Disney Research
Disney Research

Learning Fine-Grain Spatial Models for Dynamic Sports Play Prediction

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.

  • December 14, 2014
  • International Conference on Data Mining (ICDM) 2014

Authors

Abstract

Learning Fine-Grain Spatial Models for Dynamic Sports Play Prediction-Image

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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