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Automatic event detection in basketball using HMM with energy based defensive assignment

Author

Listed:
  • Keshri Suraj
  • Oh Min-hwan
  • Iyengar Garud

    (Columbia University, Industrial Engineering and Operations Research, New York, NY 10027, USA)

  • Zhang Sheng

    (Georgia Institute of Technology College of Engineering, Industrial and Systems Engineering, Atlanta, GA 30332, USA)

Abstract

We propose a unsupervised learning framework for automatically labeling events in a basketball game. Our framework uses the the optical player tracking data in the NBA. We first learn the time series of defensive assignments using a novel player and location dependent attraction based model which uses hidden Markov models (HMMs), Gaussian processes, and a “bond breaking” model for changes in defensive assignments. Next, we use the learned defensive assignments as an input to a set of HMMs that automatically detect events such as ball screens, drives and post-ups. We show that our models provide significant improvements over existing benchmarks both on defensive assignments and event detection.

Suggested Citation

  • Keshri Suraj & Oh Min-hwan & Iyengar Garud & Zhang Sheng, 2019. "Automatic event detection in basketball using HMM with energy based defensive assignment," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(2), pages 141-153, June.
  • Handle: RePEc:bpj:jqsprt:v:15:y:2019:i:2:p:141-153:n:1
    DOI: 10.1515/jqas-2017-0126
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