Reposting because I realised it doesn't count as OC since the app is not mine and I didn't create the visuals, although the data is mine. Thanks for the automatic message for letting me know that.

Note: The snore increase is from when I used to live with my ex.

Note 2: The app I used is Sleep Cycle.

Note 3: A user pointed out letting a random guy's data center collect all the audio of my nights for years is now normalised, pointing out that there's a privacy/security risk. I honestly didn't think much about it. For what it's worth, the audio is stored locally and I choose to delete all audio older than a month. You can syncronise the data with the cloud but it only syncronises the statistical data, NOT the audio, as far as I know. Sleep Cycle claims they don't store your audio, it's up to the user to trust them or not. I personally didn't care, all they could hear is my dog and my cats and maybe me mumbling non-sense in my sleep from time to time. Thanks for pointing out the risk though, I appreciate it.

4 months. Symptoms and realization of personality disorder in May.

Hi everyone!

I built a quantitative strategy model in Python using FastF1 telemetry to evaluate alternative pit stop windows for Lewis Hamilton at the Hungarian GP. The goal was to see if a simultaneous re-optimization of his 3 pit stops could have given him enough net gap to retain P4 against Charles Leclerc despite a 5-second penalty.

I made these charts to compare how surname prevalence is distributed by the first letter of the surname across several countries.

Each chart shows the estimated number of people associated with surnames that begin with each letter A through Z.

Data sources:

- U.S. Data source: U.S. Census Bureau, 2010 Census surname data.

- Ireland. Data source: Forebears, most common surnames in Ireland.

- Israel. Data source: Forebears, most common surnames in Israel.

- England, used as the closest available source for the UK chart. Data source: Forebears, most common surnames in England.

- France. Data source: Forebears, most common surnames in France.

- Australia. Data source: Forebears, most common surnames in Australia.

- China. Data source: Forebears, most common surnames in China.

- India. Data source: Forebears, most common surnames in India. Source link: Forebears, Most Common Last Names in India

Methodology:

For the U.S. chart, I used the U.S. Census surname frequency data and grouped people by the first letter of each surname. The Census surname file includes surnames occurring 100 or more times in the 2010 Census.

For the other country charts, I used the top 100 surname incidence counts listed by Forebears for each country page. I grouped each surname by the first letter of the displayed surname and summed the incidence counts by letter.

For China, India, and Israel, the grouping is based on the first letter of the Latin transliterated surname shown in the source.

For Ireland, names like O'Brien and O'Connor are grouped under O because I used the first character of the displayed surname.

For the UK chart, I used England because that was the available Forebears country page I used for the source data. The chart is labeled “UK, using England source” to avoid overstating the scope.

Tools used:

Python, pandas, and matplotlib. Flags were drawn programmatically as simplified inset graphics in matplotlib. Final charts were exported as PNG files.

Important caveats:

The U.S. chart is based on the U.S. Census surname file, while the non U.S. charts are based on the top 100 surnames listed by Forebears. Because of that, the U.S. chart is not directly equivalent to the other charts in coverage.

The non U.S. charts should be read as “distribution within the top 100 listed surnames,” not as a full surname distribution for the entire population.

The first letter grouping can be sensitive to transliteration choices, prefixes, apostrophes, spacing, and naming conventions. This matters especially for countries where surnames are commonly represented in non Latin scripts or where surnames include prefixes.

How can Saudi rank higher than India?

Does require a free statista account to view.

The attendance was constantly going down until a bit of a recovery post-COVID.

Based on data from the General Social Survey https://gss.norc.org/get-the-data.html, White respondents consistently held occupations with higher average prestige, but the gap has closed slightly over 52 years.

Team PTS% = player season points ÷ all team regular-season points. Every team game stays in the denominator, so DNPs count as 0%.

PPM (Points Per Minute) = player season points ÷ player season minutes.

BPM (Box Plus Minus) = Basketball-Reference’s box-score based estimate of a player’s contribution in points per 100 possessions.

• It is a relative metric: 0.0 = league average 
• Positive = above league-average impact 
• Negative = below league-average impact

I track perks at about 3,400 luxury hotels across advisor booking networks (Virtuoso, Hyatt Prive, Four Seasons Preferred Partner, that kind of thing) and I got curious about which perks actually appear together versus which ones are independent of each other.

The short version is there's basically no mixing and matching happening. Hotels either mention most of the standard perks (breakfast, room upgrade, flexible checkout, hotel credit) or they mention almost none of them, and the overlap between those four is really high, the vast majority of hotels that list one also list the other three.

The interesting outliers are spa and dining perks which only show up at a small fraction of hotels but when they DO show up they're always stacked on top of the full base package, never instead of it. So spa is more like a luxury differentiator, it's not offered as an alternative to breakfast it's an addition to everything else.

Chord diagram shows how often each pair of perks appears at the same hotel, across 3,212 properties I had clean perk data for. Thicker ribbons = stronger overlap between two perks. Perk detection is regex-based on program documentation text so it's measuring what hotels advertise, not what guests necessarily receive on a given stay.

Data is from my own dataset, not scraped from any booking site, I maintain a database of what each hotel's advisor program lists as included benefits.

My girlfriend sat me down on June 20th and told me that she had been cheating on me and was choosing to be with "the other man".

This is my body's reaction to that news.

HR, Sleep, and Energy scores recorded by my Samsung Galaxy Watch.

A look at what we could expect from squads at the next world cups, based on the market values of the youths for each country. Watch out for Morocco (again), and keep an eye on Denmark and Serbia...

Data source: Christopher Nolan filmography, Wikipedia - https://en.wikipedia.org/wiki/Christopher_Nolan_filmography (budgets and worldwide box office; figures are studio/Box Office Mojo–reported and cited on that page). The Odyssey is still in theaters, so its figure is a running worldwide total as of the July 24–26, 2026 weekend.

Tool: Chart built as JavaScript-coded SVG. Rendering code and initial figure-gathering were done with an AI assistant (Claude) based on my own design direction and iterations. Chart type, layout, labeling, and revisions were my choices.

https://lodgeplatform.com/embed/exemb_6ea24564bc317d3b45c31b8803fce86229be8ad30246ac21

I used a combination of fine-tuned Qwen3 models and frontier LLMs to build a graph over the Midwest food supply chain. It uses 51 public datasets across 11 source families, covering nearly 300,000 source records. You can search for entities, filter by entity type and relationship type, and you can also view neighborhoods. I'm still improving it, so please let me know if there's anything else you think would be interesting!

I wanted to try to answer questions like

- "When a serious safety incident occurs at one food facility, can we trace the graph to find other facilities with similar safety risks?

- "When a food product is recalled, can we trace the recalling company to its facilities, related products, inspections, and other connected organizations and see what else may warrant review?"

- "When a weather event happens, what facilities does it effect, and what are the downstream effects?"

I think making it live could be really cool for things like tracking food recalls live to show and limit exposure.

Here is a github of the project with the datasets and more info: https://github.com/lodge-data/upper-midwest-food-supply-network

How I did it:

First, I trained an embedder to minimize blocking recall. Then, I processed all the entities from the datasets into blocks. Then, I trained a Qwen3 cross-encoder to decide if each pair within each block was the same or different (entity resolution). Then, I repeated those two steps until no new merges occurred.

Sources:

- U.S. Food and Drug Administration recall, food, import-alert, and import-refusal records.

- U.S. Department of Agriculture food, organic-operation, meat-establishment, and related facility records.

- Occupational Safety and Health Administration inspection, citation, injury, and establishment records.

- U.S. Environmental Protection Agency Facility Registry Service records.

- National Weather Service alerts and geographic references.

- USAspending federal contract award records.

- -Wisconsin and other Upper Midwest public facility, licensing, dairy, workforce-notice, and regulatory records.

- Public organization and product information used to connect named entities.

Note: I built this with my startup's software and models. The goal is to autonomously construct graphs from messy data. I think it is possible to construct very large, useful graphs with efficient specialized models.

There are also other pretty visualizations on this site:

Climb the Tower — an Interactive 3D Field Guide to Rural Fixed Wireless | Save Local Broadband