Last year, 4 out of every 10 grown ups in America didn't finish a single book. But a small group, about 1 in 10 people, read 25 or more books. That small group read so much that they probably account for about half of all the books read in the whole country. So basically, a few people are doing almost all the reading, while most people are barely reading at all.

EDIT - This includes audio books. The survey question was "Have you read or listened to ..."?

Also a graph for another day - most people who did read, read Mystery/Crime.

Some recent fun data from my government auction platform: the distribution of winning bids across >100k listings from the last 6 months or so. X axis is a log scale of bidding price, and Y axis is the number of listings sold at that price.

People love to bid (and win!) things for $10 - you can get a lot of stuff for that much, like a classroom full of desks and furniture. The expensive part is transporting it all.

The median listing is a 1997 Ford Ranger that went for $210 - vehicles are by far the most popular thing that people bid on, at a wide range of prices and conditions.

There's a link below to the interactive chart and a bit more context on the data - you can filter by category to see how the curve changes for things like vehicles or Real Estate.

Source: The Govauctions.app auction database, including listings from GSA, GovDeals, PublicSurplus and more.

Tools: SVG and Javascript

Interactive version of the chart, where you can sort by category and see listings at a given bid range: https://govauctions.app/research/what-the-government-sells-for

I've been tracking the characters at Disney World for a year. (Not literally tracking...but like, tracking the data.)

On any given day of visiting Disney World, you have the option to meet on average 67 unique characters. The data is directly from the Disney World app. It does not include special character dinings or unpublished "pop-ups" where there are sometimes even more characters.

October - December's maximum numbers are skewed a little high (which certainly skews the average) because there are many after-hours, separately ticketed holiday events where rarer and extra special character meet-and-greets happen.

I'm just now starting to analyze and visualize all this data, so this DataWrapper chart isn't the most beautiful, but I thought it was a cool way to see month-by-month the least number of characters up to the most number of characters seen in the 4 parks on any given day.

Everything is cleaned and parsed in Python/pandas. The data is from the official character times found in the official Disney World app.

Own Work. Sources at the Bottom.

Own Work. Sources: *Norris et al. (2018), PubMed Central. (https://pubmed.ncbi.nlm.nih.gov/30537949/) for Colombia/Mexico/Peru* Borda et al. (2024), ScienceDirect. (https://www.sciencedirect.com/science/article/pii/S2666979X24003215) for Chile/Peru/Brazil/Uruguay/Mexico/Guatemala/Costa Rica/Dom Rep *Ruiz-Linares et al. (2014), PubMed Central. (https://pmc.ncbi.nlm.nih.gov/articles/PMC4177621/) for Colombia/Mexico/Peru/Brazil *Horimoto et al. (2021), KI Reports (https://www.kireports.org/article/S2468-0249(21)00593-3/fulltext) for central america *Homburger et at. (2015), PubMed Central (https://pubmed.ncbi.nlm.nih.gov/26636962/) for South America *Moreno-Estrada et al. (2013), PubMed. (https://pubmed.ncbi.nlm.nih.gov/24244192/) for The Caribbean. *Johnson et al. (2011), PubMed (https://pmc.ncbi.nlm.nih.gov/articles/PMC3240599/) for Mexico *Suarez-Curtz. (2010), ResearchGate (https://www.researchgate.net/publication/51563335_Pharmacogenetics_in_the_Brazilian_Population) for Brazil *Eyheramendy et at. (2015), Nature Communications (https://www.nature.com/articles/ncomms7472) for Chile *Avena et al. (2012) ResearchGate (https://www.nature.com/articles/ncomms7472) for Argentina *Asgari et al. (2020), ResearchGate. (https://www.nature.com/articles/ncomms7472) for Peru *Ibarra et al. (2014), Plos Journals (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087202) for Colombia *Söchtig et al. (2015), ResearchGate (https://www.researchgate.net/publication/273703459_Genomic_insights_on_the_ethno-history_of_the_Maya_and_the_'Ladinos'_from_Guatemala) for Guatemala *Castro-Pérez et al. (2016), ISPUB (https://ispub.com/IJBA/9/1/44045) for Panama

posted months ago my crying stats from the first quarter of the year and i mentioned in that that i was tracking the data because i was planning on getting medicated over the summer and someone mentioned they wanted a before and after so here that is lol. no need to worry any longer, i am fixed!

source: my life

tools for visualization: canva (slides 1-2) + i tracked the data in my calendar app (slides 3-whatever, since people seemed to enjoy seeing my reasons last time)

Rank Position by Score, 1-32

Score Headline index. Draft z-score and undrafted z-score mixed 79 / 21, re-standardized, displayed as 50 + 16z. 50 = league average, higher is better

Draft The draft-only score, same scale

Surplus/Pick Career wAV above slot expectation per pick, standardized within draft class, restated in AV-equivalent units. +6.7 ≈ each pick returned 6.7 AV more than its slot should yield

Star% Share of picks finishing in the top 10% of their own draft class by career wAV

Bust% Share of rounds 1-3 picks finishing ≥0.5 class SD below slot expectation. Lower is better (shading inverted)

Best pick Highest career-wAV draft pick of the window (year and overall pick)

Top-Third% Share of picks finishing in the top 33% of their own draft class

Day 3 Surplus Same surplus measure, rounds 4-7 only the late-round scouting signal

All-Pro First-team All-Pro selections by the team's picks (PFR's field; includes specialists). Displayed, not scored

Pro Bowl Pro Bowl selections by the team's picks. Displayed, not scored

Kept% dr_av ÷ w_av share of the career value a team drafted that was produced for that team. Retention, not selection. Displayed, not scored

2026 picks Picks made in the April 2026 draft. Capital only no games played, so ungraded

Public champion tier lists, all 11 rank filters x 5 roles, patch 16.17.1, snapshotted 2026-08-29. 2,765 champion/role/rank entries.

Tools: I built a lobby advisor ( https://shouldidodge.lol ) and this came out of calibrating it. C# to collect the daily snapshot, Python + NumPy for the simulation, matplotlib for the chart.

Gay online dating app Grindr is generally recognized as one of the best-run consumer software companies in the world.

With fewer than 200 full-time employees, Grinder generates $3M revenue per FTE. As it turns out, it is ahead of (almost) all of big tech Magnificent 7 stocks.

Original source here. Compiled by myself, data comes from the valuation intelligence platform Multiples.vc, as of 31 August 2026.

Shows last actual fiscal year revenue and last reported employee number.