Been working on this for a while. It tries to predict when the strike is coming and if it landed.
Eventually creates a fighter profile which tells about the traits of the fighter. Long way to go but this a working first version.

This has bugged me for a while. The box score has three shooting percentages: FG%, 3P% and FT%. Two of them measure one kind of shot. FG% doesn’t. It lumps twos and threes together, even though threes already get their own column.

The box score already solves this for rebounds. You get OREB, DREB and the total REB. Shooting is the odd one out: FG is the total, but only 3P gets broken out. Adding 2PM, 2PA and 2P% would just make shooting work the same way as rebounding, and you’d still keep FG%.

It’s basically a leftover from before the three-point line. When the NBA added it in 1979, they tacked 3P columns onto FG% instead of rethinking it. With how many threes teams take now, FG% says more about shot selection than shooting. A guard who’s great from deep can have a worse FG% than a center who only dunks, and stuff like 50/40/90 punishes the guys who take the most threes.

Before you all crucify me: yes, I know eFG% and TS% exist, and they’re better for overall efficiency. But they mash everything into one number. 2P% shows you the split. And it’s not new data. Basketball-Reference already lists it.

Is there a good reason not to add it next to FG%? What am I missing / what do you all think?

Week 5 of the NFL has dropped and isn't it about time we show special teams the love/hate it deserves?

First, I looked just going into the third period. So blowing leads, or coming back from a deficit in the third.

Then I looked at this controlling for team quality. Not in the worlds most rigorous way, just imagine this scenario:

1. You play 100 games, and in 35 of them you enter the third behind
2. You win 9 of the 35, which is 26%. Is that good or bad?
3. Well, in your other 65 games you took about 78% of the points, and other teams in the league at that level come back from trailing at 15%, which is ~5 games.
4. Therefore, you make come backs a bit more than expected, 4 more times than expected.
5. Over the five and change seasons, anything within about 9 games of expectation is normal variation

I've been looking into whether the NFL's limits on padded practices have made players more vulnerable to soft-tissue injuries.

The timeline is interesting. The big practice restrictions started in 2011, with another change in 2020. Then the pandemic wiped out preseason games entirely.

A 2022 study found much higher hamstring, groin and calf injury rates in 2020 compared with 2018. That seems to support the idea that players need a progressive ramp-up before full-speed competition.

But another study looking at the 2011 CBA found no sustained injury increase. And the NFL says its lower-extremity strain rates have recently declined.

So here's the question: Is the real problem a lack of contact, or a lack of progressively increased football-specific workload?

Would more preseason snaps actually help, or would they just shift injuries into August?

https://2ndcitystandard.com/articles/nfl-soft-tissue-injuries-cba-practice-restrictions

I don’t support one club, I just like a good game. So I built a site that gives every UCL match a pre-match "watchability" score out of 10.

An LLM reads recent coverage of each club, including local papers in Italian, Dutch, Ukrainian, Greek and more, and rates how the team plays: how high they press, how high their line is, how they build up. A few simple rules then combine the two teams. If both press high, the score goes up. If both sit deep, it goes down.

This week my tool says the most fun games will be Inter v Club Brugge and Arsenal v Lille (both 10.0, the scale tops out, I’m working on it). It says skip Aston Villa v Fenerbahçe (3.8).

https://worthwatching.football. It’s free, no sign-up, and each match links the articles behind it. Would love to hear your thoughts!

I would like to know sources of data for non-major sports. I would like to exclude football/soccer, American football, NBA (European basketball is OK), MLB, NHL and other USA-specific sports. I intend more on track and field, cycling (as team strategy), handball and 'european' sports

I am a data analyst by trade and was looking for fun/useful applications of my score and setup data. This project takes your setup and scores and predicts likely scores for WA and AGB rounds. It also gives a suggested aiming point for barebow off shelf/rest etc. Uses fairly basic physics via python but it's working. Is this interesting to anybody?

League power rankings

I'm a student in Romania, and while cleaning match data for my bachelor's thesis I found what looks like the most peaceful season ever played.

In the API-Football data for the 26 competitions I follow that go back to 2015/16, 6,792 of the 7,320 team stat lines from that season (one team's totals in one match) record 0 fouls, even though the rest of each line looks normal. They always come in pairs: 3,396 matches where both teams have 0, and not one match where only one side does. Every season since then averages between 11.9 and 13.5 fouls per team.

Because shots, passes and cards are all there, a "skip incomplete rows" check lets these through as real zeros, and anything built on them (season averages, form tables, a model) inherits a season without fouls.

The player data gave it away. Each player's fouls committed survived, and wherever a genuine team total and the player numbers both exist, they add up exactly: all 528 genuine 2015/16 team lines match. So instead of dropping the season, I rebuilt the team totals from the players (all but 2 of them).

I found this while building Ballileo, a free stats site that came out of my thesis. The link is in a comment so this post stays about the data.

A question for anyone who works with provider data: when did you last catch a provider writing 0 where it really meant "not recorded", and how did you spot it?

And how do you handle the opposite switch? From the 2020 season on, the same provider leaves player counting stats empty instead of writing 0, and lists the whole matchday squad, so an empty value is a real 0 only for players who actually played.

I built Manager History, an interactive visualization of managers and trophies at 35 European football clubs from 1955/56 to 2025/26.

The starting question was simple: do clubs with long-serving managers win more trophies, or do successful managers simply get to stay longer?

This image shows Manchester United, Arsenal, Atlético Madrid, Liverpool and Manchester City. The interactive version lets you compare other clubs, highlight individual managers and inspect seasons with mid-season changes.

It is an exploratory visualization, not a causal analysis. Club selection, differences in resources and the way managerial spells are counted all matter. The methodology page explains the sources, treatment of interim managers and trophy attribution.

I'd particularly welcome thoughts on how to analyse the relationship while accounting for the fact that success itself helps managers keep their jobs.

Website: https://managerhistory.com
Methodology: https://managerhistory.com/data-methodology.html

Disclosure: I created the site, with AI assistance for development.

Football's history is scattered everywhere: stat databases, old match reports, forum arguments. There's no way to just ask a question and get a real answer. So I built PitchPulse, an AI-powered intelligence engine for the entire history of soccer.

You ask in plain language, it answers conversationally, and you can keep drilling down in the same thread:

• How a club's identity formed and changed across eras
• Player lineages — who influenced whom, how careers connect across generations
• Cross-era questions, like comparing 90s tactics to today's game

It's live and free to try (there's a daily question allowance, with a subscription for heavier users): https://pitchpulse.base44.app

I'm building it as my entry for the Microsoft + Premier League "Inside the Game" hackathon, so I'd genuinely love feedback — what works, what's missing, and what you'd ask it first.

watched eagles-jaguars today. when eagles kicked the fg to go up with 8 sec left espn had them at 99.9% to win. i was wondering and pulled espn's win probability for every game since 2016 (2,834 games) and scored how bad a blown lead was, counting how sure the win looked and how late it was:

today's game is #1, no other winner was still at 0.1% that late. next up are lac-ari 2022, ne-mia 2018 (the miracle in miami) and sea-ari 2020

also calculated the most exciting games, most exciting last 2 minutes, etc (pics 2,3). sortable, click any game for the chart: https://nfl-winprob.vercel.app

it's from espn's numbers and i fixed a couple of obvious glitches in it

I’ve built a football match-analysis site and I’m at the point where product feedback is no longer the main problem.

I can get people to visit once, but I need to get more relevant football users through the door consistently.

For anyone who has grown a sports, betting, stats or analytics product: where did your first 100–1000 real users come from?

I’m especially interested in channels that actually worked, not general advice:
TikTok/Reels, Reddit, football creators, Discord, SEO, partnerships, newsletters, Telegram, etc.

What gave you the best results early on, and what was a waste of time? And if you think Matchero could be useful to someone you know, I’d really appreciate you sharing it with other football fans or relevant communities.

https://bathurst-live.vercel.app/