Open-Source Hong Kong Horse Racing ML Pipeline — Feedback Welcome [P](reddit.com)

🏇 Open-Source Hong Kong Horse Racing ML Pipeline — Feedback Welcome Hi everyone, I've been working on an open-source horse racing prediction project focused on Hong Kong Jockey Club (HKJC) data. 📦 Repo: catowabisabi/horse-racing-model-training 🌐 Live Dashboard: catowabisabi.github.io/horse-racing-model-training 🎯 Goal The goal is not to claim "AI can beat horse racing", but to build a reproducible ML pipeline and test whether there is any measurable edge after controlling for leakage. 📦 What's Included LightGBM and XGBoost training pipeline Feature engineering from HKJC historical race data With-odds and no-odds model comparison Ensemble predictions Kelly Criterion simulation Quinella, QPL, Tierce, Quartet betting simulations Out-of-sample validation HTML report dashboard Unit tests for betting math, DB schema, and odds merge logic 📊 Headline Result The interesting finding: the no-odds model outperformed the with-odds model for quinella ROI. My interpretation is that public odds already price favourites quite efficiently, while the fundamental model may still catch some mispriced combinations. 🙋 Feedback I'm Looking For Does the validation setup look clean? Better ways to avoid leakage? Are the betting simulation assumptions reasonable? Ideas for improving feature engineering? Would a ranking / listwise model make more sense than independent horse-level classification? If you find the project useful or interesting, a ⭐ GitHub star would really help me keep building it. Thanks! submitted by /u/Marshallmatta [link] [Kommentare]