Comparing Various Machine Learning Statistical Methods Using Variable Differentials to Predict College Basketball
Date of Last Revision
Bachelor of Science
Date of Expected Graduation
The purpose of this Senior Honors Project is to research, study, and demonstrate newfound knowledge of various machine learning statistical techniques that are not covered in the University of Akron’s statistics major curriculum. This report will be an overview of three machine-learning methods that were used to predict NCAA Basketball results, specifically, the March Madness tournament. The variables used for these methods, models, and tests will include numerous variables kept throughout the season for each team, along with a couple variables that are used by the selection committee when tournament teams are being picked. The end goal is to find out which machine learning method populates the most successful bracket by using key differential statistics between teams and variables of past tournament winners using Neural Network, Boosted Decision Trees, and Naïve Bayes methodologies.
Dr. Richard Einsporn
Dr. Nao Mimoto
Dr. Curtis Clemons
Bennett, Nicholas, "Comparing Various Machine Learning Statistical Methods Using Variable Differentials to Predict College Basketball" (2018). Williams Honors College, Honors Research Projects. 731.
Categorical Data Analysis Commons, Probability Commons, Statistical Methodology Commons, Statistical Models Commons