Framework
Foresportia is an analysis support tool. Results are expressed as probabilities and must always be interpreted with context (lineups, injuries, stakes).
Why explain methodology in football prediction?
Foresportia aims to help analyze matches, not to assert certainties. The objective is to explain probabilities and make visible the factors that influence a match.
The approach relies on a hybrid model: a statistical engine (Poisson, simulations, calibration) and an AI engine (learning from historical data), evaluated separately and combined through a confidence index.
For a global overview, see the pillar page: Football prediction AI
What a football prediction AI does (and does not do)
What Foresportia does
- Transform signals (form, history, xG, context) into coherent probabilities.
- Compare multiple models to estimate uncertainty.
- Provide a pedagogical, verifiable and improvable analysis framework.
What Foresportia does not do
- Promise results or guarantee outcomes.
- Replace human judgment (lineups, late news).
- Offer profit-oriented advice: this is analysis, not promises.
Simplified pipeline: how a prediction is produced
- Collection / aggregation: results, form, home/away, xG, attack/defense indicators.
- Statistical engine: expected scores (Poisson), Monte Carlo simulations, league calibration.
- AI engine: historical learning, pattern extraction (matchups, streaks, context).
- Comparison: agreement vs disagreement between AI and statistics.
- Confidence index: interpretable synthesis weighted by uncertainty.
Statistical simulation: rigor and transparency
The statistical component builds on foundational work (Maher, 1982; Dixon & Coles, 1997) modeling score distributions via Poisson processes. Each match is simulated over 1,000 times to derive coherent probabilities.
Outputs are then calibrated by league based on historical performance, adapting probabilities to each competition’s variance and style.
The AI model: learning and feedback
In parallel, a neural network is trained on a dataset of more than 5,000 matches, integrating xG, shots, possession, streaks and head-to-head history to detect patterns.
The model is regularly updated to reduce bias and integrate new configurations.
Limits: why a prediction remains a probability
Even the best models face randomness: red cards, penalties, individual errors or weather effects.
A probability is not a promise. It is a plausibility estimate given available information, hence the importance of transparency and explicit uncertainty.
Conclusion: a verifiable and improvable analysis framework
Foresportia provides a scientific, transparent and evolving framework for football match analysis. The hybrid approach confronts AI and statistics to better read uncertainty.