Sports Game Simulator

A game simulator uses Monte Carlo methods to model thousands of potential outcomes. By sampling from statistical distributions calibrated to each team's historical performance, we generate a full distribution of possible final scores. The frequency of each outcome across 10,000 simulations gives us a robust probability estimate that accounts for the inherent randomness of sports.

High-scoring sports like NFL and NBA are modeled with a normal (Gaussian) distribution, where scores cluster around a mean with a known standard deviation. Low-scoring sports like soccer and NHL use the Poisson distribution, which models the probability of discrete goal counts. MLB uses a modified Poisson that accounts for the higher variance in run scoring. Each model is calibrated to the real-world scoring patterns of its sport.

Simulation probabilities translate directly to fair odds. If Team A wins 60% of simulations, the fair moneyline is -150. Compare this to the sportsbook line: if they offer -130, you have a +EV bet. The same logic applies to spreads (what percentage of sims does Team A cover -3.5?) and totals (what percentage of sims exceed 47.5 total points?). The edge is the gap between your simulated probability and the sportsbook's implied probability.

These simulations use average stats and assume independent scoring events. Real games involve momentum shifts, injuries, weather, coaching adjustments, and correlated scoring. Our model does not account for rest days, travel, or lineup changes. Use these results as one input among many in your handicapping process. The value is in understanding probability distributions, not in treating any single number as gospel. Always combine with qualitative analysis.