SoccerCPD: Formation and Role Change-Point Detection in Soccer Matches Using Spatiotemporal Tracking Data

被引:6
作者
Kim, Hyunsung [1 ]
Kim, Bit [1 ]
Chung, Dongwook [1 ]
Yoon, Jinsung [1 ]
Ko, Sang-Ki [1 ,2 ]
机构
[1] Fitogether Inc, Seoul, South Korea
[2] Kangwon Natl Univ, Chunchon, South Korea
来源
PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022 | 2022年
基金
新加坡国家研究基金会;
关键词
Sports Analytics; Spatiotemporal Data Analysis; Formation Analysis; GPS Tracking Data; Change-Point Detection; MULTIVARIATE;
D O I
10.1145/3534678.3539150
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In fluid team sports such as soccer and basketball, analyzing team formation is one of the most intuitive ways to understand tactics from domain participants' point of view. However, existing approaches either assume that team formation is consistent throughout a match or assign formations frame-by-frame, which disagree with real situations. To tackle this issue, we propose a change-point detection framework named SoccerCPD that distinguishes tactically intended formation and role changes from temporary changes in soccer matches. We first assign roles to players frame-by-frame and perform two-step change-point detections: (1) formation change-point detection based on the sequence of role-adjacency matrices and (2) role change-point detection based on the sequence of role permutations. The evaluation of SoccerCPD using the ground truth annotated by domain experts shows that our method accurately detects the points of tactical changes and estimates the formation and role assignment per segment. Lastly, we introduce practical use-cases that domain participants can easily interpret and utilize.
引用
收藏
页码:3146 / 3156
页数:11
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