The increasing use of analytics in sports has changed the way organizations, coaches, and players approach game planning, strategies, and evaluation of performance. One common metric to describe a team's chances of winning in progress contests is win probability. Win probability is comprised of many game state variables, including down, distance, yards to goal, and score differential, along with team strength ratings such as point spread and offensive or defensive ratings. In this study, we will examine what variables are useful in determining win probability for in progress NCAA Division I football games in the overtime periods. We use logistic regression and decision trees to create win probability models using various game state and team strength variables. Our results are promising, as they perform well in describing a team's win probability given various overtime game state situations. We can use our results to evaluate coaching decisions made in the overtime periods, in addition to determining which plays had the greatest impact on a team's chances of winning the game.


Pasteur, Drew

Second Advisor

Huang, Qimin




Data Science

Publication Date


Degree Granted

Bachelor of Arts

Document Type

Senior Independent Study Thesis



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