Monte Carlo Simulation for Фудбал: Predicting Season Outcomes

Monte Carlo simulation is a computational technique that generates match and season predictions by running thousands of randomized scenarios. Rather than predicting a single outcome, Monte Carlo methods produce probability distributions that capture the full range of possible results — making them ideal for football prediction where uncertainty is inherent and single-point predictions are inherently limited.

How Monte Carlo Simulation Works

The simulation then "plays" each match thousands of times, randomly sampling from these distributions to generate a result for each iteration.

Season-Long Projections

Monte Carlo simulation is particularly powerful for season-long projections. These projections are dynamically updated after each matchday, incorporating new results and adjusting the remaining simulations accordingly.

Capturing Tail Risk

One of Monte Carlo simulation's greatest strengths is its ability to capture unlikely but important scenarios. While a deterministic model might predict that a team finishes 8th, a Monte Carlo simulation reveals the full distribution: perhaps there's a 2% chance of finishing 4th (European qualification) and a 3% chance of finishing 18th (relegation). These tail probabilities are valuable for understanding the range of possible outcomes beyond the most likely result.

Limitations and Calibration

Monte Carlo simulation is only as accurate as the underlying probability distributions it samples from.