Every team at the 2026 World Cup, placed by two numbers: how often they put a shot on target (across the bottom) and how often they turned those into goals (up the side). Teams that went deep in the tournament rise off the glass, so the champion floats near the top. The gap between the two is how clinical a team was: score a lot from few shots on target and you sit high and to the left.

The flat view is one layer of a taller model. Stack 2018, 2022 and 2026 and you can see a team drift year to year, or rotate the whole thing in 3D and scrub through time to watch the three tournaments move. Click any flag to follow a single nation across all three.

The last image switches from teams to the men who took the shots: every 2026 finisher plotted against their expected goals (xG). Above the diagonal they scored more than their chances were worth, below it they left goals on the grass. Bellingham finished at nearly double his xG.

Interactive version, where you can drag the model around, scrub the years, and follow any nation: https://viz.luarai.com/worldcup-conversion

I build a interactive tool allow you to replace the moon with other planets to see how they looks like on earth.

Source online and welcome to have a try and share what you find: https://www.runcell.dev/tool/true-size-map/moon-replaced-by-planets

The national number hasnt moved all that much (-2.7%) and remains close to what's considered herd immunity, so the case surge looked like it came out of nowhere until you look at the top of the funnel (kindergarten MMR rates at the state-level granularity). The national average is a population-weighted blur that California's four million kids hold steady no matter what happens in the small states. Once you give each state its own dot, the bottom of the distribution has clearly come apart and we're starting to see a disease that was eliminated in the US start to crawl back. Sixteen states are now below 90% (six were in 2009), the lowest is Idaho at 78%, and more than 5,000 individual schools sit under 80% coverage. In recent years, non-medical exemptions have doubled.

Charts built from CDC SchoolVaxView, NNDSS, and Washington Post school-level data; full sources and methodology in the comments.

Source: Japan Tourism Agency (観光庁), Accommodation Survey (宿泊旅行統計調査), 2025 annual final values. The prefecture x nationality breakdown is sheet 参考第1表(年計) in the annual workbook:

Release page: https://www.mlit.go.jp/kankocho/tokei_hakusyo/shukuhakutokei.html

Direct workbook (xlsx, 2.5MB): https://www.mlit.go.jp/kankocho/content/002010340.xlsx

Tools: Python, Matplotlib

How the world really watched this tournament: 12.79B match views across 198 markets, blending legal broadcasts and piracy into one global TV viewership picture. The chart breaks down average audiences and peak games round by round, showing how attention climbs from early group matches to the knockout stages and the Final.

Data & tools: Global Viewership - round by round audiences summed across the tournament to give cumulative match views. Each figure is Eyeballr's modelled estimate of total match views, legal broadcast plus modelled piracy, including out-of-home viewing, not panel-measured or official broadcaster data.

Built with: custom SVG/JavaScript & data prepared in Python.

Source: https://unofficialpartner.substack.com/p/overheard-at-the-sportspro-investor

Hey everyone, I've been working on a live intelligence mapping platform called Intel Mapper. It monitors OSINT sources 24/7, uses AI to geolocate and verify reports, and displays them on an interactive map with frontline data.

Features: real-time events, territorial control, military flight tracking, source attribution with confidence scoring.

Would love your feedback!

intelmapper.com

Pay per stream is taken to be average of the range listed on Royalty Exchange as of 2026: https://royaltyexchange.com/blog/how-much-do-streaming-platforms-pay-per-stream

The visualization tool is Python, Seaborn package

Methodology comment:

Montagne Parfums is a clone fragrance house (inspired-by versions of designer scents -- not affiliated). I kept buying ones that smelled too close to stuff I already owned, so one weekend I pulled all 4,782 customer reviews across their 167 products and tried to map the catalog by how people describe the scents.

Most of the work was getting usable text. Reviews are full of shipping complaints, price talk, and "love it!", so I ran each one through an LLM to keep only the smell-related content, and stripped out fragrance names so the model couldn't cheat by clustering on those. That left 2,834 descriptions. Anything with fewer than 4 reviews got cut, which took 167 fragrances down to 117.

For embeddings I used Qwen3-embedding-8B (4096 dimensions). The raw similarities were useless at first, everything looked about 50% similar to everything else, which is the curse of dimensionality doing its thing. Running PCA down to 50 dimensions spread the range out to -49% to 100%, enough to separate "these smell alike" from "these share nothing."

Sanity checks mostly pass. Buko and Buko Intense (same scent, different concentration) come out at 88%. The tobacco fragrances form the tightest cluster. The most "central" fragrance, most similar to everything on average, is Pineapple Royale.

The big (and perhaps obvious) caveat is this measures how reviewers talk, not scent chemistry. Reviewers echo whatever notes are listed on the product page, and low-review fragrances have way more uncertainty, so "most unique" partly just means "least described." What the project really convinced me of is that we have no vocabulary for smell. People don't describe scents, they describe memories and characters. Two real reviews from the dataset: "Makes me feel like a librarian that frequents a classy bar after work for a Manhattan on the rocks" and "I feel like a badass pirate captain who just walked into the tavern." Embedding models handle this kind of text surprisingly well, which is sort of the point of the whole exercise.

Tools: Python, PaCMAP for the projection, scipy for hierarchical clustering, Plotly for the interactive heatmap. Source code and an interactive version are on GitHub if anyone wants to poke at it, happy to answer questions about the pipeline.

Sources: FIFA Training Centre public post-match reports

Tools: Python/pdfplumber, Bruin cli, BigQuery, and SVG

Limitations: The teams faced different opponents, so this describes their tournament paths rather than an opponent-adjusted rating

Grixo is a unified intelligence platform designed to collect, process, and visualize real-time data through dedicated dashboards.

The platform currently includes:

✈️ Aircraft Intelligence

Live ADS-B aircraft tracking

Flight analytics

Interactive maps

Historical data exploration

🎈 SondeHub Intelligence

Live radiosonde tracking

Atmospheric telemetry

Weather balloon observations

Historical launch data

Rather than combining everything into a single dashboard, each intelligence module is developed independently while sharing the same platform and architecture.

The long-term vision is to expand Grixo with additional modules, including:

🌦️ Weather Intelligence

🚆 Rail Intelligence

🛰️ Earth Observation

📡 Signal Intelligence

Grixo is built using Flask, PostgreSQL, Linux, Nginx, Python, and external APIs, with AI assisting throughout the development process.

The goal is to create a scalable platform that makes complex, real-time datasets easier to explore through interactive maps, visualizations, and analytics.

I'd appreciate any feedback on the concept, design, or ideas for future modules.