In:
Journal of Quantitative Analysis in Sports, Walter de Gruyter GmbH, Vol. 15, No. 2 ( 2019-06-26), p. 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.
Type of Medium:
Online Resource
ISSN:
1559-0410
,
2194-6388
DOI:
10.1515/jqas-2017-0126
Language:
English
Publisher:
Walter de Gruyter GmbH
Publication Date:
2019
detail.hit.zdb_id:
2233187-6
SSG:
31
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