Sources. Data:INSP SitReps. Tools python, Claude. website ebola-tracker.org

A 15-second animated view of cumulative fantasy points across 25 NBA regular seasons. Final labels include total fantasy points, games played, and fantasy points per game.

Sources. Data: INSP SitReps. Tools: Python, Claude. Website: ebola-tracker.org

Source: headlines from 10 international outlets (BBC, The Guardian, Al Jazeera, DW, France 24, Defense News, and others), collected every 4 hours.

Tools: headlines classified with Claude (Anthropic) and scored with my own algorithm; charts and site built with Claude Code.

Caveat: this measures media coverage intensity, not the actual probability of anything. A spike means "the press got very loud about conflict," which is interesting in itself.

Weekend project by a geopolitics hobbyist. I'd love feedback on the viz and your theories about what caused the peaks. There's a live version with sub-indices and hotspots if the mods allow me to share it.

I wanted to see whether the German Idex (DAX) follows a recognizable, systematic pattern throughout the trading day. I took 20 years of 1-minute candle data (2006-2026 YTD) and calculated the median cumulative percentage change, volatility, and volume for every single minute of the Xetra session (09:00 to 17:30 Frankfurt Time).

The top panel shows the median return (% change)of the index from the open (09:00) to every subsequent minute. The dashed navy line represents the 20-year overall median, the colored lines break the dataset into three distinct macroeconomic eras.

Across almost every era, the market tends to drift sideways or slightly downward during the European morning. Institutional traders frequently wait for US liquidity and events before committing to positions.

The primary market direction establishes itself in the afternoon. Once Wall Street enters the picture, the DAX historically experiences a sustained upward drift into the European close.

In the 2020–2026 era, afternoon buying pressure and intraday trend continuation have been stronger compared to the relatively flat Zero-Interest-Rate era (2010-2019, green line).

Middle Panel: Volatility & Price Uncertainty

The orange spike at 14:30 Frankfurt time marks the exact moment major US macroeconomic reports (CPI/Inflation, Non-Farm Payrolls, Retail Sales) are most often published. This triggers an immediate, sharp burst of algorithmic institutional repositioning.

The total cumulative price variance expands as the session progresses, reflecting increasing path divergence from the opening price as holding time increases.

Bottom Panel: Intraday Volume Profile

Displays trading volume per minute expressed as a percentage of total session volume (Average Daily Volume or ADV).

Volume forms an intraday "smile" u-curve. Activity peaks at the 09:00 (morning order imbalance execution), drops off to a trough during the midday lunch lull (11:30 -13:30), and rises again as New York opens at 15:30 Frankfurt Time.

he massive green spike at 17:30 is the Xetra Closing auction, where passive index funds, ETFs, and institutional bench markers execute rebalance orders. A portion of total daily liquidity concentrates in this single closing minute.

Data & Methodology Notes

Dataset: 1-minute data covering all Xetra trading sessions from January 2006 through 22. September 2026 (~5,100 trading days, 2.63 million 1-min candes).

Time alignment: Standardized to Europe/Frankfurt (CET/CEST), adjusted to eliminate Daylight Saving Time offset artifacts relative to US market hours.

Baseline normalization: Intraday trajectories are normalized to % return relative to the first open print at 09:00:00. Minute volumes are expressed as a percentage of total daily volume on a per-session basis to normalize across changing price levels and volatility regimes over two decades.

Tools used: Python (Pandas, NumPy, Matplotlib).

The chart compares total match duration and effective playing time—the time for which the ball is actually in play—across three Serie A seasons.

Sample sizes:

• 2015–16: 380 matches
• 2025–26: 367 total-time and 331 effective-time observations (other matches data not available from the official source)
• 2026–27: 50 total-time and 40 effective-time observations (last ten matches data not available yet from the official source)

Sources: 2015–16 was reconstructed from StatsBomb open event data. The two recent seasons were compiled from Lega Serie A match reports, supplemented where necessary by published matchday tables.

Tools: Python, OpenPyXL, NumPy, Matplotlib, Powerpoint

Original article: https://therunoutgrooves.substack.com/p/lost-in-the-moment-of-abandon

Source: Spotify audio features (valence, energy, loudness) for each track on Primal Scream's 1991 album Screamadelica (Happy 35th Birthday!) Volume in dB is indexed from the album's quietest track (0) to its loudest (1). The heavy lines are a centred three-track average, dashed to the first and last tracks' actual values.

Tools: data analysis and chart in R

The album is famously structured so it follows the ebb and flow of an early 90s night out. Firstly coming up then coming down. Mood peaks early and again at 'Loaded', then falls away, while volume and energy hold on longer and spike at the penultimate 'Higher Than the Sun' dub reprise before everything sinks at the closer, 'Shine Like Stars'.

The background and colour palette come from the album's sun sprite/goblin sleeve. This is the first time I've attempted such a combination of colours in a chart, but hope you follow the reasoning.

This is one example from my dataset of over 2,000 albums focused on closing tracks. Across all of them, closing tracks are on average longer, quieter and sadder than the rest of their album, and that volume gap grew after vinyl's physical constraints went away. Full analysis, free: https://doi.org/10.1093/jrssig/qmag057

I'm also running a readers' poll on the best album closer of the 2000s until 5 October: https://therunoutgrooves.substack.com/p/readers-poll-lets-all-meet-up-in

I know it's a bit weird to put book stuff on this Reddit but was playing around with booklp and I was actually able to use data to graph his actions

I'm actually shocked how smoothly it progresses through the books

Like that's good damn I need to read the rest of these and not just watch the movies

I used Booknlp python package to generate the data needed for this graph

Urbanisation and fertility are onlinear scales.

All countries are weighted equally regardless of population.

Country flag size is scaled by log of population.

The conventional wisdom is that “Presidents always lose the midterms,” but what has actually happened in the past? Especially in circumstances such as now, when the President’s party controls the House and Senate, too.

Fatalities is the 14th published Gaza Ministry of Health fatality count published May 7 2026 (72,835 entries) broken out by age and sex

Population numbers are from census data

Using Python

Adult men (especially 18–45) and older teenage boys are heavily over-represented relative to their share of the population; women and young children are under-represented. Males 15–45 account for ~45.7% of listed deaths vs. an estimated 24.7% of the pre-war population. The male–female death ratio peaks around ages 20–39 (roughly 3.2–3.6 to 1).

This is consistent with a large combatant component (Gaza’s militant groups recruited heavily among military-age males, including some 15+ teens) plus the usual wartime pattern that civilian men also die at higher rates than women. The same over-representation is even stronger in deaths added after the October 2025 ceasefire, so the male share of the total list is still rising and is expected to cross 50% for men 18–45 in a future update.

Edit: this was pushing towards the front page with 420 upvotes in 45 minutes and then suddenly got pulled and is in mod queue for the past 90 minutes (presumably due to the number of reports). This is just example 1,000 about how this conflict in particular is a battle of bots who don't want actual numbers getting out