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Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo and Japan

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1 Papers

New Indicator for Centrality Measurements in Passing-network Analysis of Soccer

Topics: Data Mining; Big Data; Data Science

Authors: Masatoshi Kanbata , Ryohei Orihara , Yuichi Sei , Yasuyuki Tahara and Akihiko Ohsuga

Affiliation: Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo and Japan

Digital Object Identifier: 10.5220/0007377506160623

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

A number of fields including business, science, and sports, make use of data analytics. The evaluation of players and teams affect how tactics, training, and scouting are conducted in soccer teams. Data such as the number of shots and goals in match results are often used to evaluate players and teams. However, this is not enough to fully understand the potential of the players and teams. In this paper, we describe a new analysis method using passing-distribution data from soccer games. To evaluate the performance of players and teams, we applied graph mining. We also used an index called centrality, which evaluates individual contributions with an organization. In this research, we propose a new centrality model to improve existing conventional models. In the calculating the centrality of a given player pair, we consider not only the shortest sequence of passing but also longer ones. In this research, we verified the significance of these indicators by applying the data of UEFA EURO 2008, 2012, and 2016. As a result, we found our method to be more consistent with game results than conventional methods.

Related Ontology Subjects/Areas/Topics (10)

Artificial Intelligence	Data Mining	Sensor Networks
Big Data	Databases and Information Systems Integration	Signal Processing
Data Engineering	Enterprise Information Systems	Soft Computing
Data Management and Quality