● POKER MATH // MONTE CARLO ENGINE
Poker Variance & Downswing Simulator
Simulate 100,000+ hand poker sessions using Monte Carlo random walks to model downswing depths, confidence intervals, and probability of loss.
Simulation Parameters
INPUTS 5.0 bb/100
Expected long-term win rate: 1.0 to 12.0 bb/100
85 bb/100
6-Max NLHE: ~80-110 bb/100 | PLO: ~120-170 bb/100
50,000 Hands
Stochastic Distribution Metrics
OUTCOMESExpected Mean Profit
+2,500 BB
+25.0 Buy-ins
Probability of Loss
10.2%
chance of deficit
Avg Max Downswing
-740 BB
~7.4 Buy-ins
Worst-Case Downswing (95th %ile)
-1,480 BB
~14.8 Buy-ins
95% Confidence Interval
[-1,280 BB to +6,280 BB]
Analytical Diffusion Formula
σ_total = σ_hourly × √(Hands / 100) | P(Loss) = Φ(-μ·√N / σ) Multi-Session Sample Paths (Canvas 2D)
Visualizing 25 independent simulated trajectories over the selected sample horizon.
Mean EV Break-even (0 BB)