Framework
A football prediction model does not produce certainties: it produces probabilities. The objective is to quantify uncertainty and verify, through calibration, whether announced probabilities behave correctly over time.
What is a football prediction AI model?
A football prediction model is a statistical or AI-based system that converts match data into probabilities of outcomes (home win, draw, away win). It does not guess results; it answers a frequency question:
Over many comparable matches, how often does this scenario occur?
For a complete overview of methodology and limits, see the reference page: Football prediction AI.
What data does a football prediction model use?
- Historical results: goals, outcomes, trends.
- Recent dynamics: short-term form with temporal weighting.
- Home advantage: average pitch effect, varying by league.
- League context: variance, draw rates, statistical stability.
Some factors remain partially invisible (late injuries, cards, internal context). These increase uncertainty rather than providing perfect corrections.
How are probabilities produced?
- Strength estimation: attacking and defensive levels.
- Score modeling: goal distributions and simulations.
- Aggregation: conversion into outcome probabilities.
- Stabilization: calibration and safeguards against overconfidence.
A detailed methodology is available here: Scientific methodology.
Why a good model can still be wrong
Football has high structural variance. Few events, randomness, and match-specific incidents can reverse outcomes. This is why probabilities, not certainties, are the correct framing.
How to assess reliability
Reliability is evaluated historically through calibration. If a model announces 60%, we verify whether about 60% of similar cases were correct.
Past performance is transparently available here: Past results.