Extracting the roles of different players in soccer using an automatic clustering algorithm

Nowadays analyzing team sports using artificial intelligence algorithms has become one of the most interesting topics for data scientists. On the other hand, it is really important for the coaches and managers to analyze their own and opponents’ performance. So team sport companies support data scientists to get good results based on the valuable knowledge extracted by the data scientists from massive and unstructured datasets. Clustering, which is the process of grouping data points in a dataset in to several clusters based on their similarities, is one of the most important data mining and big data mining tools which has been widely used in different fields. In this research a new automatic big data clustering algorithm, based on a swarm intelligence method, is used to cluster a big dataset containing centers of players’ performance in different soccer matches to extract the role of different players in a soccer match. The proposed method consists of two phases. In the first phase the algorithm tries to find proper number of clusters and in the second phase it tries to find the position of the centroids. The dataset which is used in this research contains the center of several players’ performance in about 93000 soccer matches which means that the dataset has 93000 objects.

Keywords:

artificial intelligence , Big data , Big data clustering , football analysis , swarm intelligence.