Football pass network based on the measurement of player position by using network theory and clustering

The present study proposed the new method to create a pass network based on the measurement of the pass positions. The pass positions were determined from the player positions measured by the automatic tracking system for soccer players. The pass positions were classified into clusters by clustering method. The pass network was created by the number of passes between different clusters. The present study analysed nine official games of Fagiano Okayama of Japan Professional Football League Division 2 in 2016 and 2017 years. The results showed that the pass network could abstractly represent the successful passes. Then, the network metrics such as the total links, degree centrality, scaled connectivity and cluster coefficient were evaluated. The total links and degree centrality were proportional to the number of passes. The scaled connectivity decreased with increasing the number of passes because the degree of the particular nodes increased. Moreover, the cluster coefficient of the node in the top 25% degree increased with increasing the number of passes. These results could provide useful information with respect to the team performance on the field.