Learning fine-grained spatial models for dynamic sports play prediction - Yue, Y.; Lucey, P.; Carr, P.; Bialkowski, A.; Matthews, I. (IEEE International Conference on Data Mining (ICDM), 2014)
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-game predictions. We provide a detailed inspection of our learned factors, and show that our model is interpretable and corresponds to known intuitions of basketball gameplay.