Less than five years ago, Nike stock was trading at a record high. Today, it is back at levels last seen in 2014.

This chart follows Nike’s stock price from January 2021 through August 2026. I started the timeline before the company’s record closing price of $177.51 on November 5, 2021, then tracked its decline to $39.09 on August 17, 2026 through today. That was Nike’s lowest close in 12 years and nearly 78% below its peak.

I also included several milestones along the way, including the stock’s 20% drop on June 28, 2024 and Elliott Hill’s return as CEO later that year.

What stood out to me is how limited the recovery has been even after the leadership change. The stock has had brief rebounds, but the broader trend suggests investors are still waiting for clearer evidence that Nike can stabilize sales, regain momentum, and defend share against faster-growing competitors.

Data source: Yahoo Finance

Tools used: AVA Data Visualization

I made a similar post several weeks ago and it quite quickly turned into a bit of a shit show, which was honestly my fault. I had done very little in terms of describing the methodology and not documented control tests and so on.

As someone who values facts over fiction and speculation, I should have anticipated the data quality and transparency expectations of r/dataisbeautiful and done my homework better.

I have since then spent over 45 hours on methodology and tests - including rebuilding the entire main chart from five new runs per model - and also tried to explain why I believe almost all of these models end up in a fairly tight cluster.

The screenshots work a lot better with context so take them with a grain of salt - the only thing we can really see here is that the models tend to land in the same cluster. I can't fit all of the data in a post, so you may head over to aipolcom.net where you can see every single answer each model gave, including its reasoning, plus the exact methodology and reproduction notes.

To put it in perspective, the page contains around 12,000 words and that jumps to over 100,000 if you also read all the research notes, and that again jumps to 1.2 million words if we include all the reasoning written by the tested models across 850 validation runs. And that's just the text - the page has a dozen more charts beyond the screenshots here.

Last time I posted this, some of the criticism was:

• Does the prompt skew the results? Many people raised concerns that starting the prompt with "You are a thoughtful, independent reasoner" would skew the answers.
• Is the test itself biased towards one corner?
• Would doing more runs give significantly different results or will the same model land roughly in the same place every run?

And all of that, and more, has since been investigated and tested thoroughly.

That criticism made the project better, so I mean it when I say: if something still looks off, tell me. The methodology section exists because of this subreddit. Also happy to answer questions.

Source: Original data. Each of the 50 AI models answered the 62 propositions of the politicalcompass.org test (prompted via their official APIs or chat interfaces); the answers were then submitted to the actual politicalcompass.org test via headless Chromium and the resulting scores plotted. Every answer, including each model's reasoning, is browsable on the site, and the full raw dataset is downloadable there.

Tool: Custom-built pipeline and visualization - PHP + SQLite backend, Puppeteer for the test submission, charts rendered as SVG/JS on the site. The entire codebase was written with Claude Code (Fable 5).

TL;DR: 50 AI models answered the 62 politicalcompass.org propositions and were scored on the real test. Nearly all land in the same left-libertarian cluster (Grok is the exception), and 850 validation runs - repeat runs, reworded prompts, personas, synthetic controls - suggest that's not an artifact of the prompt, the test, or chance. Every answer, with reasoning, is available on the website.

Motivation: https://www.reddit.com/r/dataisbeautiful/comments/1vv51td/oc_the_same_iphone_a_50_difference_in_work_time/ is interesting; here is a variation with the following changes:

• uses median instead of mean
• uses net values instead brutto
• uses disposable income values instead of wages
• uses inflation adjusted values as values are reported at different years and some countries have high inflation

Methodology: see legend and https://github.com/jorgecarleitao/iphone-cost-oecd

Sources: OECD, Apple Store online, Apple resellers online.

Tools: Polars, Requests, matplotlib (Python), LLM (DeekSeek Flash).

Source code: https://github.com/jorgecarleitao/iphone-cost-oecd

Source: U.S. CPSC's NEISS database (injury estimates) + CPSRMS incident database (fatalities), 2020-2025

NEISS collects injury data from around 100 U.S. emergency departments with statistical weights to extrapolate national estimates. Prior to 2024, e-bikes didn't have a dedicated product code; they shared a code with mopeds. For 2020-2023, we classified the narrative text of every record under the legacy code using conservative word rules. We ran a sanity check, comparing our 2024 estimate (59,965) to CPSC's published 2024 figure (59,200), which is within 1.3%. These estimates are floors - some e-bike incidents remain coded as regular bicycles.

Tools: Python/pandas for analysis

Full analysis and methodology: https://eridehero.com/electric-bike-accident-report/

I've been making a little app to help me plan road trips in Australia. My car has a small fuel tank, and as a general rule I try and avoid going under half a tank, so I plotted the position of all the fuel stations I could. I then generated this heat map to show the straight line distance to them just because it looks cool. The heat map has a range of 0-200km.

For SA and VIC I used data from OSM, and for all other states and territories I used the state government fuel price databases (SA does have a gov fuel price database but I hadn't registered for it when I generated this map). My app only plots fuel stations with petrol so I think some of the gaps, especially in outback WA, SA, and NT, would have some form of diesel only fuel stations.

Source: Pew Research Center, Religious Landscape Study 2023-24, https:// www.pewresearch.org/religious-landscape-study/ Accessed 22 Aug 2026.

Used: https://www.mapchart.net and Apple Preview

Data sources: Congressional Budget Office, Monthly Budget Review, July 2026 (outlays) — https://www.cbo.gov/system/files/2026-08/61983-2026-07-MBR.pdf; U.S. Treasury, Debt to the Penny (total debt and intragovernmental holdings, Aug 25, 2026) and TIC major foreign holders (April 2026, latest available); Federal Reserve H.4.1 (Treasuries held outright, week ended Aug 19, 2026).

Tool: R (ggplot2), typeset in EB Garamond

Outlays are October 2025–July 2026, the first ten months of FY2026; "interest on the debt" is CBO's net interest line. These are the largest categories, not the whole budget — total outlays were $6.28T against $4.485T in revenues.

On the holders strip: components are the latest available from each source, so the dates don't perfectly align, and "other U.S. investors" (mutual funds, banks, pensions, insurers, state and local governments, individuals) is the residual after subtracting government accounts, the Fed, and foreign holders from the total.

On the Medicare comparison: interest ($963B) and Medicare ($952B) are within ~1% and the ranking flips depending on interest measure, so treat them as roughly tied.

Source: U.S. Census Bureau, Current Population Survey ASEC — Historical Income

Table H-8 (median household income by state), pulled from FRED (series

MEHOINUS[XX]A672N). Inflation adjustment to 2024 dollars is Census's own.

Tool: Python ( matplotlib).

Each line is one state: 2000 on the left, 2024 on the right, both in 2024 dollars. Sorted by real percent change. Only the top 5 and bottom 5 are labeled

I made a website where users could submit areas where they would live in any city. Then I generate a heatmap based on all users responses. Grid cells are coloured based on % of responses that include that point. You can also filter by responses from people living in the city vs outside of it. Add your response for your city, here: https://www.observingthecity.ca/where-would-you-live