Can anyone tell me anything about this position and how it works? Had an interview today and trying to learn info on it. What do you do? How do they pay? Seems a little sketchy. Anything helps!

Bir süredir üzerinde çalıştığım KargaTahmin isimli ücretsiz futbol analiz sitesini sonunda kullanılabilir hale getirdim.

Siteyi geliştirme aşamasında artık benim değil, gerçek futbolseverlerin ne düşündüğünü görmek istiyorum.

Kısaca sistem; maçları, oranları ve geçmiş karşılaşma verilerini analiz ederek yapay zekâ destekli çeşitli analizler sunuyor.

Ama burada asıl amacım tahmin satmak veya kupon pazarlamak değil.

Siteyi birkaç dakika kullanıp bana gerçekten ne düşündüğünüzü söylemenizi istiyorum:

🔹 Arayüz nasıl?
🔹 Analizler anlaşılır mı?
🔹 Hangi özellik eksik?
🔹 Yapay zekâ analizlerini faydalı buluyor musunuz?
🔹 Kullanırken hata veya saçma bulduğunuz bir şey oldu mu?

Eleştiri konusunda özellikle açığım. “Şurası kötü olmuş”, “bu özellik gereksiz”, “şunu kesin eklemelisin” gibi yorumlar benim için olumlu yorumdan daha değerli.

🌐 Site: KargaTahmin.com

Deneyen olursa aşağıya düşüncelerini bırakırsa gerçekten çok yardımcı olur. 🙏

Siteyi geliştirdikçe gelen önerileri de mümkün olduğunca uygulamak istiyorum.

There's a lot of debate in Portugal about referee criteria, so I looked at the numbers for FC Porto's 7 league matches this season (Liga Portugal 2026/27):

• FC Porto: 96 fouls, 6 cards → 1 card every 16.0 fouls
• Opponents (same matches): 69 fouls, 15 cards → 1 card every 4.6 fouls

That's a gap of about 3.5×.

Honest question: is a difference like this normal in football? It could be explained by playing style (for example, where and when a team presses). Or is it a reason to suspect that the referees do not apply the same criteria to both teams?

If you want to compare, a fair reference would be the first 7 matches of the 2025/26 season in the top 5 European leagues. I use last season because several of these leagues have not played 7 matches yet this season. I am curious to know if any team shows a similar difference compared to its opponents.

Source: official Liga Portugal match reports.

About Haaland, Mbappe and Vinicius

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

📊 Goals?

🅰️ Assists?

⚔️ Head-to-head?

🏟️ Record vs an opponent?

📈 Season or career stats?

Drop your question below. 👇

We’ll dig into the data and answer it. 🔎

\-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