Sports Analytics Weekly by kubeia.io - 52/2025
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đź“ť Sports Analytics
When Teams get Tired, Football gets Predictable
substack.com - Alex Marin Felices
Football looks chaotic. Players roam, press, stretch, collapse. But beneath the noise, teams quietly settle into patterns; and this paper shows how and when that happens.
Using full‑match tracking data from a Premier League game, Duarte et al. (2012) treated teams as complex systems, not collections of individuals. Instead of focusing on passes or shots, they tracked how a team’s shape evolved over time: how much space it occupied, how stretched it became, and how regularly those patterns repeated.
What they found is subtle, but powerful.
Teams didn’t become more rigid as matches progressed, quite the opposite. The magnitude of variation increased: teams covered more space, stretched more, and deviated further from their average shape. Yet at the same time, the structure of that variation became simpler. Using approximate entropy, the authors showed that team behaviour grew increasingly regular and predictable within each half.
In plain terms: teams move more, but in fewer ways.
🤖 Machine Learning
Which Machine Learning Models Perform Best for Football Match Prediction?
substack.com - Alex Marin Felices
Machine learning models that incorporate domain-specific rating features consistently outperform traditional statistical models. Ensemble methods, particularly gradient-boosted trees (e.g., XGBoost, CatBoost), excel when paired with tailored features like Elo, pi-, or Berrar ratings. Feature engineering, notably from limited datasets, remains "a critical component in the predictive power of models". Random Forests and SHAP provide valuable interpretability, which is especially useful in applied contexts like coaching.Nevertheless, standard benchmark datasets are rare, complicating direct model comparison. The 2017 Soccer Prediction Challenge is one of the few shared evaluation platforms. Deep learning methods have yet to consistently outperform ensembles, often due to data constraints or lack of appropriate temporal feature modeling.
🎙️ Podcast
Episode 97: Anthony Kaminskas on AKBets on Modern Bookmaking, Restrictions
smartbettingclub.com - Peter Ling
In Episode 97 of the Smart Betting Club Podcast, I am joined for the third time by Anthony Kaminskas, founder of AKBets and a former professional punter turned bookmaker.​Nearly three years on from the launch of AKBets, this conversation takes stock of how the business has grown, how the betting industry has shifted, and why life as a modern bookmaker is far tougher than most punters realise – including a healthy discussion on betting restrictions.AK speaks candidly about operating under UK regulation, handling restrictions, managing compliance risk, and absorbing rising costs at every level of the business.​He also lifts the lid on the real economics of running a sportsbook, sharing hard numbers on payroll, data feeds, duties, levies, payment processing and platform fees.​It is an honest look at why betting markets look the way they do, why margins are tightening, and why both punters and bookmakers are being squeezed by the same pressures.