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Kelly Staking Calculator

Use your estimated edge to size stakes responsibly. The Kelly criterion suggests a bankroll fraction that maximizes growth while managing risk.

  • Calculate a stake size from your bankroll.
  • Adjust for your estimated win probability.
  • Compare full Kelly to more conservative staking.

Tools are informational only, not betting advice, and do not guarantee profit.

This calculator is an educational tool for understanding bankroll-based stake sizing using the Kelly criterion. It does not provide betting advice, tips, or predictions.

Kelly Staking Calculator

Size stakes using the Kelly criterion based on your estimated edge.

Bankroll management

Classic Kelly formula: f* = (b·p − q) / b where b = odds − 1, p is your win probability, and q = 1 − p. Many traders prefer half-Kelly or less to reduce volatility.

Kelly staking links your edge to your stake size. When you believe you have a strong edge it suggests staking more; when your edge is small it recommends reducing stakes. Many traders use half-Kelly or smaller fractions to control drawdowns and avoid aggressive swings in bankroll.

How the calculation works

Core formula: f* = (b·p − q) / b where b = odds − 1, q = 1 − p

Worked example: Bankroll £1,000, odds 2.5, win chance 45%.

  • b = 1.5, p = 0.45, q = 0.55.
  • f* = (1.5 × 0.45 − 0.55) ÷ 1.5 = 0.0833.
  • Stake = 0.0833 × £1,000 = £83.33 (full Kelly).

Always gamble responsibly. This calculator is for information only and does not guarantee profit.

Is Kelly staking aggressive?

Full Kelly can be aggressive. Many traders choose half-Kelly or lower to manage drawdowns.

What if my estimated probability is wrong?

Overestimating your edge can lead to over-staking. Use conservative inputs if unsure.

Can I use Kelly for exchange trading?

Yes. Enter your odds, bankroll and probability to estimate a stake size for exchange bets.

When to use a Kelly staking calculator

The Kelly criterion is a mathematical formula developed by John L. Kelly Jr. in 1956 for optimising the growth rate of a bankroll over repeated wagers. It tells you what fraction of your bankroll to stake based on two inputs: the odds available and your estimated probability of winning. Unlike flat staking, Kelly adjusts your bet size dynamically — larger when the edge is wide and smaller when it is thin.

This calculator is most useful when you have a genuine, quantifiable edge. If you believe a selection has a 50% chance of winning and the exchange offers 2.5 (implying only 40%), the Kelly formula will recommend an optimal stake that maximises long-run growth while managing risk.

Detailed worked example

Your bankroll is £2,000. You estimate a horse has a 30% chance of winning at decimal odds of 4.5 on Betfair.

  • b (net odds): 4.5 − 1 = 3.5
  • p (win probability): 0.30
  • q (loss probability): 1 − 0.30 = 0.70
  • Full Kelly: f* = (3.5 × 0.30 − 0.70) / 3.5 = (1.05 − 0.70) / 3.5 = 0.10 (10%)
  • Full Kelly stake: £2,000 × 0.10 = £200
  • Half Kelly stake: £2,000 × 0.05 = £100
  • Quarter Kelly stake: £2,000 × 0.025 = £50

Most experienced traders use half or quarter Kelly because the full formula assumes your probability estimate is perfectly accurate, which is rarely the case. Reducing the fraction protects against estimation errors while still capturing most of the long-run growth benefit.

Common mistakes with Kelly staking

  • Overconfident probability estimates: Kelly magnifies errors. If your true edge is half of what you think, full Kelly will significantly overbet and increase drawdown risk.
  • Using full Kelly on every bet: Full Kelly produces maximum theoretical growth but also maximum volatility. Half or quarter Kelly gives a much smoother equity curve with only a small reduction in long-run growth.
  • Ignoring commission: Exchange commission reduces your net odds. Always use odds after commission in the Kelly formula, otherwise you will overstake.
  • Applying Kelly without a genuine edge: If your probability estimates are no better than the market's, Kelly will recommend zero stake. The formula only works when you have superior information.

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