How a Value Betting Engine Uses Kelly Criterion and ML to Beat Bookmakers

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A developer built a value betting engine that identifies mispriced odds by comparing bookmaker quotes against probability estimates generated by an ensemble ML model combining LightGBM and XGBoost trained on StatsBomb data. The system queries odds from over 350 bookmakers via OddsPapi and flags only bets with a positive expected value, meaning the market is paying more than the actual risk warrants. To determine stake sizes, the engine applies a fractional Kelly Criterion, a conservative variant of the formula that maximizes long-term geometric capital growth while reducing sensitivity to probability estimation errors. The strategy was backtested on data from 2022 to 2024, simulating bankroll evolution across losing streaks to verify its resilience even when expected value is positive. The project's core takeaway mirrors trading desk logic: disciplined position sizing matters as much as opportunity detection, and a modest model with sound risk management outperforms a strong model with poor staking.

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

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