Machine learning modeling for environmental factors affecting horse racing

Abstract

This study examines the impact of environmental factors on horse racing performance using machine learning techniques, offering insights into how climate and track conditions affect race outcomes. Horse racing is significantly influenced by external conditions, with variables such as temperature, humidity, track surfaces, and wind patterns playing crucial roles. By analyzing historical race data, this research helps trainers, bettors, and race organizers understand these factors. Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Random Forest, and AdaBoost were employed to model race performance, with SVM achieving the highest accuracy. Unlike other sports where athletes control their environment, racehorses must adapt to external conditions. Traditional statistical methods often fail to capture the complex relationships between these factors. Machine learning, however, can identify nonlinear patterns in data and provide a more dynamic approach to analyzing race performance. The study finds that temperature, humidity, wind, and track conditions are key influences. Moderate temperatures (10-21°C) are ideal for optimal performance, while extreme heat causes fatigue and cold leads to stiffness. Higher humidity adds stress, and wind patterns can either hinder or assist a horse’s speed. Track surfaces, including dirt, turf, and synthetic, also affect a horse's grip and stability, with wet conditions slowing horses down. The study's findings contribute to a data-driven approach in horse racing, allowing trainers to adjust strategies based on evidence. Ultimately, this research demonstrates how machine learning can revolutionize horse racing, offering more precise predictions, improved strategies, and a focus on equine welfare in response to environmental challenges.