Sports Betting Optimization with Machine Learning Algorithms

dc.contributor.author	Sullivan, Kent	en
dc.contributor.author	Myers, Daffney	en
dc.contributor.author	Harman, Alicia	en
dc.contributor.author	Srivastava, Dhiraj	en
dc.date.accessioned	2023-05-08T16:31:41Z	en
dc.date.available	2023-05-08T16:31:41Z	en
dc.date.issued	2023-05-08	en
dc.description.abstract	An automated framework that retrieves data, builds/updates a database, and compares the predicted outcome of machine learning algorithms against daily betting odds to optimized user wagers. Scope was limited to the National Basketball Association (NBA) and moneyline wagers, however the system could be expanded to include other types of sports as well as other types of wagers (spread, over/under, etc.). 14 different machine learning algorithms were explored along with 5 different feature sets and 4 optimization strategies to show that with the right balance of systematic risk (user), unsystematic risk (optimizer), and model performance - the system can be profitable. https://code.vt.edu/mdaffney/capstone/-/wikis/home	en
dc.identifier.uri	http://hdl.handle.net/10919/114967	en
dc.rights	CC0 1.0 Universal	en
dc.rights.uri	http://creativecommons.org/publicdomain/zero/1.0/	en
dc.title	Sports Betting Optimization with Machine Learning Algorithms	en
dc.type	Master's project	en