Journal of International Technology and Information Management
Document Type
Article
Abstract
This paper presented the S&C Racing system that uses Support Vector Regression (SVR) to predict harness race finishes and analyzed it on fifteen months of data from Northfield Park. We found that our system outperforms the most common betting strategies of wagering on the favorites and the mathematical arbitrage Dr. Z system in five of the seven wager types tested. This work would suggest that an informational inequality exists within the harness racing market that is not apparent to domain experts.
Recommended Citation
Schumaker, Robert P.
(2013)
"Data Mining the Harness Track and Predicting Outcomes,"
Journal of International Technology and Information Management: Vol. 22:
Iss.
2, Article 6.
DOI: https://doi.org/10.58729/1941-6679.1330
Available at:
https://scholarworks.lib.csusb.edu/jitim/vol22/iss2/6
Included in
Business Intelligence Commons, Communication Technology and New Media Commons, Digital Communications and Networking Commons, Information Literacy Commons, Management Information Systems Commons, Management Sciences and Quantitative Methods Commons, Science and Technology Studies Commons, Technology and Innovation Commons