Predicting football matches is one of the hardest problems in sports analytics. Three possible outcomes, dozens of variables, and a healthy dose of randomness make it a challenge that has humbled statisticians for decades. At CalibrSports, we built a machine learning pipeline that processes over 500 data points per match to generate probabilities across 12+ betting markets. Here is how it works.
The Data Foundation
Our models are trained on 10 years of historical match data across five major European leagues: the English Premier League, La Liga, Serie A, Bundesliga, and Ligue 1. For every match, we compute more than 500 features spanning several categories:
- Elo Ratings: A dynamic rating system that adjusts after every match, capturing each team's overall strength relative to the league. We track separate home and away Elo ratings to account for venue effects.
- Expected Goals (xG): Rolling averages of expected goals scored and conceded, giving a truer picture of attacking and defensive quality than raw scorelines.
- Team Form: Recent results over the last 5 and 10 matches, weighted by recency and opponent strength. A win against the league leader counts more than a win against the bottom side.
- Head-to-Head Records: Historical meetings between the two teams, including venue-specific results and goal patterns.
- Player Availability: Sidelined players tracked through comprehensive injury databases, including injury start and end dates. A team missing three starters is materially different from full strength.
- Standings and Position: League table position, points per game, goal difference, and promotion or relegation pressure indicators.
The Ensemble Model
We use a dual-model gradient-boosted ensemble rather than a single model. Both are gradient-boosted decision tree algorithms, but they handle categorical features and regularization differently. By averaging their outputs, we get predictions that are more robust and less prone to overfitting than either model alone.
The 1X2 model predicts the probability of Home win, Draw, and Away win. A separate Poisson regression model estimates expected goals for each team, which we then use to derive probabilities for Over/Under, Both Teams to Score, Asian Handicap, Correct Score, and other markets. In total, we cover 12+ betting markets per fixture.
Chronological Validation
Unlike many academic approaches that use random train/test splits, we enforce strict chronological ordering. The model only ever sees past data during training. This mirrors real-world conditions and prevents data leakage that would inflate accuracy metrics.
The AI Advisor
Raw model output is only half the story. Every bet our system considers is reviewed by an AI Advisor powered by a frontier large language model. This is a unique two-pass system:
- Pass 1 (Analysis): The AI performs mathematical analysis of edge tables, line comparisons, and catalyst flags. It does not make recommendations at this stage.
- Pass 2 (Decision): A second pass uses the analysis to confirm, adjust, veto, or propose new bets. It can search the web for breaking news and query our performance database for league-specific track records.
The result is a system that combines the consistency of machine learning with the contextual reasoning of a large language model. A model might flag value on a home win, but the AI Advisor can recognize that three key defenders were ruled out in the morning press conference and adjust the stake accordingly.
Kelly-Optimal Staking
Finding value is not enough. How much you bet matters just as much as what you bet on. We use a tiered fractional Kelly system that sizes bets proportionally to the edge detected. Higher-confidence bets receive larger allocations, while marginal edges get smaller stakes. This approach maximizes long-term bankroll growth while controlling drawdown risk.
Transparency
We publish every prediction and every result, wins and losses alike, on our performance page. There is no cherry-picking and no hiding behind selective reporting. If the model has a losing week, you will see it. That transparency is what separates a real prediction system from marketing.