Analysis
This article dives into the crucial aspect of feature engineering in horse racing AI, emphasizing the pitfalls of directly using raw data. It highlights how transforming data like finishing positions, race times, and jockey/stable codes leads to more accurate and profitable predictions, showcasing a practical approach to boosting AI performance in this domain.
Key Takeaways
Reference / Citation
View Original"However, in horse racing data, if you 'put raw data as is', even if the apparent AUC increases, ROI may decrease."