Machine Learning for Sports Betting

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Authors
Rugg, Hank
Advisor
Wendt, Theodore
Fasteen, Jodi
Smillie, Mark
Editor
Date of Issue
2024
Subject Keywords
Publisher
Citation
Series/Report No.
item.page.identifier
Title
Machine Learning for Sports Betting
Other Titles
Type
Thesis
Description
Abstract
With sports betting becoming more widely legal, the use of machine learning algorithms for improving an individual’s odds of placing successful sports bets has increased. In general, applying machine learning algorithms comes with challenges such as data selection, feature engineering, and dealing with time series data. In the context of gambling, it also comes with ethical considerations such as the use of such models to gamble, the accuracy of the model, and the transparency of the model. This research focuses specifically on predicting the total combined score of NBA games. This is directly applicable to the Over/Under bet – “Over” if you believe the combined total score will be above the number set by the sportsbook and “Under” if you believe the combined score will be less than the number set by the sports book. The goal of this research is to create a machine learning model that can accurately predict the total combined score of NBA games.
Sponsors
Degree Awarded
Bachelor's
Semester
Spring
Department
Mathematics