Spatiotemporal Patterns in Basketball

New technologies deployed in the NBA enable automatic collection of fine-grained spatio-temporal data that tracks player and ball movements, opening up a wide range of pattern recognition and sports analysis problems. We develop and apply machine learning, data analysis, and visualization tools to both validate long-held beliefs and uncover the hidden truths behind the game.

Project members

Project Publications

Deconstructing the Rebound with Optical Tracking Data