Monte Carlo Simulation
Definition
Monte Carlo simulation is a computational technique that uses random sampling to model the probability of different outcomes. In betting, it simulates thousands of possible bankroll trajectories based on your historical edge and variance to estimate risk of ruin, expected growth, and confidence intervals.
Example
OddsLab runs 10,000 simulations of your next 500 bets based on your historical 3.5% edge and average odds of 1.90. The results show a 95% probability of being profitable and a 2% risk of a 30%+ drawdown.
Related Terms
Variance
StatisticsVariance measures how widely your betting results spread around the expected value. High variance means large swings (common with longshot bets at high odds), while low variance means more predictable results. Understanding variance helps set realistic expectations and avoid tilt.
Standard Deviation
StatisticsStandard deviation is the square root of variance and quantifies the typical size of swings in your betting results. It is expressed in the same units as your returns (dollars or units), making it more intuitive than variance for assessing risk.
Sample Size
StatisticsSample size refers to the number of bets in your track record. In sports betting, small samples are dominated by variance and tell you very little about true skill. Statisticians generally recommend at least 500-1,000 bets at similar odds ranges before drawing conclusions about edge.
Risk of Ruin
StatisticsRisk of ruin is the probability that a bettor will lose their entire bankroll. It depends on edge, variance, and bet sizing. Aggressive staking (high % of bankroll per bet) dramatically increases risk of ruin even with a positive edge. Proper bankroll management keeps risk of ruin acceptably low.
Track your Monte Carlo Simulation with OddsLab
OddsLab automatically calculates and tracks key metrics for every bet you place — no spreadsheets required.
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