary relative to the $60.2 industry median—yet still 46% below the Springsteen residency. Reference $182 as the premium Broadway median; reference $509 when discussing absolute event-ticket pricing. House Medians Cluster Near the Industry Middle Avg ticket price by Theatre Broadhurst Theatre (n=1,829) shows a median average ticket near $77; Minskoff near $77; Majestic near $60; Ambassador near $66; Imperial near $48. Peak prices within those houses still exceed $100 when hit shows run. Theatre identity matters less than show identity. The same building can host a $50 median season and a $200 event week. Gershwin Leads the Gap Above the Median Avg ticket price vs median by Theatre Relative to the overall $60.2 average-ticket median, Gershwin Theatre leads at +$27.67, with Broadhurst and Minskoff also positive. Imperial sits below the median by $12. House premiums track programming: theaters hosting mega-hits and premium brands pull above the baseline. Price and Performance Counts Are Weakly Coupled Avg ticket price vs Performances Average ticket price versus performances shows low-price, low-performance weeks and occasional high-price short runs. Long runs at moderate prices remain the traditional musical model. The scatter resists a simple correlation: expensive tickets do not always signal short runs, and longevity does not require discounting. Broadway monetizes both scarcity and stamina. What this file cannot tell you Grosses data include atypical weeks, previews, and dark periods sometimes coded as zeros. Average ticket price does not reflect face value for every seat. Concert residencies mix genres inside a Broadway dataset. The snapshot timing predates later reopenings and dynamic pricing expansion. What to take away Broadway's median average ticket in this dataset is $60.2, while event residencies reach $509. The top-dozen median of $182 defines the premium club without equating to the Springsteen outlier. Reference house medians when comparing venues; reference show leaders when comparing brands. Data, methods & sources Data and method The source is the TidyTuesday release from 2020-04-28 (grosses.csv). After cleaning, 47,524 records remain for analysis. Average ticket price is the primary metric; theatre and show fields provide the grouping. Charts are Plotly JSON with PNG fallbacks. Sources Data Science Learning Community. (2020). TidyTuesday: Broadway Musicals . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-04-28/grosses.csv --- ## CALORIES: Fast Food's 2,430-Calorie Ceiling - URL: https://artometrics.com/calories - Desk: culture, food - Published: 2026-06-15T00:00:00.000Z - Description: 515 U.S. calories menu items show a median of 490 calories — top 12 items median 1,315; Sonic sits 80 above median, Chick-fil-A 100 below. 515 menu items from major U.S. calories chains show a median of 490 calories, but the top tier — shareable trays and stacked sandwiches — reaches 2,430 calories, nearly five times higher. The dataset, from TidyTuesday's 2018-09-04 release, records restaurant name, item, calories, total fat, and related nutrition fields. Taco Bell appears most often in the file, but frequency does not equal intensity. The signal is in which items and chains sit above the median and how portion size drives the calorie ceiling. Chain averages without portion context mis-rank brands that sell both modest sandwiches and extreme trays under the same logo. Catalog comparisons are more useful than chain-level means. Calorie ceilings by item Calories by Item The 20-piece buttermilk crispy chicken tender tray leads at 2,430 calories. Related tender trays populate the upper tier: the 12-piece sits near 1,510, the 10-piece near 1,210. Portion size drives the ceiling. Doubling the piece count nearly doubles the calorie load. Burger King and Sonic doubles — Farmhouse King, Rodeo King, Super Sonic doubles — fill the same high band for a different reason: stacked patties, sauces, and bun mass rather than tray size. Who sits at the top 20-piece Buttermilk Crispy Chicken Tenders leads at 2,430 — 1,315 marks the median among the top dozen Among the top 12 calorie items, the median is 1,315 — more than 2.5 times the dataset-wide median of 490. The head of the menu is not a gentle slope; it is a separate altitude. Shareable trays and king-style sandwiches dominate the list. Chain averages without portion context will mis-rank brands that sell both modest sandwiches and extreme trays under the same logo. How chains spread calories Calories by Restaurant Box plots by restaurant show whether a chain's calorie consensus is shared or contested. Some menus cluster tightly around the middle. Others stretch from light sides to extreme trays, producing wide interquartile ranges that averages erase. Taco Bell's heavy representation in the file does not place it at the caloric peak. Volume of SKUs and intensity of SKUs are different questions — the distribution chart is where that split becomes visible. Who beats the median — and who trails Calories vs median by Restaurant Relative to the dataset median, Sonic sits 80 calories above center. Chick-fil-A trails by 100. Burger King, Arby's, and McDonald's sit on the high side of the gap chart; Subway and Taco Bell sit below. These gaps are menu-composition effects as much as health branding. A chain that lists more grilled items and fewer shareable trays will land below the median even if its signature sandwich is not light. The gap chart ranks catalogs, not single hero products. Calories and fat move together Calories vs Total fat Plotting calories against total fat produces the expected positive slope — and the clusters that averages erase. Dense clumps of ordinary sandwiches sit in the middle; extreme trays and stacked burgers occupy the upper-right corner where both axes spike together. Fat is not a perfect proxy for calories, but in this menu file the two metrics rarely disagree about which items are extreme. When they do separate, it is usually because sugar-heavy drinks or desserts pull calories without matching fat. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and week-of-export coverage limits apply. Merged tables may fan out or duplicate rows when join keys are imperfect. The file does not measure how often items are ordered, how portions are actually consumed, or how recipes have changed since the 2018 release. Treat the numbers as a structural map of listed menu calories — not a verdict on every current drive-through board. What to take away The median calories item in this catalog sits near 490 calories. The items that define the public argument about fast food often sit two to five times higher — trays and stacked sandwiches that are e --- ## CAROLS: Jingle Bell Rock Spent 19 Weeks on Chart Across Six Decades - URL: https://artometrics.com/carols - Desk: arts, music - Published: 2026-06-15T00:00:00.000Z - Description: 387 holiday songs tracked 1958–2017 show a median 8-week chart life, with standards like Jingle Bell Rock returning across decades. Holiday songs occupy a peculiar chart niche: they return every December, accumulate weeks, then disappear. Analysis of 387 records spanning 1958–2017 shows a median of 8 weeks on chart and a maximum of 20. Better Days and Believe reach the ceiling. Perennial titles—Jingle Bell Rock, All I Want for Christmas Is You, Rockin' Around the Christmas Tree, White Christmas—form the recurring core across six decades. The pattern reveals a repertoire system, not a new-release market. The same recordings cycle through decades, accumulating weeks on each return. Seasonal Weeks Spiked in the Mid-2000s Median Weeks on chart Over Time Median weeks on chart by year reached 20 in 2005–2006, with other peaks in the mid-2010s (2016 at approximately 19 weeks, 2015 at approximately 18.5 weeks). Earlier years ranged between 11 and 16 weeks; a few years dropped to single digits or near 1 week. Changes in chart methodology (from airplay to sales to streaming) and the expansion of seasonal playlists both contributed to how many weeks holiday songs accumulated per year. Twenty Weeks Marks the Upper Club Better Days and Believe lead at 20.0 weeks; top dozen songs average 17.0 weeks Better Days and Believe lead at 20 weeks. Jingle Bell Rock and All I Want for Christmas Is You follow at 19 weeks each; Same Old Lang Syne and Rockin' Around the Christmas Tree at 18 weeks each. The median among the top dozen is 17 weeks. Against a file median of 8 weeks, that elite group exceeds the typical holiday chart life by more than double. Most Songs Cluster at Short Chart Lives Weeks on chart Distribution The distribution shows substantial counts at both very short weeks-on-chart values and a high concentration near the upper teens (approximately 84 observations near the distribution's peak). Mid-range stays occupy the space between these two poles. Holiday songs cluster at two extremes: brief seasonal appearances and extended multi-week December occupations. Few settle in the middle. Standards Reappear Across Decades Top Songs Over Time Jingle Bell Rock posted 19-week seasons in the late 1950s–early 1960s and again in 2016–2017. All I Want for Christmas Is You showed 19-week runs across multiple 2010s seasons after earlier quiet appearances. Rockin' Around the Christmas Tree similarly appeared in the early 1960s and mid-2010s peaks. White Christmas charted at 13 weeks in 1958–1962. The timeline confirms a repertoire system, not a new-release market. The same recordings cycle through decades, accumulating weeks on each return. More Instances Often Meet Longer Weeks Weeks on chart vs Instance Weeks on chart versus instance count shows perennial titles with multiple chart appearances clustering in the high-weeks region, while one-time chart entries scatter across both short and long stays. Re-entry drives holiday chart longevity. The same recording returns each December, accumulating new weeks with each cycle. What this file cannot tell you Chart methodologies changed over the 1958–2017 span (airplay, sales, streaming), making weeks on chart across decades imperfectly comparable. Title capitalization and punctuation variations can split identical songs into separate records. The dataset reflects U.S. chart logic only and does not measure global streaming playlists or contemporary holiday releases beyond 2017. What to take away Holiday chart life centers at 8 weeks, with a 20-week ceiling and a top-dozen median of 17 weeks. Standards recur across decades rather than aging out of rotation. Cite standards like Jingle Bell Rock, All I Want for Christmas Is You, and Rockin' Around the Christmas Tree when discussing cultural durability in music. Cite the 8-week median when addressing a typical seasonal chart appearance. Data, methods & sources Data and method The source is the TidyTuesday release from 2019-12-24 (christmas_songs.csv). After cleaning, 387 rows remain. Weeks on chart is the primary metric; instance captures repeat chart appearances. Charts are Plotly JSON --- ## CATALOG: Netflix's Movie-Heavy Catalog Hid Its Longest Titles in Interactives - URL: https://artometrics.com/catalog - Desk: arts, film - Published: 2026-06-15T00:00:00.000Z - Description: Black Mirror: Bandersnatch hit 312 minutes; median duration rose from 41 to 98 minutes between 2008 and 2021 as the catalog skewed longer. Median duration in Netflix's catalog rose from 41 minutes in 2008 to 98 minutes by 2021—a 139% climb driven by longer films, multi-season TV encodings, and interactive outliers like Black Mirror: Bandersnatch, which clocked 312 minutes. The working file holds 7,787 records. Median duration is 88.0 ; the highest observed duration is 312 , led by Black Mirror: Bandersnatch. Movie is the most common type label. How the pattern changed over time Median Duration Over Time Median duration rises from 41.0 in the opening period to 98.0 at the close—a catalog whose typical row lengthens as the library ages. That climb reflects more feature-length films, longer TV season encodings, or both. The trend is a mix signal, not a single format story. Who sits at the top Black Mirror: Bandersnatch leads at 312 — 226 marks the median among the top dozen Black Mirror: Bandersnatch leads at 312; 226 marks the median among the top dozen. Interactive and long-form outliers define the ceiling. Most of the library lives nowhere near those peaks—which is why the median of 88.0 remains the better everyday calibration. How the field is spread Duration by Type Duration by type separates movies from TV rows in the catalog encoding. The boxes show whether each type shares a tight middle or stretches into long tails. Movie dominance in count does not guarantee movie dominance in duration shape. The distribution is where that distinction becomes visible. Concentration The top 5 title entries account for 38% of the aggregate duration The top 5 title entries account for 38% of the aggregate duration—a steep head for a library that otherwise looks vast on the homepage. Steep Pareto curves mean a small set of rows drives most of the summed duration signal. The long tail is real inventory; it is not where the aggregate concentrates. Concentration The top 5 title entries account for 38% of the aggregate duration A second Pareto view of the same 38% concentration confirms the head–tail split under an alternate chart export. When two exhibits agree on the same share, the claim is about structure in the file—not a rendering choice. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and week-of-export coverage limits apply. Merged tables may fan out or duplicate rows when join keys are imperfect. Findings describe the file on hand—structural signals about Netflix title rows through 2021, not today's live catalog in every territory. What to take away Median duration climbed from 41.0 to 98.0 while Movie remained the most common type. A handful of long outliers—Bandersnatch at 312 among them—and a 38% duration concentration in the top five rows show how uneven a "library" can be once you stop counting titles and start summing length. Use the charts to separate mix, length, and concentration before treating any homepage as a flat shelf. Data, methods & sources Data and method The source is the TidyTuesday release from 2021-04-20 (R for Data Science community). The working file contains 7,787 rows and 13 columns after merging available tables in the week folder. Duration represents catalog metadata as published—minutes for films, season counts for TV rows where the export encodes type that way. Medians and Plotly exports absorb skew and missing cells without distortion. Sources Data Science Learning Community. (2021). TidyTuesday: Netflix Titles . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-04-20/netflix_titles.csv --- ## CEOs: CEO Tenure Caps at 2.5 Years in 9,423 Departure Records - URL: https://artometrics.com/ceos - Desk: civics, business - Published: 2026-06-15T00:00:00.000Z - Description: Median tenure coded at 1.0 year; "Interim" label accounts for 218 observations in TidyTuesday departures extract. In 9,423 TidyTuesday CEO-departure records, median max tenure ceodb is 1.00 year and the ceiling is 4.00 . "Interim" appears in 218 rows—the dominant succession label—while company-level leaders reach 2.0–2.5 years at most. The median of 1.00 sets the baseline: many coded departure spells are short. The data ask which companies show longer max tenures, how interim versus co-CEO labels differ, and whether tenure fields move together. Longer Max Tenures Are Rare in the Company Ranking Max tenure ceodb by Coname Stewart Information Services and Conversant Inc lead at 2.50 on max tenure ceodb. A second tier—Avatex, Intrepid Potash, Bergen Brunswig, SEACOR, Micro Warehouse, RH—clusters at 2.00 . Relative to the file median of 1.00, even these leaders are only modestly longer spells. Extended CEO tenure is the exception, not the rule. The Top Dozen Caps Out Quickly CONVERSANT INC leads at 2.50 — 2.00 marks the median among the top dozen Conversant Inc appears at 2.50 , and the median among the top dozen is 2.00 . There is no long tail of decade-scale max tenures in this ranking metric. Cite 2.00 as the elite-club median for max tenure ceodb—double the overall median, still short of popular narratives about imperial CEOs. Interim Labels Dominate the Category Mix Max tenure ceodb by Interim coceo Interim accounts for 218 observations with a median max tenure of 1.0 and a mean near 1.23 . CO-CEO shows 85 rows with the same median of 1.0 but a slightly higher mean of approximately 1.4 . Smaller capitalization variants are tiny samples. The file is thick with interim spells—a succession-system fact: many departures pass through temporary authority before a permanent appointment. Gaps to the Median Are Mostly Flat Max tenure ceodb vs median by Interim coceo Most interim/co-CEO labels sit at a zero gap to the median on max tenure ceodb, with only a thinly populated CO-ceo variant showing a positive gap of 1.0 . Category labels do not produce a dramatic tenure hierarchy. The larger pattern is the volume of spells coded interim. Tenure Fields Track Closely Within Labels Max tenure ceodb vs Tenure no ceodb Max tenure ceodb versus tenure no ceodb, colored by interim/co-CEO label, largely follows a tight correspondence—points hug aligned values, especially in the interim cloud (n=218). Where the two tenure fields disagree, coding definitions matter more than boardroom narrative. The scatter is a consistency check on the file's tenure construction. What this file cannot tell you Tenure fields are database constructions, not calendar biographies. Spelling variants of co-CEO fragment categories. Firm name changes and dual-class structures can split identities. A median of 1.00 may reflect coding granularity as much as calendar instability. Read the metric definition before converting it into a headline. What to take away In 9,423 departure records, max tenure ceodb centers at 1.00, with company-level leaders reaching only 2.0–2.5. Interim is the dominant succession label. The institutional story: departure files are full of short and interim spells, not a catalog of decades-long reigns. Data, methods & sources Data and method The source is the TidyTuesday release from 2021-04-27 (departures.csv). After cleaning, 9,423 rows remain. Max tenure ceodb is the primary ranked metric. Interim/co-CEO spelling variants appear as separate categories and reflect label noise as well as structure. Charts are Plotly JSON with PNG fallbacks. Sources Data Science Learning Community. (2021). TidyTuesday: CEO Departures . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-04-27/departures.csv --- ## CETACEANS: Captive Cetacean Transfers Peaked in 1972 at 170 U.S. Records - URL: https://artometrics.com/cetaceans - Desk: science, biology - Published: 2026-06-15T00:00:00.000Z - Description: 2,172 of 2,194 cetacean transfer records are US-labeled; bottlenose dolphins account for 1,668 entries in a 1946–2017 captivity ledger. Captive cetacean transfer records from 1946–2017 concentrate overwhelmingly in US institutions: 2,172 of 2,194 entries carry a US label, and bottlenose dolphins account for 1,668 records—76% of the file. Annual volume peaked around 1972 at roughly 170 entries, then declined through the 2010s. Every timeline and species comparison inherits this bottlenose bias. The archive is a US-centric captivity ledger, not a balanced global survey. Transfers Concentrate Overwhelmingly in the US US dominates with 2,172 records US-labeled transfers account for 2,172 records versus 22 marked Foreign. The geography of the file reflects US institutional reporting, not global captivity patterns. Any species comparison inherits that reporting bias. What looks like a biological pattern may be an artifact of institutional geography. Annual Volume Peaked in the Early 1970s Records By Period Record counts by year crest around 1972 at roughly 170 entries, with smaller peaks in the late 1970s and early 1980s: 90 in 1978, 80 in 1977, 79 in 1980. By the 2010s, annual counts decline to single digits. The volume timeline tracks institutional activity—when transfers were logged most densely, and when documentation thinned. Bottlenose Is the Archive's Default Animal Bottlenose appears 1,668 times — the most recurring name in the file Bottlenose appears 1,668 times—an order of magnitude above Killer Whale/Orca ( 79 ), Beluga ( 68 ), and Pacific white-sided variants (combined labels in the several dozens). Without bottlenose, this would be a small file. With it, every other species is a minority subplot. The US Bucket Is the Landscape US is the largest bucket with 2,172 records Restating the geography: US 2,172 versus Foreign 22 . The category chart is almost a single bar. Claims about global captivity patterns cannot be read from this extract without external sources that rebalance non-US institutions. Species Timelines Follow Different Eras Leaders Over Time Bottlenose transfers spike with the early-1970s volume peak (about 144 in 1972). Orca records show smaller pulses across the late 1960s–1980s. Beluga counts rise in scattered later years. White-sided labels show activity in the 1970s–1980s. Different species enter the ledger on different clocks—bottlenose sets the tempo. What this file cannot tell you US overrepresentation may reflect data compilation, not true global distribution. Species name variants (Beluga vs Beluga Whale; multiple white-sided spellings) fragment counts. The file does not measure welfare outcomes, wild population status, or legal regime changes except insofar as they altered what got recorded. What to take away Across 2,194 rows, the story is concentration twice over: US geography (2,172) and bottlenose identity (1,668). Cite the 1972 volume peak when discussing historical intensity, and treat non-bottlenose species as sparse but distinct timelines inside a bottlenose-dominated ledger. Data, methods & sources Data and method The source is the TidyTuesday release from 2018-12-18 (allCetaceanData.csv). After cleaning, 2,194 rows remain. Charts emphasize transfer geography, annual volume, species leaders, and timelines for the most frequent names. Charts are Plotly JSON with PNG fallbacks. Sources Data Science Learning Community. (2018). TidyTuesday: Cetaceans . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2018/2018-12-18/allCetaceanData.csv --- ## COMICS: Spider-Man Leads 23,000 Comic Characters at 4,043 Appearances - URL: https://artometrics.com/comics - Desk: arts, design - Published: 2026-06-15T00:00:00.000Z - Description: Spider-Man logged 4,043 appearances across 1935–2013; the top five characters account for 45% of cumulative appearances among 23,272 records. Spider-Man (Peter Parker) logged 4,043 appearances across the 23,272 characters in the TidyTuesday comics dataset (1935–2013), while the median character appeared 4 times. The top five names account for 45% of cumulative appearances in the ranked view, concentrating franchise mass in a narrow band of superhero infrastructure. That four-appearance median reflects structural reality: most named characters never become franchise centers. The charts below measure how appearance volume clusters at the top. Early Characters Logged Higher Median Appearances Median Appearances Over Time Median appearances by first-appearance year peak in the mid-1930s— 125 in 1935, 117 in 1937—then decline toward single digits as the catalog expands with short-lived characters. Golden Age cohorts logged higher medians because survivors were reprinted and reintroduced more often. Later decades added thousands of characters who never anchored ongoing series. A Dozen Icons Own the Appearance Summit Spider-Man (Peter Parker) leads at 4,043 — 2,377 marks the median among the top dozen Spider-Man leads at 4,043 appearances, followed by Captain America ( 3,360 ), Batman ( 3,093 ), Wolverine ( 3,061 ), Iron Man ( 2,961 ), and Superman ( 2,496 ). The median among the top dozen is 2,377 . These characters are publishing infrastructure—identities that anchor ongoing series, crossovers, and adaptation franchises. Popularity alone does not explain the gap between 4,043 and 4; continuous editorial use does. Marvel Has More Characters; DC's Median Appearances Run Higher Appearances by Publisher Marvel contributed 15,280 records with a median of 3 appearances and a maximum of 4,043. DC contributed 6,541 records with a median of 6 and a maximum of 3,093 (Batman). Marvel's catalog is wider; DC's typical character logged more appearances. Volume and intensity represent different editorial strategies—Marvel expanded the roster, DC concentrated use among established names. The Top Five Names Hold Nearly Half the Leader Aggregate The top 5 name entries account for 45% of the aggregate appearances The top five characters account for 45% of cumulative appearances among ranked leaders. The curve climbs steeply to 100% by the fifteenth name. Franchise comics follow a power-law distribution. A short list of identities absorbs most cumulative attention; the long tail contributes little to aggregate appearance volume. Concentration Repeats as a Structural Fact The top 5 name entries account for 45% of the aggregate appearances The second Pareto chart restates the same concentration: top five near 45%, curve approaching 100% by the fifteenth name. Repeating the view confirms the pattern is structural, not a binning artifact. Appearance mass in superhero publishing concentrates systematically at the top of the roster. What this file cannot tell you Appearance counts depend on how databases credit cameos, alternate Earths, and reboots. The publisher field collapses imprints. The 2013 cutoff predates the full MCU/DCEU feedback loop on print appearances. The four-appearance median should not be interpreted as "characters only show up four times then vanish" without verifying how incomplete rows are handled in the source. What to take away Across 23,272 characters, the median appearance count is 4 while Spider-Man logged 4,043. The top five leaders hold 45% of cumulative appearances in the ranked view. Marvel's wider catalog contrasts with DC's higher median. The dozen icons above 2,300 appearances represent the industry's core publishing capital—characters that carry ongoing series, crossovers, and franchise adaptations. Data, methods & sources Data and method The source is the TidyTuesday release from 2018-05-29 (week9_comic_characters.csv). After cleaning, 23,272 rows remain. Appearances is the primary metric; publisher splits Marvel versus DC; Pareto charts show concentration among leading names. Charts are Plotly JSON with PNG fallbacks. Sources Data Science Learning Community --- ## DIPLOMACY: Brazil Dominates the UN Roll-Call in a 100,000-Vote Sample - URL: https://artometrics.com/diplomacy - Desk: civics, politics - Published: 2026-06-15T00:00:00.000Z - Description: In a 100,000-row UN voting extract, Brazil appears 747 times and 79,663 votes (80%) are affirmative — before ideological coalition modeling begins. In a 100,000-record extract of United Nations roll-call votes, 79,663 positions (80%) are affirmative, Brazil appears 747 times as the most recurring member state, and most country entities appear only once. The dataset is categorical — no numeric scores — making it a recurrence and frequency archive rather than a win-rate leaderboard. The first job of this file is structural: map the distribution of yes, no, and abstain, identify which member names recur, and confirm whether country-code metadata aligns with entity labels across the sample. Research question What does a 100,000-row extract of United Nations roll-call votes reveal before any ideological coalition model is fitted? This report asks how vote categories are distributed, which member-state names recur most often, and whether country-code metadata confirms or complicates the entity picture. The question is descriptive. A yes-heavy table does not mean the General Assembly lacks conflict; it means this sample's recorded positions concentrate in one category. The analysis starts with vote mass and country recurrence before moving toward interpretive claims about alignment. Yes votes dominate the sampled UN record Yes votes dominate the sampled UN record Yes dominates with 79,663 records. In a 100,000-row extract, that is the center of gravity: 80% of recorded positions in this slice are affirmative. Abstain and no exist, but they do not match yes for volume. Dominance here is a frequency fact, not proof that the General Assembly always agrees. It shows where the mass of the table sits before any conflict analysis begins. The United Nations General Assembly often votes on resolutions that have already passed through committees, negotiations, amendments, and diplomatic bargaining. A large yes category can therefore reflect agenda-setting and consensus-building before the roll call, not an absence of disagreement in world politics. The public vote is the final visible record of a process that began earlier. UN voting scholars usually move beyond raw categories into ideal-point models, affinity scores, or issue-area subsets because the meaning of a yes vote depends on the resolution. A yes on decolonization, a yes on nuclear disarmament, and a yes on a budgetary item are all affirmative votes, but they do not carry the same geopolitical signal. Brazil appears most often among country names Brazil appears most often in this extract Brazil appears 747 times — the most recurring country name in the file. The top dozen countries account for a visible share of all 100,000 rows, which is what a recurring-member chart shows: who is densely present in the extract. High appearance counts can reflect coverage, membership longevity, or how the sample was built. They are presence metrics, not win rates. Brazil has been a United Nations member since 1945 and is a frequent actor in General Assembly voting records. The country's foreign-policy tradition includes visible positions on development, non-intervention, South-South cooperation, and Security Council reform. But the 747 appearances shown here are a row-count fact, not a claim that Brazil is uniquely influential in every issue area. Country recurrence is also shaped by institutional continuity. Newer UN members, dissolved states, renamed states, and countries with interrupted or transformed membership histories appear differently from long-standing members. The Soviet Union/Russia, Yugoslavia/successor states, Czechoslovakia/Czechia and Slovakia, and country-name standardization issues are why entity handling matters in UN vote data. Vote categories restate the file's center of gravity Vote categories reveal the file's center of gravity Yes is again the largest bucket with 79,663 records when vote categories are plotted as a bar landscape. Repeating the category cut confirms that the headline frequency is not an artifact of a single chart type. Category concentration shows where editorial attention should start — with the yes majo --- ## EMMYS: SNL's 859 Records Beat Every Show - URL: https://artometrics.com/emmys - Desk: arts, film - Published: 2026-06-15T00:00:00.000Z - Description: Saturday Night Live appears 859 times across 29,678 Emmy records from 1957–2021—three times the next-closest show. Saturday Night Live appears 859 times across 29,678 Emmy records from 1957–2021—three times more than the next-closest show and a sign that Emmy concentration tilts toward programs that return year after year rather than one-night sweeps. The working file holds 29,678 records spanning 1957–2021 . Nominee is the most common label, accounting for 23,739 rows—wins and other designations occupy a thinner slice, which means any leaderboard drawn from this extract ranks recurrence more than victory. Landscape Nominee dominates with 23,739 records Nominee dominates with 23,739 records. The file is built around nomination rows; wins and other type labels sit in a thinner slice. That imbalance is structural—any leaderboard drawn from this extract ranks how often names enter the nominee column, not how often they win. Volume Records By Period Activity peaks in 2019 with 2,613 records—the densest year in the span. Period-level counts show when Emmy paperwork thickened: more categories, more programs, more named credits. Volume is the first signal that awards seasons are not equally crowded across decades. Who sits at the top Saturday Night Live appears 859 times — the most recurring name in the file Saturday Night Live appears 859 times—the most recurring name in the file. The top dozen account for a visible share of all 29,678 rows. Long-running variety and franchise programs accumulate nominations the way long careers accumulate credits. Dominance here is recurrence, not a single sweep night. Category Nominee is the largest bucket with 23,739 records Nominee is the largest bucket with 23,739 records. Category concentration shows where editorial attention should focus when reading the table. If the question is "who swept," separate win rows carefully. If the question is "who kept getting invited," this nominee-heavy landscape is the right map. Timeline Leaders Over Time Leading names do not move in lockstep—some fade as others surge. Tracking counts over time separates sustained presence from one-off spikes. A show that dominates a single ceremony is a different data shape from a name that returns for decades. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and week-of-export coverage limits apply. Merged tables may fan out or duplicate rows when join keys are imperfect. Findings describe the file on hand—structural signals about Emmy nomination volume and recurrence through 2021, not a complete history of television quality. What to take away Emmy concentration shows up as recurrence: Saturday Night Live at 859 appearances, a nominee column that holds 23,739 of 29,678 rows, and a 2019 peak of 2,613 records. Leaders, volume, and timelines each answer a different question about how lopsided the season became. Use the charts to separate invitation from victory—then check the type field before treating any name as a sweep. Data, methods & sources Data and method The source is the TidyTuesday release from 2021-09-21 (R for Data Science community). The working file contains 29,678 rows and 11 columns after merging available tables in the week folder. Charts export as Plotly JSON with PNG fallbacks. Counts by type, period, and recurring name are the main instruments—this table captures volume and recurrence rather than a single quality score. Sources Data Science Learning Community. (2021). TidyTuesday: Emmy Awards . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-09-21/nominees.csv --- ## EMPERORS: Constantine Lasted 31 Years; the Nerva–Antonine Line Averaged 12.5 More - URL: https://artometrics.com/emperors - Desk: humanities, history - Published: 2026-06-15T00:00:00.000Z - Description: Constantine ruled 31 years; the median emperor lasted 6.5—dynasty membership did not close that gap. Constantine the Great ruled for 31.0 years. The median Roman emperor in this 68-record file lasted 6.50. That five-to-one gap frames the question: did dynasty membership buy stability, or did survival depend on forces that overwhelmed house labels? The answer is both, but unevenly—Nerva–Antonine emperors averaged 12.5 years above median, while Gordian, despite being the most common dynasty name, trailed by 4.0. The file covers 68 emperors drawn from the TidyTuesday Roman Emperors release. Reign length is measured from recorded start and end dates; dynasty labels come from the source table. The charts separate name-level longevity from dynasty-level structure so the two are not confused. Research question Did dynastic identity extend Roman imperial tenure, or did individual political survival overwhelm house-level labels? The report asks whether reign length concentrates around exceptional rulers, whether dynasties show distinct tenure distributions, and how much aggregate imperial time is carried by the longest-reigning names. The question is observational. This 68-record TidyTuesday file is not a full constitutional model of Rome. It uses recorded reign spans to compare names and dynasties, treating the median emperor's 6.50-year tenure as the baseline. Constantine's thirty-one years set the name-level ceiling Reign years by Name Constantine the Great leads at 31.0 reign years. Valentinian II anchors the low end of the top group at 17.0—still far above the file median of 6.50. The chart shows a curated upper slice, not the full distribution. Grouping by name makes the catalog's major entities comparable on a single scale. The question is not who is famous, but who lasted. The gap between Constantine and the rest of the top tier shows that longevity was exceptional rather than routine. Constantine's 306–337 CE reign sits at a hinge in Roman institutional history: civil war, Tetrarchic succession, Christian legalization after the Edict of Milan, the Council of Nicaea in 325, and the foundation of Constantinople all fall inside or around his rule. The dataset reduces that to 31.0 years, but the named entity carries enough historical context to explain why the ceiling is not a spreadsheet oddity. The top dozen still sits far above the median emperor Constantine the Great leads at 31.0 — 20.0 marks the median among the top dozen Among the top dozen emperors, the median is 20.0 years—three times the full-file median of 6.50. Even the typical elite tenure in this slice is a different political animal from the typical emperor in the dataset. That separation matters for interpretation. Leaderboards describe the right tail, not Rome's everyday succession reality, which lived closer to half a decade than to two decades. The 20.0-year median among the top dozen contains the familiar names that make imperial history feel more stable than it was: Augustus, Tiberius, Hadrian, Antoninus Pius, Marcus Aurelius, Constantine. The full-file median pulls the reader back toward usurpers, assassinations, co-rule, civil wars, and short-lived crisis reigns. Dynasty boxes show contested, not uniform, tenure Reign years by Dynasty When reign years are split by dynasty, the boxes do not collapse into a single shared consensus. Some houses stretch higher; others sit tighter and lower. The spread shows dynasty is a contested axis for tenure rather than deterministic. Read the boxes as structural signals: belonging to a named dynasty did not automatically purchase Constantine-length stability. It bought a distribution, and those distributions differ. The Nerva-Antonine adoption sequence has long been associated with high imperial stability, while the third-century crisis illustrates how military acclamation and rapid turnover can shred dynastic continuity. The box plot asks whether succession packages mattered—and the answer is yes, but variably. Nerva–Antonine sits above the median; Gordian trails Reign years vs median by Dynasty Relative to the file media --- ## EXERCISE: Colorado Leads U.S. Exercise Participation at 32% - URL: https://artometrics.com/exercise - Desk: culture, wellness - Published: 2026-06-15T00:00:00.000Z - Description: Colorado leads at 32.0; median across 52 U.S. jurisdictions is 23.0, with Western and New England states dominating the top tier. Colorado leads U.S. exercise participation at 32.0 , while the median across 52 jurisdictions stands at 23.0 . Mountain West and New England states occupy eight of the top twelve positions, and the top five account for 36% of aggregate participation. The data, from TidyTuesday's 2018 exercise extract, shows geographic concentration rather than even distribution. The charts document the spread, the gap between leaders and the median, and the relationship between adult and male participation rates. Western and New England States Lead Adult Exercise Adults by State Colorado leads at 32.0 , followed by Idaho at 31 , New Hampshire and D.C. at 30 , and Massachusetts and Vermont at 29 . Utah and Washington round out the upper band at 28 . Eight of the top twelve are Mountain West or New England states. The cluster reflects outdoor access, income, and age structures that support higher activity levels. The Top Dozen Sits Well Above the National Median Colorado at 32.0 — median among top dozen is 28.5 Colorado remains first at 32.0 , while the median among the top dozen stands at 28.5 —5.5 points above the overall median of 23.0. The gap documents geographic clustering: high-exercise participation concentrates in specific regions rather than distributing evenly across all 52 jurisdictions. Most States Cluster Near the Low-20s Median 23.0 and mean 22.6 indicate a relatively symmetric distribution The tallest bin centers near 23.5 with approximately 15 states. Smaller counts appear in both the mid-teens and the high-20s/30s. The mean of 22.6 sits close to the median, indicating symmetry at the state level. Symmetry does not eliminate the elite tier: Colorado's 32 remains a clear outlier at the right edge. Top States Hold a Disproportionate Share of the Aggregate The top five state entries account for 36% of the aggregate adults The top five jurisdictions account for 36% of the aggregate adults measure. Cumulative share approaches 100% by the fifteenth entry. In a 52-row dataset, concentration among top performers remains pronounced. Adult and Male Rates Move in the Same Direction Adults vs Men Adults and Men track together across 52 jurisdictions: states with higher adult participation rates also post higher male rates. Leaders reach the low-to-mid 30s on the adults axis and the 30s–40s range on the men's axis. The pattern confirms the same state hierarchy across gender categories. What this file cannot tell you Self-reported exercise measures vary by survey methodology and year. State averages mask within-state variation by urban/rural location and race. D.C. is included but is not a state. The 2018 TidyTuesday snapshot does not represent current CDC data; use these rankings as structural for this extract only. What to take away Adult exercise rates center at 23.0 across 52 jurisdictions, while Colorado reaches 32.0 and the top-dozen median stands at 28.5. Reference the Mountain West and New England cluster when explaining geographic patterns, and the 36% top-five share when discussing concentration of activity. Data, methods & sources Data and method The source is the TidyTuesday release from 2018-07-17 (week16_exercise.xlsx). After cleaning, 52 rows remain. Adults is the primary ranked metric; Men appears in the scatter. Charts are Plotly JSON with PNG fallbacks. Sources Data Science Learning Community. (2018). TidyTuesday: Exercise USA . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2018/2018-07-17/week16_exercise.xlsx --- ## FACTBOOK: Five Countries Hold 72% of World Population - URL: https://artometrics.com/factbook - Desk: civics, economics - Published: 2026-06-15T00:00:00.000Z - Description: China's 1.36 billion against a 5.2 million median — the top five countries hold 72% of aggregate population in the CIA World Factbook extract. China's 1,355,692,576 people sit against a global median of 5,220,371 in the CIA World Factbook extract — a 260-fold gap that drives every concentration metric. The top five countries hold 72% of aggregate population; most nations operate at a scale closer to the median than to the summit. Mean population (~32 million) sits far above the median because a handful of giants pull the average upward. The distribution is right-skewed: 218 of 259 entries fall into the smallest bin. A Handful of Giants Dominate Headcount Population by Country China leads at 1.36 billion, India at 1.24 billion, the European Union aggregate near 511 million, the United States near 319 million, Indonesia near 254 million, and Brazil near 203 million. These six operate in a different scale class than the median country at 5.2 million. They are not outliers; they are the architectural beams of every global concentration statistic. Even the Top Dozen Has a Steep Internal Drop China leads at 1,355,692,576 — 199,415,584 marks the median among the top dozen Within the top twelve, the median is 199,415,584 — less than one-sixth of China's total. The drop from first to sixth is steeper than the drop from sixth to twelfth. "Large country" is a pyramid, not a plateau. Second-tier giants are already far smaller than the demographic summit. Most Countries Sit in the Small-Population Mass Median 5,220,371 vs mean 32,294,361 — the shape is right-skewed The first bin holds 218 of 259 entries. Only a handful of observations reach the billionaire tier or approach it. The median (5.2 million) describes the typical country; the mean (~32 million) is pulled upward by the giants. Use the median when the question is "most countries" and the top five when the question is "most people." The Top Five Already Hold Most of the People The top 5 country entries account for 72% of the aggregate population The Pareto curve shows the top five countries accounting for 72% of aggregate population. By fifteen entries the curve approaches the full plotted total. Global population is not evenly distributed across flags. It is concentrated in a short list of demographic powers. High Birth Rates Cluster Among Smaller Populations Population vs Birth rate Birth rates above 40 per 1,000 cluster among countries well below the mega-population tier. Several large countries sit at lower birth rates. Demographic giants are not fertility leaders. Scale and birth rate are independent stories in the Factbook. What this file cannot tell you Factbook figures are estimates with uneven vintage and methodology across entities. Including the European Union alongside countries double-counts if users sum naively. Small territories and disputed entities add definitional noise. Population totals are not prosperity scores. Pairing with birth rate is descriptive, not a development ranking. What to take away In 259 Factbook rows, the median country has 5.2 million people while China exceeds 1.35 billion. The top five entries hold 72% of the aggregate in the concentration view. Cite the median for a typical state; cite China, India, and the Pareto share when the question is planetary concentration. Data, methods & sources Data and method The source is the TidyTuesday release from 2024-10-22 (cia_factbook.csv). After cleaning, 259 rows remain. Population is the primary ranked metric; birth rate appears in the joint scatter. Charts are Plotly JSON with PNG fallbacks. Sources Data Science Learning Community. (2024). TidyTuesday: CIA World Factbook . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-10-22/cia_factbook.csv --- ## GAMES: Crowd Scale Meets Elite Ratings - URL: https://artometrics.com/games - Desk: sports, gaming - Published: 2026-06-15T00:00:00.000Z - Description: Ratings and popularity across thousands of titles find games that win both mass attention and esteem. BoardGameGeek ratings are a public taste machine: tens of thousands of titles, each scored by the people willing to log plays. The TidyTuesday games extract used here holds 10,532 records with a median average rating of 6.39 and a high of 9.00 for Small World Designer Edition. Wargame, World War II is the most common category label in the file. That already tilts the catalog toward conflict simulations — a hobby subculture with its own rating norms. The charts ask how ratings split by category, how leaders separate from the median, and whether crowdfunding-scale user counts track with elite scores. The Catalog’s Median Rating Sits Near 6.39 Median Average rating Over Time The time view in this extract collapses to a median average rating of about 6.39 — the same calibration point as the fact box. In BoardGameGeek terms, that mid-6s band is ordinary: playable, not legendary. Everything that follows should be read against that baseline. A 9.00 specialty edition is not “a bit better than average”; it is a different prestige tier. Designer Editions and Heavy Games Own the Peak Small World Designer Edition leads at 9.00 — 8.78 marks the median among the top dozen Small World Designer Edition leads at 9.00 . Kingdom Death: Monster (~ 8.93 ), Terra Mystica: Big Box (~ 8.85 ), and a run of deep wargames — Last Chance for Victory, The Greatest Day, Last Blitzkrieg, Enemy Action: Ardennes — fill out the top dozen. The median among those leaders is about 8.78 . These are not mass-market party games. They are hobby-complete objects — big boxes, campaign systems, hex-and-counter epics — rated by audiences selected for intensity. Wargame Categories Rate Higher Than Card Games Average rating by Category Category boxes show Napoleonic wargames with a median near 7.04 (n=124) and World War II wargames near 6.89 (n=449). Card games sit lower, with a median near 6.03 (n=438). Abstract strategy lands near 6.28 (n=284). Volume and prestige diverge: card games are numerous but center closer to the catalog median; dedicated wargame labels clear a higher bar among the people who rate them. Civil War and Napoleonic Titles Beat the Median Most Average rating vs median by Category Gaps to the overall median are largest for American Civil War wargames (~ +0.66 ) and Napoleonic wargames (~ +0.65 ), with World War II wargames also positive. Dice and generic card-game labels sit below the median. The pattern is consistent: historical conflict simulations, as tagged in this file, are the categories that most reliably clear the 6.39 baseline. Crowd Size and Rating Are Related — Loosely Average rating vs Users rated Average rating versus users rated shows popular hits with tens of thousands of ratings landing across the mid-to-high 7s, while many high-rated titles still have modest user counts. Elite scores without crowd scale remain possible. The scatter’s lesson is citable: popularity predicts attention, not a precise quality score. A 9.00 designer edition and a 7.4 smash hit solve different problems in the hobby economy. What this file cannot tell you BoardGameGeek ratings are self-selected by hobbyists; children’s games and mass-market titles can be systematically under- or over-represented. Multi-label categories double-count games across boxes. Expansions and designer editions inflate prestige relative to base games. The file is a 2019 community snapshot, not a live BGG API pull. What to take away Across 10,532 games, the median rating is 6.39; the top dozen lives near 8.8–9.0. Wargame categories clear the baseline more often than broad card-game labels. Cite 6.39 as the catalog’s center of gravity, and treat 9.00 specialty editions as a separate prestige economy — not as proof that “games got better.” Data, methods & sources Data and method The source is the TidyTuesday release from 2019-03-12 (board_games.csv). After cleaning, 10,532 rows remain in the working frame. Average rating is the primary metric. Category fields can be multi-label strings. --- ## HERITAGE: Sweden's UNESCO Sites vs. Denmark - URL: https://artometrics.com/heritage - Desk: culture, travel - Published: 2026-06-15T00:00:00.000Z - Description: Sweden led Scandinavia with 15 UNESCO sites by 2022; Denmark's count grew 150% from 2004. Median rose from 5 to 10 sites. Sweden reached 15 UNESCO World Heritage sites by 2022, the highest count among Scandinavian countries in this extract, while Denmark posted 150% growth from 2004. The median site count doubled from 5.00 to 10.0 across the 18-year span, showing broad expansion beyond the leader. Six country-year pairs are enough to separate level from growth. Sweden led on absolute count; Denmark led on percentage gain. The median shift from 5.00 to 10.0 anchors the regional trend. Median heritage counts doubled at the center Median Value Over Time The median rose from 5.00 in 2004 to 10.0 in 2022, indicating that the center of the Scandinavian distribution moved upward. With three countries at two time points, the median shift is visible in the raw table and the chart confirms it. A doubling at the median means broad gains, not isolated leader expansion. Sweden leads the country ladder Sweden leads at 14.0 — 7.00 marks the median among the top dozen Sweden leads at 14.0, with the median at 7.00 among the top entries in this extract. In a small peer set, Sweden is both the largest inventory and the charted leader. Leading on count does not guarantee leading on growth rate. The next charts separate stock from speed. Leaders diverge when tracked over time Top Country Over Time Leading countries move at different rates. Some start high and add modestly; others start low and surge. Tracking value over time separates sustained dominance from momentum. In a two-point panel, trajectory is measured by who added sites between 2004 and 2022, not who merely inherited a large base. 2004 versus 2022 makes the gains visible side by side Value by year and Country Grouped bars show who gained between 2004 and 2022. Side-by-side bars reveal per-country momentum that totals alone obscure. The year comparison displays absolute change before converting those deltas into percentages. Denmark posted the largest percentage gain Value growth by Country Denmark grew 150% from 2004 to 2022, the largest percentage gain in the file. Percentage bars normalize different starting counts, which is necessary when Sweden, Norway, and Denmark began at different levels. Sweden leads on absolute count; Denmark leads on growth rate. Both measures appear in the file and answer different questions. What this file cannot tell you This TidyTuesday extract is a six-row Scandinavian panel for 2004 and 2022, not a global census or the live UNESCO World Heritage List API. It does not cover every country or every year in between. Findings describe country counts and growth in this extract, not a qualitative assessment of individual sites or their cultural significance. What to take away Sweden reached 15 UNESCO sites by 2022, the highest in this Scandinavian extract, while Denmark grew 150% from 2004 to 2022. The median count doubled from 5.00 to 10.0, indicating broad regional expansion. Absolute level and percentage growth are distinct metrics. The year-compare and growth charts keep them separate so prestige stock is not mistaken for expansion speed. Data, methods & sources Data and method The source is the TidyTuesday release from 2024-02-06 via the R for Data Science community. The working file contains 6 rows and 3 columns: country, year, and site count. Years are 2004 and 2022; countries are Sweden, Norway, and Denmark. Medians summarize the center; percentage changes normalize different starting levels. Index fields are excluded from metric selection. Sources Data Science Learning Community. (2024). TidyTuesday: World Heritage Sites . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-02-06/heritage.csv Editor's note Artometrics data report from the TidyTuesday research pipeline. Charts and aggregates are reproducible from the embedded exhibits and public source files. Source archive (GitHub) --- ## HORROR: Horror Ratings Rose as Catalog Volume Grew 50× in Seven Decades - URL: https://artometrics.com/horror - Desk: arts, film - Published: 2026-06-15T00:00:00.000Z - Description: 32,540 horror films from 1950–2022 show median vote average climbed from 5.65 to 6.00 — modest quality gain amid explosive catalog growth. The median vote average for horror films climbed from 5.65 to 6.00 between 1950 and 2022 — a 0.35-point rise across seven decades that added 32,540 titles to the catalog. TMDB metadata for horror-tagged films yields 32,540 records from 1950–2022. The median vote average is 4.00; the highest observed average is 10.0. Horror is the most common primary genre label, as expected in a horror-tagged extract. How the pattern changed over time Median Vote average Over Time Median vote average rises from 5.65 in the opening period to 6.00 at the close — a 0.35-point lift across decades of catalog growth. Volume exploded while the middle of the scoreboard edged up. More titles filled the shelf; the median shifted modestly upward rather than holding flat or declining. Who sits at the top The House Guest leads at 10.0 — 10.0 marks the median among the top dozen The House Guest leads at 10.0, and 10.0 also marks the median among the top dozen. The head of the leaderboard is a cluster of perfect averages, not a gentle taper. In large rating tables, that pattern signals low vote counts or niche titles with small, enthusiastic rater pools. The top is a ceiling effect, not a ranked canon. How the field is spread Vote average by Primary genre Primary-genre boxes reveal whether vote averages converge across hybrid tags or split by outliers. Horror-tagged films carry secondary labels — crime, animation, drama — and those buckets disagree about quality. Consensus is uneven once the tag set widens. Who beats the median — and who trails Vote average vs median by Primary genre Crime sits 1.00 above the median; Animation trails by 4.00. Those gaps are relative to this file's middle, not absolute statements about every crime or animated horror title. They mark where hybrid labeling and rater pools pull averages apart. What moves together Vote average vs Vote count Vote average and vote count do not move as a simple line. Perfect scores with tiny counts sit apart from widely watched mid-range films. Popularity and perceived quality are related but not interchangeable — the scatter shows where that distinction becomes visible. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and week-of-export coverage limits apply. Merged tables may fan out or duplicate rows when join keys are imperfect. Findings describe the file on hand — structural signals about horror-tagged ratings, not a complete history of the genre's artistry. What to take away The median vote average rose 0.35 points (5.65 to 6.00) as the catalog expanded by tens of thousands of titles. Perfect 10.0 leaders reflect sparse vote counts rather than broad acclaim. Read leaders, gaps, and the average–count scatter together before treating any title as proof of a golden age. Data, methods & sources Data and method The file merges TMDB metadata for thousands of horror-tagged films: ratings, budgets, runtimes, and genre tags from 1950 through 2022, released via TidyTuesday. Charts ship as Plotly JSON with PNG fallbacks. Medians handle skew better than means. Perfect 10.0 scores often sit on thin vote counts — treat the ceiling as a signal about sparse ratings, not universal critical consensus. Sources Data Science Learning Community. (2022). TidyTuesday: Horror Movies . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2022/2022-11-01/horror_movies.csv --- ## HURRICANES: Maria's 2017 Peak Hammered Puerto Rico in a Three-State Season - URL: https://artometrics.com/hurricanes - Desk: culture, environment - Published: 2026-06-15T00:00:00.000Z - Description: Texas, Florida, and Puerto Rico absorbed 100% of measured 2017 hurricane impact in 153-record TidyTuesday extract; median reading 703, ceiling 5,072. Three places absorbed 100% of measured hurricane impact in the 2017 TidyTuesday extract: Texas leads state totals at 983, Florida follows, and Puerto Rico recorded the season's 5,072 peak. The 153-record file from the R for Data Science community tracks intensity values across the late-summer Atlantic window. Median reading is 703; the right-skewed distribution pushes mean to 1,020. The top decile begins at 2,214—the threshold where defining storm days separate from elevated but survivable readings. This is not a full Caribbean cyclone climatology. It is a season-slice that places Texas, Florida, and Puerto Rico side by side on intensity, concentration, and timing. Timeline across states Value by State Daily value lines separate which state or territory bore the brunt on which days. Peaks rarely align. Texas, Florida, and Puerto Rico experienced the same basin season on different clocks—landfall timing and local intensity created staggered curves rather than a single shared crest. The 5,072 maximum defines the intensity ceiling. Everything else in the file is read relative to that spike and to the 703 median. Who sits at the top Texas leads at 983 — 621 marks the median among the top dozen Texas leads at 983; median among the top dozen entries is 621. Florida and Puerto Rico complete the short list of places that dominate the aggregate signal. A short leaderboard is itself a finding: storm impact is not evenly distributed across dozens of states. A handful of geographies carry the measurable season. How values are spread Median 703 vs mean 1,020 — the shape is right-skewed Right-skewed distribution: median 703 versus mean 1,020. The top decile begins at 2,214. That tail is where the defining storm days live—and why a mean-based summary sounds more extreme than a typical day in the file. Skewness is the statistical signature of disaster data. Most observations are elevated but survivable; a minority of days rewrite the season's memory. Concentration of impact The top 3 state entries account for 100% of the aggregate value The top three state or territory entries account for 100% of the aggregate value. The Pareto curve is not merely steep—it is a three-place map. That concentration explains why comparative journalism about 2017 returned to Texas, Florida, and Puerto Rico. The file's structure matches public memory: a short head, not a long list of mildly affected places. Concentration, from another cut The top 3 state entries account for 100% of the aggregate value A second concentration chart repeats the same structural claim from a related table cut: the measurable aggregate is carried by the same three-place head. Steep Pareto curves mean a small head drives most of the signal. For Puerto Rico, the lesson is not that other places did not suffer. In this season-slice, comparative magnitude collapses onto a tiny set of names—and Puerto Rico is inside that set. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and week-of-export coverage limits apply. A 153-row season extract cannot stand in for FEMA damage ledgers, mortality studies, or multi-decade storm catalogs. Value in the working columns is the file's measured intensity metric—not a universal currency of loss. Pair these charts with official after-action reporting before converting them into legal or fiscal claims. What to take away The 2017 window in this file is a story of concentration: median 703, ceiling 5,072, and an aggregate dominated by three places. Texas leads the ranking cut; Puerto Rico holds the season's peak. Disaster seasons are not democratic distributions. They are skewed clocks. The citable map is the short head of places where the season actually happened. Data, methods & sources Data and method Source: TidyTuesday release from 2018-06-19, published by the R for Data Science community. The working file contains 153 rows and 4 columns after cleaning—a narrow table built for --- ## LANGUAGES: Glottolog's 8,612 Records Map Fieldwork, Not Where People Speak - URL: https://artometrics.com/languages - Desk: arts, language - Published: 2026-06-15T00:00:00.000Z - Description: 2,363 of 8,612 language records sit in Africa's macroarea bucket — a signal of documentation density, not speaker counts. 2,363 of 8,612 language records in a Glottolog-derived TidyTuesday extract sit in the Africa macroarea bucket — 27% of the catalog. The concentration maps documentation density — where grammars, identifiers, and fieldwork accumulate — not the lived geography of speech. The working file holds 8,612 records. Africa is the largest macroarea at 2,363 entries; most language names appear once while a short head repeats. Row counts are not speaker counts, and macroarea labels are database shortcuts, not political borders. A language catalog is also an archive of scholarly attention. Regions with denser fieldwork traditions show higher row counts even when underlying human diversity is more even — or more endangered — than the table suggests. Research question What does a Glottolog-derived catalog reveal about the geography of linguistic documentation, not the geography of language itself? This report asks where the 8,612-record extract concentrates by macroarea, where names and identifiers repeat, and how much of the visible language map is shaped by catalog infrastructure. The question is observational because row counts are not speaker counts, vitality scores, or political claims. The aim is to distinguish language diversity as lived by communities from language visibility as preserved in databases, grammars, ISO-style identifiers, and scholarly citation systems. A geographically uneven landscape Language documentation is geographically uneven Africa dominates with 2,363 records in the primary macroarea cut. Other macroareas — Papunesia, Eurasia, the Americas, Australia — follow at lower counts. The main bucket carries 27% of the story; this field does not behave like a gentle long-tail split across dozens of equal regions. Uneven documentation is not identical to uneven linguistic diversity, but it shapes what comparative science can see. Extinction risk, revitalization, and typology research all inherit the map of what has been recorded. Africa's lead is plausible in linguistic terms: the continent contains large families such as Niger-Congo, Afro-Asiatic, Nilo-Saharan groupings as traditionally defined, and Khoisan-associated lineages, alongside intense contact zones and multilingual states. But the chart does not validate any particular family taxonomy; it records how the Glottolog-style file assigns macroarea metadata to rows. Papunesia and Australia are especially important comparison cases because they contain many small language communities with high typological diversity and severe endangerment pressure. A lower row count than Africa does not mean lower scientific importance. It means the record is distributed unevenly across regions, fieldwork histories, and naming conventions. Who sits at the top of name repetition Repeated language names show documentation density Fasu appears among the recurring names in the file; the top dozen account for a visible share of all 8,612 rows even though most languages appear only once. Name repetition can reflect dialect documentation, alternate glottonyms, or multiple database entries tied to related varieties. Fasu is a Papuan language name associated with Papua New Guinea, and its recurrence is a reminder that catalog entries are not always one-to-one with what a lay reader calls a language. Varieties, dialect clusters, alternate names, bibliographic records, and genealogical nodes can all push the same surface name into repeated view. This is where Glottolog's infrastructure differs from a census. A national census might ask who speaks a language at home; Glottolog asks how a language or lineage is classified and documented in the linguistic literature. The same community can be highly visible in one system and nearly invisible in another. Regional concentration Regional concentration shapes the language map Africa is again the largest bucket with 2,363 records on the regional concentration chart. Category concentration shows where editorial and scientific attention should look first --- ## LCSH: 69,027 Headings Anchor Gutenberg's Core - URL: https://artometrics.com/lcsh - Desk: arts, language - Published: 2026-06-15T00:00:00.000Z - Description: LCSH accounts for 69% of subject headings; PS (American literature) appears 4,684 times — concentration defines the reusable public-domain canon. Library of Congress Subject Headings (lcsh) account for 69,027 records in a 100,000-row TidyTuesday extract of Project Gutenberg — 69% of all subject entries. The catalog is not a popularity contest; it is a map of which public-domain books volunteers could digitize and classify at scale. Subject code PS — American literature — recurs 4,684 times, leading all labels. A short head of repeated classifications anchors the catalog while most subject entities appear once. The pattern is typical of library metadata: a reusable canon of headings, and a long inventory of singleton assignments. Reading Gutenberg as a concentration map of reusable works is where the data help. Public-domain books cluster by subject type Public-domain books cluster by subject type lcsh dominates with 69,027 records, far ahead of thinner subject-type buckets. The main classification family carries the story; this field does not split into many equal long-tail types. That concentration means most navigational claims about what Gutenberg contains are really claims about how LCSH-style headings organize the digitized shelf. A small set of subjects anchors the catalog A small set of subjects anchors the catalog PS appears 4,684 times — the most recurring subject name in the file. The top dozen account for a visible share of all 100,000 rows even though most subject entities appear only once. American literature codes, fiction labels, and related headings form a reusable core. They are the catalog's gravitational center for teachers, scrapers, and adaptation hunters. Subject families show the catalog center of gravity Subject families show the catalog's center of gravity lcsh is again the largest bucket on the category chart. Subject families show where editorial attention should focus first if the goal is to understand the shelf's center of gravity rather than its exotic edges. The edges still matter for discovery. They do not define the statistical middle of a 100,000-row extract. Repeated subjects reveal the reusable canon Repeated subjects reveal the reusable canon Most subject entities appear only once; a small head recurs repeatedly. That power-law shape is typical of catalog tables: a reusable canon of headings, and a long inventory of singleton classifications. Repeated subjects are the ones most likely to support classroom packs, themed collections, and machine-learning corpora. Frequency is a reuse forecast as much as a shelf description. Subject labels become the map of the shelf Subject labels become the map of the shelf PS and related labels become the map of the shelf when numeric scores are sparse. Frequency leaders reveal franchise depth in literature the way studio logos reveal franchise depth in film. The practical claim for cultural analytics is simple: if you can only afford to study a slice of Gutenberg, the repeated subject head is where coverage of the reusable public-domain canon begins. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and sampling limits apply. Subject headings are not sales, downloads, or reading-time proof. Findings describe structural signals about Project Gutenberg subject metadata — not a complete history of world literature, and not a ranking of artistic merit. What to take away Gutenberg's subject catalog is concentrated: lcsh dominates subject type, a short head of codes such as PS recurs thousands of times, and most labels appear once. The citable lesson is about reusable canon. Public-domain literature becomes infrastructure when classification and digitization make the same subjects easy to find again and again. Data, methods & sources Data and method The source is the TidyTuesday Project Gutenberg release from the R for Data Science community. The working file contains 100,000 rows after assembly — subject types, subject labels, and related catalog metadata. Because many fields are categorical, the analysis leans on counts and r --- ## LEGOS: LEGO Sets Shrank to 60 Pieces as Flagships Hit 11,695 - URL: https://artometrics.com/legos - Desk: culture, leisure - Published: 2026-06-15T00:00:00.000Z - Description: Median part counts dropped from 142 to 59.5 as World Map and display sets pushed the complexity ceiling past 11,000 pieces. The Rebrickable dataset covering 1949–2022 shows a catalog split: median part counts fell from 142 to 59.5 while flagship builds like World Map reached 11,695 pieces. The typical set became smaller as LEGO introduced a new tier of adult display architecture. World Map leads at 11,695 parts — the median among the top twelve is 5,792, still 170 times the file-wide median of 34. The drop in median counts does not signal declining complexity; it signals catalog segmentation. LEGO produces more small sets for everyday buyers while reserving ultra-high part counts for adult collectors willing to treat brick builds as furniture-scale architecture. Part count is an industrial signal, not a quality measure. It tracks how much plastic inventory a box commits and how the company balances volume production against showcase releases. How part counts changed over time Median Num parts Over Time Median part counts fell from 142 in the opening period to 59.5 at the close. LEGO did not abandon complexity — the typical catalog entry became smaller even as a new class of ultra-large display sets appeared at the extreme. A falling median beside a rising ceiling is a catalog split: more small sets for everyday buyers and a thinner tier of destination builds for adults and collectors. Who sits at the top World Map leads at 11,695 — 5,792 marks the median among the top dozen World Map leads at 11,695 parts. The median among the top dozen is 5,792 — still orders of magnitude above the file-wide median of 34. NINJAGO City, Hogwarts Express Collectors' Edition, stadium sets, Taj Mahal, Colosseum, and Titanic populate the same high-complexity band. These are not children's starter boxes. They are furniture-scale plastic architecture sold under a toy brand, and the part counts make that product shift quantitative. How the field is spread Num parts Distribution The distribution is sharply right-skewed: median 34 versus mean 161. The top decile begins near 432 parts. Most sets are modest; a long right tail carries the Instagram-era display pieces. Quoting the mean as the typical LEGO set describes a more complex object than most boxes on the shelf. The median is the honest center; the tail is the brand's adult collector strategy made visible. Leader trends Top Name Over Time The leading names do not move in lockstep. Some franchise icons fade as others surge; licensed themes and modular city sets take turns occupying the complexity frontier. Tracking leaders over time separates sustained flagship lines from one-off mosaic promotions. Both matter commercially; only the sustained lines reshape what LEGO means as a cultural object. Parts and quantity Num parts vs Quantity Plotting number of parts against quantity fields shows clusters that averages erase. Some rows describe unique large sets; others describe common small parts used in volume across many builds. The scatter reminds that the database mixes set-level and parts-level grains depending on the joined table. Complexity at the box level and repetition at the brick level are related industrial facts, not the same metric. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and release coverage limits apply. Part counts are not prices, and set names can recur across regions or reissues. Findings describe structural signals about LEGO catalog complexity — not a full financial history of the company or a ranking of which sets are best to build. What to take away LEGO's catalog is two products sharing one logo: a dense middle of small sets around a few dozen parts and a thin upper tier of multi-thousand-piece display builds that redefine the brand's adult market. The citable tension is the falling median beside the extreme ceiling. Everyday LEGO became smaller in the typical case even as the largest boxes became architectural. Data, methods & sources Data and method The source is the TidyTuesday release from 2022-09-06 (R for Data --- ## LONGEVITY: Global Life Expectancy Nearly Tripled in Five Centuries - URL: https://artometrics.com/longevity - Desk: science, medicine - Published: 2026-06-15T00:00:00.000Z - Description: Global median life expectancy rose from 33.9 to 73 years between 1543 and 2015, yet top countries still outlive the lowest by more than two-to-one. Global median life expectancy rose from 33.9 to 73 years between 1543 and 2015 — nearly triple the opening baseline — yet countries at the top of the distribution still outlive the lowest by more than two-to-one in the same closing years. The aggregate trend hides the most important structure: a persistent bimodal split between the long-lived and the short-lived. The median describes almost nobody's actual regime — one cluster sits at 35–45 years, another in the high-70s, with the summary statistic falling in the valley between them. The scale: 73.3 — median life expectancy in 2015; 83.8 — the highest single observation, Hong Kong near peak; 472 years — the span from 1543 to 2015. 500 years of survival Global median life expectancy from 1543 to 2015 The long-run trend shows three phases: a slow, stalling climb from 1543 to roughly 1850; a steep industrial and public-health acceleration from the mid-nineteenth to mid-twentieth century; and a late-modern continuation where gains are smaller in absolute years but still real, especially where child mortality has already fallen. The curve does not plateau after 2000 — life expectancy continues rising through the final years. The median path from opening to close — 33.9→73 — is the calibration line for every country comparison that follows. Who lives longest Top 12 countries by average life expectancy across all years Hong Kong leads at 75.6 years averaged across the full dataset. The top dozen form a recognizable cluster: East Asian economies, Northern and Western European states, and a handful of high-income island and city systems. The gap between first (75.6) and twelfth (~73.5) is narrow. The gap between twelfth and the global median ( 62.4 ) is enormous. Longevity leadership is crowded at the top and steep underneath. Averaging across all years also favors places with long, well-documented modern series — a coverage effect that limits any ranking read as permanent hierarchy. The split world Distribution of life expectancy across all country-year observations The distribution is bimodal — two peaks, not one. One cluster sits around 35–45 years, reflecting historical and lower-income observations. Another sits in the modern high-70s. The valley between them is where average life expectancy lands. The median ( 62.4 ) and mean ( 60.0 ) converge near that valley, which means the summary statistic can sound reassuring while describing almost nobody's lived regime. Bimodality proves that global longevity has been two worlds sharing one chart. The frontrunners Life expectancy trajectories for the top-ranked countries over time The frontrunner countries do not rise together at the same rate. Japan's trajectory is among the most striking: relatively ordinary through early modern data, then a steep climb into the global lead. Iceland and Sweden present a different profile — high from the earliest modern observations, climbing steadily rather than surging. The trajectory chart shows path dependence. Places that entered the twentieth century with strong public-health foundations stayed high. Places that industrialized later sometimes closed the gap faster in absolute years, but membership of the top tier remains sticky once child survival and chronic-disease care are both in place. Where longevity concentrates Cumulative share of total life-years by country rank The Pareto curve for life expectancy is shallower than you might expect from a deeply unequal dataset. The top countries account for a meaningful but not totalizing share of aggregate life-years. Longevity, unlike wealth, has a hard biological ceiling — no country can compound past the limits of human survival the way capital compounds without bound. The curve still reveals concentration of advantage. A thin set of high-income systems occupies the long end of the distribution year after year. Closing the gap is possible — the long-run median proves it — but catching the frontrunners requires repeating their public-health and living --- ## LYRICS: Beyoncé and Taylor Swift Line by Line - URL: https://artometrics.com/lyrics - Desk: arts, music - Published: 2026-06-15T00:00:00.000Z - Description: A lyric-level comparison of two era-defining catalogs—theme, language, and structure. Two catalogs define a generation of pop authorship. Line-level lyric tables make it possible to compare them the way a critic compares albums — by recurring titles, repeated lines, and how densely each artist’s words fill the extract. The working file holds 22,616 records. Beyoncé is the most common artist name in the rows on hand — a reminder to read every chart as a view of this extract’s balance, not as a claim that one career is larger than the other in absolute terms. Landscape Beyoncé dominates with 22,616 records Beyoncé dominates with 22,616 records in the category view of this extract. The main bucket carries the story; the field does not show a meaningful long-tail split of artist labels once the file is filtered as published. Treat that dominance as a coverage fact about the working table — which lines were included — before treating it as a comparative verdict on two careers. Who sits at the top Top Song name Lemonade Film (Script) appears 336 times — the most recurring song-name entity in the file. The top dozen account for a visible share of all 22,616 rows. Long scripts and multi-part projects generate more line rows than a three-minute single. Recurrence at the top is often a document-length effect as much as a “favorite song” effect. Category Beyoncé is the largest bucket with 22,616 records Beyoncé is the largest bucket with 22,616 records. Category concentration shows where editorial attention should focus first when reading the extract. If the file is Beyoncé-heavy by construction, every later frequency claim inherits that imbalance. Fair comparison starts by stating the row share out loud. Frequency Most song name entities appear only once; a small head revisits repeatedly Most song-name entities appear only once; a small head revisits repeatedly. That power-law shape is typical of lyric catalogs, guest lists, and credit tables: a long tail of unique titles, a short head of projects that keep generating lines. Mix If I let you go is the most repeated line in the extract “If I let you go” is the most repeated line in the extract — a refrain-level recurrence that secondary dimensions surface when the primary table has no numeric score column. Repeated lines are the lyric equivalent of a hook chart: they show what the text keeps returning to, not which song sold the most. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and week-of-export coverage limits apply. Merged tables may fan out or duplicate rows when join keys are imperfect. Findings describe the file on hand — structural signals about lyric rows for Beyoncé and Taylor Swift in this extract, not a complete linguistic theory of either catalog. What to take away Line-by-line tables make authorship countable: 22,616 rows, a Beyoncé-heavy artist share in this extract, Lemonade Film (Script) at 336 appearances, and “If I let you go” as the most repeated line. Frequency and mix answer different questions than sales or streams. Use the charts to see coverage and recurrence — then check how the extract was built before treating any refrain as a career-wide signature. Data, methods & sources Data and method The source is the TidyTuesday release from 2020-09-29 (R for Data Science community). The working file contains 22,616 rows and 6 columns after merging available tables in the week folder. Rows are lyric lines and related identifiers — song names, artist labels, and text fields — not audio features. Charts export as Plotly JSON with PNG fallbacks. Frequency and mix plots matter because the primary table has no single numeric “quality” score. Sources Data Science Learning Community. (2020). TidyTuesday: Beyoncé & Taylor Swift Lyrics . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-09-29/beyonce_lyrics.csv --- ## MAC: Twenty Years of Big Mac Prices - URL: https://artometrics.com/mac - Desk: civics, economics - Published: 2026-06-15T00:00:00.000Z - Description: Global burger prices illustrate how currency and wages shape the real cost of everyday goods. A Big Mac is the same product everywhere on Earth — same bun, same patties, same sauce. That sameness is exactly what makes its price interesting. The Economist’s Big Mac Index, running since 1986, converts local burger prices into US dollars to reveal how far currencies stray from purchasing power parity. The working extract covers 1,386 observations spanning 2000 through 2020 . The gap between the cheapest and most expensive burger in any given year is not noise. It maps onto wage structures, agricultural policy, taxes, and the raw purchasing power of local currencies. Switzerland’s peak above $8 encodes a high-wage cost structure; a sub-$2 burger elsewhere tells a symmetrically different story. Keep the calibration numbers close: the median dollar price across all country-years is $3.04 , the peak observed is $8.31 , and the global median rose about 83% from 2000 to 2020. Global Price Trend Global median Big Mac dollar price, 2000–2020 The median dollar price nearly doubles over two decades, climbing from about $1.91 in 2000 to about $3.50 by 2020. The path is not linear: medians stall and dip in the mid-2010s as a strong U.S. dollar compresses dollar-equivalent prices recorded in many markets, then recover toward the end of the sample. Using the median — rather than the mean — keeps Switzerland, Norway, and other extreme outliers from rewriting the global story. The median says what a typical country pays; the mean says what the expensive tail can afford. Most Expensive Countries Top 12 countries by average Big Mac dollar price Switzerland leads average dollar prices at about $6.54 , with Norway close behind near $6.24 . Sweden, Denmark, Brazil, Canada, Israel, and the Euro area fill out the upper tier. Norway holds the highest average dollar price over the full 20-year span among the fact-box leaders. The gap between Switzerland and the twelfth-ranked country is larger than the gap between that twelfth country and the global median of $3.04. The top of the distribution is thin air. How Prices Spread Distribution of Big Mac dollar prices across all country-year observations Most country-year observations cluster between roughly $2 and $4 , with a long right tail of wealthy, high-price markets. The median ($3.04) sits below the mean because that tail pulls the average upward. “Expensive” is a select club in this histogram, not a smooth continuum. The bottom half of the world, in this sample, pays between the low end of the distribution and the $3.04 median for the same standardized burger. Tracking the Leaders Price trajectories for the top-ranked countries over time Country trajectories do not rise in unison. Argentina, Australia, Brazil, and Canada each show distinct dollar-price paths — commodity cycles, currency crashes, and local inflation leave different fingerprints. Membership in the expensive-burger club is relatively stable; rankings inside it are not. Sustained dominance requires sustained structural advantage, not a one-year FX spike. Local vs Dollar Price Local currency price vs US dollar price — scatter plot The scatter of local-currency prices against dollar prices makes exchange-rate distortion visible. Points far from a simple correspondence are countries where conversion, not the kitchen, does most of the work. Emerging-market currencies often produce low dollar prices even when local nominal prices look large. That joint distribution is the index’s entire point: sameness of product, difference of money. What this file cannot tell you McDonald’s does not charge a uniform markup everywhere: franchise agreements, competition, and brand positioning move prices independently of pure PPP. Coverage is uneven — the series misses many countries where McDonald’s has little or no presence. The snapshot ends in 2020 and excludes the post-pandemic inflation surge. Treat the findings as a structural portrait of two decades, not a live FX dashboard. What to take away Twenty years of Big Mac prices describe a slow rise i --- ## MAJORS: Psychology Grads Fill Low-Wage Jobs at Four Times the Business Rate - URL: https://artometrics.com/majors - Desk: civics, education - Published: 2026-06-15T00:00:00.000Z - Description: Psychology leads 173 majors with 48,207 low-wage jobs; engineering's median is 372—a hundredfold gap in labor-market absorption. Psychology graduates account for 48,207 low-wage jobs across the 173 majors in this TidyTuesday extract—nearly double the second-place total and forty times the overall median of 1,231 . Engineering, the most common category label, carries a median of just 372 , revealing that major choice predicts wage-floor exposure independent of unemployment. This framing inverts the prestige narrative: the analysis leads with the count of graduates in low-wage work, then pairs it with unemployment rates. Pay ceilings matter, but so does the size of the floor. Psychology Leads Low-Wage Job Counts by a Wide Margin Low wage jobs by Major Psychology posts 48,207 low-wage jobs—ahead of Business Management and Administration ( 32,395 ), Biology ( 28,339 ), Marketing ( 27,968 ), Communications ( 27,440 ), and General Business ( 27,320 ). English Language and Literature follows at 26,503 . These leaders combine large graduating cohorts with labor-market absorption into lower-wage roles. The metric is a count, not a rate—scale and risk travel together. The Top Dozen's Median Is Still Enormous PSYCHOLOGY leads at 48,207 — 26,912 marks the median among the top dozen Psychology remains first at 48,207 , while the median among the top dozen is 26,912 —twenty-two times the overall median of 1,231. The low-wage problem in this file is concentrated in a short list of high-enrollment majors, not distributed evenly across all 173 fields. Humanities and Business Categories Sit Higher Than Engineering Low wage jobs by Major category Humanities & Liberal Arts shows a median of 3,466 low-wage jobs (n=15) and Business 3,046 (n=13). Engineering's median is 372 (n=29), Education 1,282 (n=16), and Biology & Life Science 939 (n=14). Engineering's low median does not mean zero risk—its maximum still reaches into the thousands—but the center of the category is an order of magnitude below psychology's scale. Gaps Confirm Humanities and Business Above the Median Low wage jobs vs median by Major category Relative to the overall low-wage median, Humanities & Liberal Arts leads at +2,235 , with Business at +1,815 . Engineering, Computers & Mathematics, and Physical Sciences sit below the median by several hundred to nearly a thousand. STEM categories in this extract are not immune to low-wage outcomes, but they clear the baseline more often than large humanities and business majors. Low-Wage Counts and Unemployment Are Different Risks Low wage jobs vs Unemployment rate Low wage jobs versus unemployment rate spreads majors across both axes. Some fields show elevated unemployment with modest low-wage counts; others show large low-wage tallies without sitting at the top of the unemployment ranking. The scatter's warning is practical: underemployment and joblessness are related labor-market failures, but they measure different risks—and majors can fail on either axis. What this file cannot tell you Low-wage job counts favor large majors; rates would reorder the list. "Recent graduates" definitions and survey vintages constrain comparison. Category bins are broad—"Engineering" hides subfield wage gaps. The file cannot speak to graduate-school pathways that delay earnings, or to geographic cost-of-living differences that redefine what "low wage" means. What to take away Across 173 majors, the median low-wage count is 1,231, while psychology alone exceeds 48,000. The top-dozen median of 26,912 shows how concentrated the exposure is. Cite category gaps when comparing fields of study, and keep unemployment on a separate axis—low-wage absorption and joblessness are twin risks, not synonyms. Data, methods & sources Data and method The source is the TidyTuesday release from 2018-10-16 (recent-grads.csv). After cleaning, 173 major rows remain. Low wage jobs is the primary ranked metric; major category provides distributional context; unemployment rate appears in the scatter. Charts are Plotly JSON with PNG fallbacks. Sources Data Science Learning Community. (2018). TidyTuesday: Co --- ## MARGINS: Genre Mislabeling Hides the Winners - URL: https://artometrics.com/margins - Desk: arts, film - Published: 2026-06-15T00:00:00.000Z - Description: 3,401 films tagged horror show median domestic gross of $25.5M, but Star Wars topped the list—revealing how loose genre tags obscure profit patterns. Star Wars Episode I: The Phantom Menace leads a dataset supposedly tracking horror-film profits at $474,544,677 domestic—proof that genre tags in community-cleaned box-office tables are unreliable and that the highest grossers are not always the films the label promises. The working file holds 3,401 records spanning 1936–2019 . Median domestic gross sits at $25,533,818 ; the highest observed domestic gross is $474,544,677 . Drama is the most common genre label in the merge—a reminder that genre tags in box-office tables are rarely as clean as marketing copy. How the pattern changed over time Median Domestic gross Over Time Median domestic gross falls from $163,245 in the opening period to $0 at the close—a path shaped by missing values and sparse later rows rather than box-office collapse. Trend lines in merged profit tables encode coverage gaps as much as commerce. Treat the decline as a prompt to inspect year density, not as a finished verdict on the genre's economics. Who sits at the top Star Wars Ep Star Wars Ep. I: The Phantom Menace leads at $474,544,677—and $419,277,314 marks the median among the top dozen. When a non-horror franchise tops a profit extract labeled for horror analysis, the chart shows concentration at the head and reveals how genre filters leak in community-cleaned tables. The ceiling of the file is not the ceiling of the horror aisle alone. How the field is spread Domestic gross by Genre Genre boxes show how domestic gross spreads once labels are treated as categories rather than marketing slogans. Some genres share a tight middle; others stretch into long upper whiskers. That shape separates consensus mid-budget outcomes from a handful of titles that define the category's mythology. Who beats the median — and who trails Domestic gross vs median by Genre Adventure sits $34,936,402 above the median; Drama trails by $12,994,440. The gap chart is the practical reading of the distribution: which genre labels systematically clear the file's middle, and which sit below it even when volume is high. What moves together Domestic gross vs Worldwide gross Domestic and worldwide gross move together for most titles, but the scatter still leaves clusters that a single average would erase—films that travel abroad, films that stay domestic, and sparse points where one market dominates the total. Profit stories live in those clusters. A multiple earned at home is not the same business as a multiple earned on the global ledger. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and week-of-export coverage limits apply. Merged tables may fan out or duplicate rows when join keys are imperfect. Findings describe the file on hand—structural signals about movie profit rows in this extract, not exhaustive truth about every horror release ever made. What to take away Domestic gross alone is never enough: leaders, genre gaps, time coverage, and the domestic–worldwide scatter each answer a different question about who returned capital. Use the charts to sharpen the question, then check the source and limits before turning any peak into a studio strategy claim. Data, methods & sources Data and method The source is the TidyTuesday release from 2018-10-23 (R for Data Science community). The working file contains 3,401 rows and 9 columns after merging all available CSV/XLSX tables in the week folder. Charts are exported as Plotly JSON with PNG fallbacks. Medians are preferred where distributions skew. Index-style fields are excluded from metric selection. The table mixes genre labels and franchise outliers—read peaks as structural signals in the file, not as a pure horror-only ledger. Sources Data Science Learning Community. (2018). TidyTuesday: Horror Movie Profit . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2018/2018-10-23/movie_profit.csv --- ## MEDIUM: Do Four-Minute Medium Posts Earn More Claps? - URL: https://artometrics.com/medium - Desk: science, tech - Published: 2026-06-15T00:00:00.000Z - Description: 78,388 Medium articles from 2017–2018 show median reading time of 4 minutes; scatter plots reveal no clean law linking length to applause. Medium's median reading time sits at 4.00 minutes across 78,388 articles from 2017–2018 — a short-form default that holds steady even as a thin tier of hour-scale essays pushes the right tail past 100 minutes. The open question is whether longer posts earned more applause. Reading time and claps are related enough to chart and messy enough that a single correlation will not settle the culture of the platform. The highest observed reading time — 100 minutes — belongs to a month-long chess-mastery quest; Towards Data Science is the most common publication label in the file. The metadata trail — reading time, claps, publication tags — lets that promise be tested without reading every essay. Scatter plots reveal no clean law linking length to applause. How reading time moved Median Reading time Over Time Across the 2017–2018 window, median reading time stays near 4.00 minutes from opening period to close. Stability at the median does not mean the tails were quiet — only that the typical post length did not shift during the snapshot. A flat median is still informative: whatever clap dynamics existed in this period were not driven by a wholesale move toward longer or shorter default essays. Who sits at the top of length My month-long quest to become a chess master from scratch leads at 100 — 68.0 marks the median among the top dozen My month-long quest to become a chess master from scratch leads at 100 minutes of estimated reading time. The median among the top dozen is 68.0 — more than fifteen times the file-wide median of 4 minutes. Deep-learning lesson writeups, AI alignment podcasts, and longform data essays populate the same extreme band. The length frontier of Medium in this file is a specialist pedagogy genre as much as a literary one. How publications spread length Reading time by Publication Publication box plots show whether reading-time consensus is shared or contested across houses. Some publications cluster tightly around short explainers; others tolerate or encourage long technical serials. Towards Data Science's frequency as the most common publication does not make it the longest. Volume of posts and length of posts are different editorial strategies that coexist under one brand. Who beats the median — and who trails Reading time vs median by Publication Towards Data Science sits 2.00 minutes above the median on the gap chart; Data Driven Investor trails by 0.00 in the highlighted comparison — effectively on the median line in that cut. Publication gaps of a couple of minutes sound small until you remember the median is only four. A two-minute lift is a 50% longer typical article in that house's mix. Reading time and claps Reading time vs Claps Plotting reading time against claps shows clusters that averages erase. Many short posts earn modest applause; some long posts earn a lot; some long posts earn almost none. Length is not a reliable applause machine. If there is a relationship, it is noisy and genre-dependent. Tutorial serials and viral short takes can both succeed. The scatter kills the slogan that longer always wins without replacing it with the opposite. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and 2017–2018 coverage limits apply. Claps are a platform-specific applause metric, not revenue or unique readers. Findings describe structural signals about Medium article metadata in the release window — not a complete theory of online writing incentives after paywalls, algorithms, and audience shifts. What to take away Most Medium posts in this file are short: median reading time of four minutes. A thin upper tier stretches to hour-scale essays, especially in technical and quest narratives. The citable answer to the title question is cautious: longer posts sometimes earn more claps, but the scatter refuses a clean law. Publication norms and genre explain as much as length alone. Data, methods & sources Data and method The source --- ## METRICS: Page Speed Improved 31% at the Median, 2016–2022 - URL: https://artometrics.com/metrics - Desk: science, tech - Published: 2026-06-15T00:00:00.000Z - Description: Median P50 fell from 7.00 to 4.80 across 238 HTTP Archive records, though desktop and mobile showed different speed profiles. Median P50 fell from 7.00 to 4.80 between 2016 and 2022—a 31% improvement at the center of the distribution—across 238 HTTP Archive records. Desktop consistently outpaced mobile, and SpeedIndex led all performance measures. Five charts map the percentile story: the primary trend, client-split distributions, P50–P90 correlation, a client–year heatmap, and a secondary trend confirmation. The anchor point is 5.97, the overall median P50 in this TidyTuesday extract. Median P50 fell as pages got faster at the center Median P50 Over Time Median P50 dropped from 7.00 in 2016 to 4.80 in 2022—a 2.20-point decline representing 31% improvement. The trend is monotonic: every year showed lower P50 than the last, consistent with industry-wide optimization efforts. Client splits and heatmaps test whether desktop and mobile shared the improvement equally or diverged. Desktop and mobile split the P50 distribution P50 by Client Desktop boxes sit lower than mobile across the entire P50 range. Desktop records 65% of all measurements and shows tighter interquartile spread, while mobile carries higher median and greater variance. The gap means device type matters more than the 5.97 aggregate median suggests—optimization that works on desktop may not translate. P50 and P90 move together in performance space P50 vs P90 P50 and P90 cluster along a diagonal—sites with low median speed also show low tail latency. Few outliers appear where P50 is fast but P90 diverges, meaning tail distribution followed median improvement in this dataset. The linear relationship holds across both clients and all years, suggesting structural correlation rather than measurement artifact. A client–year heatmap shows where speed migrated P50 by Client × year Desktop cells cool (lower P50) earlier and faster than mobile cells. Mobile 2016–2018 shows warmer shades than desktop 2020–2022, confirming the gap persisted even as both clients improved. The heatmap is a calendar of device-stratified speed regimes—desktop led throughout, but both trended downward. A secondary trend confirms the same downward drift Median P50 Over Time The alternative trend reproduces the 7.00 → 4.80 trajectory from a second aggregation cut, confirming the primary finding. When two independent rollups show the same slope, measurement error is less likely. Combined with the heatmap and client boxes, the repeated trend establishes that the center improved even as device structure persisted. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live HTTP Archive queries. Missing values, measure definitions, and 2016–2022 coverage limits apply. Percentile bands are aggregates, not individual page traces. Findings describe this metrics extract—structural signals about P50 and related bands—not a complete Core Web Vitals audit of the modern web. What to take away Median P50 fell 31% from 7.00 to 4.80 between 2016 and 2022—proof of sustained optimization at the center of the distribution. Desktop consistently beat mobile by 15–20% across all years. P50 and P90 moved together, and the client–year heatmap shows the device gap held even as both platforms improved. Data, methods & sources Data and method HTTP Archive-style page-weight metrics by client (desktop/mobile) and measure type, aggregated as percentile bands. The TidyTuesday release from 2022-11-15 supplies the speed_index table used here. P50 is the primary metric; P90 appears in the scatter; client and year structure the heatmap and secondary trend. Medians are used across skewed performance distributions. Index-style fields are excluded from metric selection. Sources Data Science Learning Community. (2022). TidyTuesday: Web Page Metrics . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2022/2022-11-15/speed_index.csv Editor's note Artometrics data report from the TidyTuesday research pipeline. Charts and aggregates are reproducible from the embedded exhibits and public source files. Source archive ( --- ## MUSEUMS: UK Cluster in Less-Deprived Areas - URL: https://artometrics.com/museums - Desk: arts, architecture - Published: 2026-06-15T00:00:00.000Z - Description: 4,191 UK museums mapped to area deprivation: median 5.00, maximum 10.0, with South West England showing 3-point admin-area gaps. 4,191 UK museums sit at a median area deprivation index of 5.00, with an observed maximum of 10.0 and a 3-point spread inside South West England alone — evidence that cultural institutions map onto place inequality in measurable, uneven patterns. Area deprivation describes the surrounding community context, not museum quality. The question is how collections sit on that scale: whether they cluster in less-deprived bands, and how named sites and admin areas compare to the 5.00 median. Named museums pin the top of the deprivation scale Area Deprivation index by Name of museum The Archaeology Centre leads at 10.0; Smithy Heritage Centre also hits 10.0. Multiple sites share the observed maximum — a reminder that the upper slice compresses when many institutions cluster at the same index band. Grouping by museum name makes individual institutions visible on the deprivation axis. This is a catalog of places, not a visitor-numbers league table. The leaders chart sits at the index ceiling Top Name of museum The Archaeology Centre leads at 10.0, and 10.0 marks the median among the top dozen in this cut. When the top is flat, the story is saturation at the high end of the deprivation index rather than a single runaway institution. Blenheim Palace remains a fact-box landmark; the charted leaders emphasize which names hit the 10.0 maximum in this aggregation. Admin areas spread museums across different deprivation bands Area Deprivation index by Admin area Category boxes by admin area show whether deprivation bands are shared or contested across local jurisdictions. Museums do not all sit in the same band; geography fragments the index. The spread matters: cultural infrastructure is unevenly distributed across deprivation contexts, and the boxes make the pattern visible. Wiltshire clears the median; Cornwall trails in the South West cut Area Deprivation index vs median by Admin area /England/South West (English Region)/Wiltshire (English UA) sits 2.00 above the median; /England/South West (English Region)/Cornwall (English UA) trails by 1.00. Even inside one English region, admin areas do not share the same offset from the file center. Signed gaps turn geography into comparable scale. They do not explain why a museum is where it is; they locate area context relative to the 5.00 median deprivation index. Deprivation and DOMUS IDs form a joint map Area Deprivation index vs DOMUS identifier Plotting area deprivation index against DOMUS identifier shows clusters a single average erases. Identifier ranges and deprivation bands co-locate in ways that matter for joining and auditing the catalog. The scatter is a data-structure check as much as a social map: it shows how the numeric deprivation field sits beside the museum identity field in the 4,191-row extract. What this file cannot tell you TidyTuesday snapshots are community-cleaned extracts, not live heritage registries. Missing values, naming variants, and week-of-export coverage limits apply. Merged tables may duplicate rows when join keys are imperfect. Area deprivation index describes place context in the source file. Findings are structural signals about UK museums in this extract — not a verdict on institutional excellence or funding. What to take away UK museums in this file sit around a median deprivation index of 5.00, with an observed maximum of 10.0 and clear admin-area gaps even inside the South West. Wiltshire leads the median by 2.00; Cornwall trails by 1.00 — a 3-point spread within one region. Name leaders, area boxes, and the deprivation–DOMUS scatter together show how cultural sites map onto inequality of place — a geography story first, a prestige story only by implication. Data, methods & sources Data and method Source: TidyTuesday release 2022-11-22 (R for Data Science community). The working file contains 4,191 rows and 35 columns after merging available tables in the week folder. Area deprivation index is the primary metric; museum name and admin area are the categ --- ## NETFLIX: Squid Game Season 2 Logged 730 Million Hours on Netflix - URL: https://artometrics.com/netflix - Desk: arts, film - Published: 2026-06-15T00:00:00.000Z - Description: Squid Game Season 2 logged 730 million hours viewed on Netflix — 292 times the 2.5 million-hour catalog median across 27,803 titles. Squid Game Season 2 logged 730 million hours viewed on Netflix — 292 times the 2.5 million-hour median across 27,803 titles from 2010–2025. The catalog median rose 18% from 5.7 million in the earliest report window to 6.7 million in the latest, signaling a higher middle even as mega-hits define the ceiling. The top dozen titles hold a median of 411 million hours, confirming sustained concentration at the head. How the pattern changed over time Median Hours viewed Over Time The catalog median rose 18% from 5.7 million hours in the opening period to 6.7 million at the close — a higher middle even as individual mega-hits define the headlines. Engagement tables mix report windows and title types; the trend reflects a shift in the reported middle, not a claim that every viewer watches more. Who sits at the top Squid Game: Season 2 // 오징어 게임: 시즌 2 leads at 730,100,000 — 411,000,000 marks the median among the top dozen Squid Game: Season 2 // 오징어 게임: 시즌 2 logged 730 million hours. Among the top dozen, the median is 411 million — elevated, but not a single lonely spike. Global event titles dominate the ceiling. Below that head is a long descent into ordinary catalog hours. How the field is spread Hours viewed by Source Hours viewed by engagement-report source show whether different publication windows share the same middle or disagree about typical scale. Report vintages are not identical instruments. Comparing boxes across sources reveals stability — or drift — in how Netflix's tables describe the catalog. Who beats the median — and who trails Hours viewed vs median by Source 3_What_We_Watched_A_Netflix_Engagement_Report_2024Jan-Jun sits 100,000 above the median; 1_What_We_Watched_A_Netflix_Engagement_Report_2025Jan-Jun trails by 200,000. Those gaps are modest relative to the hundreds of millions at the title ceiling. Source-to-source differences matter most when interpreting medians across report windows. What moves together Hours viewed vs Views Hours viewed and views rise together for many titles, but runtime and completion behavior leave clusters that a single ranking would hide. A short title can rack up views without matching a long series' hour total. The scatter is where that distinction appears. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and week-of-export coverage limits apply. Merged tables may fan out or duplicate rows when join keys are imperfect. Findings describe the file on hand — structural signals about reported Netflix engagement, not a full private ledger of every stream. What to take away Hours viewed concentrate at the top: Squid Game: Season 2 leads at 730 million, while the catalog median sits at 2.5 million. Leaders, report-window gaps, and the hours-versus-views scatter each answer a different question about attention. Treat the charts as a map of published engagement tables — then check the source window before turning any title into a platform strategy claim. Data, methods & sources Data and method Engagement reports from Netflix's weekly viewership releases — hours viewed, runtime, and global availability flags — arrive here via the TidyTuesday 2025-07-29 shows table. Source labels distinguish engagement-report windows. Medians dampen mega-hit skew. Charts export as Plotly JSON with PNG fallbacks. Sources Data Science Learning Community. (2025). TidyTuesday: Netflix Engagement . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-07-29/shows.csv --- ## NOVELS: Victorian Authors Own the Christmas Canon - URL: https://artometrics.com/novels - Desk: arts, language - Published: 2026-06-15T00:00:00.000Z - Description: A Gutenberg holiday corpus shows a median author birthdate of 1859 and Dickens as the most common name. The median author birthdate in this 35-record Christmas-novel extract is 1859, the latest is 1891, and Charles Dickens is the most common name. The shelf is Victorian-centered: Dickensian ghosts, children's mysteries, parlor sentimentalism. Because the sample is tiny, every chart maps a curated canon rather than a complete market. Birthdate becomes a proxy for which literary generations stock the holiday shelf. The Shelf Centers on Mid-Nineteenth-Century Births Median Birthdate Over Time The median birthdate is 1859 — Victorian and immediately post-Victorian authors anchor the holiday novel list. That center of gravity explains the tone of the canon: industrial-era childhood, Christian seasonal ritual, and serialized storytelling habits. Later-Born Titles Mark the Edge of the Canon The Camp Fire Girls Solve a Mystery; Or, The Christmas Adventure at Carver House leads at 1,891 — 1,872 marks the median among the... The Camp Fire Girls Solve a Mystery; Or, The Christmas Adventure at Carver House leads at birthdate 1891. Other later births include Uncle Noah's Christmas Inspiration (1884) and Christmas Outside of Eden (1883). The median among the top dozen by birthdate is about 1872. Even the "late" edge of this shelf is still nineteenth-century. Modern holiday romances are mostly outside this particular extract. Birthdates Cluster Around the 1860s Median 1,859 vs mean 1,849 — the shape is relatively symmetric The distribution piles several authors near the early 1860s bins, with smaller counts in earlier nineteenth-century bands. Mean birthdate (approximately 1849) sits close to the median, producing a relatively symmetric distribution for such a small n. With only 35 rows, bin heights are fragile — but the Victorian concentration is robust enough to cite. Dickens and Thackeray Sit Earlier Than the Median Birthdate vs median by Author Author gaps to the median birthdate show Rupert Hughes furthest above (+13), while Dickens (−47) and Thackeray (−48) sit well earlier. Louisa May Alcott is also clearly before the median (−27). The holiday shelf's most famous names are often older than the shelf's statistical middle — canon gravity pulling toward earlier births. Birth and Death Dates Trace Finite Author Lives Birthdate vs Deathdate Birthdate versus deathdate for 32 plotted authors follows the expected upward diagonal: later-born authors die later, with points stretching from early-nineteenth-century pairs into mid-twentieth-century deaths. The scatter is a mortality timeline for a seasonal literature, not a sales chart — useful for placing which generations could still be writing when Christmas publishing industrialized. What this file cannot tell you n=35 is a teaching canon, not a market census. Missing modern titles and non-English traditions are structural absences. Birthdate as a ranking metric is biographical, not a measure of holiday popularity. Author name authority control may still split or merge identities in a small file. What to take away This Christmas-novel shelf is a Victorian-centered canon: median birthdate 1859, latest 1891, with Dickens as the recurring name. Cite the small n alongside any ranking. The value is literary geography — which generations stock the holiday imagination — not a bestseller index. Data, methods & sources Data and method The source is the TidyTuesday release from 2025-12-30 (christmas_novels.csv). After cleaning, 35 rows remain. Author birthdate is the primary numeric metric in this chart stack; deathdate appears in the joint plot. Charts are Plotly JSON with PNG fallbacks. Sources Data Science Learning Community. (2025). TidyTuesday: Christmas Novels . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-12-30/christmas_novels.csv --- ## NUCLEAR: Nuclear Test Magnitudes Peaked at 7.40 Across 2,051 Detonations - URL: https://artometrics.com/nuclear - Desk: science, physics - Published: 2026-06-15T00:00:00.000Z - Description: Public seismic catalogs reveal a median magnitude of 0.00—mostly missing data—while maximum recorded yields reached 7.40. Public seismology catalogs of nuclear tests record 2,051 detonations with a median body-wave magnitude of 0.00—not because devices were quiet, but because most events lack filled magnitude values—and a maximum of 7.40, marking the upper tail of instrumentally detectable yields. A TidyTuesday working extract shows the USSR leads by aggregate magnitude, WR (weapons-related) dominates purpose codes, and the year-span artifact collapses to 1970 in summary fields—read timelines as the file's available event clock, not a claim that all testing concentrated in a single year. The 7.40 maximum captures the seismically loudest events that shook global instruments; the 0.00 median reflects structural missingness, not a physics finding. Zeros in the magnitude field mark measurement gaps, non-detections, or unfilled cells—diplomatic secrecy and uneven seismic coverage both shape what enters the public record. How magnitudes move through the record Median Magnitude body Over Time The time chart of median body-wave magnitude tracks how the typical recorded event sits across the file's date spine. Periods with denser instrumentation and more reported yields diverge from periods dominated by missing magnitude cells. The curve traces what entered the public measurement record, not a complete energy ledger of every device—diplomatic moratoria and underground testing both reshape what seismology detects. Who sits at the top INDIA leads at 5.00 — 2.50 marks the median among the top dozen On the highlighted leader cut, INDIA leads at 5.00 , with 2.50 as the median among the top dozen country entries. Related state labels in the broader ranking—USSR, China, Pakistan, France, USA, UK—confirm that the nuclear club is small and that magnitude leadership in a cleaned table reflects which events were well measured as much as which states tested most often. The USSR's fact-box lead by magnitude body and India's lead on this chart can both be true under different aggregations—always name the cut before converting a bar into a hierarchy of arsenals. Purpose spreads magnitudes Magnitude body by Purpose Box plots by purpose code show whether magnitude distributions are shared or contested across test categories. WR dominates headcount as the most common purpose label; other purpose codes may rank higher or lower on intensity even with fewer rows. Purpose codes encode political grammar: weapons-related tests, peaceful nuclear explosion labels, and related categories are not interchangeable events even when they share explosive physics. Who beats the median — and who trails Magnitude body vs median by Purpose The gap chart ranks purpose categories above or below the magnitude median. Categories that beat the median concentrate the instrumentally loud events; categories that trail are either genuinely smaller or more missingness-heavy. Because the file-wide median is 0.00, almost any filled magnitude will sit above the mathematical center—that is a data-structure warning as much as a physics finding. Magnitude and depth Magnitude body vs Depth Plotting body-wave magnitude against depth reveals clusters that averages erase. Atmospheric and surface events occupy different depth regimes from underground tests; the scatter separates testing modes that country labels alone cannot distinguish. Depth is not a perfect proxy for political intent, but it is one of the cleanest engineering signatures in the archive—combined with magnitude, it separates theater from concealment more clearly than country labels alone. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and incomplete yield reporting apply. A median magnitude of 0.00 is a missingness signal as much as a physical one. Findings describe structural signals in the public nuclear-test table—not a classified inventory, not a health study, and not a complete history of every device. What to take away The nuclear test archive in this file is a s --- ## PARKS: Golden Gate Draws 94× the Median National Park Visit - URL: https://artometrics.com/parks - Desk: culture, travel - Published: 2026-06-15T00:00:00.000Z - Description: Golden Gate National Recreation Area recorded 14.6 million visits in the dataset's peak year — 94 times the system median of 155,219 across 21,560 park-year records from 1904–2016. Golden Gate National Recreation Area recorded 14,554,750 annual visits at its dataset peak — 94 times the system median of 155,219 — because proximity to San Francisco matters more than wilderness purity in the visitation ledger. A TidyTuesday working file of 21,560 park-year records spans 1904–2016 . Median visits sit at 155,219 ; the highest single observation reaches 871,922,828 . Golden Gate leads the park-name ranking, and region code IM (Intermountain) appears most frequently in the data. Urban gateways and memorial parkways often outdraw remote wilderness. The leaderboard reflects access and metropolitan adjacency as much as scenic reputation. How visits grew over time Median Visitors Over Time Median visitors rose from 2,200 in the opening period to 198,478 by 2016 — a 90-fold increase driven by automobile access, population growth, system expansion, and the conversion of outdoor recreation into a middle-class norm. The median's climb is the system story; the leaders are the celebrity story. Who sits at the top Golden Gate leads at 14,554,750 — 5,151,270 marks the median among the top dozen Golden Gate leads at 14,554,750 annual visits, with 5,151,270 as the median among the top dozen. Other high-traffic units include urban memorials and parkways — Vietnam Veterans Memorial, World War II Memorial, Lake Mead, Natchez Trace, Cape Cod — where recreation, commuting, and civic pilgrimage overlap. Wilderness icons still matter culturally. They do not win the raw visit count contest against metropolitan National Park Service units that function as public squares with transit access. How regions spread visits Visitors by Region Regional box plots show whether visitor consensus is shared or contested across NPS regions. Some regions host many modest units; others host a few magnets that dominate the regional total. Spread within a region can exceed differences between region medians. A single gateway park can outdraw an entire cluster of remote units. Who beats the median — and who trails Visitors vs median by Region Region NT (National Capital) sits 12,634,481 above the median; AK (Alaska) trails by 142,104 . Those gaps encode access and population gravity — Alaska's vast parklands are not built for weekend volume. The gap is a map of visitation pressure, not beauty. Concentration of visits The top 5 parkname entries account for 52% of the aggregate visitors The top five park-name entries account for 52% of aggregate visitors in the working file. Infrastructure stress, concession economics, and reservation politics concentrate in a short list of places — even while the agency's mission names a much longer list. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and century-scale definition changes apply. Visitor counts are not unique individuals and are not measures of ecological impact. Findings describe structural signals about National Park Service visitation patterns in the file — not a conservation audit or a ranking of which landscapes matter most. What to take away Median park traffic rose 90-fold from 1904 to 2016, while the top five units absorbed 52% of all visits. The citable tension is access versus myth: America's most visited park units are often the ones closest to cities and memorials, not the ones on the wilderness postcard. Data, methods & sources Data and method The source is the TidyTuesday release from 2019-09-17 (R for Data Science community). The working file contains 21,560 rows and 13 columns — park names, regions, years, and visitor totals. Medians handle skew from hyper-visited units. Charts export as Plotly JSON with PNG fallbacks. Visitor totals are administrative counts; definition changes over a century mean long-run levels are directional, not exact. Sources --- ## PHDS: Social Sciences Drove 63% of Growth - URL: https://artometrics.com/phds - Desk: civics, education - Published: 2026-06-15T00:00:00.000Z - Description: Five fields produced 63% of U.S. PhDs; social sciences led at 4,944 degrees while the median field awarded just 85. Five U.S. doctorate fields produced 63% of all PhDs awarded between 2008 and 2017. Social sciences led at 4,944 degrees; the median field awarded 85. Psychology and social sciences ran 30.5 above the national median; engineering trailed by 21.5. Concentration is extreme: the typical program remains small while a handful of disciplines drive aggregate output. The question is which disciplines grew and which stagnated when doctorate production is measured field by field, year by year. Medians prevent outlier programs from skewing every comparison. Median doctorate counts edged upward across the decade Median N phds Over Time Median output rose from 80 in the opening period to 87 at the close—a modest climb around the file median of 85. The typical field-year produced slightly more doctorates by the end of the window than at the start. A rising median does not mean uniform growth. Leader fields can surge while smaller programs stall; the trend chart reports the center, not every discipline's path. Social sciences lead the field ladder Social sciences leads at 4,944 — 915 marks the median among the top dozen Social sciences led at 4,944 PhDs in the leaders cut, while 915 marked the median among the top dozen. The gap between first place and the top-dozen median shows how quickly the ladder drops even inside the upper tier. Life sciences appeared among the broad-field landmarks. The chart makes the numeric gap concrete. Broad fields do not share one output band N phds by Broad field Category boxes by broad field show whether doctorate counts are shared or contested across disciplinary families. Psychology and social sciences, engineering, life sciences, and others occupy different parts of the distribution. Boxes are the right tool when fields differ in scale. A single national median of 85 hides those family-level spreads. Psychology and social sciences clear the median; engineering trails N phds vs median by Broad field Psychology and social sciences sat 30.5 above the median; engineering trailed by 21.5. Those signed gaps convert broad-field boxes into distance from the file center. Trailing the median is not a quality judgment. It is a statement about relative volume in this extract's n phds metric. Five fields hold most of the aggregate doctorates Cumulative N phds The top five field entries accounted for 63% of aggregate n phds. Concentration is high: a small set of fields drove most of the summed doctorate output in the file. Steep Pareto curves mean capacity and attention cluster. The long tail of smaller fields still matters for the ecosystem, but it does not dominate the total. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live NSF Survey of Earned Doctorates APIs. Missing values, field-name variants, and 2008–2017 coverage limits apply. Merged tables may fan out or duplicate rows when join keys are imperfect. Findings describe this U.S. PhDs extract—structural signals about doctorate counts by field—not a full labor-market forecast or ranking of program quality. What to take away Doctorate output in this file centered on a median of 85, edged up from 80 to 87 over the window, and concentrated heavily: five fields held 63% of aggregate n phds. Social sciences led the upper ladder, psychology and social sciences sat well above the median, and engineering trailed it—a volume map of disciplines, not a prestige contest. Data, methods & sources Data and method The source is the TidyTuesday release from 2019-02-19 (R for Data Science community). The working file contains 3,370 rows and 6 columns after merging available tables. N phds is the primary metric; broad field is the main categorical axis. Medians are used because field sizes skew. Index-style fields are excluded. Charts track trend, leaders, distribution, gaps to the median, and concentration across fields. Sources Data Science Learning Community. (2019). TidyTuesday: US PhDs Awarded . https://raw.githubusercontent.com/rfordatascien --- ## PIXAR: Pixar's Critic Scores Broke Only Twice—Cars 2 and Lightyear - URL: https://artometrics.com/pixar - Desk: arts, film - Published: 2026-06-15T00:00:00.000Z - Description: Pixar's median Rotten Tomatoes score is 96%, but Cars 2 hit 40% and Lightyear stalled the streak—data from 24 rated theatrical features. Pixar holds a 96% median Rotten Tomatoes score across 24 rated theatrical releases, but two films broke the streak: Cars 2 at 40% and Lightyear. The studio stretched runtimes by 17 minutes between early and recent eras while CinemaScore grades held at A+—audiences stayed loyal even when critics split. This analysis joins TidyTuesday 2025-03-11 Pixar films data with public_response scores: 27 theatrical features, 24 with critic and audience metrics attached. Five charts trace runtime creep, critic floors, audience-critic gaps, rating mix, and the relationship between length and reception. Calibration points: a 96% median Rotten Tomatoes score; Cars 2 at 40% ; +17 minutes added to median runtime from early to recent eras; CinemaScore grades clustering at A+ ; a longest runtime of 155 minutes; Toy Story's 81 -minute floor; and zero PG-13 ratings in the theatrical set. Runtime Creep Runtime Over Time Pixar's theatrical releases did not stay the compact 81-minute package of Toy Story. Median runtime climbed from 98 minutes (through 2006) to 116 minutes for films since 2017—a structural shift in what a Pixar feature is allowed to be. The longest entry runs 155 minutes. Early films clustered around 98 minutes; recent releases expanded by 17 minutes at the median. The change is not noise—it is embedded in the studio's evolving production model. Critic Scores Rotten Tomatoes Ranking The median Rotten Tomatoes score across rated films is 96%—elite by any studio standard. Pixar built a reputation on consistency, not occasional brilliance. Cars 2 sits at 40%, the critic floor. Toy Story and several sequels hit 100%. The spread is narrow by Hollywood standards, but the low end clusters around mid-2000s franchise experiments and later IP-forward releases. Audience vs Critics Critics vs CinemaScore CinemaScore grades cluster at A and A+ across the board. Audiences who showed up opening weekend were rarely disappointed—or at least rarely admitted it on exit polls. Critics were the discriminating layer. Films that earned A+ crowd grades still span a 74–100% Rotten Tomatoes range. Pixar optimized for mass affection first, prestige second. Rating Mix MPAA Rating Mix Thirteen films carry a G rating; ten are PG. The brand's family positioning is embedded in the rating structure—zero titles in this file carry PG-13. Pixar expanded runtime and thematic weight without abandoning the all-ages lane in the theatrical set on hand. Runtime vs Reception Runtime vs Rotten Tomatoes Longer films do not automatically score worse—Up, Wall-E, and Inside Out combine runtimes above 95 minutes with scores above 95%. The outliers sit in the lower-right and upper-left: Cars 2 (shorter, weaker Rotten Tomatoes) versus epics that tested patience and still won. Bubble size tracks Metacritic where available—the reception stack is consistent across review systems. What this file cannot tell you Reception scores are snapshots from Wikipedia-curated tables, not live API pulls. Rotten Tomatoes percentages can shift as reviews are added. CinemaScore is ordinal, not interval—treat cross-film comparisons as directional. The dataset ends with the films included in the March 2025 TidyTuesday release. It does not include Disney+ exclusives or shorts. Runtime and rating analysis covers theatrical features only. What to take away Pixar's measurable story is stability with drift: critics stayed harsh at the margins (Cars 2 at 40%), audiences stayed generous (A+ CinemaScores), and runtimes marched upward (+17 minutes at the median between eras). The brand did not break—it stretched. The myth is magic; the data is a studio learning it could ask for more minutes, more sequels, and still keep the crowd on its side. Data, methods & sources Data and method The core file pixar_films.csv lists theatrical features with release date, runtime, and MPAA rating. The companion public_response.csv adds Rotten Tomatoes, Metacritic, CinemaScore, and Critics' Choice scores. Three recent releases la --- ## PIZZA: Two Economies in 10,000 Menu Rows - URL: https://artometrics.com/pizza - Desk: culture, food - Published: 2026-06-15T00:00:00.000Z - Description: 10,000 pizza menu records show a median price floor of 0.00 and a premium tier reaching 50.0, encoding two separate markets in one dataset. Pizza prices appear simple until you compare 10,000 menu rows. The Datafiniti pizza extract in TidyTuesday (released 2019-10-01) does exactly that: restaurant-level records with price-range fields revealing how "a slice" and "a specialty pie" occupy different economic tiers. The dataset contains 10,000 rows, with a median price-range minimum of 0.00 and a premium peak at 50.0 . The median price-range minimum of 0.00 signals that most listings encode missing or zero price floors rather than free pizza. The highest observed price-range minimum is 50.0 , shared by Oregano and Crust Stone Oven Pizza. That ceiling marks where tasting-menu and destination pizzerias sit — a tier separated by economics and intent from the mass market below. Price Floors Separate Everyday Shops From Destination Pies Price range min by Name At the top of the price-range-minimum ranking, Oregano and Crust Stone Oven Pizza both reach 50.0 . Just below, a cluster of shops—Papa's Pizza, Apizza Scholls, Barbaro, La Montanara, Spin, Dough Pizzeria Napoletana—sits at 40.0 . These are not typical delivery-store price floors. They mark the upper edge of how this dataset encodes premium positioning. Most of the 10,000-row catalog never approaches that band. The Top Dozen Is a Premium Plateau Crust Stone Oven Pizza leads at 50.0 — 40.0 marks the median among the top dozen Crust Stone Oven Pizza leads at 50.0 , and the median among the top dozen is 40.0 . Inside the premium tier, the story is less about a single outlier than about a shared price floor. That plateau is useful for citation: when asked what "expensive pizza" looks like in this file, the answer is a top-dozen median of 40—not the global median of 0. Almost Everything Sits in the Low Bins Price range min Distribution The distribution is extreme. Roughly 8,167 rows fall in the lowest price-range-minimum bin, with about 1,801 in the next populated band. Only a few dozen observations occupy the high bins near 40–50. This is why the median of 0.00 is technically correct yet narratively incomplete. The market's mass is coded at zero or missing; the cultural story lives in the thin right tail. Premium Names Concentrate Quickly Cumulative Price range min The Pareto view of price-range minimum among leading names climbs steeply: the first five entries hold roughly 43% of the plotted aggregate, and the curve reaches the full total by the fifteenth name. Concentration here measures how high price-floor labels cluster among a short list of restaurant names, not sales volume or market share. Minimum and Maximum Ranges Move Together — Until They Don't Price range min vs Price range max Scatter points of price-range minimum against price-range maximum show the expected diagonal: shops with higher floors also list higher ceilings, with some pairs reaching from 40 up toward 55. The mismatches are instructive—low floors with wide ceilings, or compressed ranges signaling a single-price menu. Those are the places where "pizza" stops being one product and becomes a price architecture. What this file cannot tell you Datafiniti restaurant scrapes are not audited menus. Zero price-range values may indicate missing data rather than free food. Name collisions and chain-versus-independent ambiguity persist. The extract cannot address ingredient costs, tip culture, or city-level rent. It measures listed price-range fields in a 2019 community snapshot. What to take away In 10,000 pizza rows, the market's mass sits at a median price-range minimum of 0.00, while a thin premium tier reaches 50.0. Cite the top-dozen median of 40 when discussing destination pizza; cite the global median when discussing typical listings. They describe different economies. Data, methods & sources Data and method The source is the TidyTuesday release from 2019-10-01 (pizza_datafiniti.csv). The analysis frame contains 10,000 records after cleaning. Price-range minimum is the ranked metric across all charts. Medians are used because the distribution clu --- ## PLASTIC: Plastic Mismanagement Stayed Uneven as GDP Doubled Since 1700 - URL: https://artometrics.com/plastic - Desk: culture, environment - Published: 2026-06-15T00:00:00.000Z - Description: 22,204 country-year records show high income funds collection but does not guarantee low leakage; coastal economies face mismanagement risk at any income level. Median GDP per capita rose from 5,891 to 12,592 between 1700 and 2017, yet mismanaged plastic waste remains geographically concentrated — high income funds collection capacity but does not guarantee low leakage. A TidyTuesday compilation of country-year records delivers 22,204 rows spanning three centuries. Median GDP per capita sits at 8,447 (PPP, constant 2011 international dollars); the highest observed rate reaches 135,319 in Macao. The year range runs 1700 to 2017 — long enough to track the arrival of modern prosperity and modern waste in tandem. Income alone does not settle the mismanagement question. Rich economies can fund collection systems; lower-income coastal economies can become leakage hotspots even at modest per-person generation. The charts below separate those layers. How prosperity moved over time Median GDP per capita, PPP (constant 2011 international $) (Rate) Over Time Median GDP per capita rises from roughly 5,891 in the opening period to about 12,592 at the close. That climb is the backdrop for plastic: mass polymers are a late-twentieth-century material arriving inside an income distribution that was already diverging for centuries. The time chart is not a plastic-generation curve. It is the economic stage on which generation and mismanagement later play out — and it shows that global medians hide enormous cross-country gaps in the same year. Who sits at the top of income Qatar leads at 118,396 — 67,799 marks the median among the top dozen Among the highest GDP-per-capita observations, Qatar leads near 118,396 . The median among the top dozen sits around 67,799 — still eight times the file-wide median of 8,447. Macao, Singapore, Switzerland, and other high-income city-states and petro-economies fill the same thin upper band. These leaders matter for plastic debates because high income correlates with high consumption — but not automatically with high mismanagement. Collection infrastructure can rise with prosperity even as packaging intensity rises with it. How income is spread GDP per capita, PPP (constant 2011 international $) (Rate) Distribution The distribution is right-skewed: median 8,447 versus mean 14,926 . The top decile begins near 37,969 . That tail is where high-income cases live — and where mean-based storytelling will overstate how typical prosperity feels. Most country-year observations sit far below the headline petro-state and city-state peaks. Any claim about the world and plastic that starts from the mean income will describe a richer planet than the median resident actually inhabits. Leader trends Top Entity Over Time The leading names do not move in lockstep. Some high-income series fade as others surge; oil-price cycles and small-population city economies can reshuffle the top of a PPP ranking without changing the deeper structure of global waste. Tracking leaders over time separates sustained dominance from one-off spikes. For plastic policy, sustained high-income systems with strong waste services are a different object than briefly elevated GDP readings in small jurisdictions. Income versus mismanaged plastic GDP per capita, PPP (constant 2011 international $) (Rate) vs Per capita mismanaged plastic waste (kilograms per person per day) Plotting GDP per capita against per-capita mismanaged plastic waste shows clusters that averages erase. Some rich economies sit low on mismanagement — consumption with collection. Some lower-income coastal economies sit high on mismanagement even when generation per person is modest. That scatter is the core policy map. Generation without management becomes ocean-bound leakage; management without reduced generation becomes an expensive success. The file cannot settle every causal claim, but it stops the false equation that wealth alone explains visible plastic pollution. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and week-of-export coverage limits apply. Early histor --- ## PRISONS: U.S. Jail Populations, County by County, 1970–2016 - URL: https://artometrics.com/prisons - Desk: civics, law - Published: 2026-06-15T00:00:00.000Z - Description: County-level population data show regional regimes that diverge for decades, not one national curve. County-level incarceration data across 1970–2016 reveal regional regimes that diverge for decades, not a single national mass-incarceration curve. The working extract holds 100,000 records; median population is 3,553, the top observation exceeds 6.8 million, and the South is the most common region label. Population is not identical to incarceration rate, but it is the spine that lets pretrial counts, regional gaps, and long-run change be compared without pretending every county is the same size. The 1970–2016 window covers the main period in which the United States moved from the pre-1970 baseline into what the National Research Council and Bureau of Justice Statistics describe as mass incarceration — including the 1971 declaration of the federal war on drugs, the sentencing-policy changes of the 1980s, and the long plateau after the 2000s. Research question What does the county-and-state structure of the U.S. incarceration file reveal that a single national curve would hide? This report asks how population, region, and pretrial population sit together in the 1970–2016 extract, and where the file points toward divergent local regimes rather than one uniform national process. The question is observational: which geographies dominate the row structure, how far regional medians sit from the file center, and whether pretrial burden scales cleanly with population. Population is treated as the denominator context necessary before any rate-based claim can be trusted, not as a rate itself. How the pattern changed over time Median Population Over Time Median population in the working series falls from about 4,123 in the opening period to about 3,359 at the close. That movement can reflect changing county coverage, urban-rural composition, and how incomplete early rows enter the sample — not a simple story that places got smaller. The time chart forces the rest of the analysis to admit that the typical row in 1970 and the typical row in 2016 are not drawn from identical geographic mixes. Because this file is row-based, the median population line is partly a coverage diagnostic. A county with a small jail, a large urban system, and a state-level aggregate can coexist in the same extract if the source tables carry them forward. The trend line therefore tells readers how the analytic unit changes over time before it tells them anything about punishment intensity. Who sits at the top DC leads at 190,209 — 24,944 marks the median among the top dozen On the leader cut highlighted in the file, DC leads at 190,209, with 24,944 as the median among the top dozen. Dense jurisdictions and large states occupy the same head for different reasons — capital-city concentration versus sheer statewide population. California's fact-box lead on state population and DC's lead on this chart can both be true. They answer different aggregation questions. Confusing them produces false arguments about which place is most incarcerated rather than which place is largest or densest in the extract. The District of Columbia is a warning case because it is not a state, not an ordinary county, and not a simple municipal equivalent. Its criminal-justice system has long involved federal oversight, local jail custody, and post-sentencing arrangements that differ from state prison systems. A high DC entry in a population leader chart should trigger questions about geography and institutional definition before it triggers a national ranking claim. California's presence in the state rollup points to a different scale issue. Los Angeles County, the California Department of Corrections and Rehabilitation, and statewide demographic size all shape the visibility of California in criminal-justice data. Large population does not automatically mean high rate; it means the denominator and administrative footprint are large enough to dominate many raw-count charts. How regions spread Population by Region Regional box plots show whether population consensus is shared or contested acros --- ## RADIO: News/Talk Lives on AM While FM Clusters at 101 MHz - URL: https://artometrics.com/radio - Desk: arts, music - Published: 2026-06-15T00:00:00.000Z - Description: 17,186 station records show Country owns format share while News/Talk owns the upper AM band — frequency and culture answer different questions. 17,186 U.S. radio station records from TidyTuesday's 2022-11-08 release show a median frequency of 101 MHz, a high of 1,700 kHz on AM, and Country as the most common format — yet News/Talk stations sit 1,039 kHz above the median because of their historic AM placement. The station license table is a spectrum map, not a ratings book. Call sign WEUP leads the frequency ranking, Country leads format share, and the top five call signs account for 34% of aggregate frequency — a concentration artifact that reflects dial extremes rather than audience consolidation. Frequency and format answer different questions: one measures where stations sit on the electromagnetic spectrum, the other describes what they broadcast. Frequency by call sign Frequency by Call sign WJCC appears near 1,700 kHz at the high end of the call-sign frequency cut, with neighbors such as KGED near 1,680 kHz. These are upper-AM coordinates, not audience-size claims. Grouping by call sign exposes how the metric varies across licensed identities — a map of where stations sit on the spectrum, not which morning shows win their cities. Who sits at the top Top Call sign WJCC leads at 1,700 kHz, with 1,695 kHz as the median among the top dozen. The entire top band is a tight cluster of high-AM assignments. The finding is the tightness itself: the top of frequency is a narrow technical neighborhood, while format diversity lives on a different axis. Frequency by format Frequency by Format Box plots by format — Country, News/Talk, Sports, Classic Hits, and related labels — show whether frequency consensus is shared or contested across programming types. Country leads station count but does not automatically own the highest dial positions. Format is culture; frequency is spectrum. Confusing the two produces false stories about which genres rule the airwaves. Who beats the median — and who trails Frequency vs median by Format News/Talk sits 1,039 kHz above the median; Contemporary Christian trails by about 9. News/Talk's elevation reflects its heavy presence on AM frequencies far above the FM-centered median of 101 MHz. The gap is a band-plan story as much as a format story. Talk radio's historic AM home pulls its frequency distribution into a different universe from music formats clustered on FM. Concentration on the dial The top 5 call sign entries account for 34% of the aggregate frequency The top five call-sign entries account for 34% of aggregate frequency in the working cut — a concentration statistic that reflects how large the highest dial numbers are relative to the median, not a claim that five stations own a third of listening. Summing dial positions is not the same as summing audiences. Pareto-on-frequency is a reminder that spectrum physics and market share are separate metrics. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and license-status quirks apply. Frequency is not listenership, and format labels can be stale or contested. Findings describe structural signals about U.S. radio station metadata — not a ratings analysis, and not a complete history of every translator and HD multicast. What to take away The U.S. radio dial in this file centers at 101 MHz with AM extremes near 1,700 kHz, Country as the most common format, and News/Talk pulled high by AM band placement. The citable split is spectrum versus culture: call-sign frequency leaders and format share leaders answer different questions about the same airwaves. Data, methods & sources Data and method The source is the TidyTuesday release from 2022-11-08, maintained by the R for Data Science community. The working file contains 17,186 rows and 11 columns after cleaning — call signs, frequencies, formats, and license status fields. Medians stabilize a dial that mixes FM-looking centers with AM extremes. Charts export as Plotly JSON with PNG fallbacks. Each license row represents permission to exist on the dial, not a measure --- ## RAMEN: Malaysia Leads Global Ramen Ratings by Half a Star - URL: https://artometrics.com/ramen - Desk: culture, food - Published: 2026-06-15T00:00:00.000Z - Description: 3,180 instant-ramen reviews show a 5.00-star ceiling and country gaps of 0.25–0.50 stars around the 3.75 median. The median instant-ramen rating in a 3,180-record TidyTuesday sample is 3.75 stars on a five-point scale, but dozens of brands share the 5.00 ceiling — compressing the top into a plateau where Malaysia's 0.50-star lead above the median becomes the most meaningful country gap. Star ratings collapse broth clarity, noodle chew, spice balance, and packaging nostalgia into a single number. A TidyTuesday working file from June 2019 shows 3,180 reviews with a median of 3.75 and a maximum of 5.00 . Nongshim leads the brand ranking in the fact box, and Japan is the most common country label by product count — though output volume and rating leadership do not align. When perfect scores are common, the useful story lives below the ceiling: which countries sit above the median, and whether high ratings rest on thin or thick review support. Stars by brand Stars by Brand Brand rollups at the top of the chart crowd the 5.00 ceiling. TTL, Tseng Noodles, A-Sha, Prima Taste, and other specialists occupy the perfect-score band. When the top saturates, discrimination shifts to consistency across varieties and the number of reviews behind the mean. Nongshim's fact-box lead can coexist with a chart full of smaller perfect-score brands because aggregation choices — mean across varieties, review weighting, or maximum score — change the ranking order. Who sits at the top TTL leads at 5.00 — 5.00 marks the median among the top dozen TTL leads at 5.00 , and the median among the top dozen is also 5.00 . The elite band is a plateau, not a ladder. Perfect scores are common enough that first place is a crowded room. Tiny rank differences among five-star brands carry little information. The useful comparisons appear in the middle of the distribution and across countries. Stars by country Stars by Country Country box plots show whether star consensus is shared or contested across producing nations. Japan leads by product count; Malaysia, Thailand, South Korea, and Singapore compete on rating location rather than volume. A country can dominate shelf presence without dominating the upper quartile of stars. The spread chart separates industrial scale from taste prestige. Who beats the median — and who trails Stars vs median by Country Malaysia sits 0.50 stars above the median; Thailand trails by 0.25 . Those gaps look small until you remember the scale runs to 5.00 and the median is already 3.75. Half a star above center is a material national reputation effect in a compressed rating system. It is also narrow enough that a few legendary varieties can move a country's summary. Stars and review number Stars vs Review number Plotting stars against review number reveals clusters that averages hide. Some high-rated varieties have thin review support; some heavily reviewed products sit nearer the middle. Attention and admiration are related, not identical. Review-number fields in the source can behave as identifiers as much as popularity counts. The scatter prevents readers from equating a perfect score with a broadly tested consensus. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and brand-name duplicates apply. Stars are not sales, and country labels do not always indicate where a variety is consumed most. Findings describe structural signals in ramen rating metadata, not a definitive world championship of instant noodles. What to take away Instant ramen ratings in this file center near 3.75 stars, with a crowded perfect-score ceiling at 5.00 and country gaps of 0.25 to 0.50 stars around the median. When the top is a plateau of five-star brands, the meaningful geography is which countries sit above the middle and how much review support stands behind the praise. Malaysia's 0.50-star lead and Thailand's 0.25-star deficit matter more than first-place ties at the ceiling. Data, methods & sources Data and method The source is the TidyTuesday release from 2019-06-04, maintained by the R fo --- ## RANKINGS: How the Chart Top Got Crowded - URL: https://artometrics.com/rankings - Desk: arts, music - Published: 2026-06-15T00:00:00.000Z - Description: Large-scale Hot 100 data show how rankings compress at the summit. The Billboard Hot 100 is a weekly ranking machine: 100 positions, endlessly refreshed. The TidyTuesday billboard extract used here holds 100,000 song-week rows, with a median week position of 50.0 — exactly mid-chart — and a highest observed week position of 100 (the bottom rung). That median of 50 is almost tautological in a full 1–100 ranking, which is why the interesting questions shift to concentration, re-entries (instance), and which titles spend time at the chart’s edges. “Down By The River” appears among the fact-box leaders for week position in this working file. Song-Level Position Averages Crowd the Bottom Rung Week position by Song When songs are ranked by week-position summaries, a block of titles ties at 100 — including Girl (You Captivate Me), Time Seller, Gee Baby (I’m Sorry), and others. Those are songs whose observed positions in this aggregation sit at the chart floor. The chart is less a hall of fame than a map of how many titles live at the margins of the Hot 100’s attention economy. Leaders by Week Position Are a Flat Elite (Quarter To Four) Stomp leads at 100 — 100 marks the median among the top dozen (Quarter To Four) Stomp and peer titles in the leaders view hit 100 , and the median among the top dozen is also 100 . There is no graded ladder here — the “leaders” by this numeric field are a plateau at the bottom rank. That flatness is informative: many songs brush the Hot 100 without climbing. Presence is not the same as dominance. Weekly Positions Fill the Chart Almost Uniformly Week position Distribution The distribution of week positions across the 100,000-row sample is remarkably even: bin counts hover near 5,000 observations across the 1–100 range. A full Hot 100 every week produces that rectangular shape by design. Uniform position counts are the baseline. Deviations in other charts — concentration among songs, re-entry instances — are where the cultural story lives. Concentration Among Songs Is Mechanical at First Cumulative Week position The Pareto curve over leading song entries rises in near-equal steps in this view, reaching 100% by the fifteenth plotted entry. Within the restricted leader set, share accumulates steadily rather than in a single spike. Full-catalog fame concentration is a different question; this curve describes the ranked subset on display, not every Hot 100 occupant since the 1950s. Re-Entries Stretch a Song’s Life Without Guaranteeing Rank Week position vs Instance Week position versus instance (re-entry or appearance count) shows titles returning to the chart at many ranks. High instance values appear across both strong and weak positions. Longevity on Billboard is not only about peaking at No. 1. It is also about how many times a song is allowed to reappear — a second career path the scatter makes visible. What this file cannot tell you Billboard’s chart methodology has changed over decades (sales, airplay, streaming). A pooled file mixes eras with different rules. Song-title collisions and punctuation variants can split or merge identities. Numeric “leaders” by week position in this stack highlight the bottom of the chart, not the cultural canon. Read rank direction carefully before citing. What to take away A 100,000-row Hot 100 extract is mostly a uniform grid of weekly ranks — median 50, full coverage from 1 to 100. The drama is in which songs repeatedly occupy that grid and which only touch position 100. Cite the uniform distribution when explaining the chart’s structure; cite instance versus position when explaining how hits persist. Data, methods & sources Data and method The source is the TidyTuesday release from 2021-09-14 (billboard.csv). After cleaning, the analysis sample contains 100,000 records. Week position is the primary metric; lower numbers are better ranks in Billboard’s native scale, but several charts here rank the numeric position field directly as stored. Charts are Plotly JSON with PNG fallbacks. Sources Data Science Learning Community. (2021) --- ## RESTAURANTS: NYC Inspection Scores Hit 156—Eleven Times the Median - URL: https://artometrics.com/restaurants - Desk: culture, food - Published: 2026-06-15T00:00:00.000Z - Description: NYC restaurant inspection scores cluster at 15; highest violation count reaches 156 in 100,000-record TidyTuesday extract. NYC restaurant inspection scores cluster at a median of 15.0 violation points, but the distribution stretches to 156 in a 100,000-record TidyTuesday extract — a span that letter grades compress into A, B, and C stickers. The highest-scoring establishment in this extract, The Slope Lounge and Restaurant , logged 152 points. The top dozen violators post a median of 122 . Grade A remains the most common public label, while the year span runs from placeholder 1900 through 2018 , with inspection density concentrated in the 2010s. Higher scores mean more violations, not better food — an inversion easy to forget once letter grades take over. How scores moved over time Median Score Over Time Median score over time tracks whether the typical inspected restaurant became cleaner, worse, or remained stable as the program matured. Flat stretches can signal stable compliance or consistent scoring practice. Early placeholder years carry little weight; most interpretive signal sits in the dense 2010–2018 window. Who sits at the top of violation scores The Slope Lounge and Restaurant leads at 152 — 122 marks the median among the top dozen The Slope Lounge and Restaurant posts 152 points, with a median of 122 among the top dozen violators. These scores sit at the extreme end of a distribution where the overall median is 15. The head of the distribution is where enforcement drama concentrates. Single bad inspections can define a DBA's ranking even if later visits improve. Scores by grade Score by Grade Box plots by grade show the numeric ranges that map onto A, B, C, and related labels. Grade A dominates the count; higher-letter grades occupy the upper score territory where violation points accumulate. The chart makes the letter system's compression visible: many different numeric outcomes share an A, while public debate concentrates on rarer worse grades. Who beats the median — and who trails Score vs median by Grade The gap chart ranks grades above or below the median score. Worse letter grades sit far above the median by design; A sits at or below it. The geometry is the grading rule made visual. What remains notable is the within-grade spread on the previous chart: even A is a band, not a point, and policy arguments often happen inside that band. Scores and identifiers Score vs Camis Plotting score against CAMIS identifiers is mostly a diagnostic scatter: restaurant IDs are not a substantive X-axis, but the cloud shows how scores disperse across the universe of establishments rather than clustering on a few names. When substantive covariates are thin, an ID scatter refuses to invent a prettier relationship than the table supports. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and sampling to 100,000 rows apply. Scores are inspection outcomes, not taste ratings. Findings describe structural patterns in NYC inspection scoring — not a complete health study, and not a recommendation about where to eat without checking current grades. What to take away NYC restaurant inspections cluster at a median of 15 violation points, with a long tail extending past 150. Grade A dominates the public display; extreme scores are rare but visible in the data. The inversion remains: higher numbers are worse, letter grades compress a wide numeric band, and the window sticker is only the headline of a point system running underneath. Data, methods & sources Data and method The source is the TidyTuesday release built from NYC restaurant inspection open data via the R for Data Science community. The working file contains 100,000 rows sampled from the full dataset, with fields including DBA names, boroughs, scores, grades, and inspection dates. Medians matter because a thin tail of high-violation restaurants pulls means upward. Charts export as Plotly JSON with PNG fallbacks. Grades derive from scores under city rules; treating them as independent categories misses the underlying point --- ## SCHOOLS: School Diversity Peaks at 85.3 While the Median Holds at 2.38 - URL: https://artometrics.com/schools - Desk: civics, education - Published: 2026-06-15T00:00:00.000Z - Description: Butteville Union Elementary scores 85.3 on the Multi index while the national median sits at 2.38 across 27,944 local education agencies. Butteville Union Elementary scores 85.3 on the Multi diversity index while the national median sits at 2.38 across 27,944 local education agencies. The 35-fold gap between the top district and the median defines the shape of U.S. school diversity: a flat center and an extreme upper tail. Texas is the most common state in the file by record count, but frequency does not equal high diversity scores. Oklahoma sits 4.25 above the median; New Jersey trails by 0.81. The Multi–Variance scatter reveals that schools cluster rather than form a smooth cloud, indicating that composition patterns vary independently of overall diversity level. Research question Where does student-body diversity concentrate when the unit of analysis is the local education agency rather than the nation as a whole? This report asks whether the Multi metric behaves like a broad national baseline or a long-tailed distribution in which a few LEAs and states sit far above the typical row. The observational question is constrained to the source file: how stable is the median, which LEA names define the upper tail, how do state abbreviations structure the spread, and how does Multi relate to Variance? It does not treat one diversity metric as the full moral or demographic story of American education. A flat median hides how extreme the upper tail is Median Multi Over Time The median Multi sits at 2.38 in the opening period and 2.38 at the close — a flat line at the center of the distribution. Stability at the median does not mean stability everywhere; it means the typical school's Multi did not move in this snapshot window. That flat center is the baseline against which the leaderboard should be read. The action in school diversity here is not a national median march — it is how far individual LEAs sit above that center. U.S. school diversity is often narrated through landmark legal periods: Brown v. Board of Education in 1954, the busing and desegregation orders of the 1960s and 1970s, the release from court supervision in later decades, and contemporary residential segregation. Those histories are real, but this chart is not a long legal history; it is a snapshot-style metric view where the center does not move. A stable center can coexist with sharp local change because public education is geographically partitioned. District boundaries, housing markets, charter growth, school assignment rules, and private-school exit can all alter individual LEAs without moving the national median much. The chart tells readers to look for distributional extremes rather than assume a single national trend. Butteville Union Elementary leads a steep LEA ladder Top LEA NAME Butteville Union Elementary leads at 85.3 Multi. Among the top dozen LEAs, the median is 52.0 — still many times the file-wide median of 2.38. The leaderboard is an upper-tail map of districts whose composition scores sit far from the typical school. LEA names on this chart carry the highest observed Multi values in the file. They are not a random sample of American schooling; they are the extreme cases the metric surfaces first. Butteville Union Elementary is a named local education agency rather than a state system, which is exactly why the scale warning matters. A small district can produce an extreme composition metric if its enrollment mix is unusual, if denominators are small, or if categories split more evenly than in larger systems. The top of the ladder is a place to investigate, not a national template. The top-dozen median of 52.0 confirms that the upper tail is not one data-entry spike. Several LEAs sit in a high-Multi band that is many times the file-wide center. In education data, that kind of gap often reflects the interaction of neighborhood demography, district boundaries, grade configuration, and reporting categories. State boxes show how Multi spreads geographically Multi by ST Splitting Multi by state (ST) produces category boxes that reveal whether diversity scores are shared or contested acr --- ## SHERLOCK: Twelve Words per Tokenized Record - URL: https://artometrics.com/sherlock - Desk: arts, language - Published: 2026-06-15T00:00:00.000Z - Description: Tokenized Holmes corpus from TidyTuesday shows median 12.0 words, mean 10.9, with top five titles holding 34% of aggregate count. The tokenized Sherlock Holmes corpus from TidyTuesday contains 65,958 records with a median word count of 12.0 and a mean of 10.9—evidence of a nearly symmetric distribution. The top-ranked title, The Yellow Face, reaches 13.0 words per record, just one word above the file-wide median, while the top five books together account for 34% of aggregate word count. The Adventure of the Beryl Coronet appears as the top-ranked book by word count in the fact box; The Yellow Face leads the charted breakdown at 13.0. This analysis tracks textual mass in a tokenized extract, not literary merit or narrative arc. Story-level medians sit in a narrow band Word count by Book The Yellow Face leads at 13.0 words per record; A Case of Identity anchors the low end at 12.0. The visible spread is narrow, reflecting tight clustering across many titles in the tokenized corpus. Grouping by book exposes length variation across the catalog without requiring plot summaries. The question here is structural concentration, not detective methodology. Even the leaders barely clear the file median The Yellow Face leads at 13.0 — 12.0 marks the median among the top dozen The Yellow Face leads at 13.0, while the median among the top twelve is 12.0—identical to the file-wide median. Leading in this extract does not imply a dramatic leap; it means a single-word edge inside a compressed distribution. That compression is the finding. Holmes stories in this tokenization do not diverge into wildly different word-count regimes at the center of the leaderboard. Median 12.0, mean 10.9 — a mostly symmetric shape Word count Distribution The full-sample distribution shows median 12.0 and mean 10.9—near symmetry rather than heavy right skew. The top decile begins at 14.0; that upper slice contains the longer textual segments in this metric. Symmetry matters for citation. A median-centered account of Holmes text length is more faithful than an average distorted by outliers. Five books account for a third of aggregate word count Cumulative Word count The top five book entries account for 34% of aggregate word count. Concentration is real but not absolute: a meaningful head exists, yet a long tail of other titles still holds the majority of textual mass. This concentration curve means editorial attention can begin with a small set of books without ignoring the rest of the canon. A second concentration cut confirms the same head Cumulative Word count A second cumulative chart repeats the concentration view: the top five book entries again account for 34% of aggregate word count. Parallel concentration cuts serve as a robustness check when the pipeline exports multiple figures. The takeaway holds. A compact set of titles carries a disproportionate share of the summed word-count field in this 65,958-row extract. What this file cannot tell you TidyTuesday snapshots are community-cleaned teaching datasets, not live literary APIs. Tokenization choices, spelling variants in titles, and week-of-export coverage limits apply. Word count here is the field in the file—not an independent scholarly page count or a proxy for narrative complexity. Findings describe this extract. They are structural signals about textual mass across books, not claims about literary quality or cultural importance. What to take away The tokenized Holmes corpus centers tightly: median word count 12.0, mean 10.9, top decile from 14.0, and leaders such as The Yellow Face only one word above the median. Concentration is modest—the top five books hold 34% of aggregate word count—but no single title dominates. The textual mass accumulates without extreme median drift, and the distribution remains nearly symmetric across 65,958 records. Data, methods & sources Data and method The source is the TidyTuesday release from 2025-11-18, published by the R for Data Science community. The working file contains 65,958 rows and 4 columns after merging available tables. Book titles serve as the primary categorical axis; word cou --- ## SIMPSONS: Marcia Wallace, 175 Guest Appearances - URL: https://artometrics.com/simpsons - Desk: arts, film - Published: 2026-06-15T00:00:00.000Z - Description: Marcia Wallace logged 175 guest appearances across 30 Simpsons seasons—2.7× more than the second-place performer in the TidyTuesday dataset. Marcia Wallace logged 175 guest appearances across 30 Simpsons seasons—2.7× more than the second-place performer and the highest count in the TidyTuesday dataset released August 27, 2019. The snapshot captures 1,381 guest records spanning Seasons 1–30. Season 24 recorded the highest guest density with 65 appearances, while most guests appeared only once. Volume Records By Period Activity peaks in Season 24 with 65 records. Period-level counts reveal when guest-appearance frequency intensified. Who sits at the top Top Guest star Marcia Wallace appears 175 times—the most recurring name in the file. The top dozen account for a visible share of all 1,381 rows. Timeline Leaders Over Time The leading names do not move in lockstep—some fade as others surge. Tracking counts over time separates sustained presence from one-season spikes. Frequency Most guest star entities appear only once; a small head revisits repeatedly Most guest stars appear only once; a small cluster revisits repeatedly. This power-law shape is typical of guest lists, credits, and catalog-style tables. Mix 24 is the most repeated season in the extract Season 24 is the most repeated season in the extract. Secondary dimensions add context when the primary table has no numeric score column. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and week-of-export coverage limits apply. Merged tables may fan out or duplicate rows when join keys are imperfect. Findings describe the file on hand—treat them as structural signals about Simpsons guest appearances, not exhaustive truth about the full domain. What to take away Read as a teaching map, this dataset shows why one metric is rarely enough: leaders, tails, trends, and relationships each answer a different question about guest-appearance patterns. The best reading is modest: use the chart to sharpen the question, then check the source and limits before turning it into a claim. Data, methods & sources Data and method The source is the TidyTuesday release from 2019-08-27 (R for Data Science community). This working file contains 1,381 rows and 7 columns after merging all available CSV/XLSX tables in the week folder. Charts are exported as Plotly JSON with PNG fallbacks. Medians are used for robustness where distributions skew. Index-style fields (row numbers, sequential IDs) are excluded from metric selection. Sources Data Science Learning Community. (2019). TidyTuesday: Simpsons Guest Stars . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2019/2019-08-27/simpsons-guests.csv --- ## SLICES: A Dozen Steam Titles Captured Half the Audience - URL: https://artometrics.com/slices - Desk: sports, gaming - Published: 2026-06-15T00:00:00.000Z - Description: Median concurrent players climbed from 212 to 309 across the decade, but Dota 2's 475,924 average dwarfs the top-twelve median of 64,656. Median average concurrent players on Steam rose from 212 to 309 between 2012 and 2021, a 46 % climb that still leaves the typical title-month at 203 in an 83,631-row extract. Dota 2 leads at 475,924 Avg — 7.4 times the top-twelve median — and the file's single highest observation reaches 1,584,887. January runs 36.9 above the median; October trails by 23.3. The file is the TidyTuesday 2021-03-16 video-games release, merged from community-cleaned Steam player-count snapshots. Charts trace the median's climb, rank the leaders by Avg, map month offsets, and plot Avg against Gain to show how level and change co-occur in a long-tailed market. Median concurrency climbed across the decade Median Avg Over Time Median Avg rises from 212 at the series open to 309 at the close, crossing above the file median of 203 midway through the period. The 46 % gain reflects either platform growth, survivor bias among tracked titles, or both. The chart reports the center's path. Individual titles distribute around that median in ways the trend line does not capture. Dota 2 leads a top tier that drops off fast Dota 2 leads at 475,924 — 64,656 marks the median among the top dozen Dota 2 leads at 475,924 Avg, 7.4 times the top-twelve median of 64,656. The gap from first to the elite-tier median defines the live-service economy: a few titles sustain city-scale populations; the rest of the top dozen are large but operate in a different order of magnitude. The file's highest single Avg — 1,584,887, appearing in the PLAYERUNKNOWN'S BATTLEGROUNDS record set — sits above even the charted leaders, marking the extreme ceiling of the distribution. Months carry different concurrency distributions Avg by Month Box plots by month reveal whether Avg spreads uniformly or clusters around distinct medians across the calendar. Seasonal launches, holidays, and school schedules can shift who is online. February's frequency in the file is a coverage fact, not a player-count claim; the boxes measure dispersion within each month, not record volume. January clears the median; October trails Avg vs median by Month January sits 36.9 above the file median; October trails by 23.3. Those signed offsets convert the calendar into a comparable scale against the center, revealing structural month effects in this extract. The offsets are not proof of a universal gaming season — coverage dates, launch windows, and sample composition all shape the pattern — but they are concrete enough to cite when asking when concurrency runs hot or cold in this snapshot. Avg and Gain form a joint cloud of winners and churn Avg vs Gain Plotting Avg against Gain reveals clusters that medians erase: high average with stable gain is a different audience story from high average with volatile gain, and low average with positive gain signals a trajectory invisible in rank tables. The scatter maps how level and change co-occur across the 83,631 title-months, showing the joint distribution that single-metric summaries cannot capture. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live SteamCharts APIs. Missing values, title variants, and 2012–2021 coverage limits apply. Merged tables may fan out or duplicate rows when join keys are imperfect, inflating counts. Avg and Gain are the metrics as defined in the source tables. Findings describe concurrency structure in this extract, not complete commercial P&L for every title or the full Steam catalog. What to take away The file median of 203 Avg anchors an 83,631-row distribution that rose from 212 to 309 at the median between 2012 and 2021, a 46 % climb. Dota 2 leads at 475,924, 7.4 times the top-twelve median, and the single highest observation reaches 1,584,887. January sits 36.9 above the median; October trails by 23.3. Avg–Gain scatter plots show how level and momentum travel together in a long-tailed live-service market where a few titles capture city-scale audiences and the typical game serves hundreds. Data, methods & --- ## STEAM: Ten Hours per Dollar Owns the Shelf - URL: https://artometrics.com/steam - Desk: sports, gaming - Published: 2026-06-15T00:00:00.000Z - Description: 26,688 Steam titles show critic scores rising from 70 to 73 between 2004–2018 while million-owner games beat the median by 10 points. Median critic score for Steam titles rose from 70.0 in 2004 to 73.0 by 2018, while games with 2–5 million owners scored 10 points above the catalog median and the smallest-owner tier trailed by 5 points, according to 26,688 titles logged in the Steam Spy extract. Metascore tracks critical consensus; owner tiers measure commercial reach. The catalog median of 73.0 divides the critically strong from the ordinary. Little Triangle leads at 98.0. Median Metascore edged up across the store's expansion years Median Metascore Over Time Median Metascore rose from 70.0 at period open to 73.0 at close — landing on the catalog median. The typical scored game ended the window three points higher than it began. A rising median reflects stronger releases, expanded critic coverage of titles receiving scores, or both. Little Triangle tops a tightly packed critical elite Little Triangle leads at 98.0 — 95.5 marks the median among the top dozen Little Triangle leads at 98.0, while 95.5 marks the median among the top dozen. The elite band compresses near the ceiling — high scores clustered together rather than a lonely outlier. DEEP SPACE WAIFU: NEKOMIMI also appears among notable titles in the ranking rules used in fact boxes. Owner tiers carry different Metascore distributions Metascore by Owners Games with millions of owners are not the same statistical population as the 0–20,000 band that dominates row counts. Boxes show whether Metascore consensus is shared or contested across popularity bands. Volume in the smallest owner bucket is a catalog fact; the boxes reveal how scores spread inside each ownership tier. Mid-million owner games clear the median; tiny audiences trail Metascore vs median by Owners The 2,000,000–5,000,000 owners band sits 10.0 above the median; the 0–20,000 band trails by 5.00. Broader ownership tiers sit higher on Metascore relative to the catalog center than the sparsest shelf. That gap is an association, not proof that owners cause scores — but it is a citable structural pattern. Metascore and price form a joint market map Metascore vs Price Plotting Metascore against price shows clusters that averages erase — cheap high scores, expensive middling scores, and every bargain or premium pattern between. The scatter answers the storefront question: what combinations of critic score and sticker price actually appear in the 26,688-row extract. What this file cannot tell you TidyTuesday snapshots are not live Steam or Metacritic APIs. Missing Metascores, owner-band binning, price currency assumptions, and 2004–2018 coverage limits apply. Findings describe this Steam Spy extract — structural signals about Metascore, owners, and price — not a complete quality ranking of every game ever shipped. What to take away Median Metascore rose from 70.0 to 73.0 across the 2004–2018 window; Little Triangle leads at 98.0. Games with 2–5 million owners sit 10 points above the catalog median, while the smallest ownership tier trails by 5 points. Metascore–price clusters show how critic standing and sticker price co-occur on the shelf. Data, methods & sources Data and method The source is the TidyTuesday release from 2019-07-30 (R for Data Science community). The working file contains 26,688 rows and 11 columns after merging available tables. Metascore is the primary metric; owners is the categorical size axis; price appears in the scatter. Medians are used because scores and prices both skew. Sources Data Science Learning Community. (2019). TidyTuesday: Video Games Steam Spy . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2019/2019-07-30/video_games.csv Editor's note Artometrics data report from the TidyTuesday research pipeline. Charts and aggregates are reproducible from the embedded exhibits and public source files. Source archive (GitHub) --- ## SUPERBOWL: Five Brands Capture 87% of Ad Views - URL: https://artometrics.com/superbowl - Desk: civics, communication - Published: 2026-06-15T00:00:00.000Z - Description: NFL leads at 403,641 views; median dropped from 111,814 to 33,766 as the 2006–2021 archive filled with ordinary uploads. Five brands capture 87% of all post-game Super Bowl ad views on YouTube, according to 247 uploads spanning 2006–2021. Median views fell from 111,814 to 33,766 as the archive expanded, but the ceiling reached 176,373,378—proof that a handful of commercials become folklore while most settle into inventory. NFL leads the top-twelve brand median at 403,641 views; Doritos ranks first in the full-file aggregation. Post-game streaming attention operates as its own economy. Broadcast buys the first audience; YouTube decides which spots circulate for years. The dataset tracks that second life through trend, brand leaders, format distribution, concentration, and the relationship between views and likes. Median views fell as the archive got denser Median View count Over Time Median view count fell from 111,814 in the earliest period to 33,766 at the end of the span. That decline does not signal deteriorating ad quality—it reflects the archive filling out with more ordinary uploads, pulling the center down while the ceiling remained stratospheric at 176,373,378. The falling median and the fixed ceiling coexist because attention is power-law distributed: most ads cluster near the median, a few explode into viral territory. NFL leads the brand ladder on YouTube views NFL leads at 403,641 — 46,661 marks the median among the top dozen NFL leads the top-twelve brands at 403,641 views; the median within that group sits at 46,661. Doritos remains the top brand in the full-file aggregation; the chart isolates the highest performers in this particular cut. The gap between #1 and the top-dozen median shows how quickly the ladder drops. A few brands own the conversation; most of the competitive set operates closer to tens of thousands than hundreds of thousands. Almost everything in the file is a YouTube video View count by Kind Splitting view count by format produces a near-monoculture: youtube#video is the dominant kind. The distribution confirms the archive's platform identity rather than revealing contested formats. This matters for scope. The dataset tracks post-game internet lives of Super Bowl ads on YouTube, not a complete census of every commercial that aired during the broadcast. Five brands hold eighty-seven percent of aggregate views Cumulative View count The top five brands account for 87% of aggregate views—a steep Pareto curve. Post-game attention is not distributed evenly; a small group drives the bulk of summed engagement. Concentration at this level explains why brand strategy obsesses over a handful of breakthrough spots. The long tail exists but does not move the aggregate the way the head does. Views and likes travel together — with clusters View count vs Like count Plotting view count against like count reveals clusters that a single correlation coefficient would obscure. Some ads convert attention into likes efficiently; others accumulate views without the same approval signal. The scatter maps how the two engagement measures co-move in this 247-row extract, not a quality ranking. What this file cannot tell you TidyTuesday snapshots are community-cleaned archives, not live YouTube APIs. View and like counts are frozen at export; missing values, brand spelling variants, and coverage gaps apply. Merged tables may duplicate or fan out rows when join keys are imperfect. Findings describe this Super Bowl ads extract and its structural signals, not a comprehensive history of every commercial creative or every platform where ads circulated. What to take away Post-game Super Bowl attention is concentrated: median views sit at 41,379, the ceiling reaches 176,373,378, and five brands capture 87% of aggregate views. The falling median from 111,814 to 33,766 reflects archive expansion, not declining ad quality. NFL and Doritos-scale leaders anchor the archive's memory. Views and likes move together in clusters, which is how engagement looks when a few spots become folklore and most settle into inventory. Data, methods & sources Data a --- ## TELEVISION: Prestige TV Scores Drifted Up—Slowly—From 1990 to 2018 - URL: https://artometrics.com/television - Desk: arts, film - Published: 2026-06-15T00:00:00.000Z - Description: Median season ratings climbed from 7.86 to 8.15 across 2,266 records—a raised floor, not a sudden elite leap. Median season ratings rose 0.29 points—from 7.86 to 8.15—between 1990 and 2018, raising the typical show's score without the sharp elite-vs-mediocrity split that golden-age rhetoric promised. The working file holds 2,266 records from 1990–2018 . Median average rating is 8.11 ; the highest observed average is 9.68 . Parenthood leads the title ranking in the extract. How the pattern changed over time Median Av rating Over Time Median average rating rises from 7.86 in the opening period to 8.15 at the close—a 0.29-point climb that describes the typical row, not the headline cases. The middle of the scoreboard edged upward while prestige TV became a cultural brand. That is not the same shape as an explosion of elite outliers. Who sits at the top Touched by an Angel leads at 9.60 — 9.24 marks the median among the top dozen Touched by an Angel leads at 9.60; 9.24 marks the median among the top dozen. The head of the board is densely packed at the high end. High averages can reflect passionate rater pools as much as critical consensus. Read the leaders as the file's ceiling, then compare them to the 8.11 median that describes the typical row. How the field is spread Av rating Distribution Median 8.11 versus mean 8.06—the shape is nearly symmetric. The top decile begins at 8.79; that tail is where defining cases live. A symmetric prestige-TV scoreboard contradicts the thin-elite-versus-wide-mediocrity story. The distribution is tighter than golden-age rhetoric suggests. Leader Trends Top Title Over Time Leading names do not move in lockstep—some fade as others surge. Tracking medians over time separates sustained dominance from one-off spikes. A peak season is not the same data shape as a long plateau above 8. What moves together Av rating vs SeasonNumber Average rating and season number show clusters that a single summary would erase—early-season highs, late-season drift, and shows that hold the middle across many seasons. Longevity and quality are related but not identical. The scatter is where that distinction becomes visible. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and week-of-export coverage limits apply. Merged tables may fan out or duplicate rows when join keys are imperfect. Findings describe the file on hand—structural signals about prestige-TV ratings through 2018, not a complete canon of the streaming era that followed. What to take away The median climbed 0.29 points—from 7.86 to 8.15—while the overall middle stayed near 8.11. A raised floor, not a sudden leap. Leaders near 9.6 and a top decile from 8.79 show how much room remains above the typical row. Read trends, distribution, and season scatter together before treating any single title as proof that critics and audiences agreed. Data, methods & sources Data and method The source is the TidyTuesday release from 2019-01-08 (R for Data Science community)—the IMDb/Economist TV ratings extract. The working file contains 2,266 rows and 8 columns after merging available tables in the week folder. Charts export as Plotly JSON with PNG fallbacks. Medians handle skew; season numbers join ratings so longevity can be plotted against score. Sources Data Science Learning Community. (2019). TidyTuesday: TV Golden Age . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2019/2019-01-08/IMDb_Economist_tv_ratings.csv --- ## TUITION: Vermont Led Public Tuition at $13,486 as Medians Rose 56% - URL: https://artometrics.com/tuition - Desk: civics, education - Published: 2026-06-15T00:00:00.000Z - Description: Median state tuition climbed from $5,876 to $9,141 across 2004–2015; Vermont led at $13,486 while five states held 38% of aggregate value. Median state tuition rose from $5,876 in 2004 to $9,141 in 2015—a 56% increase—across 600 state-year observations. Vermont led at $13,486, the top decile began at $11,204, and five states held 38% of aggregate tuition value. The median of $7,607 anchors the distribution. Vermont's lead, the climb from baseline to close, and the $11,204 top-decile threshold all measure distance from that center. Median tuition climbed from the mid-five-thousands to the low-nine-thousands Median Value Over Time Median tuition rose from $5,876 in 2004 to $9,141 in 2015. The typical state moved upward across the period, and no year showed a reversal. A rising median signals shared experience. High-cost states amplify the climb; low-cost states still participate in the upward shift. Vermont leads the state ladder in the charted cut Vermont leads at 13,486 — 10,850 marks the median among the top dozen Vermont leads at $13,486; the median among the top dozen states is $10,850. New Hampshire ranks second in the fact-box summary; the leaders chart shows the full numeric ladder. The gap from first place to the top-dozen median confirms a competitive but elevated upper tier—expensive relative to the file median of $7,607. Median and mean sit close; the top decile starts above eleven thousand Median 7,607 vs mean 7,899 — the shape is relatively symmetric Median $7,607 versus mean $7,899—the distribution is relatively symmetric for a price series. The top decile begins at $11,204; high-tuition states occupy that upper slice. Near-symmetry at the center does not erase the expensive tail. It confirms the typical state is not distorted by extreme outliers in the average. Leading states do not move in lockstep over time Top State Over Time Leader trajectories diverge—some states fade while others surge. Tracking medians over time separates sustained high-price states from one-off spikes. Side-by-side paths matter when the policy question is whether expensive states stay expensive or the ladder reshuffles. Five states account for thirty-eight percent of aggregate value The top 5 state entries account for 38% of the aggregate value The top five state entries account for 38% of aggregate tuition value. Concentration is meaningful without being absolute: a visible head and a broader set of states carrying most of the remaining total. Pareto steepness reminds us that national tuition debates often orbit a minority of high-cost states even when the median tells a quieter story. What this file cannot tell you TidyTuesday snapshots are not live College Board or IPEDS APIs. Missing values, state naming variants, and 2004–2015 coverage limits apply. Tuition definitions follow the source file's value field. Findings describe this extract—structural signals about state price levels and trends—not net-price analysis after aid. What to take away State tuition rose from $5,876 to $9,141 at the median, centered on $7,607, with an expensive tail beginning at $11,204 in the top decile. Vermont led at $13,486, five states held 38% of aggregate value, and leader trajectories diverged—a price map of public higher education's mid-2000s to mid-2010s climb. Data, methods & sources Data and method The source is the TidyTuesday release from 2018-04-02 (R for Data Science community). The working file contains 600 rows and 3 columns after merging available tables. Value is the tuition metric; state is the entity axis; year spans 2004–2015. Medians are preferred for skewed price distributions. The file reports a mean of $7,899 against the median of $7,607—relatively close, confirming modest skew. Sources Data Science Learning Community. (2018). TidyTuesday: US Tuition Costs . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2018/2018-04-02/us_avg_tuition.xlsx Editor's note Artometrics data report from the TidyTuesday research pipeline. Charts and aggregates are reproducible from the embedded exhibits and public source files. Source archive (GitHub) --- ## VOTERS: National Turnout Hit 132 Million; Most States Stayed Near 1.2 Million - URL: https://artometrics.com/voters - Desk: civics, politics - Published: 2026-06-15T00:00:00.000Z - Description: National turnout reached 132.6 million in 2014 while the median state recorded 1.17 million votes across 936 elections from 1980–2014. National turnout reached 132,609,063 in 2014 while the median state recorded 1,170,867 votes, according to 936 election records from 1980–2014. The national aggregate appears as a separate entity in the dataset, sitting 113 times higher than the typical state. Five charts separate trend, leaders, distribution shape, state trajectories, and the relationship between votes cast and eligible voters. The median of 1,170,867 divides smaller states below from larger electorates and national totals above. Median votes rose across three decades of elections Median Votes Over Time Median state turnout rose 52% from 910,290 in 1980 to 1,387,622 in 2014. Population growth, eligibility expansion, and election-type composition all contribute to the upward trend. The rising median provides context for state-level comparisons: leader states and national totals amplify the trend, while smaller jurisdictions track within the shifting distribution. The United States total dwarfs the state ladder United States leads at 90,912,015 — 4,209,538 marks the median among the top dozen The national aggregate averages 90,912,015 votes across the period, while the median among the top dozen states sits at 4,209,538. The national row represents the sum of all state turnout, not a peer comparison. The peak of 132,609,063 reflects this national-scale aggregation. Individual state rankings occur far below that ceiling. A right-skewed distribution with a heavy upper tail Median 1,170,867 vs mean 3,074,280 — the shape is right-skewed Median state turnout of 1,170,867 sits below the mean of 3,074,280 because large states and national totals create a long right tail. The top decile begins at 4,659,000, separating high-volume states from the rest. Right skew explains why turnout analysis requires medians and rates rather than raw averages alone. Leading states reshuffle across election cycles Top State Over Time State trajectories diverge over time: some maintain high turnout while others surge or fade. Tracking individual states separates sustained volume from cycle-specific spikes. Presidential versus midterm composition within the dataset can shift the state ladder even when the overall median rises. Votes scale with eligible voters — in clusters Votes vs Eligible voters Votes cast rise with eligible-voter pools, but the scatter plot reveals clusters that averages obscure. Larger eligibility produces larger turnout, yet the pattern's texture shows variation in how tightly that relationship holds across states and years. The joint distribution captures turnout as a rate problem without requiring a calculated rate column: eligibility and votes move together, and the clusters show where exceptions occur. What this file cannot tell you TidyTuesday snapshots are community-cleaned extracts, not live election records. Missing values, jurisdiction-naming variants, and 1980–2014 coverage limits apply. National and state rows coexist in ways that require care when ranking. Findings describe this voter-turnout extract — structural patterns in vote totals — not a complete model of registration law, suppression, or mobilization. What to take away Median state turnout rose 52% from 910,290 to 1,387,622 between 1980 and 2014, while the national aggregate reached 132,609,063 at its peak. Vote totals are right-skewed around the median of 1,170,867, with the top decile beginning at 4,659,000. State rankings shift across election cycles, and votes track eligible-voter pools in clusters — a volume atlas of American elections across 35 years. Data, methods & sources Data and method The source is the TidyTuesday release from 2018-10-09 (R for Data Science community). The working file contains 936 rows and 7 columns after merging available tables. Votes is the primary metric; eligible voters appears in the scatter plot; state and national totals form the entity axis. Median turnout is the central measure because vote totals are right-skewed: mean of 3,074,280 versus median of 1,17 --- ## WASTEWATER: Singapore's Wastewater Plants Discharge 200× the Global Norm - URL: https://artometrics.com/wastewater - Desk: science, engineering - Published: 2026-06-15T00:00:00.000Z - Description: Facility-level data show median discharge at 1,079 m³/day; Singapore's plants average 223,683. Median wastewater discharge across 58,502 facilities is 1,079 m³/day; Singapore's plants average 223,683, while one U.S. facility exceeds 3 million—a 2,800× spread that maps density, infrastructure investment, and the uneven geography of treatment reporting. The TidyTuesday extract from September 2022 covers 58,502 plants worldwide. Median waste discharge ( WASTE DIS ) is 1,079 m³/day; the maximum exceeds 3,073,754 . The United States leads aggregate discharge due to reporting volume, while Secondary treatment is the most common level label by count. Scale without treatment tier is incomplete: a large plant with advanced filtration is a different civic asset from a large plant stuck at primary or secondary standards. Discharge by country WASTE DIS by COUNTRY Country rollups place high-intensity systems in view. Singapore leads near 223,683 m³/day on average plant discharge, while Nicaragua sits near 33,649 —a reminder that averages mix plant size, urban concentration, and reporting coverage. The United States leads aggregate discharge by facility count, not per-plant intensity. Large federations with many logged plants dominate totals even when median plants elsewhere run bigger. Who sits at the top Singapore leads at 223,683 — 56,849 marks the median among the top dozen Among the top dozen countries by intensity, Singapore leads at 223,683 m³/day, and the median of that group is 56,849 —far above the file-wide median of 1,079. Dense city-states and highly instrumented reporting systems rise on these lists. The ranking reflects what gets measured and attributed as much as what flows through pipes. Treatment levels spread discharge WASTE DIS by LEVEL Category boxes by treatment level—secondary, advanced, and related labels—show whether discharge norms are shared or split across tiers. Secondary dominates headcount; advanced plants can dominate intensity. The civic stakes: upgrading level without changing plant counts can shift environmental outcomes more than building another median-sized secondary facility. Who beats the median — and who trails WASTE DIS vs median by LEVEL Advanced treatment sits 681 m³/day above the median; Secondary trails by 342 . The direction fits only if advanced plants are often larger urban facilities, not merely cleaner ones. Level labels encode technology and city scale. Reading the gap as a pure quality ranking without plant-size context overfits the moral and underfits the engineering. Discharge and quality fields WASTE DIS vs QUAL WASTE Plotting waste discharge against quality scores reveals clusters that averages erase. Some plants combine high discharge with strong quality marks; others pair modest discharge with weaker flags. The scatter is where big and good stop being synonyms. Infrastructure policy needs both axes: capacity to serve population, and treatment quality sufficient for receiving water. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live APIs. Missing values, spelling variants, and uneven national reporting apply. Proposed or non-operational plants can sit beside active ones if status filters are imperfect. Discharge units and quality fields are HydroWASTE-derived attributes in the release—not a substitute for local permit databases or real-time effluent monitoring. What to take away Wastewater is a skewed industry: median discharge near 1,079 m³/day, extremes above three million, and country totals dominated by large reporting systems such as the United States. The citable distinction is treatment level versus size. Secondary is common; advanced sits 681 m³/day above the median; quality and discharge together decide whether a plant is merely large or actually protective. Data, methods & sources Data and method The source is the TidyTuesday release from 2022-09-20 (R for Data Science community). The working file contains 58,502 rows and 25 columns after merging country, treatment level, discharge, and quality fields. Medians handle skew from a h --- ## WEALTH: Middle Brackets Gained Income Share; Top Tiers Trailed the Median - URL: https://artometrics.com/wealth - Desk: civics, economics - Published: 2026-06-15T00:00:00.000Z - Description: Middle income groups claimed 16% more share; top earners held 6.7 points less than the 10.9% median between 1967 and 2019. Middle-income households captured a larger slice of total income than the median, while top earners held less. Across 2,916 Census records from 1967 to 2019, the median income share was 10.9%; households earning $50,000–$74,999 sat 6.6 points above that mark, and those earning $150,000–$199,999 trailed by 6.7 points. The data track how the median drifted, which race categories led, how each dollar bracket compared to the center, and how income share correlated with median household income. The calibration point is 10.9% — the midpoint of the share field in this extract. The median income-distribution marker drifted upward Median Income distribution Over Time Median income distribution rose from 10.9 in the opening period to 11.2 at the close — a 0.3 percentage-point increase across the 52-year window. The center of this field moved higher, though not by orders of magnitude. Modest median drift can coexist with large bracket-level gaps. The trend chart reports the center; the gap chart reports the structure underneath. Hispanic (Any Race) leads the charted race ladder Hispanic (Any Race) leads at 12.0 — 10.7 marks the median among the top dozen Hispanic (Any Race) leads at 12.0, while 10.7 marks the median among the top dozen race categories in this cut. Black Alone remains a fact-box landmark for the ranking rule used there; the leaders chart makes the numeric top of this aggregation explicit. The ladder is tight near the center of the file — leaders sit close to the overall median of 10.9 rather than in a different statistical universe. Income brackets carve different distribution bands Income distribution by Income bracket Category boxes by income bracket show whether the income-distribution field is shared or contested across dollar bands. Under $15,000 is the most common bracket label by frequency; the boxes ask how the metric spreads inside each band. Bracket structure is the skeleton of inequality analysis in this file. A single median of 10.9 hides those band-level differences. Mid brackets clear the median; high brackets trail in this cut Income distribution vs median by Income bracket $50,000 to $74,999 sits 6.60 above the median; $150,000 to $199,999 trails by 6.70. Those signed gaps are among the starkest bracket contrasts in the extract. Trailing or leading the median here is a statement about the income-distribution field's values by bracket, not a casual claim about who is "richer" in narrative terms. Cite the signed distances as structural offsets. Distribution values and income medians form joint clusters Income distribution vs Income median Plotting income distribution against income median shows clusters that averages erase. Race-year points co-locate in patterns that a single summary cannot hold. The scatter is relational: it shows how the two income fields move together across the 1967–2019 extract. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live Census microdata APIs. Missing values, race and bracket labeling choices, and 1967–2019 coverage limits apply. Merged tables may fan out or duplicate rows when join keys are imperfect. Findings describe this wealth-and-income extract — structural signals about the income-distribution field — not a complete wealth account including assets and debt. What to take away Income distribution in this file centers on 10.9, edges up to 11.2 at the median, and still shows sharp bracket gaps — mid bands above the median, top dollar bands trailing in this metric's signed distances. Race ladders and the distribution–median scatter add group and joint structure. Together they map concentration and composition without reducing fifty years of income data to a single slogan. Data, methods & sources Data and method The source is the TidyTuesday release from 2021-02-09 (R for Data Science community). The working file contains 2,916 rows and 10 columns after merging available tables in the week folder. Income distribution is the primary metric; r --- ## WINE: Austria and Chile, Two Points Apart - URL: https://artometrics.com/wine - Desk: culture, food - Published: 2026-06-15T00:00:00.000Z - Description: Austria leads Chile by 2.00 points; the median sits at 88.0 in a dataset where the top dozen titles all score 100. 100,000 wine ratings from the TidyTuesday archive show a median score of 88.0, a perfect-score ceiling among elite titles, and Austria leading Chile by 2.00 points. The points–price scatter reveals clusters that averages erase: cheap high scorers, expensive middling bottles, and a familiar cloud where spending more often accompanies higher scores without guaranteeing them. The calibration point is 88.0 — the mathematical center of a scoring culture that uses the high end of the scale and leaves the bottom half empty. Perfect scores crowd the title breakdown's top slice Points by Title Château Léoville Barton 2010 Saint-Julien leads at 100; Cardinale 2006 Cabernet Sauvignon (Napa Valley) anchors the low end of the plotted top group at 100 — a flat elite band at the ceiling. When the top slice is all 100s, the chart shows saturation at the maximum, not a ladder of near-misses. The leaders chart is a wall of 100-point wines Château Léoville Barton 2010 Saint-Julien leads at 100 — 100 marks the median among the top dozen The top dozen's median is 100, not 95 or 97 — the elite is compressed at the ceiling. Charles Smith 2006 Royal City remains a fact-box landmark for the title ranking rule used there, but the wall of perfect scores is the structural signal. Countries spread points around a high center Points by Country Category boxes by country show whether points consensus is shared or contested across origins. The United States is the most common country by frequency; the boxes test how much score variation place carries in this file. Austria clears the median; Chile trails Points vs median by Country Austria sits 2.00 points above the median; Chile trails by 2.00. Those signed gaps are modest in points terms — a reminder that this scoring culture lives in a narrow band around 88.0. Small gaps can still be meaningful when the whole scale's action happens between the high 80s and the low 90s for most bottles. Points and price form the real test of the dinner-table theory Points vs Price Plotting points against price shows clusters that averages erase: cheap high scorers, expensive middling bottles, and the familiar cloud where spending more often accompanies higher scores without guaranteeing them. The scatter is the relationship the title promised — it shows the joint distribution in the 100,000-row extract without claiming a coefficient beyond what the chart displays. What this file cannot tell you Community-cleaned TidyTuesday snapshots are not live Wine Enthusiast APIs. Missing prices, title variants, and coverage limits apply. Critic points reflect the source's scoring practice, which uses a compressed high end of the scale. Findings describe this wine extract — structural signals about points, country, and price — not a universal law of taste. What to take away Wine scores in this file center on a median of 88.0, peak at 100 among title leaders, and show only modest country gaps around that high center. Austria sits 2.00 points above the median and Chile 2.00 below, while the points–price scatter is where the cost-versus-score question becomes visible without reducing 100,000 tastings to a slogan. Data, methods & sources Data and method The source is the TidyTuesday release from 2019-05-28 (R for Data Science community). The working file contains 100,000 rows and 13 columns after merging available tables. Points is the primary metric; price appears in the scatter; title and country are categorical axes. Medians are used because wine scores cluster tightly and prices skew right. Sources Data Science Learning Community. (2019). TidyTuesday: Wine Ratings . https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2019/2019-05-28/winemag-data-130k-v2.csv Editor's note Artometrics data report from the TidyTuesday research pipeline. Charts and aggregates are reproducible from the embedded exhibits and public source files. Source archive (GitHub) --- ## POKEMON: Dragon-types Earn Their Attack Stats—Bug-types Don't Recover - URL: https://artometrics.com/pokemon - Desk: sports, gaming - Published: 2026-05-06T00:00:00.000Z - Description: Game Freak's type system encodes stat tradeoffs, power creep clusters at the tail, and Cosmoem weighs 999.9 kg at 0.1 meters. Dragon-types post a median Attack of 100 and no stat below 80. Bug-types sit at 60 across the board. The difference isn't lore — it's the stat schema Game Freak has enforced across 949 Pokémon and nine generations. The franchise looks like a cultural phenomenon. The PokéAPI-derived TidyTuesday dataset shows it's a design system with measurable rules, deliberate specialists, and power creep that concentrates in the tail rather than the median. The TidyTuesday 2025-04-01 Pokémon dataset covers every Pokémon through Generation IX. Three questions land cleanly: Do different types actually play differently, or is that a perception? Has Game Freak been inflating Pokémon power over time? And what does physical size reveal about how Pokémon are designed? Research question Can Pokémon's creature design be measured as a system rather than described only as franchise lore? This report asks whether primary type, generation, base experience, height, and weight reveal consistent design rules across the PokéAPI-derived TidyTuesday dataset. The observational question is practical: do Dragon, Steel, Fighting, Bug, Water, and other types carry statistically distinct stat identities; does the upper tail of base experience creep across generations; and do physical dimensions follow a coherent size grammar with deliberate exceptions such as Cosmoem and Wailord? Stat Identity By Type Stat Heatmap Dragon sits at the top of the heatmap for a reason. With a median Attack of 100 and no single stat below 80, Dragon-types are the closest thing Pokémon has to a generalist elite — high across the board, weak nowhere. The type wasn't always rare by accident. Game Freak made Dragon deliberately difficult to access in the early games because the numbers justified the gatekeeping. The more revealing story is in the specialists. Steel-types clock a median Defense of 116 — the highest single cell on the entire chart — while their Special Attack sits at 60, dead last among Steel's own stats. Fighting-types mirror that tradeoff on offense: Attack of 100, Special Attack of 42, the sharpest stat gap in the dataset. These aren't balanced Pokémon. They're designed to do one thing extremely well and pay for it everywhere else. The heatmap makes that design philosophy impossible to ignore. Bug sits alone at the bottom. Across all six stats, Bug-types post the lowest or near-lowest medians in every column — 60 HP, 65 Attack, 60 Defense, 55 Special Attack, 60 Special Defense, 60 Speed. The type has been statistically undertuned since Generation I and the data across nine generations confirms it hasn't been corrected. Whether that's a deliberate choice to keep early-game Pokémon weak or an oversight that calcified into canon, the result is the same: Bug-types are the worst-statted primary type in the game by a significant margin. One caveat: Flying appears near the top of the chart with an unusually high Speed median of 116, but Flying is almost never a primary type — it's almost always secondary. The dataset's primary Flying entries (Tornadus, Gyarados, Chatot, and a handful of others) are a small and unrepresentative sample, which inflates the type's apparent standing relative to how Flying Pokémon actually play in practice. Generation Creep Generation Ridgeline Every generation from Gen 1 through Gen 7 shows the same basic distribution shape — a peak around 60–70 base experience on the left, a second hump around 150–170 for mid-tier Pokémon, and a long tail trailing off toward the high end. What changes is that tail. With each successive generation, it stretches a little further right. The center of mass isn't dramatically shifting, but the ceiling keeps rising. The outliers tell the real story. That thin red thread extending past 600 on Gen 2's row is Blissey — a Normal-type with 255 base HP and the highest base experience yield of any Pokémon in the game at the time of its introduction. Gen 1 has its own isolated tail around 400, anchored by Chansey and the original l --- ## READMITTED: The 3% Stick Medicare Keeps Swinging - URL: https://artometrics.com/readmitted - Desk: science, medicine - Published: 2026-04-21T00:00:00.000Z - Description: In CMS's latest Hospital Readmissions Reduction Program extract, 48.15% of hospital–condition pairs still exceed the expected readmission ratio after a decade of penalties capped at 3% of Medicare payments. This R2 report maps HRRP history, who pays, safety-net effects, observation and mortality debates, international comparators, and eight charts for hospital executives. IN THIS REPORT Research question Executive summary HRRP since 2012: cap, peers, and who pays National readmissions fell, then plateaued Geography: penalties without a simple map Conditions: hip/knee leads the ERR table Safety-net pressure and teaching hospitals Observation stays and the mortality debate How other systems measure returns Ownership and penalty tiers Decision tools for hospital executives Limitations Conclusion Data, methods & sources Editor's note In 2009, Jencks and colleagues reported that nearly one in five Medicare fee-for-service patients returned to the hospital within thirty days. Congress answered with the Hospital Readmissions Reduction Program (HRRP): starting in October 2012, CMS began shaving inpatient prospective payment system (IPPS) dollars when risk-standardized thirty-day readmissions exceeded targets for six conditions. The maximum cut rose from 1% in fiscal year 2013 to 3% in FY2015, and it applies to all Medicare DRG payments—not just the condition that missed (CMS, QualityNet; MedPAC, 2024). The October 2026 CMS supplemental extract (dataset 9n3s-kdb3 , measurement window July 1, 2021–June 30, 2024) still flags 48.15% of hospital–condition pairs above an excess readmission ratio (ERR) of 1.0. ERR divides predicted readmissions by expected readmissions for each hospital’s patient mix. Above 1.0 triggers penalty math; below 1.0 earns no bonus. The national mean ERR is 1.002 . The program clarified the problem faster than it eliminated it. RESEARCH QUESTION After fourteen years of HRRP, where do penalties still concentrate in the latest CMS file—by state, condition, ownership, and safety-net proxy—and what should hospital executives infer about transitional care, observation-status gaming, mortality tradeoffs, and CMS’s shift toward episode-based models such as TEAM? EXECUTIVE SUMMARY What it means. Medicare still penalizes roughly half of measured hospital–condition pairs for excess thirty-day readmissions, with elective joint surgery posting the highest average ERR and for-profit systems carrying more high-tier exposure than non-profits. How we know. We rebuilt working tables from CMS HRRP supplemental data and Hospital General Information, then exported eight charts ( npm run brief:readmitted ). Penalty-timeline points for FY2025–FY2026 come from Advisory Board analyses of CMS preliminary payment-reduction files; 2012–2015 national rates follow Zuckerman et al. ( NEJM , 2016, doi:10.1056/NEJMsa1513024 ). What explains it. HRRP financialized a logistics problem—discharge planning, medication reconciliation, follow-up access—without paying for the fixes. Observation stays moved returns off the inpatient ledger (Zuckerman et al., 2016). Safety-net hospitals face higher dual-eligible mix; CMS peer-groups on dual proportion, but case-mix adjustment remains imperfect (Joynt & Jha, 2013, doi:10.1001/jama.2012.94856 ). OECD countries track readmissions with different payment levers ( OECD Health at a Glance ). Method & data box Population: 11,720 hospital–condition pairs with non-suppressed ERR after CMS’s ≥25-discharge rule (six conditions, ~2,700 hospitals). Primary files: CMS Provider Data Catalog 9n3s-kdb3 (HRRP) joined offline to xubh-q36u (ownership and hospital type). Reproduce: npm run brief:readmitted — refreshes articles/readmitted/data/ , renders PNG + Plotly JSON, syncs to public/ . Rounding: National shares use two decimal places on the exact ratio (48.15%, not 48.2%), matching articles/readmitted/data/SOURCE.txt . HRRP SINCE 2012: CAP, PEERS, AND WHO PAYS The three percent cap landed in FY2015; severe penalties (≥1% reductions) ticked up again in preliminary FY2026 data HRRP is a hospital-level payment adjustment, not a per-readmission fine. CMS multiplies a hospital-specific factor into Medicare operating DRG payments for the fiscal year. Who pays? The hospital’s Medicare revenue pool—finance, not quality officers, feels the hit first. Teaching hospitals and safety-net sys --- ## CAESAR: Power at 0.95, Then the Ides - URL: https://artometrics.com/caesar - Desk: humanities, history - Published: 2026-04-18T00:00:00.000Z - Description: Caesar's LTA profile shows 0.95 Need for Power and 0.31 Distrust — he built an empire on magnanimity and died when three pardoned senators proved the theory wrong. Julius Caesar scored 0.95 on Need for Power and 0.31 on Distrust under Leadership Trait Analysis — near-ceiling confidence in his right to rule, pathologically low suspicion of betrayal. He resettled 80,000 veterans, reformed Roman debt law, and stabilized grain supply in five years. Rome's GDP per capita rose 30% in the two centuries after his death, built on his reforms. He was stabbed 23 times by men he had pardoned and promoted. The house he built lasted five centuries because Augustus redesigned it to survive without him. Caesar was born in 100 BCE in the Subura — Rome's roughest district — to a patrician family with a noble name and thin wallets. His father died when he was fifteen, leaving him head of household with no resources. At twenty-five, Cilician pirates captured his ship, demanded twenty talents ransom, and watched him laugh, volunteer fifty, spend thirty-eight days writing poetry and calling them illiterates, then return with a fleet and crucify every one — exactly as promised. This is not an anecdote. It is the earliest behavioral evidence of his operating theory: the self is the institution. By 44 BCE, Caesar had crossed the Rubicon, won a civil war, reformed debt, redistributed land to 80,000 veterans and urban poor, rebuilt the calendar, and declared himself dictator for life. He was then stabbed twenty-three times by sixty senators — led by Brutus and Cassius, two men he had pardoned, appointed to praetorships, and promised consulships. The house he built outlasted him by five centuries. The soul that built it collapsed the structure the moment it failed. Leadership Trait Analysis Caesar's LTA profile has a distinctive asymmetric shape that makes the psychonomic argument visible before you read a word Caesar's LTA profile has a distinctive asymmetric shape. Two variables spike toward the outer ring — Need for Power (P = 0.95) and Belief in Ability to Control Events (B = 0.92). One variable collapses toward the center — Distrust of Others (D = 0.31). That triangle is the architecture of his career. He believed, with near-total conviction, that he could personally determine outcomes and that he should be the one doing so. He also extended trust readily and withdrew it slowly. The combination works at every scale — until the people you trust decide the same values you embody apply to removing you. Caesar exceeds the average world leader baseline on every variable except Distrust, where he falls dramatically below (0.31 vs. 0.48). He was not an extreme version of a normal leader. He was normal in every domain except pathologically low distrust and pathologically high power-need running simultaneously. Mao's comparison is instructive: Mao scores 0.74 on Distrust and 0.81 on In-Group Bias — the variables that explain the Cultural Revolution. Both scored near-ceiling on Need for Power. Mao purged. Caesar pardoned. Mao's successors dismantled his cult. Caesar's built an empire on his name. The single most important number on this chart is D = 0.31. A leader who extends trust this freely, at this level of power concentration, builds a system with no redundancy. Every node that fails hits the center directly. The Economic Fingerprint The most important feature of this chart is not Caesar's era — it is what comes after it The most important feature is what comes after Caesar. Roman GDP per capita during the late Republic sat at approximately $440–$490 (1990 international dollars), barely clearing the subsistence floor of $400 (Maddison, 2007). The Scheidel-Friesen (2009) income distribution model places the top 1.5% capturing roughly 20% of all income. Grain supply chains failed regularly. Debt bondage was common. The Republic's fiscal machinery was captured by the factions fighting civil wars for control of it. Caesar's dictatorship lasted less than five years (49–44 BCE). His reforms held after his death, but GDP data shows no immediate uplift. The economy barely moves during his lifetime. It moves under Augustus, --- ## GIANTS: Three Titles in Five Years at Payroll Nine - URL: https://artometrics.com/giants - Desk: sports, baseball - Published: 2026-04-18T00:00:00.000Z - Description: San Francisco's 2010–2014 dynasty won three titles with a draft-built rotation at mid-tier payroll—then couldn't replicate the model. The Giants won the 2010 World Series while ranking ninth in MLB payroll, fielding a rotation assembled almost entirely from the draft. They won again in 2012 at rank eight and in 2014 at rank four—three titles in five years without monopolizing free agency. Then the window closed. This report examines the 2010–2014 dynasty through franchise-scale data: 141 seasons of win percentage, attendance across the Candlestick-to-Oracle transition, and payroll efficiency during the championship window. The mechanism is clear—homegrown pitching on pre-arbitration cost—and the limit is structural. When Lincecum, Cain, and Bumgarner required market contracts and the draft pipeline slowed, the model collapsed. The franchise that opened as the New York Gothams in 1883 carries one of the longest arcs in American sports: eight World Series titles split cleanly by geography, a continental relocation, three stadiums, and dynasties separated by decades of near-misses. Five titles came in New York (1905, 1921, 1922, 1933, 1954). Three came in San Francisco (2010, 2012, 2014). Between them sits Candlestick's fifty-year frustration and, after 2000, a privately financed ballpark that doubled the attendance floor and rebuilt the economic foundation. Using the Lahman Baseball Database, the charts trace that arc across winning over time, the stadium's economic break, and how a draft-built rotation converted a revenue floor into rings. 141 years of winning—and the gaps that defined them Franchise win percentage from the Gothams to Oracle The New York Giants were one of baseball's best franchises for the first half of the twentieth century. The smoothed trend holds above .500 from the 1890s through 1957, with five World Series titles and long runs under John McGraw and Bill Terry. McGraw's 1903–1932 tenure remains one of the sport's longest managerial dynasties. This was not occasional success—it was the standard. The 1958 move to San Francisco broke that momentum and did not restore it for fifty years. Candlestick produced pennants in 1962 and 1989—seven-game losses and an earthquake-interrupted Series—but the trend flatlined just below .500. Mays, McCovey, and Marichal were Hall of Fame talent without a San Francisco championship. The 1978 low of .383 was the worst since the Dead Ball era. The Oracle era marks the sharpest structural break. From 2000 onward the raw line grows more volatile, but the smoothed trend climbs into the 2010–2014 window before receding after 2016. What the smoother cannot show is the mechanism: a front office that developed its own pitching, built around Buster Posey, and won three titles in five years with a roster that looked nothing like a bought dynasty. The post-2016 dip toward .400 marks the Zaidi rebuild—an unfinished chapter the data tracks but does not resolve. Oracle Park raised the floor before the dynasty arrived San Francisco attendance from Candlestick to the waterfront Candlestick was miserable in ways difficult to convey to anyone who never sat a night game there. Wind off the Bay knocked over coffee cups; Vin Scully called it the worst ballpark in baseball. Attendance followed: multiple 1970s seasons under 700,000, the worst figures in the National League. Even the 1989 pennant winners drew 2.1 million—a total Oracle routinely exceeded in rebuilding years. Oracle Park opened April 11, 2000, and drew 3.3 million in its first season. That jump—from 2.1 million in Candlestick's final year to 3.3 million without a title team—is the most important financial event in franchise history. Privately financed on the waterfront, it was the first MLB stadium without public money since Dodger Stadium in 1962, meaning the Giants owned the revenue streams. From 2000 onward, a floor Candlestick never established held. Post-Bonds years (2007–2009) dipped to 2.8 million at the low—still 700,000 above Candlestick's competitive peaks. Dynasty years pushed attendance back above 3.3 million. COVID was the only real interruption; recov --- ## WARRIORS: Seventy-Three Wins Rewrote Basketball Offense - URL: https://artometrics.com/warriors - Desk: sports, basketball - Published: 2026-04-18T00:00:00.000Z - Description: Golden State won 73 games in 2015–16, launched 15 more threes per game than the league, and changed how every franchise builds offense. Golden State won 73 games in 2015–16—the most in NBA history—and by that season were attempting 14.9 more threes per game than the league average. Within a decade, every franchise copied the strategy. The Warriors began as the Philadelphia Warriors in 1946. For 35 consecutive seasons between 1975 and 2012, they hovered below or near .500, briefly competitive but rarely relevant. Then Stephen Curry learned to shoot from 30 feet, Steve Kerr committed to small lineups, and the franchise became the most analytically influential in modern basketball. Seven championships across three cities, a 73-win regular season that still stands alone, and 3,747 career threes by Curry through 2024 mark the discontinuity. What follows quantifies how dominant the dynasty windows were, how the three-point revolution unfolded in real numbers, and what it means to win 73 games in a league where almost nobody else has even approached that ceiling. Two dynasty windows, and a desert between them Warriors win percentage, 1946–2024 The Warriors have had exactly two dynasty windows in 78 years. The Philadelphia era—anchored by the 1947 and 1956 championships—produced the first sustained run, though several seasons hovered in the 40–45% win range even then. The 1975 title was a one-season outlier: a Rick Barry-led peak at .720, then a collapse back into irrelevance. What follows is 35 consecutive seasons below or hovering at .500, interrupted only by the We Believe playoff run in 2007 and a few Mullin–Hardaway winning years in the early 1990s. The Curry era is categorically different. From 2015 through 2019, the Warriors never won fewer than 57 games in a non-strike-shortened season—five consecutive years above .695, unmatched in the modern NBA. The 2015–16 spike to .890 is the visual anchor. The 2019–20 collapse—from .707 to .232, a 47-point drop after Klay Thompson's ACL tear and a COVID-shortened season—is the steepest single-season fall in franchise history. The 2022 championship rebound, from .232 to .671 in two seasons without a lottery pick or blockbuster trade, is the fastest recovery in the dataset. How one franchise forced the league to relearn offense Warriors vs. league three-point attempts per game From 1980 through 2012, Golden State shot about as many threes as everyone else. The lines run almost on top of each other for three decades—not because the Warriors were average in talent, but because no team yet treated the three-point line as basketball's central structural advantage. A shot worth 50% more than a two did not need a mathematician; it needed Curry. Divergence starts in 2013 and accelerates immediately. By 2015–16—the 73-win season—the Warriors launched 41.6 threes per game while the league averaged 26.7, a 14.9-attempt gap. They were attempting more threes per game than the entire NBA would average five years later. The 2017–2019 dip in volume is Kevin Durant, not retreat: easier scoring closer to the rim let Steve Kerr pull volume without sacrificing efficiency. After 2019, the league does not retreat when the dynasty wobbles—it accelerates. By 2022 the NBA average hit 35 attempts per game and Golden State's advantage had evaporated. Competitive diffusion: one franchise proves a thesis, wins four titles doing it, and within a decade the sport reorganizes around the same idea. Six teams reached 68 wins. Only one owns 73. Historic 68+ win seasons and who closed the title Only six teams in NBA history have won 68 or more regular-season games. The cluster at 68 and 69 is the ordinary ceiling of greatness. The 1995–96 Bulls sat alone at 72 for nearly two decades. Then Golden State broke the mark by one. Winning 73 games did not produce a championship. The 2015–16 Warriors lost the Finals to LeBron James and Cleveland in seven games—the first Finals team to blow a 3–1 lead. Of the six seasons on this chart, only the Warriors and the 1971–72 Bucks failed to win the title in their record year. The 73-win season is simultaneously the greate --- ## COFFEE: Ethiopia Scores Highest on Coffee Quality—With Zero Starbucks Stores - URL: https://artometrics.com/coffee - Desk: culture, food - Published: 2026-04-14T00:00:00.000Z - Description: Ethiopia's median CQI cup score is 85.3 — the highest of 16 countries with 20+ samples — yet Starbucks operates zero stores in the top three scoring nations. Ethiopia's median Coffee Quality Institute cup score is 85.3 — the highest of any country with 20 or more rated samples — yet Starbucks operates zero stores there. The same is true for Kenya and Uganda, the second- and third-highest scoring nations. Coffee's quality map and its retail map describe two unrelated geographies. This analysis uses CQI cupping scores and Starbucks store counts to test whether sensory quality and branded retail power accumulate in the same places. They do not. The countries that grow the best-rated coffee capture little of the value that branded retail extracts downstream. Research question Does the geography of high-scoring coffee match the geography of retail coffee power? This analysis tests whether Coffee Quality Institute cupping scores and Starbucks store counts describe the same global map or two disconnected positions in the commodity chain. The question is where Arabica quality concentrates once country medians and sensory sub-metrics are plotted, and whether that origin map aligns with the markets where a dominant branded retailer operates stores. The answer matters because origin value, sensory quality, and retail capture are often discussed as if they reinforce one another naturally. Where Great Coffee Comes From Country Ridgeline Ethiopia occupies a different part of the distribution entirely. The median cup score across all 16 qualifying countries is 82.5. Ethiopia's distribution peaks near 85 and has a long right tail that no other origin approaches. Kenya and Uganda sit in second and third, but their distributions overlap substantially with the rest of the field. Ethiopia's does not. Ethiopia is the genetic origin of Arabica coffee — the Kaffa region in southwestern Ethiopia is where the species was first documented growing wild. The country's range of elevation, soil diversity, and processing tradition produces a flavor complexity that Q Graders reward with above-median scores across every sub-metric. The bottom of the chart is equally clear. Nicaragua, Mexico, Honduras, and Taiwan cluster between 81 and 83 — technically specialty grade, but at the floor. Brazil, the world's largest coffee producer by volume, lands in the lower half — market dominance and cup quality are not the same thing. The Quality-Retail Disconnect Quality Vs Retail There is no positive relationship between a country's quality score and Starbucks' retail presence there. The three highest-scoring origins — Ethiopia, Kenya, and Uganda — all sit at zero Starbucks locations. Hawaii (USA) lands near the top of the y-axis not because of its quality rank but because its ISO code maps to the US, which hosts thousands of Starbucks locations. This is structural. Starbucks builds stores in markets where it can sell coffee, not only in markets where it sources coffee. Ethiopia's retail consumer market is too small and too low-income to support the Starbucks price point at scale. The company sources Ethiopian beans, but that sourcing relationship does not translate into a domestic retail presence. Colombia reinforces the pattern: a recognized origin with specialty scores that shows little Starbucks store footprint relative to the fame of its beans. Economic value from growing specialty coffee does not automatically accumulate where the coffee is grown. The Sub-Metric Fingerprint Submetric Heatmap Ethiopia scores higher on every single sub-metric among the eight-country heatmap field. Its median acidity, cupper points, and flavor lead the set. Kenya comes close on aroma and flavor but trails on most other dimensions. The heatmap also reveals how flat the remaining countries are. Brazil sits in a narrow mid band across attributes — consistent and undifferentiated. Honduras and Mexico are nearly indistinguishable on several scores. When buyers talk about commodity coffee, this is the sensory data behind the phrase. Guatemala and Colombia show elevated acidity and balance relative to that flat middle — profiles that translate ea --- ## IMPERIAL: What Empire Chronicles Leave Out - URL: https://artometrics.com/imperial - Desk: humanities, history - Published: 2026-04-14T00:00:00.000Z - Description: A historical-economics lens asks who captured wealth when rulers claimed order. Every empire in history has told the same story about itself: we brought order, we built roads, we made the trains run on time. What the chronicles leave out is the spreadsheet. Who actually got richer? How long did the prosperity last? And when it ended, how fast did it go? Those questions have answers now. The Maddison Project Database — the most comprehensive long-run economic dataset ever assembled — lets us put Rome, Britain, the Ottomans, Imperial China, the Mongols, and Habsburg Spain on the same chart and ask: what does empire actually look like as an economic system? The answer is not what the history textbooks imply. GDP goes up. Real wages mostly don’t. The rise takes centuries. The fall takes decades. And in every case, the institutions built to extract surplus during expansion become structurally incapable of sharing it during maturation. That’s the pattern. Six empires. No exceptions. Research question When empire is measured through long-run GDP per capita and real wages, who captures the surplus: the imperial core, ordinary workers, or institutions built for extraction? This report asks whether Rome, Britain, the Ottomans, Imperial China, the Mongols, and Habsburg Spain show comparable trajectories once their economic series are indexed and capped to historical lifespans. The observational question is not whether empires built infrastructure or expanded trade. It is whether aggregate output translated into living standards, how quickly imperial GDP peaks unwound, and whether the extraction gap between GDP and wages is visible in the best available long-run economic evidence. Gdp Per Capita Across Empire Cores Gdppc All Empires For the first 1,800 years of this chart, five of the six empire cores are essentially stationary. Rome peaks early and declines. The Ottomans and Imperial China drift sideways for centuries. Habsburg Spain — despite controlling the most silver-producing territory in human history — never generates a GDP breakout. The Mongol blip in the 1260s appears and vanishes within a century. The chart’s visual drama is entirely a British phenomenon, and it doesn’t begin until roughly 1800. That flatness is not a data artifact. Pre-industrial economies were governed by a hard ceiling: agricultural productivity. Every empire eventually hit the same constraint — you can conquer more land, you can tax more efficiently, you can build better roads — but you cannot grow aggregate output faster than the soil can produce food. This is why five empires operating across four millennia produce nearly identical GDP trajectories. They were all playing the same zero-sum game. The temptation when looking at Chart 1 is to conclude that Britain governed better. This is wrong. The British divergence is a technology story, not an institutional one. Steam power — specifically the transition from organic energy (human labor, animal muscle, firewood) to fossil energy (coal) — removed the agricultural ceiling entirely. British GDP per capita in 1900 is not high because of better laws or smarter monarchs. It is high because coal-powered factories could produce manufactured goods at a scale that no farming economy could match, and Britain industrialized first. The Roman GDP line in this dataset is a back-extrapolation built from Maddison (2007), Scheidel and Friesen (2009), and Lo Cascio and Malanima (2009) — the best available estimates, with significant uncertainty bands. At roughly $1,000–1,200 in 2011 USD PPP, the Roman Empire’s peak was genuinely impressive for its era. It represents something like double the GDP per capita of its contemporaries and suggests a level of market integration — trans-Mediterranean trade, monetized labor, professional armies — that would not reappear in Europe for a thousand years after the Western collapse. Rise Fast, Fall Faster Empire Lifespan Indexed Chart 2 removes the distortion of absolute scale. By indexing every empire’s GDP per capita to its own peak (peak = 100) and aligning on a ±2 --- ## YOUTUBE: The Platform's $36B Engine and One Podcast's Borrowed Peak - URL: https://artometrics.com/youtube - Desk: civics, communication - Published: 2026-04-14T00:00:00.000Z - Description: Alphabet reported $36.1B in YouTube ad revenue in FY2024 while Nielsen puts YouTube at 15.8% of U.S. TV-screen usage (Nielsen Gauge, August 2026, before Nielsen's September 2026 method change). Against that scale, H3 Podcast's Frenemies era tripled monthly views—and set a ceiling the show has not touched since. IN THIS REPORT Executive summary Research question YouTube ads crossed $36B TV-screen share is now a lead indicator Creator payouts and the Partner Program split Shorts vs long-form economics Cross-market comparisons Case study: H3's borrowed peak Counterexamples Decision tools for executives Limitations Conclusion Data, methods & sources Editor's note Executive summary YouTube is a disclosed advertising business inside Alphabet, a TV-screen habit in Nielsen's monthly Gauge, and a payout machine for the creator economy. Alphabet reported $36.1 billion in YouTube advertising revenue for fiscal 2024—roughly the same order of magnitude as Netflix's $39.0 billion in total streaming revenue that year, though the two lines measure different things (auction-based ads vs subscription receipts). YouTube said in September 2025 it has paid more than $100 billion to creators, artists, and media companies over the prior four years. YouTube led Nielsen's Media Distributor Gauge with 15.8% of U.S. TV watch-time in August 2026, ahead of Disney (9.3%) and Netflix (7.7%). Nielsen's Media Distributor Gauge placed YouTube at 13.4% of U.S. television usage on TV screens in July 2025, ahead of Disney (9.4%) and Netflix (8.8%) in the same release. The mechanism underneath those platform numbers is a revenue-share contract. Google documents a 55% partner share of net watch-page advertising revenue on long-form video; Shorts pools pay creators 45% in YouTube's published Partner Program updates. Goldman Sachs Research sized the broader creator economy at about $250 billion in 2022, projecting roughly $480 billion by 2027, with short-form ad monetization as a growth driver— including estimates that Shorts ads could rise from a low single-digit share of YouTube ad revenue toward one quarter of the total by 2027. This report's case study sits inside that macro frame. The H3 Podcast's nine-month Frenemies co-host era (Ethan Klein and Trisha Paytas, September 2020–June 2021) tripled monthly channel views and established a viewership ceiling the podcast has not approached since. When the partnership ended mid-episode on camera, views reverted to baseline within a month. The show that remained is longer—220+ minute median episodes in 2024 versus 42 minutes in 2017—and smaller than the Frenemies peak. Platform economics explain why a borrowed audience matters: in a $36B ad market, missing your own peak is not necessarily failure, but it is a strategic fact. Research question How do disclosed YouTube platform economics (Alphabet revenue, Nielsen TV-screen share, Partner Program splits, and creator-economy scale estimates) contextualize what happens when a major podcast builds its apex around a guest co-host—and what should executives infer about durability, format length, and community risk? Data & method (at a glance) Platform tables — Fiscal-year YouTube ads revenue from Alphabet Form 10-K filings (2018–2024); FY2024 Netflix total revenue for scale comparison. Nielsen figures from published Media Distributor Gauge and Gauge press releases (TV-screen methodology; YouTube TV excluded). Partner splits from Google YouTube Help and YouTube Blog. Goldman Sachs creator-economy and Shorts estimates (April 2023 research; labeled projections). H3 case study — 1,638 videos across four channels (YouTube Data API v3, April 2026), 680 podcast episodes with duration (Podchaser), Reddit monthly post counts (Arctic Shift: r/h3h3productions, r/h3snark). Era labels are editorial (Baseline, Frenemies, Leftovers, post-2023). Charts — Seven economics figures built from committed CSVs in articles/youtube/data/ via npm run brief:youtube ; three H3 figures from the Artometrics h3 repository pipeline. YouTube ads crossed $36B YouTube ads revenue grew from $11.2B (FY2018) to $36.1B (FY2024) in Alphabet's disclosed line item. What it means. YouTube is material to Alphabet—not a rounding error inside Google Services. The filing line is narrow (advertising on YouTube properties) a --- ## FRANCHISE: Merchandise Owns the Ledger - URL: https://artometrics.com/franchise - Desk: arts, film - Published: 2026-04-08T00:00:00.000Z - Description: 140 franchises on the October 2026 Wikipedia ledger sum to $1.45T lifetime revenue. Merchandise is 55.8%; box office 18.3%; Pokémon leads at $130.7B. The US now out-earns Japan ($816B vs $394B), but Japan runs the higher merchandise share (71.4% vs 52.0%). Nine of 2025's ten biggest films were franchise entries. IN THIS REPORT Executive summary Research question One ledger, two playgrounds Merchandise pools — Japan’s media-mix vs Hollywood’s vertical stack Who tops the table — and who is not crowned Wikipedia moved (2019 vs 2026) Japan, the United States, and the United Kingdom — the flip Original medium sets the extraction layer Box office is a thin slice for most of the top twenty Revenue per year of existence Disney consolidated — ownership literacy Owner concentration — five parents, 58% Disney acquisitions — check size vs row anchor Disney segment revenue (10-K) Theatrical seriality Sony, Universal, Nintendo — fragmented vs married IP When franchises break Decision tools for executives Limitations Conclusion Data, methods & sources Editor's note Executive summary. Franchise economics is licensing geometry: who owns repeatable rights, and which revenue streams compound after the premiere. On Wikipedia's October 2026 ledger (140 properties, $1,448.1B lifetime, S017), merchandise, licensing and retail are 55.8% of revenue; box office is 18.3% and games 17.9%. Pokémon leads at $130.7B — more than Star Wars ($46.7B) and the Marvel Cinematic Universe ($37.5B) combined. Consolidated to today's parents, Disney holds 29.7% of the ledger and five companies hold 58.2%. The regional story flipped when we rebuilt the data: US-origin franchises now out-earn Japan's ($816.5B vs $394.5B), but Japan's are the purer merchandise machines (71.4% vs 52.0%). In theaters the sequel economy deepened — franchise installments took 94.4% of 2025's worldwide top-ten gross. Several big 2019 figures (Hello Kitty $80.0B, Mario $35.0B) were source errors, not declines; the companion piece “What changed when we fixed the data” documents every revision. ONE LEDGER, TWO PLAYGROUNDS A child trades a cardboard Pikachu for lunch money; Bob Iger signs a seventy-one-point-three-billion-dollar check for Twenty-First Century Fox. Both trades assume the same premise — that characters can be mined long after opening weekend. Our x-ray is Wikipedia's “highest-grossing media franchises” list as of October 12, 2026 (revision 1379832186, S017): 140 franchises, each itemised into revenue streams that we map to one standard schema. The earlier edition of this report used the TidyTuesday file of July 2, 2019 (103 franchises, S001); we keep it as a baseline and audited it against the Wikipedia revision it was scraped from. Figures are editors' lifetime estimates of consumer spend in nominal dollars, not audited studio financials — still the cleanest public ledger for comparing how money attaches to IP. This report reads that ledger the way an anime R2 report reads MyAnimeList: as the footprint of a production system, not a popularity contest. Henry Jenkins’s Convergence Culture (2006) frames the audience side — stories that jump formats because fans follow characters. Marc Steinberg’s media-mix work on Japan shows the extreme case: sponsored television animation as billboard for merchandise (Steinberg, 2012). American conglomerates built a parallel stack — own the character, own the park turnstile, own the streaming shelf. Below, each pattern states what it means for a buyer, how we know it (source and method), what mechanism explains it, and a cross-market comparison or counterexample. RESEARCH QUESTION For franchises on the October 2026 Wikipedia ledger, (1) how much lifetime value sits in merchandise versus box office and games, (2) how do Japan-origin and US-origin properties differ in that mix, (3) how did estimates move since the 2019 file, (4) how does consolidating Disney subsidiaries change the ownership map, and (5) how dominant are franchise installments in the 2025 and 2026 box office? MERCHANDISE POOLS — JAPAN’S MEDIA-MIX VS HOLLYWOOD’S VERTICAL STACK Merchandise, licensing and retail 55.8% [44.0–66.2]; box office 18.3% [12.8–25.6]; games 17.9% [11.2–26.2]. Black ticks mark the 2019 file. What it means. At franchise scale, theatrical is usually marketing infrastru --- ## ANIME: Source Sets the Audience Floor - URL: https://artometrics.com/anime - Desk: arts, film - Published: 2026-03-31T00:00:00.000Z - Description: On the October 2026 MyAnimeList ledger, light-novel TV adaptations still draw 27.5 times the median audience of originals. Source mix and isekai tags trace how committees greenlight shows — with wide uncertainty on any single year. IN THIS REPORT Research question Each format wave lines up with a new distribution channel Source material sets the audience floor Isekai tags and source mix (2015–2025) Consistency is a studio trait Sequels score higher, consistent with mostly well-received shows getting one Popularity tracks quality, with a short list of exceptions Limitations Conclusion Data, methods & sources Editor's note Death Note has 1,610,561 MyAnimeList members, more than any other anime in the archive, and scores 8.66. Hanada Shounen-shi , a 2002 series from the same studio, Madhouse, scores 8.11 and has 8,929. A MyAnimeList archive of 18,813 clean titles (October 2026 snapshot via Tenrai + AniList; see what changed when we fixed the data ) suggests that much of a gap like that is set before anyone draws a frame, starting with where the story comes from. On television, light-novel adaptations draw a median of 196,258 platform members. Manga adaptations draw 69,623 and original series 7,133 , which puts light novels about 27.5× ahead of originals on the clean TV frame. Quality follows a different pattern. Only 3.0% of original TV series and 1.6% of TV game adaptations score 8.0 or higher on scored TV rows, compared with 14.8% of manga adaptations. Across all scored titles, a title's score and its audience move closely together, with a rank correlation of 0.73 (n = 18,704). Every comparison below is measured against the overall median score of 6.36 unless noted. This report reads the archive as the record of a production system, not a list of favorites. In that system, a production committee (seisaku iinkai) of publishers, broadcasters, music labels, and merchandisers pools the budget and splits copyright in proportion to each member's investment. The animation studio is typically a contracted manufacturer that doesn't own the IP (Hernández Hernández, 2018, §4.3; Mihara, 2018). The figures here are observed fan behavior on one platform. They show which projects found audiences, not which ones made money. RESEARCH QUESTION Which decisions made before production (format, source material, studio, and whether to make a sequel) are associated with a title's reach and fan rating, and where does the platform's popularity record miss strong work? Each format wave lines up with a new distribution channel Each wave lines up with a new channel. Direct-to-video output peaked at 115 titles in 1993, and web-first releases grew more than fivefold between 2009 and 2018. Anime output is not one growth curve. It's a series of format waves, and each wave lines up with a new channel. Theatrical film dominates the early catalog. Original video animation (OVA), sold directly on tape and disc, ran at roughly 100 titles a year from 1990 to 1993 and peaked at 115 in 1993. Television then became the core of the catalog, with 4,260 TV titles in the archive. Original net animation (ONA), made for web distribution first, grew from 40 titles in 2009 to 104 in 2013 and 216 in 2018, for 1,321 in total. The archive records release format, not financing. Reading these waves as responses to new distribution channels is our editorial frame, not a measured finding, and the data can't show who paid for each wave. The context literature documents one wave directly: starting with Tetsuwan Atom (1963), TV anime was tied to sponsor merchandising (Steinberg, 2012). Direct-to-video The first OVA, Dallos (Studio Pierrot, December 1983), was co-directed by Mamoru Oshii (Pierrot archive; Anime News Network). The format became known for high production values and experimentation (Anime News Network, 2022). Its standouts include Ginga Eiyuu Densetsu ( Legend of the Galactic Heroes , 1988), a 110-episode space epic that scores 9.10, the only OVA in the top 10 by score. Golden Boy (1995) drew the era's largest audience, 171,713 members, and Gainax's Gunbuster (1988) also stands out. Studio Pierrot logo via Wikimedia Commons (public domain, PD-textlogo; trademark of its owner) Gainax logo via Wikim ---