The Kelly Criterion

The formula that maximises long-term bankroll growth rate, derived from information theory and adopted by professional gamblers and investors alike.

The Formula

f* = (bp − q) ÷ b

f* = fraction of bankroll to stake
b = decimal odds − 1 (the net odds)
p = your estimated win probability
q = 1 − p (probability of losing)

Worked Example

Decimal odds of 2.50 (so b = 1.50). You estimate true win probability at 45% (p = 0.45, q = 0.55).

f* = (1.50 × 0.45 − 0.55) ÷ 1.50
f* = (0.675 − 0.55) ÷ 1.50
f* = 0.125 ÷ 1.50 = 8.3% of bankroll

With a $1,000 bankroll, Kelly suggests staking $83 on this single bet.

Why Kelly Works

Kelly staking maximises the expected logarithmic growth rate of your bankroll over the long run — mathematically the optimal balance between growing your bankroll aggressively and avoiding ruin. Staking more than Kelly increases variance and long-term ruin risk without improving the growth rate; staking less simply grows your bankroll more slowly than optimal.

The Critical Weakness: Estimate Sensitivity

Kelly is only as good as your probability estimate p. Overestimate your edge even slightly, and full Kelly staking becomes dangerously aggressive. This is the single biggest practical problem with the formula — most bettors' probability estimates carry real uncertainty.

Fractional Kelly: The Practical Solution

Most professional bettors use a fraction of full Kelly — commonly half-Kelly (50%) or quarter-Kelly (25%) — to dramatically reduce variance and protect against estimation error, at the cost of somewhat slower theoretical growth.

Kelly FractionStake (from example)Risk Level
Full Kelly (100%)8.3%High variance, max theoretical growth
Half Kelly (50%)4.15%Moderate variance, ~75% of growth rate
Quarter Kelly (25%)2.08%Low variance, safer for uncertain edges

When Not to Use Kelly

If you can't confidently estimate your win probability — which is most casual bettors, most of the time — Kelly will likely do more harm than good. In that case, simple fixed or proportional staking is more robust and forgiving of estimation error.

SOURCES & FURTHER READING
  • Kelly, J.L. (1956). "A New Interpretation of Information Rate." Bell System Technical Journal, 35(4), 917–926. [PDF]
  • Pinnacle — The Kelly Criterion Explained
  • Thorp, E.O. (1997). "The Kelly Criterion in Blackjack, Sports Betting and the Stock Market." 10th International Conference on Gambling and Risk Taking.
Written by James Hartley, Betting Strategy Editor · Last updated: July 2, 2026
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