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Friday, February 2 • 10:45am - 11:15am
Using Machine Learning to Classify Quality and Style of Play at the Quarterback Position

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When it comes to winning football games, success at the quarterback position is the most highly correlated and predictive variable. As such, finding, evaluating, and sustaining a high-level passing attack is one of the most important tasks in all of pro sports. Using Pro Football Focus data, we determine aspects of a quarterback’s throw profile that are the most stable season to season, as well as those that are most predictive of future performance. With a variety of machine learning techniques at our disposal, we use these insights to classify quarterback play, and these groups provide substantial information for both explanatory and predictive purposes for teams moving forward.

avatar for George Chahrouri

George Chahrouri

Analytics Lead, Pro Football Focus
Born and raised in California, George graduated with BS in Math from Loyola Marymount University in Los Angeles and then joined Teach for America. After teaching math in both Bridgeport CT and Compton CA, he joined Pro Football Focus. In addition to working on the analytics side... Read More →
avatar for Eric Eager

Eric Eager

Data Scientist, Pro Football Focus
Eric Eager received his PhD in mathematical biology from the University of Nebraska - Lincoln 2012, and joined the faculty at UW-La Crosse immediately thereafter. He is the author of over 20 papers in applied mathematics, mathematical biology and the scholarship of teaching and learning... Read More →

Friday February 2, 2018 10:45am - 11:15am CST