How Our AI Football Predictions Work

Short answer: a machine-learning model trained on 78681 historical matches weighs team strength, form, expected goals and bookmaker odds and outputs a probability for every market. Probabilities are recorded before kick-off and scored against real results.

Data

Results and odds for 16 leagues since 2012 (football-data.co.uk)
Expected goals, xG (Understat)
Elo team ratings and rest days (ClubElo)
Fixtures, live scores and line-ups (ESPN)

Model

Gradient-boosted trees (LightGBM). The model starts from the betting market's odds and learns to correct what the market misses using form, xG, strength gap and rest. Match result, both teams to score and over/under 1.5 / 2.5 / 3.5 are modelled separately; likely scorelines come from each side's expected goals. For the European cups, where history is thin, probabilities are derived from market odds alone.

How accuracy is measured

The model is always trained on earlier seasons and tested on a season it has not seen, with an automated check against information leaking from the future. The key measure is calibration: picks rated 70% should land about 70% of the time. See the scorecard and past predictions.

Limits

Football has a large share of luck; even the best model misses about half of all match-result predictions.
Our model is about as accurate as the betting market, not meaningfully better; we do not promise profit from betting.
18+. Betting loses money.