Match Simulation and Expected Scorelines: How AI Generates Score Predictions

Generating accurate scoreline probability distributions is one of the most technically demanding aspects of football prediction.

Bivariate Poisson Model

The bivariate approach captures the correlation between the two teams' goal counts (in matches where one team scores many goals, the other often scores fewer), producing more realistic score distributions than independent Poisson models.

Score Probability Matrix

Each cell contains the estimated probability of that specific scoreline. This matrix is the foundation for all derivative predictions: 1X2 probabilities are calculated by summing the appropriate cells, Over/Under probabilities by summing scorelines above or below the threshold, and BTTS by summing cells where both teams score at least once.

Fat-Tail Adjustments

Standard Poisson models underestimate the probability of extreme scorelines (5-0, 6-1, etc.) because real football exhibits more variance than the Poisson distribution assumes.