Brigham Young University's highly-visible football program stands at the nexus of The LDS Church and the beloved American cultural institution of football. This study examines the program in the context of the proliferation of "... more
Brigham Young University's highly-visible football program stands at the nexus of The LDS Church and the beloved American cultural institution of football. This study examines the program in the context of the proliferation of " analytics, " or advanced statistics, that is revolutionizing decision-making in sports, business, and other areas of life. New analytic thinking has enabled the evaluation of the program's performance under different head coaches. Its overachievement for its aggregate talent level in the past may be credited to its best talent being concentrated at the two most important positions. Currently, the program is acquiring the best talent at the most important position but not at the second and third most important, though that will likely change with recent events. Helpful to the program's recruiting efforts would be membership in a top-tier conference. The program's outgoing staff has demonstrated conscientiousness of some analytic findings. The new staff has used new metrics and standard analytic practices, though receptiveness to analytics among its high-level members has not been uniform. This study has implications for how Church-affiliated institutions respond to changes in the intellectual atmosphere.
Beane (Lemire 2015) identified injury prevention as the next frontier in sports analytics. In this study, I demonstrate that statistics is essential by examining re-injury prevention, through testing whether NCAA coaches and players,... more
Beane (Lemire 2015) identified injury prevention as the next frontier in sports analytics. In this study, I demonstrate that statistics is essential by examining re-injury prevention, through testing whether NCAA coaches and players, facing the need to limit repetitions as key players recover from injury, allocate those repetitions in a manner that maximizes win probability. I calculate in-game win probabilities using the methodology of Stern (1991) and Winston (2009), NCAA historical point spread data from Covers.com, and NCAA expected points of each down-distance-position state from Knowlton (2015). As a test case, I consider Brigham Young University's use of dual-threat quarterback Taysom Hill in his 2016 return from a prior-year Lisfranc injury. I extract play-by-play data from BYUCougars.com using R's rvest package and regular expression functions. I determine the situations in which Hill's rushing, based on his yards per carry prior to the game on standard and passing downs, maximizes changes in win probability.
