Systems Analysis for University of Virginia Football Recruiting and Performance

Abstract

The role that data analytics plays on sports teams has increased dramatically since Michael Lewis wrote Moneyball and shed some light on Billy Beane's use of analytics with the Oakland Athletics. Today, every major professional sports team has at least an analytics expert on staff, if not a whole department [1]. College teams are increasing their use of analytics as well. Our research goals were to improve the University of Virginia (U. Va.) football team in two ways: recruiting and on-field performance. Our goal of improving the recruiting process led to the development of two tools. First, we created a model that predicts how well an athlete will perform in college based on their high school statistics and demographics. This tool allows coaches to discover lesser ranked athletes who are likely to outperform their rankings. We also further developed an existing model that predicts how likely players are to commit to U. Va. This tool prevents coaches from potentially wasting valuable time and resources on players who are unlikely to commit to U. Va. In order to improve U. Va.'s on-field performance, we created two additional tools. We developed an expected points model based on existing NFL models in an attempt to evaluate the team's performance and identify areas where our play calling was consistently sub-optimal. Finally, we created matchup reports that the coaches can use to scout opposing teams. The expected points model is integrated into these reports to provide a more accurate assessment of the opponent's performance. With this tool, the coaches will be able to spend less time identifying opponents' strengths and weaknesses and more time preparing to exploit them.