Deconstructing the Rebound with Optical Tracking Data

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Rajiv Maheswaran

Yu-Han Chang

Aaron Henehan

Samantha Danesis


Abstract: This paper leverages STATS’ SportVU Optical Tracking data to deconstruct several previously hidden aspects of rebounding. We are able to move beyond the outcome of who got the rebound to discover the non-linear relationship between shot location and its impact on offensive rebound rates, implications of the height of where rebounds are obtained, and estimates of where players should move in order to improve rebounding rates. We also leverage machine-learning methods to estimate the predictability of rebounding.