Predicting Outcomes of Horse Racing using Machine Learning

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Published: Oct 27, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i9.8119
Keywords:
Machine learning, imbalanced data, SMOTE, prediction, classification model, sports betting, horse racing

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Meenakshi Gupta
School of Engineering & Technology, Sushant University, Gurgaon - 122018, Haryana
Latika Singh
School of Engineering & Technology, Sushant University, Gurgaon - 122018, Haryana

Abstract

Machine learning with its vast framework is making its way into every aspect of modern society. The segment of betting sports particularly horse racing calls for the attention from a large spectrum of research community owing to its value to the stakeholders and the amount of money involved. Horse racing prediction is a complex problem as there are a large number of influencing variables. The present study aims to contribute in this domain by training machine learning algorithms for predicting horse racing results or outcomes. For this, data for a whole racing season from 2017 to 2019 of races conducted by Turf Club of India was considered which amounts to over 14,700 races.  Six algorithms namely Logistic Regression, Random Forest, Naive Bayes, and k-Nearest Neighbors) k-NN were used to predict the winning horse for each race. Synthetic Minority Oversampling Technique (SMOTE) technique was applied to the imbalanced horse racing data set and the attributes of the horse race repository were analyzed. The results were compared with other sampling methods to evaluate the relative effectiveness of this method. The proposed framework is able to give an accuracy of 97.6% which is substantially higher when compared to other similar studies. The research can be beneficial to the stakeholders as well as researchers in the same area to do further analysis and experiments.

Article Details

How to Cite
Gupta, M. ., & Singh, L. . (2023). Predicting Outcomes of Horse Racing using Machine Learning. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9), 38–47. https://doi.org/10.17762/ijritcc.v11i9.8119
Issue
Vol. 11 No. 9 (2023)
Section
Articles

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