Sullivan, KentMyers, DaffneyHarman, AliciaSrivastava, Dhiraj2023-05-082023-05-082023-05-08http://hdl.handle.net/10919/114967An 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/homeCC0 1.0 UniversalMaster's project