Monte Carlo simulation runs a sporting event thousands of times in a computer to estimate win probabilities and the range of possible outcomes, turning uncertainty into a distribution you can actually reason about.

The method is simple in concept: define the inputs, such as team scoring rates and key uncertainties, then simulate the event over and over with random variation and count how often each outcome occurs.

Its strength is handling complexity that simple ratings cannot, such as correlated events, tournament brackets, and futures markets where one result changes everything else.

Its weakness is that the output is only as honest as the inputs, so a simulation built on guessed parameters produces precise-looking numbers that are still just guesses.