At Betmance, when we give a team a 64% chance to win, that number isn't a gut feeling—it's the result of running the match thousands of times. The technology behind this? Monte Carlo simulations. This post breaks down this powerful statistical method and how it creates the reliable probabilities you see on our platform.

What is a Monte Carlo Simulation?

In simple terms, a Monte Carlo simulation is a way to model the probability of different outcomes in a process that has inherent randomness. It's like rolling a dice thousands of times to see how often each number comes up, but for much more complex scenarios—like a 90-minute soccer match.

The name comes from the famous Monte Carlo casino, as the method relies on random sampling, similar to the randomness of games of chance.

How We Simulate a Single Match

We don't just simulate the final score. We model the core events that lead to it. For a single "run" of a match simulation, our engine:

1. Generates Chance Events: Based on team ratings, it creates potential scoring opportunities (shots).
2. Assigns Outcome Probability: Each shot is assigned a probability of becoming a goal, using metrics like Expected Goals (xG) and the defensive strength of the opponent.
3. Uses Random Sampling: For each shot, the engine effectively "rolls the dice" to determine if it becomes a goal, based on its probability.
4. Tallies the Score: After simulating the flow of the match, it records the final score (e.g., 2-1, 0-0, 3-0).

Running Thousands of Simulations for a Clear Picture

One simulation is just one possible story of how the match could play out. It could be a fluke. The power of Monte Carlo comes from repetition.

We run this process not 100, but 10,000 times or more for a single fixture. After all these runs, we count the results:

• If Team A wins 6,200 times out of 10,000, they have a 62% win probability.
• If 2,500 simulations end in a draw, that's a 25% draw probability.
• If Team B wins 1,300 times, that's a 13% win probability.

This gives us a robust, probabilistic forecast that accounts for all the chaotic, random events that can happen in a game.

Why This Method is a Game-Changer for Sports Prediction

• Quantify Uncertainty: They don't just give one answer; they show a range of likely outcomes and their probabilities.
• Model Complex Interactions: They can incorporate countless variables—from team strength and home advantage to player injuries and weather—and see how they interact.
• Are Inherently Realistic: Soccer is a game of chance. A dominant team can lose to a single counter-attack. Monte Carlo simulations naturally capture this reality, rather than assuming the "better" team always wins.

The Betmance Advantage

While the concept is standard in quantitative fields, the quality of the simulation depends entirely on the quality of the input data. This is where Betmance stands out. Our custom metrics like Adjusted Possession Impact and Weighted Shot Quality ensure that the "rules" of our simulation are far more nuanced and accurate than those using only basic public data.