How to use the kelly criterion bet size calculator
Find the fraction of your bankroll to bet to maximise long-run growth, from the win probability and payout odds. Enter probability of winning, net odds received per unit bet in the fields above; the kelly fraction of bankroll, kelly fraction as decimal are computed instantly and plotted on the chart so you can see how the answer changes.
Common searches this calculator answers include “Kelly criterion”, “optimal bet size”, “how much to bet”, “calculate kelly criterion bet size”, “find kelly fraction of bankroll”.
Formula
Variables
- p — Probability of winning
- b — Net odds received per unit bet
Worked example
With probability of winning = 0.55, net odds received per unit bet = 1, the calculator gives kelly fraction of bankroll ≈ 10 %; kelly fraction as decimal ≈ 0.1 fraction.
Frequently asked questions
What is the kelly criterion bet size calculator used for?
Find the fraction of your bankroll to bet to maximise long-run growth, from the win probability and payout odds. It helps with questions such as “Kelly criterion”, “optimal bet size”, “how much to bet”.
What is the formula for the kelly criterion bet size calculation?
Kelly fraction of bankroll (%) = 100 × (p × (b+1)-1) / b. Kelly fraction as decimal (fraction) = [Kelly fraction of bankroll] / 100. Here p is probability of winning, b is net odds received per unit bet.
What inputs do I need for the kelly criterion bet size calculator?
You need probability of winning, net odds received per unit bet. Each field is pre-filled with a typical example so you can see how the result responds as you edit the numbers.
How do I use the kelly criterion bet size calculator?
Type values into the input fields. Results and the chart update instantly. Use Reset to restore the example values, Copy results to paste them elsewhere, or Share to copy a link that preserves your inputs.
Is the kelly criterion bet size result exact?
Calculations use standard formulas and double-precision arithmetic, then display about seven significant figures. Real-world measurements carry uncertainty, so treat results as estimates unless your inputs are exact.