Sport Analytics: Using Open Source Logistic Regression Software to Classify Upcoming Play Type in the NFL - Robert E. Baker; Ted Kwartler (Journal of Applied Sports Management, 2015)
The purpose of this study was to utilize data analytics as means to classify National Football League offensive play types. The open source software R was employed to create a logistic regression based on data for the Cleveland Browns and Pittsburgh Steelers from 13 recent seasons. The regression is based on all first, second, and third downs within regulation play, totaling 26,310 data points. The initial algorithms classify rush or pass for each offense. Revealed through differing coefficients of the independent variables, each team shows a slightly different approach to play selections in response to in-game situations. Identifying the driving factors to play selection is possible by isolating each attribute within the regression. Further examination could yield improved precision to control for changes in head coach, offensive coordinators, player personnel and other factors such as weather because these may influence play type. Logistic regression shows promise as an in-game aid to determining opponent behavior. Specifically, Cleveland's offensive play selection algorithm was correct for 66.4% of plays versus 66.9% for Pittsburgh. Use of open source software and logistic regression of NFL play selection could be beneficial in aiding future game decisions. Further research is recommended to explore possible improvement of the algorithm accuracy.
Keywords: sport analytics; sport management; data mining; NFL; regression