Clustering algorithm for formations in football games
Clustering algorithm for formations in football games
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DOI：
10.1038/s41598-019-48623-1
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发表时间：
2019-09-11
影响因子：
4.6
通讯作者：
Yamazaki, Yoshihiro
中科院分区：
文献类型：
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作者：
Narizuka, Takuma;Yamazaki, Yoshihiro
在竞技团体运动中，运动员在比赛中保持一定的队形，以实现有效的进攻和防守。对于团队风格的定量博弈分析和评估，我们需要一个能够动态表征这种阵型结构的一般框架。本文提出了一种基于Delaunay方法的多场足球比赛队形聚类算法，该算法将球队队形定义为Delaunay三角剖分的邻接矩阵。我们首先表明，整个足球比赛的热图可以聚类成几个平均阵型：“442”、“4141”、“433”、“541”和“343”。然后，使用分层聚类，每个平均阵型被进一步划分为更具体的模式（集群），其中玩家的配置是不同的。我们的方法通过关注每个层次簇之间的转换，实现了不同时间尺度地层的可视化、定量比较和时间序列分析。特别是，我们可以从多个游戏中提取关于阵型中球员位置交换的团队风格。将我们的算法应用于包含足球比赛的数据集，我们提取了特定球队的典型阵型转换模式。
In competitive team sports, players maintain a certain formation during a game to achieve effective attacks and defenses. For the quantitative game analysis and assessment of team styles, we need a general framework that can characterize such formation structures dynamically. This paper develops a clustering algorithm for formations of multiple football (soccer) games based on the Delaunay method, which defines the formation of a team as an adjacency matrix of Delaunay triangulation. We first show that heat maps of entire football games can be clustered into several average formations: "442", "4141", "433", "541", and "343". Then, using hierarchical clustering, each average formation is further divided into more specific patterns (clusters) in which the configurations of players are different. Our method enables the visualization, quantitative comparison, and time-series analysis for formations in different time scales by focusing on transitions between clusters at each hierarchy. In particular, we can extract team styles from multiple games regarding the positional exchange of players within the formations. Applying our algorithm to the datasets comprising football games, we extract typical transition patterns of the formation for a particular team.