Abstract
In this chapter 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.
© 2008 CIIMA. Reprinted, with permission, from (Schumaker and Johnson (2008). An Investigation of SVM Regression to Predict Longshot Greyhound Races. CIIMA. 8(2)).
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References
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Schumaker, R.P., Solieman, O.K., Chen, H. (2010). Greyhound Racing Using Support Vector Machines: A Case Study. In: Sports Data Mining. Integrated Series in Information Systems, vol 26. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6730-5_11
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DOI: https://doi.org/10.1007/978-1-4419-6730-5_11
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