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This paper looks at how to best manage money in sports betting, focusing on the top five European football leagues from 2015 to 2025. The Kelly Criterion is the best way to grow money over time, but it can be risky and unstable, especially when dealing with related events. Many models ignore this issue. To solve this, we suggest a new method that uses a logistic regression model to find value and a Risk Parity strategy to balance risk in each bet. We tested this on over 17,403 matches and compared it to Flat Staking and Fractional Kelly strategies. The results showed that while Full Kelly had the highest peak, it was not stable and led to big losses ( \({>}\) 80%). The Optimal Fractional Kelly (15%) gave the best returns (+10.5% ROI) with smaller losses ( \({<}\) 52%). The Risk Parity method did not do well overall because of similar “favorite” bets, but it did better in low-risk markets like the Italian Serie A (+83% ROI). Overall, fixed fractional Kelly is a strong choice, while Risk Parity works well in certain low-risk leagues. This study offers a clear, data-based way to separate profit-making from risk management in sports betting.

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Risk Parity vs. Kelly Criterion: An Empirical Evaluation of Bankroll Allocation Strategies in Football Value Betting

  • 会议论文

  • 发表日期：

    2026年

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  • René Manassé Galekwa,
  • Jean Marie Tshimula,
  • Etienne Gael Tajeuna,
  • Selain K. Kasereka,
  • Kyandoghere Kyamakya

摘要

This paper looks at how to best manage money in sports betting, focusing on the top five European football leagues from 2015 to 2025. The Kelly Criterion is the best way to grow money over time, but it can be risky and unstable, especially when dealing with related events. Many models ignore this issue. To solve this, we suggest a new method that uses a logistic regression model to find value and a Risk Parity strategy to balance risk in each bet. We tested this on over 17,403 matches and compared it to Flat Staking and Fractional Kelly strategies. The results showed that while Full Kelly had the highest peak, it was not stable and led to big losses ( \({>}\) 80%). The Optimal Fractional Kelly (15%) gave the best returns (+10.5% ROI) with smaller losses ( \({<}\) 52%). The Risk Parity method did not do well overall because of similar “favorite” bets, but it did better in low-risk markets like the Italian Serie A (+83% ROI). Overall, fixed fractional Kelly is a strong choice, while Risk Parity works well in certain low-risk leagues. This study offers a clear, data-based way to separate profit-making from risk management in sports betting.