Definition
A Monte Carlo simulation runs thousands of random trials to estimate how often an outcome happens. In sports betting, it can turn player projections, team ratings, or scoring assumptions into an estimated win probability.
If a simulation runs 10,000 trials and a team wins 5,600 of them, the simulated win probability is:
5,600 / 10,000 = 56%
For a -110 bet risking $110 to win $100, expected value is:
EV = (win probability × $100) - (loss probability × $110)
Worked Example
A bettor simulates 10,000 NBA game outcomes for a team priced at -110. The team wins 5,600 trials.
Simulated win probability: 56%
Loss probability: 44%
Expected value on a $110 risk:
EV = (0.56 × $100) - (0.44 × $110)EV = $56 - $48.40EV = $7.60
The break-even rate at -110 is:
110 / (110 + 100) = 52.38%
The simulation’s 56% estimate is above the 52.38% break-even point.
Why It Matters
Monte Carlo simulation helps a bettor compare a projected probability against the sportsbook price. It is most useful when outcomes depend on many moving parts, such as pace, injuries, player minutes, and scoring variance.