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Analysis of the Accuracy of Reported Track Conditions Utilizing Predictive Modeling
- 1 University of North Carolina at Chapel Hill, School of Art and Science, Chapel Hill 27514, US
- 2 The Wheatley School, Old Westbury 11568, US
- 3 New York University, College of Art and Science, NY 10012, US
* Author to whom correspondence should be addressed.
Abstract
Horse racing is a globally viewed sport in which the performance of horses may be influenced by numerous factors, with the condition and moisture in the racing surface, known as the "going," being one of the most influential. Accurate reporting of the going is essential for ensuring fair competition. This study aims to measure the accuracy of reported track conditions by utilizing a dataset from three racecourses—Catterick, Chester, and Newmarket. We first identified discrepancies suspected to be caused by rounding in reported distances in the data and corrected them by reverting them to officially sanctioned distances. We started with linear regression models to predict winning times, using key variables such as race distance, class of the race, and the reported going. Then we applied log transformation to the data to solve heteroscedasticity. The final model will be used to generate prediction intervals for winning times under each going conditions, allowing us to figure out which goings might be reasonable for a specific race. The results indicate that approximately 6-7% of races were outside of the calculated bounds, which may lead to errors in strategic decisions by trainers and bettors. By calculating the posterior probabilities of each going condition using Bayesian inference, we created a list of reasonable goings for each new race, giving trainers and bettors more accurate information so that they can better prepare for future races in the day.
Keywords
track conditions, linear regression, horse racing, actual going prediction
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Cite this article
Li,M.;Yang,D.;Zhao,T. (2025). Analysis of the Accuracy of Reported Track Conditions Utilizing Predictive Modeling. Applied and Computational Engineering,131,18-33.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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