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We consider the problem of learning predictive models for in-game sports play prediction. Focusing on basketball, we develop models for anticipating near-future events given the current game state. We employ a latent factor modeling approach, which leads to a compact data representation that enables efficient prediction given raw spatiotemporal tracking data. We validate our approach using tracking data from the 2012-2013 NBA season, and show that our model can make accurate in-gamedoi:10.1109/icdm.2014.106 dblp:conf/icdm/YueLCBM14 fatcat:fhnphzlglravdj6drazhlduika