Article Title

An Investigation of SVM Regression to Predict Longshot Greyhound Races

Authors

Robert P. Schumaker, Hagan School of Business, Information Systems Dept. Iona College
James W. Johnson, Director of Information Technology, College of Education Indiana State University

Abstract

In this paper we investigate the role of machine learning within the domain of Greyhound Racing. We test a Support Vector Regression (SVR) algorithm on 1,953 races across 31 different dog tracks and explore the role of a simple betting engine on a wide range of wager types. From this we triangulated our results on three dimensions of evaluation: accuracy, payout and betting efficiency. We found that accuracy and payouts were inversely linked, where our system could correctly predict Wins 45.35% of the time with a betting efficiency of 87.4% (return per bet) for high accuracy low payout, or predict Superfecta Box wagers with 6.45% accuracy and a 2,195.5% return per bet, corresponding to low accuracy high payout. This implied that AZGreyhound was able to correctly identify longshot dogs and we investigate the reasons why as well as the system’s performance.

Recommended Citation

Schumaker, Robert P. and Johnson, James W. (2008) "An Investigation of SVM Regression to Predict Longshot Greyhound Races," Communications of the IIMA: Vol. 8: Iss. 2, Article 7.
DOI: https://doi.org/10.58729/1941-6687.1082
Available at: https://scholarworks.lib.csusb.edu/ciima/vol8/iss2/7