UM
Event-based non-parametric clustering of team sport trajectories
Peng, Fengchao1; Ji, Yudian1; Luo, Qiong1; Ni, Lionel M.1,2
2018-01-12
Conference Name5th IEEE International Conference on Big Data, Big Data 2017
Source PublicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January
Pages994-999
Conference Date1211, 2017 - 1214, 2017
Conference PlaceBoston, MA, United states
Author of SourceInstitute of Electrical and Electronics Engineers Inc.
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. © 2017 IEEE.
DOI10.1109/BigData.2017.8258021
Language英语
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Deparment of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong;
2.University of Macau, China
Recommended Citation
GB/T 7714
Peng, Fengchao,Ji, Yudian,Luo, Qiong,et al. Event-based non-parametric clustering of team sport trajectories[C]//Institute of Electrical and Electronics Engineers Inc.,2018:994-999.
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