Predicting the Winner: AI Machine Learning Models Take on Horse Racing's G1 Championships
product#machine learning📝 Blog|Analyzed: Apr 12, 2026 07:31•
Published: Apr 12, 2026 07:17
•1 min read
•Qiita AIAnalysis
This exciting project showcases the fascinating intersection of sports analytics and machine learning by applying custom LightGBM models to predict outcomes for Japan's top G1 horse races. The developer has thoughtfully engineered two distinct models—one using regression to predict speed indices and another using classification to forecast top-three finishes. It is thrilling to see data-driven methodologies and rigorous feature engineering bringing a highly analytical edge to traditional horse racing.
Key Takeaways
- •The system utilizes two LightGBM models: a regression model for predicting speed indices and a classification model for predicting top-three finishes.
- •Feature engineering includes detailed compatibility scores based on jockeys, venues, and weather, along with data from the horses' last three races.
- •The year-long validation experiment focuses specifically on purchasing win tickets for G1 races to track the models' return on investment.
Reference / Citation
View Original"I would like to apply my自制 horse racing prediction model to the G1 races over the next year and verify its performance."
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