AI soccer predictions for every match
Machine learning models trained on years of European soccer data. We forecast match winners, exact scores, and goal totals for 5 top leagues — with transparency about what the model gets right and wrong.
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Leagues we analyze
English Premier League
20 teams, 380 matches per season. The most-watched soccer league in the world, featuring top clubs like Manchester City, Arsenal, and Liverpool.
Spanish La Liga
20 teams, 380 matches per season. Home to Real Madrid and Barcelona, known for technical play and tactical depth.
German Bundesliga
18 teams, 306 matches per season. The highest-scoring of Europe's top leagues, with Bayern Munich as the dominant force.
Italian Serie A
20 teams, 380 matches per season. Historically defensive, with strong home advantages — our model captures these patterns well.
French Ligue 1
18 teams, 306 matches per season. PSG's dominance provides a strong statistical baseline for modeling outcomes.
What each prediction includes
Sample predictions
Man City vs Liverpool
Score: 2-1 · H 48% D 27% A 25%
Real Madrid vs Atletico
Score: 1-0 · H 44% D 30% A 26%
Bayern vs Leipzig
Score: 3-1 · H 58% D 22% A 20%
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AI predictions for every fixture across 8 European leagues.
Start Free TrialThe technology
LightGBM per league
Separate gradient-boosted models for each league. Different leagues have different scoring patterns, home advantages, and tactical styles — per-league training captures these nuances.
100+ features per match
Expected goals (xG), ELO ratings, form streaks, head-to-head records, player availability, shot statistics, and home/away splits — all engineered from raw match data.
Exact score modelling
For exact score predictions, a statistical approach purpose-built for football that accounts for the correlation between home and away goals.
Temporal validation
No data leakage, no hindsight bias. The model is only ever evaluated on matches it hasn't seen. 140 days of genuine out-of-sample predictions.
Why trust an AI model?
Unlike human tipsters, a machine learning model has no emotional bias. It doesn't favour popular teams, chase losses, or get swayed by media narratives. It processes data the same way every time.
We publish our accuracy numbers openly — including leagues where the model underperforms. You can see exactly how well predictions have tracked reality over 140 days of backtesting on our model performance page.
That said, no model is perfect. Soccer is inherently unpredictable — injuries, red cards, weather, and referee decisions introduce variance that no algorithm can fully account for. We show confidence scores so you know when the model is uncertain.
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