Even if you know your Expected Value (EV), variance, and bankroll, one important question remains:
"What will my actual betting results probably look like?"
Mathematics can calculate averages, but real betting never follows the average exactly.
This is where Monte Carlo simulation becomes extremely valuable.
A Monte Carlo simulation runs thousands of possible betting seasons using random outcomes that match your betting assumptions. Instead of producing a single answer, it generates an entire range of possible results, helping you understand both the opportunities and the risks of your strategy.
A Monte Carlo simulation is a statistical technique that repeatedly models random events based on known probabilities.
In sports betting, it simulates thousands—or even millions—of possible betting histories using your estimated edge.
Each simulation represents one possible future.
Some simulated seasons will perform exceptionally well, others will perform poorly, and most will fall somewhere in between.
Rather than asking, "What profit will I make?" Monte Carlo asks, "What profits are realistically possible?"
Every simulation starts with a few key inputs:
These values define the assumptions of your betting strategy.
The more realistic your assumptions are, the more useful your simulation becomes.
A simple Monte Carlo simulation follows these steps:
A typical simulation runs between 5,000 and 10,000 iterations, creating thousands of possible betting seasons.
After thousands of simulations, you no longer have one possible outcome—you have a complete probability distribution.
This allows you to answer practical questions such as:
These answers are far more useful than simply knowing your Expected Value.
Monte Carlo simulations often summarise results using percentiles.
These percentiles help set realistic expectations before real money is ever placed.
One of the most valuable outputs from a Monte Carlo simulation is the distribution of maximum drawdowns.
Each simulated betting season records the largest peak-to-trough decline experienced.
After thousands of simulations, you can estimate:
This information is invaluable for determining whether your bankroll is appropriately sized.
Monte Carlo simulation is most useful before you begin using a betting strategy.
If your simulation shows that a large percentage of outcomes result in bankruptcy, your stakes are probably too high.
Reducing stake size and rerunning the simulation allows you to find a more sustainable approach.
For example:
Simulation allows you to test your bankroll before risking real money.
You do not need specialist software to perform basic Monte Carlo simulations.
Common options include:
RAND() to generate random betting sequences.NumPy, which can simulate thousands of betting seasons with only a few lines of code.As your betting models become more advanced, simulations can include multiple bet types, changing stake sizes, and varying odds distributions.
Monte Carlo simulation is a powerful tool, but it is only as reliable as the assumptions you provide.
Most simulations assume:
Real betting rarely satisfies all of these assumptions.
In reality:
For this reason, many experienced bettors deliberately reduce their estimated edge by around 20–30% when running simulations to produce more conservative forecasts.
Monte Carlo simulation transforms theoretical betting mathematics into realistic expectations by modelling thousands of possible betting seasons. Instead of predicting a single outcome, it shows the full range of profits, losses, drawdowns, and bankroll paths that could occur. Used correctly, it helps bettors choose appropriate stake sizes, evaluate bankroll risk, and prepare psychologically for the inevitable effects of variance. While simulations cannot predict the future, they provide one of the most effective ways to understand the uncertainty that accompanies every positive Expected Value betting strategy.