Play a made-up game ten thousand times, count how often the favorite wins, and you have a probability. Monte Carlo sports betting is that simple at its core, which is exactly why it deserves suspicion. This lesson separates simulation noise, which shrinks as you add runs, from model error, which no number of runs can fix. You learn the standard error formula and why quadrupling the runs only halves the noise. You also see why games need shared factors like pace so outcomes hang together, and how to check the engine itself. Next time someone shows a probability to two decimals, you will ask how many runs and how sure they are of the inputs.
Learning objectives
You will learn what a Monte Carlo simulation does, why more runs only fix one kind of error, and how shared factors like pace change the spread of outcomes. You will be able to read a simulated probability with the right amount of suspicion.
