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Advances in Basketball Analytics with Alexander D'Amour Part 1

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Speaker: Alexander D'Amour is the acting Neyman Visiting Assistant Professor in the Department of Statistics at UC Berkeley. He is completing his PhD in Statistics at Harvard University, where he was a member of the Harvard Laboratory for Applied Statistical Methodology & Data Science, lead by Edoardo Airoldi. He works on a wide variety of Statistics and Machine Learning problems with the goal of developing foundational principles for applied statistics and Data Science that unify themes in design, modeling, inference, and decision rules that cut across application areas and methodologies. To this end, he has undertaken research and consulting projects in document disambiguation, text analysis, epidemiology, education technology, e-commerce, entertainment, credit access in developing economies, and sports statistics. Alex is an active member of the XY Research group, which conducts research in sports statistics with a focus on player-tracking data, and a founding partner of Damyata, LLC, a Data Science consultancy. Abstract: In the[masked] season, the National Basketball Association, in conjunction with STATS LLC, implemented a league-wide program to collect player-tracking data for all NBA games. The data feed now provides 24-FPS records of all players' XY coordinates on the court, as well as XYZ coordinates for the ball. This data source has opened up new lines of inquiry into the quantitative analysis of basketball that have previously been hamstrung by a reliance on spatially naive box-score and play-by-play statistics. In this talk I will discuss several projects undertaken by myself and the XY Research group that use newly-available spatial data to work toward answering fundamental questions about basketball. Topics covered will include expected possession value (a.k.a, EPV, or a stock-ticker for a possession), defensive shot charts, the impact of ball movement, and play detection.
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