FanPick ブログ

Quantifying Home Advantage — How to Build It Into Your Football Prediction Model

Home advantage is worth +0.30-0.45 goals per match. Learn what drives it, how COVID proved crowd effects, and four methods to build it into your prediction model.

2026年7月5日

methodology

How to Update Football Predictions in Real Time During Live Matches

Learn how Bayesian inference and non-homogeneous Poisson processes power real-time football prediction that updates with every match event.

2026年7月3日

methodology

Football Prediction for Knockout Matches — Why Cup Games Need Different Models Than League Games

Germany and the Netherlands both lost on penalties in the Round of 32. Learn why league prediction models fail in knockout football and how to adjust your Poisson, Elo, and penalty models for cup matches.

2026年7月2日

methodology

Random Forest and Gradient Boosting for Football Predictions — Tree-Based Models That Actually Work

Learn how tree-based ensemble models predict football match outcomes. Random Forest vs XGBoost vs LightGBM compared with practical tuning tips and feature engineering.

2026年6月30日

methodology

How to Calibrate Your Football Prediction Model — Brier Score, Log Loss, and Reliability Diagrams Explained

Learn how to calibrate your football prediction model using Brier score, log loss, and reliability diagrams. A practical guide to making your probability estimates trustworthy.

2026年6月29日

methodology

Does Team Form Actually Predict Match Results? How to Measure Momentum with Data

Everyone talks about momentum in football. But does team form actually predict match outcomes? A data-driven guide to measuring form with EMA and rolling averages.

2026年6月27日

methodology

Feature Engineering for Football Prediction Models — The Inputs That Actually Matter

Learn how to engineer features for football prediction models. Time-decay form, strength-of-schedule, Elo ratings, and the 10 features every model needs to beat the market.

2026年6月26日

methodology

Expected Threat (xT) — The Football Analytics Metric That Values Every Touch

Expected goals only values shots. Expected Threat (xT) values every pass, dribble, and carry on the pitch. Learn how this Markov chain model predicts football better than xG alone.

2026年6月24日

methodology

Logistic Regression for Football Predictions — The Foundation Every Model Needs

Learn how logistic regression powers football prediction models. From the sigmoid function to win/draw/loss probabilities, this guide breaks down the math that drives match forecasting.

2026年6月22日

methodology

Advanced Football Statistics Beyond xG — The Metrics That Actually Predict Match Outcomes

Expected goals is just the start. Learn PPDA, field tilt, progressive passes, xAG, and the advanced metrics that professional analysts use to predict football outcomes.

2026年6月22日

methodology

How to Read Betting Odds and Convert Them into Probabilities

Learn to convert decimal, fractional, and American odds into implied probabilities. Find value bets, understand the overround, and make sharper predictions.

2026年6月21日

methodology

The Kelly Criterion for Football Predictions — Optimal Bet Sizing for Maximum Growth

Learn how the Kelly Criterion sizes football prediction bets for maximum bankroll growth. Formula, worked examples, fractional Kelly, and a practical workflow.

2026年6月19日