EPH - International Journal of Mathematics and Statistics (ISSN: 2208-2212) http://ephjournal.com/index.php/ms en-US <ul> <li>All contributor(s) agree to transfer the copyright of this article to <strong>EPH Journal.</strong></li> <li><strong>EPH Journal</strong> will have all the rights to distribute, share, sell, modify this research article with proper reference of the contributors.&nbsp;</li> <li><strong>EPH Journal</strong> will have the right to edit or completely remove the published article on any misconduct happening.</li> </ul> chief-editor@ephjournal.com (Naeem Akhtar) support@ephjournal.com (Naveen Malik) Sun, 31 Dec 2017 09:53:36 +0000 OJS 3.1.0.0 http://blogs.law.harvard.edu/tech/rss 60 Modeling NFL Football Outcomes http://ephjournal.com/index.php/ms/article/view/375 <p>Three modeling techniques were used to develop models that explain the outcomes of NFL football games.&nbsp; The modeling techniques applied included ordinary least square regression, logistic regression, and proportional odds.&nbsp; Two seasons of NFL football games were used as the training data set and one season of NFL football games was used as the testing data set.&nbsp; The OLS model developed explained approximately 83% of the variation in the point spread of a football game.&nbsp; The logistic regression model developed estimates the probability of the home football team winning given the differences of significant in-game statistics. The proportional odds model estimates the probability of a home team winning the game by 10 or more points or less than 10 points, or losing the game by these same amounts given differences of in-game statistics found to be significant. The models all did well under the testing data set.&nbsp;</p> <p>&nbsp;</p> Rhonda C Magel, Joseph Roith ##submission.copyrightStatement## http://creativecommons.org/licenses/by-nc-nd/4.0 http://ephjournal.com/index.php/ms/article/view/375 Sun, 31 Dec 2017 09:53:18 +0000