Recently, professional team sport organizations have invested their resources to analyze their own and opponents' performance. So, developing methods and algorithms for analyzing team sports has become one of the most popular topics among data scientists. Analyzing football is hard because of its complexity, number of events in each match, and constant flow of circulation of the ball. Finding roles of players with the purpose of analyzing the performance of a team or making a meaningful comparison between players is crucial. In this article, an automatic big data clustering method, based on a swarm intelligence algorithm, is proposed to automatically cluster the data set of players' performance centers in different matches and extract different kinds of roles in football. The proposed method created using particle swarm optimization algorithm has two phases. In the first phase, the algorithm searches the solution space to find the number of clusters and, in the second phase, it finds the positions of the centroids. To show the effectiveness of the algorithm, it is tested on six synthetic data sets and its performance is compared with two other conventional clustering methods. After that, the algorithm is used to find clusters of a data set containing 93,000 objects, which are the centers of players' performance in about 4900 matches in different European leagues.

中文翻译：

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使用自动粒子群优化-聚类算法查找足球运动员的角色。

最近，专业的团体运动组织投入了资源来分析自己和对手的表现。因此，开发用于分析团队运动的方法和算法已成为数据科学家中最受欢迎的主题之一。由于足球的复杂性，每场比赛中的事件数量以及球的不断流通，分析足球非常困难。寻找球员的角色以分析团队绩效或在球员之间进行有意义的比较是至关重要的。本文提出了一种基于群体智能算法的自动大数据聚类方法，可以自动对不同比赛中运动员表现中心的数据集进行聚类，提取足球中不同类型的角色。利用粒子群优化算法创建的方法分为两个阶段。在第一阶段，该算法搜索解空间以找到聚类的数量，在第二阶段，该算法找到质心的位置。为了显示该算法的有效性，对六个合成数据集进行了测试，并将其性能与其他两种常规聚类方法进行了比较。之后，该算法用于查找包含93,000个对象的数据集的群集，这些对象是欧洲不同联赛中大约4900场比赛中玩家表现的中心。它在六个综合数据集上进行了测试，并将其性能与其他两种常规聚类方法进行了比较。之后，该算法用于查找包含93,000个对象的数据集的群集，这些对象是欧洲不同联赛中大约4900场比赛中玩家表现的中心。它在六个综合数据集上进行了测试，并将其性能与其他两种常规聚类方法进行了比较。之后，该算法用于查找包含93,000个对象的数据集的群集，这些对象是欧洲不同联赛中大约4900场比赛中玩家表现的中心。