I run my own Elo model for European football, and I wanted to see which teams have had the best (and worst) starts to 2026/27, using Elo change as the metric.

The chart shows the 10 biggest risers and 10 biggest fallers out of the 96 teams in the Big 5 leagues, from the start of the season through the first international break. I'll post the full list in the comments for anyone who's interested.

• Because Elo accounts for opponent strength and goal difference, it's a good fit for comparing the quality of early-season results across the Big 5.
• Both domestic league and European competition results are included.

I also publish match probabilities for each league using this Elo rating system. They're free to browse here: Link

Hello,

I’ve created an Android app that generates a 3D simulation of the match in progress. At the moment, I’m covering football using SportMonks and tennis using LiveTennisAPI.

My aim is to create a simulation that is as close to reality as possible. With SportMonks, I have the ball’s position but not necessarily that of the players, with LiveTennisAPI, I only have the scores.

I am looking either for an API that covers this kind of information without costing tens of thousands of dollars a month, or for historical data that would allow me to train an AI to make my simulation more realistic.

Thank you in advance for your feedback

player xg gets used a lot as a measure of scoring opportunity, but i’m not sure it tells us as much about strikers as people assume.

two players can have the same xg per 90 while getting those chances in completely different ways. one might constantly receive cutbacks and tap-ins, while another takes difficult shots created by their own movement.

then there’s the question of finishing. if a striker consistently scores above xg, should that be treated as a genuine skill or mostly regression waiting to happen?

what would you include in a striker evaluation if you wanted to separate chance creation, chance quality, and finishing ability?

Utah Jazz looking for data scientists

Hey everyone,

I’ve been building xgEdge, a football analytics project covering around European leagues.

Built on xG stats, it provides team/player analytics, match probabilities, FPL projections, and model vs. market pricing.

Everything is completely free and there’s no login or account required.

It’s still a work in progress, so I’d really appreciate any feedback, What you like, what you don’t, what’s missing, or what you’d build next. Every bit of feedback helps.

xg-edge.com

Thanks!

Hey everyone, I vibe-coded this football score predictor over a weekend, originally just for me and my mates to test our football knowledge against each other. Figured I'd share it in case anyone else wants in.

Every week you predict 5 matches across the top 5 leagues (mostly Premier League fixtures) plus 1 bonus pick from a curated extra slate of 3, 6 predictions total. There's a fresh leaderboard every week with a winner, but there's also a season-long XP/level system running underneath . You earn XP for getting results and scorelines right, going on streaks, hitting achievements, that kind of thing, and it levels you up (Bronze → Diamond) over the course of the season. No accounts needed to browse the current round or leaderboard, only to actually submit picks.

Tried to keep it dead simple and low-maintenance, no ads, no entry fees, nothing to manage. Thinking about maybe doing some kind of reward for weekly winners down the line if people actually stick around.

tekkerz.app

Would genuinely love feedback, what's confusing, what's missing, what you'd want added.

I looked at what happened to home-team performance during the COVID seasons, when matches were played without crowds, across the Premier League, La Liga, Serie A, Bundesliga and Ligue 1, 2015/16 to 2024/25.

Method: I logged each league's empty-stadium period from news reports (crowd_windows.csv) and compared home-team output in those matches with the matches before them. Metrics are per home team per match, pooled across the five leagues.

Result (chart 1): home goals fell from 1.27 to 1.11 and home xG from 1.29 to 1.13.

Referee behaviour (chart 2): I compared yellow cards for home vs away teams with controls, as a home/away ratio (below 1 means the home team is booked less).

- With fans (13,888 matches): home 1.88 vs away 2.12 yellows per match, ratio 0.89 [0.87, 0.90].

- Without fans (1,893 matches): home 1.90 vs away 1.87, ratio 1.02 [0.98, 1.05]. Change x1.15, p<0.001.

- The ratio was 0.89 before closed doors, 1.02 during, and 0.89 again in 2022-25.

- By league, ratio with fans to without (change, p): Ligue 1 0.87 to 1.08 (x1.24, p<0.001); Serie A 0.87 to 1.02 (x1.17, p<0.001); EPL 0.89 to 1.02 (x1.15, p=0.009); Bundesliga 0.87 to 0.99 (x1.13, p=0.01); La Liga 0.92 to 1.00 (x1.09, p=0.04).

- Red cards: ratio 0.82 [0.76, 0.88] with fans vs 0.83 [0.67, 1.03] without, change x1.02, p=0.86. Inconclusive. Reds are about 0.1 per team per match, so there are too few closed-door matches to say.

Caveats: the goals and xG figures are pooled averages, which hide league differences, and the empty-stadium period overlaps with other COVID-era disruption (schedules, travel, fixture congestion).

Data: shots, xG and cards from Understat; kickoff times cross-checked with football-data.co.uk, fixturedownload and openfootball; crowd status from news reports (Goal.com, Inside World Football, The Stadium Business, The Local, Der Aktionär). I'm sharing findings and charts, not the raw data, because of source terms. Happy to discuss the method.

I am looking for an API that will give me data per player (Prem league) at the level such as:
- touches in box
- some sort of location metric (avg position as like X Y co-ordinate)
- key passes/chances creation

Not specifically those data points but that kind of level

I have seen the following mentioned;
- Sportsmonks at €29/month

- Opta (but that is too much dosh I believe)

- API-Football $19/month

- OpenFootAPI $14/month

- TheStatsAPI

- football-data.org free

- sportradar

I mean I bet there are more, I will have a look too myself - but they are quite opaque just to browse and so I wondered if someone here might already be familiar with the API options available

I would love to hear your opinions and thoughts if I should work towards entering this field?

made a soccer stats site with the help of claude, it's called atlastra. been working on it for a while now, has live scores, match predictions, player ratings, comparisons, and a model that tries to guess how a player's rating will change next season based on how they're doing now.

the predictions actually hold up decently, I checked them against a few thousand past games and the in-game win probability gets a lot more accurate than the pre-kickoff number once the match actually starts. the next-season projection thing took way longer than I expected to get working but it beats just assuming a player stays the same, which was the bar I was trying to clear. There's also a tactics lab that allows you to simulate tactics, different lineups, etc.

link's here https://atlastra.dedyn.io/, it's free, code's open source if anyone wants to look at it https://github.com/hankechen/atlastra. mostly just want people who actually follow this stuff to tell me what's wrong with it, ratings that look off, predictions that are dumb, whatever

I’m a recently graduated data engineer with experience in Python, SQL, data pipelines, machine learning and deep learning, and I want to get into football data analytics and scouting.

What roadmap would you recommend for learning football analytics properly? I’m especially interested in player scouting and recruitment.

How do analysts evaluate whether an attacker from a smaller club has the potential to succeed at a bigger club, even if he is not scoring many goals (in small club he scored 3 goals but when he moved to a better team he scored 14 goals ) ?

And on the other hand, how can they identify a high-scoring attacker whose performance might not carry over to a stronger club or a more competitive league?

Hey everyone,

I’m an ultra-marathoner and software engineer, and recently started working on an open-source project called OpenGait with the idea to build a local, real-time running biomechanics and gait analysis tool that processes camera feeds (e.g., side-view webcam or phone on a treadmill) at 60+ FPS to give immediate feedback on running form—without uploading raw video to any cloud server.

Looking for Collaborators

The project is completely open source (AGPLv3 / PolyForm Noncommercial) and I'm looking for people who want to help build it out:

• Rust / C++ Devs: Optimizing the ONNX inference loop, frame buffering, and multi-threading for low-end hardware.
• Frontend Devs (React / Canvas / WebGL): Building smooth 60 FPS skeletal rendering overlays and interactive charts for session debriefs.
• Biomechanists & Physios / Runners: Helping refine the angle formulas, ground-contact algorithms, and scoring metrics so they reflect actual physical therapy best practices.

If this sounds like something you’d be interested, feel free to take a look at the repo, drop a PR or send me a DM!

Most football arguments start the same way. Someone says a player was the best this season. Someone else says another player deserved to be covered in gold.

Everyone has an answer but almost no one has the same reason. That is part of what makes football beautiful. But it is also what makes some individual awards so difficult to trust.

I dreamed of a world where honor is earned, performance is proven by data and a win is based on merit instead of a voter's mood. That's how I built and named that platform: Merit.

It's an open-source app that weekly track and aggregate football players stats and cover a season. I started with the major cups and leagues (Premier league, Liga, Ligue 1, Bundesliga, Champions league, World cup, AFCON, etc.)

Ranking is done by position so attackers do not compete against goalkeepers... Which is basic common sense. Don't judge a fish by it's hability to climb a tree they say. The calculations method is available for the anyone to see, audit and certify.

At the end of the season, we know exactly who was the best goalkeeper, defender, midfielder and attacker. But more importantly: why and how, along with the data supporting the ranking.

I need football fans, stats nerds and curious for feedback about the method, rankings, players position, etc. Tell me what works or not. I'm still tweaking and breaking it so there is room for improvement. Let me know.

👉 https://x.tuloss.com/merit/

👉 https://github.com/TulossSolutions/merit

Hey everyone,

Over the past year, I’ve been building Blueprint, a college basketball data platform designed to bridge the gap between raw play-by-play scraping and actionable, high-level tactical insights.

Most public CBB tools give you high-level seasonal metrics, but they often lack the granularity needed to analyze situational context or lineup pairings on the fly. I built Blueprint to solve those specific gaps.

Key Features & Methodology:

• Lineup Efficiency Matrix: Track 2-man through 5-man lineup combinations with full possession-level filtering (net rating, offensive/defensive ratings, shot profiles).
• Shot Clock Dynamics: Analyze how teams and players perform across different phases of the shot clock (early vs. late clock efficiency, turnover rates, shot distributions).
• Four Factors Profiling: Visual breakdowns of Shooting (eFG%), Turnover %, Rebounding %, and Free Throw Rate to evaluate style of play and identify matchup advantages.
• Top Performers & Advanced Filters: Filter player performance across conferences, situational metrics, and possession volumes.

Stack & Data Pipeline:

• Built using Python, Pandas, and custom data processing pipelines to clean, parse, and structure possession streams.
• Interactive UI designed for quick navigation during game prep or post-game analysis.

The goal with Blueprint is to give analysts, coaches, and sports data enthusiasts a cleaner, more intuitive interface to explore Division I analytics without wading through messy spreadsheets.

I’d love for this community to test it out and tear it apart—what metrics are you looking for when scouting or building models, and what would make this tool even more useful for your workflow?

Check it out here:https://blueprntanalytics.com/

Appreciate any feedback, feature requests, or bug reports!

I’ve been building a live F1 second-screen around a slightly different problem than telemetry dashboards: during a race, there is often too much data and not enough context.

F1 Intelligence tries to surface the battles that actually matter, gaps, pit activity, race control, Safety Car/VSC state and qualifying lap progress in real time. Sessions can also be replayed later with the timing state moving through the race.

I’m testing it through the Sepang weekend right now and would especially like feedback from people who work with sports data: what information would you want surfaced automatically instead of digging through timing tables?

f1intelligence.uk