Monte Carlo simulation

A computational method for estimating the distribution of outcomes when the underlying math is too complex to solve analytically. You run N independent random trials of the same process and treat the empirical distribution as an estimate of the true distribution.

CasiMath uses Monte Carlo to estimate bonus EV: for each bonus, we simulate 1,000-3,000 random walks through the wagering requirement, drawing per-spin returns from a normal distribution shaped by the game’s RTP and volatility. The mean of the resulting cashouts is our EV estimate; the percentiles give us the risk profile.

More runs = tighter estimate. 1,500 runs is enough for the point EV; 3,000+ for stable p5/p95 tails.

The Portfolio Builder exposes the run count so you can dial accuracy vs speed. The tails also feed the risk-of-ruin estimate we report next to EV.