Learning Fine-Grain Spatial Models for Dynamic Sports Play Prediction
Yisong Yue (Disney Research Pittsburgh)
Patrick Lucey (Disney Research Pittsburgh)
Peter Carr (Disney Research Pittsburgh)
Alina Bialkowski (Disney Research Pittsburgh)
Iain Matthews (Disney Research Pittsburgh)
International Conference on Data Mining (ICDM) 2014
December 14, 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.
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