The MatchSight Prediction Model: Mathematical Architecture
An open explanation of the mathematical foundations powering MatchSight football forecasts. Explore how raw fixture feeds convert into probability distributions.
Poisson Goal Distribution Modeling
Football scores are discrete count variables with low average occurrences. Standard normal distributions fail because goals cannot be negative. MatchSight utilizes Poisson distribution functions to model expected home and away goal occurrences based on empirical scoring rates.
Rolling Historical Lookback Window
To capture current team strength while maintaining statistical stability, MatchSight evaluates completed fixtures within a defined rolling 180-day window. Fixtures require strict sample thresholds—at least 20 competition matches and 5 venue matches per club—before forecasts are generated.
Venue-Specific Baseline Normalization
Home advantage is an established factor in football forecasting. Rather than applying an arbitrary fixed bonus, the engine evaluates home matches played at home and away matches played away, benchmarking each club's scoring and concession rates against competition-wide venue averages.