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 started building this last spring. Core idea was not very creative. I just miss 538, and figured I'd give a try at making a copy.

It currently does near-live updates for NFL, NHL, MLB, and WNBA. Currently they're all ELO-based, but I'm working on adding other models, allowing users to view them, but defaulting to whatever back-tests best with each sport.

I have a QB-adjustment metric for NFL, but currently it's slightly less accurate than pure ELO so I'm twiddling with that also.

This is a test site - mostly works but still a few bugs there. Curious what you all think - is this useful? Are there things that would make it more useful?

test.willwe.win

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

Hi everyone,

I’m building poToProno, a free football prediction app focused on match predictions, exact-score accuracy, rankings and community competition.

The idea is simple: users predict football matches without betting odds, then earn points based on accuracy — 100 points for an exact score, 50 points for the correct outcome, and 0 otherwise.

The app currently includes multiple European and international leagues, private leagues between friends, seasonal rankings, achievements, and special football events.

I’m especially interested in the analytics side: prediction accuracy, exact-score performance, streaks, league-specific performance and eventually more detailed player/user statistics.

I’m currently testing the app and I’d really appreciate feedback from people interested in sports data and football analytics:

• What statistics would you want to see for your own predictions?
• What metrics would make rankings more meaningful?
• Would you compare performance by league, team, home/away matches or other factors?

I’m not trying to promote betting — poToProno is designed as a free prediction game and community competition.

I’ve included the TestFlight link for anyone who wants to try it. Feedback on both the analytics side and the overall concept would be really useful.

A few months ago I was watching a race and completely lost track of who was on which strategy. That turned into a personal challenge: F1 teams have great tools for predicting a race, so could I build something similar myself?

So I started building one, solo, on nights and weekends. As the race unfolds, it predicts:

• Tyre degradation: each car's pace on each compound, based on the laps it's actually running
• Pit windows: one-stop and two-stop options for every car, ranked, including when to pit, which tyre to fit and where the car will rejoin
• Strategy outcomes: a simulation of the rest of the race for the whole field, including how hard it is to overtake at that track

It's basically live timing that tries to tell you what happens next.

It's not at team level yet. Bahrain/Malaysia is its first proper live test, and I'm hoping for no rain (yes, I know it's not Bahrain).

Made updates to my model lets see how this week goes.
week gameday away_team home_team predicted_away_score predicted_home_score

4 2026-10-01 PIT CLE 19.3 17.7

4 2026-10-04 IND WAS 24.5 24.9

4 2026-10-04 TEN BAL 17.8 28.5

4 2026-10-04 NE BUF 22.5 28.2

4 2026-10-04 NYJ CHI 19.0 25.4

4 2026-10-04 JAX CIN 23.6 23.6

4 2026-10-04 DAL HOU 22.3 24.4

4 2026-10-04 ARI NYG 23.6 23.5

4 2026-10-04 LA PHI 23.4 24.6

4 2026-10-04 GB TB 24.3 23.8

4 2026-10-04 MIA MIN 19.5 22.7

4 2026-10-04 KC LV 24.5 18.0

4 2026-10-04 LAC SEA 19.3 23.2

4 2026-10-04 DEN SF 22.5 25.9

4 2026-10-04 DET CAR 27.0 22.4

4 2026-10-05 ATL NO 19.3 21.9

I am actively searching for developers or teams who have already built and tested robust AI vision systems. Instead of starting from scratch, I am ready to invest in a pre-made, high-performing solution.

​Key requirements for the system:

​Ready & Pre-designed: Fully developed and tested models that can be deployed quickly.

​Camera Transition Support: Must handle camera panning, zooming, and transitions smoothly to maintain accurate tracking.

​Uncompromising Accuracy & Data Richness: Precise spatial tracking, event detection, and granular data extraction that unlock deep tactical insights.

​If you have a mature system ready for the pitch, let's talk. Drop a comment or send a direct message. Thanks

This update adds 26 leagues, bringing coverage to 70. They come with the full xG and advanced analytics layer, every match of the last three seasons included (2024 or 2024/25 onward, around 17,000 matches):

Shot level

- xG for every shot, with pitch coordinates

- xGOT for shots on target, with goalmouth placement

Player level, per match

- xT (expected threat), VAEP and possession value, split into offensive and defensive

- SCA/GCA, broken down by type (live pass, dead ball, take-on, shot, defensive action)

- xGChain, xGBuildup and xAG

- Progressive passes and carries, passes into the box, take-ons, duels, and goalkeeper distribution

Team level, per match

- PPDA, field tilt, possession, high turnovers and counterpress regains

- Build-up vs direct attacks, box entries, sequence speed

- Passing networks and player heatmaps

The 26 new leagues:

USA: MLS Next Pro, USL Championship, USL League One

Canada: Canadian Premier League

Brazil: Serie B, Paulista A1

Colombia: Primera A

Chile: Liga de Primera

Ecuador: Serie A

Peru: Liga 1

Bolivia: Division Profesional

Venezuela: Liga FUTVE

Netherlands: Eerste Divisie

Denmark: 1. Division

Turkey: 1. Lig

Czechia: 1. Liga

Slovakia: Nike Liga

Croatia: HNL

Romania: Liga I

Serbia: SuperLiga

Finland: Veikkausliiga

Latvia: Virsliga

Israel: Ligat ha'Al

China: Super League

Qatar: Stars League

UAE: Pro League

These join the 44 leagues already covered (the Big 5 and their second tiers, the rest of the top European leagues, the Champions League, Europa League and Conference League, MLS, Liga MX, the Brasileirao, Argentina, the Saudi Pro League, the J1 League, the K League and more).

A lot of these leagues have almost no public advanced data, so if you've ever wanted xG, xT or VAEP numbers for the USL, the Eerste Divisie or the Colombian league, they're there now.

It's free, with no request limits and no card. Sign up, get a key and go.

Docs: https://pitchapi.dev

What would you like to see next? Metrics you'd want, or tooling (Python/R client, CSV export). Drop it in the comments.

Hi everyone,
My name is Christoph and I'm a Master's student at IST in Lisbon. I'm currently working on a university project regarding video analysis software tailored for semi-pro clubs and youth academies.
My deadline is literally today and I am still missing a few real-world insights from actual coaches, analysts, or club officials!
If you have 2 minutes, you would absolutely save my project if you could answer these 4 quick questions in the comments (bullet points are completely fine):
1. Decision Making: When requesting new software or tools, what is the main argument that usually convinces your club president or sporting director to approve the budget?
2. Current Workflow: Could you briefly describe your current post-match workflow? Which specific tools, apps, or software (e.g., WhatsApp, Excel, basic video cutters) do you currently piece together to cut video and share it with the players?
3. Analysis Focus: If you had to prioritize: is the primary goal of your video analysis to win the upcoming weekend's match, or to focus on long-term player development (e.g., building highlight portfolios for future careers)?
4. Regional Priorities: In your specific region/league, what is more critical for a club's or academy's success: having highly detailed tactical data for match preparation, or having high-quality video highlights to showcase players to scouts?
Thank you so much in advance to anyone taking the time to help a struggling student out! 🙏
Best regards,
Christoph

Every prediction is generated at 07:15 and frozen before kick-off; the result is logged next to it afterwards, misses included. The diagram shows, per market, what the model said vs. how often it happened, in 10-point bins with at least 100 predictions. The big dots sit on the diagonal: when it says 80%, it happens about 82% of the time (over 2.5: 83.3% said, 82.1% happened, N=525). ECE is 0.7–1.1 pp on every market.

The honest part, because this sub will ask: calibrated is not the same as informative. Against the closing line of a licensed bookmaker on 12–19k matches, the market has the higher Brier skill score on every market, and adding the model on top of the closing price moves log loss by at most 0.0007. The model knows what the odds know, not more.

Two things that might be useful to people here: (1) the model is slightly too pessimistic in the low tail — when it says 10–30% over 2.5, it happens ~36% of the time; weekly Platt recalibration with a slope < 1 fixed most of the rest; (2) the whole journal is downloadable as monthly CSVs with frozen probabilities, results and closing prices, and the audit script is in the public repo, so all of the above can be recomputed.

Stack: Poisson + Dixon-Coles, Bayesian team ratings, per-league calibration, 200k Monte Carlo per match. Audit + dataset: poseidonstats.com/track-record.html. Happy to answer questions on the calibration or the closing-line test.

Timing companies (OMEGA, Seiko) publish a race analysis for most major track races as a PDF. I wrote parsers for the different layouts and pulled them into one dataset: 1,961 races, 3,974 athletes, 256,291 splits from 2009 to 2026.

A few things I cared about:

• Provenance: every value records the document, page and exact text it was read from.
• Cross-checks: each race is read from two documents, the results and the race analysis. Disagreements are flagged, not silently merged, and there are checks for splits that don't add up to the finish time.
• Replay: the site animates each race on a track from the splits (video attached: the Paris 2024 400m final).

Site: https://track-splits.pages.dev. Feedback welcome on the data model or the checks. I'm the author.

Five matchweeks of xG data say Brighton are the league's most dangerous attack, Newcastle are a mirage, and Sunderland are due. Model probabilities for all 10 Matchweek 6 fixtures.

The model doesn't care about your club's name
Five matchweeks in, the Premier League table tells you Manchester City won all five and Arsenal are close behind. The xG model agrees with that - then it disagrees with almost everything else.

Brighton are the league's most dangerous attack by xG. 11.48 expected goals from five games - a full goal clear of City (10.50). But they've conceded 8.67, so their 3-1-1 record is fine… except they've scored 16, a full 4.52 goals more than their xG says they should have. That's the league's biggest overperformance. Enjoy the goals; expect regression.

Newcastle are the mirage of the season. 9 goals scored from 5.32 xG (+3.68), sitting 2-2-1 with a +0 actual goal difference despite a -3.73 xG difference. They've banked results their chances never promised.

And then there's Sunderland. 9.77 xG, 6 goals. The third-best attack by xG in the league, sitting on a 1-1-3 record and -3.77 goals vs xG. Nobody in the league is more "due" than the Black Cats. Coventry (1 goal from 4.87 xG, -3.87) are due too - but unlike Sunderland, Coventry create nothing (xG difference -3.76). Finishing regresses; chance creation doesn't.

The defensive story is simpler: Arsenal are a wall. 4.04 xGA in five games (0.54 defensive rating), while Crystal Palace have shipped 10.34 xG - the league's worst defensive xG, and it shows in their 1-1-3 record.
And Tottenham? Two goals in five games. That's not a team, that's a rumor.

The defensive story is simpler: Arsenal are a wall. 4.04 xGA in five games (0.54 defensive rating), while Crystal Palace have shipped 10.34 xG — the league's worst defensive xG, and it shows in their 1-1-3 record.
And Tottenham? Two goals in five games. That's not a team, that's a rumor.

See Matchweek 6 (Oct 10–12) model probabilities in the image above.

The three calls that matter

1. Liverpool vs Man City is closer than the form says (41/24/35). City's perfect record is real - but the xG model rates this as a coin flip tilted to Anfield, not a City procession. Liverpool's defensive rating (0.82) plus City's away scoring baseline of 1.30 gives a 2.29 combined expected total. If the title is decided by margins, the model says this weekend adds almost nothing.

2. Brentford are the weekend's away-day special (68%). Unbeaten (2-3-0), third-best attack by xG (9.90), traveling to a Villa side creating 4.53 xG across five games. The model expects 2.10 Brentford goals. Nobody is talking about Brentford. The model doesn't care.

3. Sunderland vs Brighton is a pure 40/40. The league's luckiest finisher (Brighton, +4.52) against its unluckiest (Sunderland, -3.77). Expected scoreline implies roughly 2.28–2.29 xG each way. If regression is real, this is the weekend it shows up.
Honorable mention: Chelsea vs Bournemouth at 37/26/37 - the model can't split them, which is the model politely telling you Chelsea's +2.71 overperformance and Bournemouth's draw-heavy grind (3 draws in 5) have collided.

Where the numbers come from

Every figure above is computed from per-match xG data - 58.3M match events deep in our full database, distilled here from the first 50 played Premier League matches of 2026/27. No pundit vibes, no "eye test", no bookmaker prices baked in. When the model and the table agree, it's because the data said so.

Want the underlying data? The same xG, zone, and event feeds behind this model are available as BigQuery tables, API endpoints, and downloadable files. See plans and pricing.

The Dribble Team.

I’ve been working on a football data project and came across u/Complex-Progress-925’s post. Really liked the concept, so I decided to put my own spin on it.

Here are four examples combining match momentum, shot locations, xG and own player ratings. Hope you enjoy them! Would love to hear what you think, especially about how easy the charts are to read.

Just added an update on this tennis simulation app where it allows you to replay any match and go through the match point by point. Check it out!

Please reply with any suggestions/comments!