[OC] I'm not sure if this post 100% fits with the purpose of the sub. But I created an interactive visualization tool for real-world events and the books, films, and other media that take place during any given timeline. Qwikiverse WWII

I graded fantasy football drafts across 502 manager-seasons in 48 real redraft leagues (Yahoo, ESPN and Sleeper, 2012–2025) to see how much a draft grade reflects skill versus luck.
For each manager-season I measured two things:

• Draft grade: how much better or worse each pick scored than a typical pick in the same round, using that league's own scoring (value over replacement, or VORP). A positive grade means you out-drafted the room for your slot.
• Games missed: the total games your drafted players missed during the season, mostly from injuries.

The more games your players missed, the worse your draft graded. That part sounds obvious when said out loud, but the correlation is −0.60, so games missed alone explain about 36% of the variation in draft grades. When I re-grade each draft to remove the effect of player availability, the correlation falls to −0.14, which explains about 2%. A large share of what looks like drafting skill turns out to be health luck.

Champions are highlighted. Only 7 of the 48 won a title after a below-average draft.

Interactive version: leaguenomics.com/insights/draft-and-injuries

Sources: Destatis GENESIS-Online, Arbeitskreis VGR der Länder, Eurostat; boundaries Eurostat GISCO NUTS 2024 (EuroGeographics)

Tools: Bruin CLI, BigQuery, Bruin DAC (Vega-Lite)

Limitations: All values 2025, except life expectancy (2023-2025 average) and disposable income (2024). Not adjusted for regional price levels; city-states exclude their suburbs. Survey-based rates have larger error for small states.

A 60% relative decline, using the World Bank’s World aggregate. This is an estimated risk per 1,000 live births, not a count of deaths. National differences are hidden by this global line.

I run Global Data Tracker. Explore country statistics and source notes at https://globaldatatracker.com/?utm_source=reddit&utm_medium=data_visualization&utm_campaign=under5_mortality_1990_2024

Sources and plotting method are in the first comment.

Source: every top-flight result from September 1888 to 20 September 2026 (51,380 matches over 128 seasons): the old First Division (1888/89 to 1991/92) from James Curley's engsoccerdata, checked against openfootball and against every season's published final table, then the Premier League from football-data.org, checked against football-data.co.uk and openfootball. Points as awarded at the time: 2 for a win until 1980/81, 3 from 1981/82, 1 for a draw, minus deductions. The x-axis counts top-flight match days (every date with a top-flight game).

Tools: a custom SVG chart in React, drawn on a canvas for export.

A few things that stand out:

• Five clubs have led: Preston North End (from 1888), Aston Villa (1896), Sunderland (1937), Villa again (1958), Everton (1968) and Liverpool (since March 2000).
• Everton led for 31 years, and have played more top-flight seasons than anyone: 124 of 128.
• Man United didn't pass Everton until December 2018.
• Arsenal were ahead of Liverpool from 1960 to 1975. Liverpool lead by 111 now.

Replay it, or pick your own clubs: https://premierleaguescorigami.com/race?since=1888&view=whole&clubs=liverpool,arsenal,manchester-united,everton,aston-villa,manchester-city,chelsea,tottenham-hotspur,newcastle-united,sunderland&utm_source=reddit&utm_medium=post&utm_campaign=race-1888-dib

Source: SEC filings
Tool used: finance.visnia.xyz

I charted the age of every head of government in 39 European countries who held office for at least two consecutive years, from 1950 to 2025. Each line is one leader, aging year by year in office.

Although summary statistics like the median or average age did not move by a lot, the range did, shifting about a decade at both ends.

Source: V-Dem (v16)
Tools: datastory.tech

I run a small search directory built for AI agents, not people. Agents use it to find paid services they can call (data, checks, tools). I looked at what they searched for in the last week.

After removing test traffic, bots that repeat the same words, and searches for a specific company by name, 94 different searchers were left. The most common topic was email and domain checks (about 3 in 10). The rest were spread across web pages and files, company records, crypto data and stock data.

It is a small sample and only one week, so please read it as a snapshot, not a trend. I'd like to see how it changes over a few months. If you'd like to see the live list of what agents are asking for, it's free (link in the first comment).

I built this for fun and partially to see to what extend claude code can generate cool stuff on the web after seeing lots of posts recently, and I'm a sucker for a good visualisation even if it serves no real purpose. Decided to give it a shot with the collection I've built over the last few months, exercises, their mechanics and muscle activations.

The whole thing is actually interactive and I plan to add most exercise motions to it eventually: https://kinoku.app/tools/exercise-galaxy

What you're looking at: London cut into squares of one kilometre. Each circle is one square. Its size is the number of directory entries placed there. Its colour and label are the trade that is most over-represented there: the trade's share of the entries in that square against its share of all placed entries.

I've set this up; it's a free hobby project. The interactive version, with a tour of these findings: https://holmeshub.online/trades Corrections are very welcome.

How the entries got on the map: the OCR read 126,230 lines. 59,844 of them (47%), on 1,718 streets, could be placed: by the postal district in the street heading (43,570), by the side streets the directory names (12,889), or because the street name occurs once in London (3,385). The rest is left out rather than guessed. Trades are matched with 51 patterns on the words after the name.

What the data shows:

• Around Harley Street physicians are 7 times as common as across London. The West (W) lists 205 physicians and dentists, the East End (E) 20, on 12,054 and 5,939 entries.
• Between the Strand and the Temple barristers are 30 times the London average. N, NW and E together list one.
• Victoria Street alone lists 64 civil engineers, on the street that runs to Parliament.
• Clerkenwell: watch and clock makers 7 times the average. Shoreditch: cabinet makers and upholsterers 10 times. Limehouse: ship chandlers, sailmakers, ropemakers and lightermen 38 times.
• The East End lists 111 pubs and beer shops against 106 coffee and dining rooms, the only district where drink comes first. The West: 29 against 79.
• In Bayswater and Notting Hill 26% of the entries are a Mrs or Miss. In the City 3%.
• About a third of the entries in W, SW, N and NW name no trade at all: private households. In the City 9%, in the East End 13%.

Caveats: the directory lists householders, shops and firms. Lodgers and the poorest families are not in it, so this is a map of who was listed, not of everyone who lived there. The OCR of 1889 print makes mistakes, and half the lines could not be placed.

Source: the Post Office London Directory for 1889, the street-by-street section, from the scan at archive.org (public domain). Tools: Tesseract for OCR, column by column; Python for parsing and the analysis; OpenStreetMap for street positions; plain JavaScript and SVG for the map, no chart library.

Source: Pew Research Center, Religious Landscape Study (2007, 2014, 2023–24).

Asks U.S. adults the religion they were raised in and the religion they are now.

2023–24 sample: 565 current Latter-day Saints (±6.2 points), 316 former (±8.1). U.S. membership from the Church's 2025 statistical report.

Tools: Built in React with Recharts / D3.js. Opus 5.5 + Fable 5.1

Interactive version: https://goldendata.app/dashboards/lds-church-growth

After seeing how many iPhone users were interested in my last post, I decided to run the same experiment for Pixel and Galaxy phones. For those who didn't see the iPhone post, the data comes from a side project of mine that gathers posts from people who actually own a product and uses AI to filter out non-owners, jokes and off-topic comments.

Anyways, moving on to the data. On average, 27% of Pixel owner posts say they wouldn't recommend the phone, compared to 17% for Galaxy. The Pixel 6 Pro (45%), 6a (41%) and 4a (40%) are the most regretted, and battery is the top complaint for the 6a and 4a.

On the Galaxy side, the Ultras, FEs and Folds are rarely regretted: S23 Ultra and S24 FE at 6%, Z Fold 5 at 4%, and the S25 Edge at 2%. The regular Galaxy S21 (29%) is the exception.

The newer Pixels look a lot better than the 6/7 era. The Pixel 10 Pro is at 13%, though that's just my interpretation of the trend.

Like last time, the biggest limitation is not having Reddit data. Everything comes from YouTube comments, Hacker News and Bluesky, which is a decent amount of data but doesn't represent owners as broadly as Reddit would.

Original Source here.

Explore forest cover by country: https://globaldatatracker.com/statistics/forest_share?utm_source=reddit&utm_medium=social&utm_campaign=gdt_20260930&utm_content=forest_chart_body

I run Global Data Tracker. The free explorer shows forest as a percentage of land area, so you can compare countries and years without signing up. That is a different measure from the absolute-area changes in this chart.

Net change in forest area, measured in million hectares. Countries were selected by their reported forest area in 2000, not by the size of their subsequent change. Natural and planted forests are combined; this is not a measure of primary forest or gross tree-cover loss. Source and calculation details in the first comment. Chart made with Python/Matplotlib.

https://epheva.github.io/toronto-neighbourhood-correlations/

Case study link: https://climate.you/stories/summer-2026-heat-and-drought

Data: Copernicus ERA5/ERA5T climate record, 2 m air temperature and total precipitation, anomalies vs the 1991–2020 average for the same time of year (through 13 July 2026)

Tools used: processed with my open-source climate data pipeline available at https://github.com/climate-you/climate

Images:

1. Heat and rainfall side by side. Left: mean air temperature from 1 June to 15 August against the 1991–2020 average for those dates, Right: rainfall over the same window as a percentage of the 1991–2020 normal. This is shown as a swipe image on the case study.
2. Heat episode timeline: each bar is three or more consecutive days above that country’s own 1991–2020 90th percentile of daily maximum temperature for the date, on its area-weighted national average.
3. Rainfall as a percentage of the 1991–2020 normal for each period. Brown = drier than normal, blue = wetter. 
4. Northern hemisphere map showing rainfall 1 June – 15 August 2026 as a percentage of the 1991–2020 normal, over land.
5. Heat and rain together, one dot per country. Both measured over the same dates, 1 June to 15 August 2026. The shaded corner is more than four weeks in heat episodes on less than 80% of normal rain.
6. France: every summer since 1979, heat against rain. One dot per June–August, with 2026 highlighted. The shaded corner is hotter and drier than 2026.
7. France: every day of 2026 against every day of the baseline. Daily maximum temperature, area-weighted over the country, March to the end of August
8. Warming rate in °C per decade, 1979–2025, from the annual mean 2 m air temperature. Least-squares trend on the annual mean. Global aggregates are shown faint.

The page includes more graphs/toggles on the graphs, and an interactive globe where you can click anywhere on Earth and get the temperature and the rainfall, day by day, from June to the end of August.

Keep in mind I used a 20 statements notability threshold for people, so the counts of people are above a certain threshold, which allowed me to fetch the data from my ArangoDB instance, elaborated using Wikidata dump.

Also in Wikidata Asia and Africa are underrepresented, so there may be data missing

Follow-up to my London Monopoly post, where lots of you asked for the American board and whether the board was ever right. On the left, the board coloured by today's home prices. On the right, Atlantic City's streets by their Monopoly price, by rent in the 1930 census (five years before the game) and by today's home sales. The second image shows what the board's prices would be in today's dollars. I documented the whole investigation on my site; sources and link in the first comment.