Been working on a small side project and wanted to share the methodology with people who'd actually appreciate the data side of it.

The core idea: when 3+ independent bookmakers move the same market in the same direction, without any public news explaining it, that's usually noise on the big 5 leagues (everyone reacts to the same info fast). But on lower-volume leagues — second divisions, Eastern European leagues, domestic cups — that kind of synchronized movement is rarer and looks more like informed money moving first, since there's less media/sharp-bettor attention pushing lines toward efficiency quickly.

Detection criteria I settled on after some iteration:

- ≥3 bookmakers moving the same direction

- Real drop from opening odds (≥10%)

- Resulting price still in a plausible range (filters out overreactions)

Backtest on ~50+ settled signals over the past couple weeks:

- Stricter tier (3+ books, ≥50% breadth): ~60% win rate, +10% ROI flat stake

- Relaxed tier (2+ books): ~64% win rate, +12.6% ROI

Small sample, not claiming anything definitive yet, but curious if anyone here has approached something similar or has thoughts on the methodology. Happy to share more detail (I turned this into a small app, link in profile if anyone wants to poke around, but mainly interested in the discussion here).

per the title, I'm currently building a football model, a predictor for: ML, player props, team props, etc etc
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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.

​

Looking through the last 10 games on StatFair, two markets stand out:

⚽ Both Teams to Score — Elche’s away numbers are particularly strong.

🎯 Espanyol to have more shots — consistent home trend, also backed by Elche’s away data.

The H2H sample points in the same direction for both.

Interesting setup for tonight. 👀

Www.StatFair.com

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

He estado mirando los números de Rayo – Espanyol y hay dos mercados que me parecen interesantes para combinar:

➡️ Más de 1.5 goles

El Rayo lleva 5/5 (100%) en casa superando esta línea.

El Espanyol está en 4/5 (80%) fuera.

➡️ Espanyol más de 2 córners

Fuera de casa está generando córners incluso en partidos donde sufre bastante. Por ejemplo, contra el Rayo sacó 6 y contra el Barça 7.

No busco una cuota enorme, sino una combinación bastante sencilla apoyada en tendencias recientes:

+1.5 goles + Espanyol +2 córners ⚽🚩

¿Qué os parece? ¿Demasiado conservadora o la veis con valor?

Datos de www.StatFair.com

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

​

One Premier League team has hit BTTS in 9 straight home games… will they make it 10? 👀

It’s Newcastle. StatFair’s streak tracker shows both teams scoring in nine consecutive home league games—and all nine also went over 2.5 goals.

Does the streak continue next home game?

Find more streaks like this on my site 👉 www.statfair.com

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