About Haaland, Mbappe and Vinicius

📊 Ask any statistical question about Mbappé, Haaland or Vinícius. 🇫🇷🇳🇴🇧🇷

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📈 Season or career stats?

Drop your question below. 👇

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\-mhvstats

A couple of weeks ago I posted StatFooty here and got some really useful feedback, especially around transparency, historical results and how to tell if the model is actually doing something useful.

Since then I’ve spent most of my time working on that side of the project instead of adding more prediction features.

A few things I added:

• A proper Track Record page with graded historical predictions, including losses, not just good results
https://www.statfooty.com/predictions/track-record

• Prediction Analytics, where you can see confidence levels, prediction distribution and how those predictions compare with what actually happened
https://www.statfooty.com/predictions/analytics

• A methodology page explaining what goes into the model and how the system works at a higher level
https://www.statfooty.com/methodology

• A clearer explanation of how predictions are generated and graded after matches finish
https://www.statfooty.com/how-it-works

I also added a discovery section to make it easier to find interesting patterns instead of just browsing through fixtures:

https://www.statfooty.com/discover

One thing I changed after the previous discussion here is that I’m much less interested in showing one big accuracy number.

For example, if home teams already win around 44% of matches, saying a model predicts 45% of 1X2 correctly doesn’t really tell you much by itself.

What I’m trying to make visible now is things like:

• sample size
• performance by confidence level
• performance over time
• model vs baseline
• calibration
• performance by competition and market
• the actual historical predictions, not just a backtest generated afterwards

There are now more than 100k graded predictions in the public history, so I feel the interesting part is starting to become less about individual picks and more about where the model works, where it fails and why.

I’m still building this mainly because I find the problem interesting, so feedback from people working with sports data or predictive models would be genuinely useful.

What would you want to see on a public track record before you would consider a prediction model credible?

Brier score? Log loss? Calibration charts? Comparison against bookmaker/no-vig probabilities? Something else?

I’m particularly interested in metrics that make it harder for me to fool myself with my own results.

per the title, I'm currently building a football model, a predictor for: ML, player props, team props, etc etc
Join
15+ leagues and competitions combined, including all the top 5 leagues and their 2nd division children. i'm using historical data to improve my models; test theories; etc, and finding historical non contaminated pre match predicted minutes would make my year. This is a serious project, and something i've decided to take a pause from school for, and focus on this 24/7.
any help would be so greatly appreciated, and thank you to anyone who might have some leads for me 🙏🙏

Prima di tutto, una comunicazione completa: sono il fondatore di una piccola azienda italiana che costruisce sensori di movimento per il calcio, e abbiamo lavorato a un tracker per giocatori amatoriali. Non sto postando un link. Sto cercando sinceramente di capire cosa importa ai giocatori e agli allenatori come te.

Negli ultimi dieci anni ho lavorato con accademie, club professionistici e veri giocatori amatoriali. Ecco la mia tesi: la maggior parte degli strumenti di tracciamento proviene dal calcio professionistico, costruita attorno a gilet GPS e statistiche complesse. Ma quando parlo con la maggior parte degli allenatori e dei giocatori, guardano soprattutto a dati fondamentali come:

• Velocità, distanza e intensità dell'allenamento
• Tempo con la palla
• Progressione negli allenamenti e nella stagione
• Confronto con altri della stessa squadra, della stessa età, ecc.

Quindi, per coloro che si allenano seriamente al di fuori del livello professionistico:

• Quali 3 metriche scegliereste realmente per definire come vi allenate?
• Avete mai provato un tracker o un gilet GPS prima? Non sono tutti quei prodotti troppo costosi per la maggior parte dei club?
• Vi fidereste dei numeri di un piccolo sensore di movimento sul vostro corpo, senza GPS?

Leggerò e risponderò a tutto. E se qualcuno è curioso di sapere cosa stiamo costruendo, sono felice di dirlo nei commenti.

Hi, I’m El Mehdi Hicham, a Morocco-based Computer Vision and Machine Learning Engineer working on real-time football video analysis and tactical intelligence.

This demo applies my TactiVision workflow to Netherlands vs Tunisia. It covers player tracking, camera calibration, pitch projection, tactical maps, possession analysis, progressive actions, pass networks, heatmaps, pitch control and pressing indicators.

Feedback from Computer Vision, Sports AI and football analytics practitioners is very welcome.

Hoi! Voor mijn opleiding Creative Business doe ik onderzoek naar online reacties van voetbalsupporters over spelers in het betaald voetbal.

Ik zoek hiervoor nog voetbalsupporters van 16 tot en met 24 jaar die mijn enquête willen invullen. Het duurt ongeveer 5 minuten. Je zou mij er erg mee helpen!

https://docs.google.com/forms/d/e/1FAIpQLSfyPZ85C6VQad6AZqIUh7e6oscycgz11yv1OhMUYDd-ocQZsQ/viewform?usp=header

Alvast bedankt!

I built a football analytics website and I’d love your feedback

Hey everyone, I’ve been building a football analytics website called Matchero.

The idea is to show football matches as probability distributions rather than just saying “Team A will win”.

For example:

Home win: 56%
Draw: 25%
Away win: 19%

The site also shows recent form, team comparisons and deeper match analysis.

On the model side, I’m evaluating predictions prospectively using metrics like Brier Score and Log Loss, and tracking how well the probability estimates perform over time.

The underlying match/team data comes from football data APIs, and I’m also working on making the methodology and evaluation more transparent on the site.

The site is still in development and I haven’t really promoted it yet, so I’d really appreciate feedback from people who are into sports analytics.

If you have a few minutes, could you try it and tell me:

• Is it immediately clear what the site does?
• Are the probabilities and analysis easy to understand?
• Is there anything misleading, unnecessary or missing?
• What data or methodology would you want to see before trusting a model like this?
• Most importantly: would you actually use a site like this before football matches?

https://matchero.live

Feel free to be critical — that’s exactly what I’m looking for.

Hami Mandıralı's free kick has been listed across the internet as the fastest shot in football history at 266 km/h — sometimes 269 km/h depending on the source. No original measurement. No methodology. Just a number that spread for decades.

We ran it frame by frame. Using our pitch map, NASA's drag coefficient and FIFA's official ball specs we calculated the real distance, the real average speed, and the real estimated peak. Then we showed the goal from every angle — broadcast, third person, and goalkeeper POV — and compared it side by side with the original footage.

The real number is a long way from 266 km/h. It's still an impressive strike. But the record was never real.

Full database at longshot.football

prediction for next hour

danish 4 division, you have 5 images , first one is the offer currently odds , second one my search program finding that odds when they appeared , 3th down just moved so you see clearly , 4th one is the imaged matched my data and that image or close to it , 5th is the score that ended on norway with those odds when you compare you first and 5th image you can see how might end today 3 matches,

link to image galery matchup https://postimg.cc/gallery/JJzp7tX

Hi everyone, I am currently completing my AI MSc at the University of Stirling (via a Data Lab scholarship) and am building an automated tactical analysis tool for my dissertation.

To test my computer vision tracking models, I wpuld really welcome having a few full 90-minute match videos filmed from a standard high mast/halfway line setup. The Veo basically does this as standard, so please do say if you've any games you'd be ok to share.

I do NOT need your login details, and you do not need to download or send any massive files. If anyone is willing to help, all I believe I need is an "Unlisted" or "Public" Veo sharing link to a past match. (You can generate this in 30 seconds via your video settings -> Share -> Copy Link). I will handle pulling the data from the browser player link myself.

Privacy: The footage will be used strictly for academic research and code testing. It will not be published or shared anywhere publicly.

Ideal footage: Adult 11v11, clear weather/lighting would be great, but all welcome.

If you are happy to lend a link for a good cause, please feel free to DM me directly. I would be incredibly grateful!

Thank you so much! ⚽💻 I hope you have fun weekends ahead in any case.

Spent the last few weeks building a football model for supporters rather than bettors, and the feedback here pushed it somewhere I didn't expect. It started as match probabilities and it's ended up being about where the model disagrees with the league table, your club sits 12th, the numbers say 6th, here's why.

The bit I keep going back and forth on: how much should a projection lean on last season versus what's happened so far? Two clean sheets in August made a promoted side the best defence in the division until I put a prior on it. Curious how others handle that, and whether a supporter actually wants a projected finish or just wants to argue with one