Poisson models, Elo ratings, machine learning and ensembles explained step by step — what goes in, what comes out and how to judge the result.
Prediction models turn data into probabilities. This section explains the main families step by step: goal-based models, rating systems, machine learning and combinations of models, and how their accuracy is measured.
Start with How AI Predicts Football Matches and try the Elo calculator.
How an expected goals model is built from thousands of past shots, why different providers give different xG, and what the number can and cannot tell you.
Why even the best football models rarely give any outcome more than a strong chance, how randomness shapes goals, and what a realistic forecast looks like.
Forty terms from AI and sports analytics explained in one or two plain sentences each, from algorithm and overfitting to xG, tracking data and Brier score.
Why an average of several forecasts is often more accurate than the best single one, from Galton's ox to modern ensembles, and when combining stops helping.
What a football prediction model actually looks at, why it gives probabilities instead of certainties, and where it still gets things wrong.
How cameras and AI turn a match into positions for every player and the ball, what FIFA's semi-automated offside system measures, and the limits.
