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

Keshri Suraj (), Oh Min-hwan (), Zhang Sheng () and Iyengar Garud ()
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Keshri Suraj: Columbia University, Industrial Engineering and Operations Research, New York, NY 10027, USA
Oh Min-hwan: 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
Iyengar Garud: Columbia University, Industrial Engineering and Operations Research, New York, NY 10027, USA

Journal of Quantitative Analysis in Sports, 2019, vol. 15, issue 2, 141-153

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.

Keywords: gaussian process; HMM; defensive assignment; NBA; unsupervised learning (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/jqas-2017-0126

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