Scope
This article documents a continuous improvement process. The objective is not to promise outcomes, but to make the prediction model more honest, stable and auditable. Football remains a high-variance sport: probability is never certainty.
The symptom: rising probabilities without proportional performance
An initial signal appeared: over a recent period, the model was publishing more high probabilities, while success rates did not increase accordingly. In other words, confidence was inflating without evidence that it was justified.
This type of drift is common: a probabilistic model can remain structurally sound, yet misrepresent uncertainty.
Key point
In football, it is normal to have many matches around 50–55%. Genuine 80% scenarios are rare unless external information is injected.
Diagnosis: when probabilities are over-processed
Foresportia probabilities originate from a probabilistic score grid, then pass through stability modules (home advantage, form, etc.). Risk emerges when transformations directly sharpen confidence.
- Probability temperature: useful for smoothing, risky when < 1
- Draw calibration: necessary but sensitive in football
- Renormalization: reducing draws mechanically inflates win probabilities
A higher number is not a better prediction. A useful model is one whose confidence matches statistical reality.
Corrections applied: stabilizing without artificial boosting
1) Temperature control: anti-sharpening safeguard
Temperature adjusts distribution shape: T>1 smooths, T<1 sharpens. In production, sharpening is disabled: T ≥ 1 is enforced.
2) Draw calibration: bounded league-level scaling
Draws depend heavily on league style and scoring patterns. Foresportia applies bounded, league-specific factors based on historical data, with strict renormalization.
3) Seasonal ramp-up: confidence grows only when data allows it
- progressive weighting of recent matches
- gradual integration of standings
- shrunk home advantage early in the season
- reduced Dixon–Coles correlation at season start
Monitoring: how the model detects and corrects drift
- LogLoss: strong penalty for overconfidence
- Brier Score: quadratic probability error
- Calibration curves: announced vs observed frequencies
- p_max distribution: detection of artificial inflation
- Temporal windows: rolling performance analysis
Philosophy: increasing probabilities is not a goal
Foresportia does not seek impressive numbers. The objective is clarity and honesty. If the model does not detect a strong signal, it must express uncertainty.
What this evolution changes in practice
- probabilities structurally aligned with football reality
- better absorption of league-specific behavior
- clearer emergence of genuinely strong signals
- greater long-term consistency
The result is a more readable, stable prediction model, aligned with the inherent uncertainty of football.