## What Monte Carlo Simulation Does
Monte Carlo simulation uses random sampling to model the distribution of possible outcomes from a repeated process. For betting: it simulates thousands of possible betting records given your edge and variance, producing a distribution of possible results.
## Why Run Monte Carlo Simulations?
Before experiencing a 20-unit losing run, you should know:
- What is the probability of a 20-unit losing run in my strategy?
- How long could a losing run plausibly last?
- What is my expected maximum drawdown over 1,000 bets?
Monte Carlo answers these questions from first principles — before the events occur.
## A Simple Monte Carlo Setup
**Inputs:** Edge (ROI), bet frequency, odds distribution, number of simulations
**Process:**
For each simulation (1,000 iterations):
For each bet in the simulation (1,000 bets):
Generate a random draw (win/lose based on probability)
Calculate profit/loss
Update running total
Record: final profit, maximum drawdown, longest losing run
**Output:** Distribution of final profits, drawdown distribution, losing run distribution
## Interpreting the Results
After 10,000 simulations of 1,000 bets at 3% ROI:
- 5th percentile final profit: −8 units (unlucky scenario)
- 50th percentile: +30 units (median scenario)
- 95th percentile: +68 units (lucky scenario)
- 95th percentile maximum drawdown: 32 units
This tells you: in the worst 5% of scenarios, you will still be profitable at 1,000 bets (−8 units is a loss, but within risk limits). And your stop-loss should be set at 30+ units to avoid triggering it in normal variance scenarios.
## Tools for Monte Carlo
- Excel: RAND() function with IF statements for win/loss simulation
- Python: NumPy random module with a simple loop
- R: runif() and custom function
- Online tools: several free betting variance simulators (e.g. Luck vs Skill by Pyckio)
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