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Event-Based Non-Parametric Clustering of Team Sport Trajectories
Peng, Fengchao; Ji, Yudian; Luo, Qiong; Ni, Lionel M.; Nie, JY; Obradovic, Z; Suzumura, T; Ghosh, R; Nambiar, R; Wang, C; Zang, H; BaezaYates, R; Hu, X; Kepner, J; Cuzzocrea, A; Tang, J; Toyoda, M
2017
Conference Name2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
Pages994-999
Publication Place345 E 47TH ST, NEW YORK, NY 10017 USA
PublisherIEEE
AbstractStrategy design and analysis is important in team sports, such as basketball and soccer. In this paper, we take basketball as an example and study how to cluster movement trajectories in the games to identify the strategies. This problem is challenging in that the trajectories are diverse and that it is unknown how many or what strategies are employed in the games. As a result, traditional parametric clustering methods are not directly applicable to the raw trajectory data. Therefore, we propose to align trajectories around the basket and simplify them based on movement directions and game events, including dribbling, passing, and shooting. Furthermore, we propose a non-parametric density peak (NPDP) method to cluster these simplified event trajectories. Our experiments on an NBA game dataset of 50,000 offenses show that, without parameter tuning, NPDP clusters all trajectories into groups of high similarity and identifies distinguishing movement strategies.
URLView the original
Indexed ByCPCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS IDWOS:000428073701004
The Source to ArticleWOS
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Document TypeConference paper
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Peng, Fengchao,Ji, Yudian,Luo, Qiong,et al. Event-Based Non-Parametric Clustering of Team Sport Trajectories[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:994-999.
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