Risk of Ruin Simulator

Monte Carlo risk-of-ruin simulator — runs 1,000 simulated trips and shows the full distribution of outcomes.

The bankroll calculator tells you your theoretical risk of ruin using a closed-form formula. That’s useful, but it’s one number. This page runs 1,000 simulated trips through your setup and shows you the full distribution of outcomes — so you can see not just how often you’d go broke, but how wide the gap is between a good run and a bad one.

Risk of Ruin Simulator

Runs 1,000 simulated trips through your setup. Shows the distribution of outcomes, not just the expected value.

Your Setup

Negative values simulate playing without an edge
Representative of a season / trip series, not one night

Simulation Results

Ruined (lost entire bankroll)
Ended in profit
Ended with a loss (not ruined)
Median final bankroll
Best outcome (99th percentile)
Worst outcome (1st percentile)
Ready to simulate. Click "Run 1,000 Simulations" to see how your setup performs across a thousand alternate realities.
How this works

Each simulation plays the specified number of hours hand-by-hand. Each hand's outcome is drawn from a normal distribution with mean equal to your edge × bet size and standard deviation equal to bet size × √1.32. That's the standard simplification for blackjack variance modeling — real blackjack has a fatter-tailed distribution (splits, doubles, blackjacks), but for risk-of-ruin purposes the normal approximation is within a few percent of reality over the timescales that matter.

A simulation is marked "ruined" if bankroll drops below zero at any point during the run — we stop that run and count it. Runs that survive to the end are recorded with their final bankroll. The histogram shows the full distribution; the stats summarize it.

1,000 simulations is enough to estimate your actual risk of ruin to within ~3 percentage points. If you want tighter precision, you'd need 10,000+ simulations, which gets slow in a browser.

Why the distribution matters

“Expected value” is an average. If you play with a 1% edge, the average outcome is slow, steady profit. But you don’t live in the average — you live in one specific run. And one specific run can look wildly different from the average, especially over short time frames.

The histogram above shows what “variance” actually looks like. The vertical dashed gold line is your starting bankroll. Every bar is a bucket of simulations that ended with a final bankroll in that range. The red bar on the far left is the simulations that went broke. Run the simulation with a 1% edge over 100 hours and you’ll see: most of the mass is to the right of where you started (you’re profitable on average), but there’s a non-trivial chunk to the left — runs where you lost money despite having an edge. That chunk is variance, and it’s the reason bankroll discipline matters.

What to look for when you run it

The ruined count. This is your risk of ruin. Most serious counters target under 5%. If yours is 20%, you’re under-bankrolled for your bet size — either grow the roll or cut the bet.

The spread between 1st and 99th percentile. This is the range of outcomes you should be emotionally prepared for. If median is +$2,000 but 1st percentile is −$3,000, that’s a real thing that happens to 1 in 100 counters with your setup. If you’d be shaken by a $3,000 loss, bet less.

The shape of the distribution. A well-bankrolled counter with a real edge should see a distribution that’s mostly to the right of the starting line with a long but thin left tail. If your distribution is wide and centered near the start line, variance is overwhelming your edge — you need more hours, not just different bets.

Things the simulator doesn’t model

A few honest caveats. The simulator uses a normal distribution for per-hand outcomes. Real blackjack is close to normal but not exactly — the long tail is fatter because of things like splits, doubles, and blackjacks. For risk-of-ruin purposes over many hours, the normal approximation is fine. For predicting the precise shape of short-run variance, it’s optimistic.

The simulator also assumes your bet size is constant and your edge is constant. Real counters bet differently based on the count (that’s the whole point) and the edge the counter actually realizes depends on penetration, counting accuracy, and how aggressively you spread. Set the “edge” input to what you honestly think you’ll realize, not what’s theoretically possible with perfect play.

Finally, the simulator stops a run when bankroll hits zero. It doesn’t model the “I’ll rebuy” case. If you’d actually reload your bankroll after a loss, you’re not playing the game this simulator is simulating — you’re playing a different, larger game with a larger total bankroll. Input that larger number instead.

For the closed-form theoretical version of these numbers, see our bankroll calculator. For how to achieve the edge you’re inputting, start with the trainer.