In this thesis, we predict end-of-game results for National Football League regular season games after week 6 (not including playoffs) using machine learning methods. The features we use in our analysis are readily available to the public. Most of the features will be in-game statistics, such as passing yards and rushing yards. The out-of-game statistics we use include win percentage and average team scores (for both sides). Methods include logistic regression, k-nearest neighbors, random forest, and support vector machine. In addition, a combined model is created. The accuracy, sensitivity, and specificity of our models is compared to the accuracy, sensitivity, and specificity of the point spread.
|Commitee:||Korosteleva, Olga, Safer, Alan|
|School:||California State University, Long Beach|
|Department:||Mathematics and Statistics|
|School Location:||United States -- California|
|Source:||MAI 57/01M(E), Masters Abstracts International|
|Subjects:||Statistics, Artificial intelligence|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be