Bayesian Football Match Predictor

Problem

Football prediction is often reduced to gut feeling about “who is stronger,” but engineers need an inspectable probabilistic model: how form, injuries, home advantage, attacking and defensive data, head-to-head history, and market odds combine into win-draw-loss probabilities and whether those estimates differ from market prices.

How It Works

The skill organizes analysis with Bayesian posterior probability. It starts from a prior based on historical win rates, then applies correction factors such as recent form, home/away context, injury impact, H2H, attack/defense data, and league position to produce probabilities and a most likely scoreline. It also supports comparing bookmaker odds by converting odds into implied probabilities, checking value coefficients, and tracking odds trends to see whether an outcome is underpriced. In practice, this is useful for turning scattered match inputs into repeatable probability tables, json, or markdown reports that can be reviewed, backtested, or embedded in an analysis workflow.

Boundaries

This is an assistive estimator, not a certainty. Injuries, weather, referee decisions, squad motivation, and individual surges can all change outcomes. If data sources are incomplete or odds look abnormal, confidence should be discounted. Treat the output as a probability estimate, not betting advice.