GoodRun

A BETTER READ ON YOUR GAME

How many buy-ins do I need?

Turn a win rate, variance estimate and risk target into a bankroll.

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Your game, in numbers

ASSUMED · Live NLHE cash

Variance starting points & conversion
30 is an assumption. Edit it for your table; 27–30 is a starting estimate, not a measurement.

ASSUMED · Live NLHE: 140 BB/100. A modeling starting point, not a measured live population value.

Read this starting point’s source →

The default is ASSUMED for live NLHE, not measured from your play.
Maximum lifetime chance of hitting zero under this model.

Use your net win rate after rake and costs. Nothing is saved or sent.

Defaults are examples, not a bankroll rule. Tournament expected profit uses buy-ins: 20% ROI is 0.20 per entry. Tournament variance can be much larger than the example.

A separate growth-based comparison

Compare live cash stakes with quarter, half and full Kelly →. That tool models proportional rebalancing with its own assumptions; this bankroll model keeps a fixed stake and a lifetime ruin target.

What this estimate assumes

B = −σ² × ln(r) / (2 × μ)

B is bankroll, μ is expected net profit per block, σ is the standard deviation of one block, and r is lifetime ruin probability. Win rate and standard deviation must use the same block: an hour, 100 hands, or one tournament.

This is a Brownian-motion approximation: fixed stakes, independent results, a constant edge and variance, no withdrawals or top-ups, and play continuing indefinitely. “Ruin” means hitting zero. Actual poker has discrete bets and, especially in tournaments, large jumps; you can be unable to afford a buy-in before reaching zero.

It does not account for a changing game, uncertain win rates, living costs or moving down in stakes. A positive win rate must be an estimate of your true edge, not just a recent upswing. With a zero or negative edge, any finite bankroll eventually runs out in this model.

Source: Chin & Ingenoso, Risk Adjustment for Poker Players, §1 (diffusion model). The bankroll tool algebraically inverts that formula, then rounds up to whole buy-ins.

Starting points, with their evidence

There is no universal “normal game” preset. Stack depth, table size, rake, player style and straddles change variance. All values below describe the SD of a 100-hand block, in big blinds. A source’s measured value is not a measurement of your play.

Live NLHE cash
ASSUMED: 140; published benchmark range 120–160 BB/100. Not a measured population estimate.
Live PLO cash
ASSUMED: 175; published benchmark range 150–200+ BB/100. Deeper play can exceed it.
Online NLHE 6-max
ASSUMED: 90; published benchmark range 75–120 BB/100. These three benchmark ranges come from PrimeDope’s variance guide, not an independently audited sample.
Online GG NL50 6-max
MEASURED BY SOURCE: 78, with a middle-half range of 71–85 BB/100. Pokalab’s own reported data: regulars with 20,000+ hands, 4–6 players, no ante, after rake. GoodRun has not independently audited those hand records. Do not generalize it to live poker or every online pool.
Online PLO 6-max
ASSUMED: 140; 120–160 BB/100 is the modeled range used in SolvePoker’s simulations. Simulated inputs are not measured player SDs.

Single-player live estimates are not presets: one published estimate was revised after regrouping sessions. Neither establishes a population benchmark. Unequal-length session rates need proper normalization; taking their ordinary SD is not a common 100-hand SD estimate.

Sources checked 31 October 2026. Editing a preset changes its label to custom. Personal measurements are preferable; these inputs do not compute SD from raw hand histories.

Live hours ↔ online hands

win rate/hour = win rate/100 × hands per hour / 100

SD/hour = SD/100 × √(hands per hour / 100)

SD scales with the square root of volume; win rate scales directly. At 30 hands/hour, an assumed 140 BB/100 corresponds to about 76.68 BB for a one-hour block. Changing hands/hour estimates volume, not the table’s playing style.

Track this automatically with GoodRun.

Keep sessions, stakes and expenses together, so your next estimate starts with your own history.

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