[2H2] [General Session] 3. Data Mining

[2H2-02] New indicator for centrality measurement in passing network analysis of football

〇Masatoshi Kanbata¹, Ryohei Orihara¹, Yuichi Sei¹, Yasuyuki Tahara¹, Akihiko Ohsuga¹ (1. The University of Electro-Communications)

Keywords:graph mining, football, big data

Data analytics is used in various field including business, science and sports. The evaluation of players and teams affects tactics, training and scouting in football teams. Players and teams are often evaluated by data such as shots and goals in game results. However it is not enough to fully understand potential of the players and teams. This paper describes a new analysis method using football passing data. In order to evaluate performance of players and teams, we employ graph mining. There is an indicator called centrality that evaluates individual contribution within an organization and it is used to evaluate the players and the teams. In calculation of the centrality for a given player pair, we consider not only the shortest path of passing but also longer ones. As a result, we found our method to be more consistent with game results than conventional methods.