Data Visualization of Football Using Degree of Centrality

Previous research indicated that passing networks can increase the performances of players in a football team. This can be achieved with the aid of data visualization and analysis using post-match data. This paper provides a taxonomy of sports data in football visualization and summarizes the data f...

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Bibliographic Details
Published in:Lecture Notes in Bioengineering
Main Author: Mazlan M.S.; Sainan K.I.; Mohamed Z.
Format: Conference paper
Language:English
Published: Springer Science and Business Media Deutschland GmbH 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161411359&doi=10.1007%2f978-981-99-0297-2_7&partnerID=40&md5=441ebd8a0b7e3b1a12c7858779bbbd8d
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Summary:Previous research indicated that passing networks can increase the performances of players in a football team. This can be achieved with the aid of data visualization and analysis using post-match data. This paper provides a taxonomy of sports data in football visualization and summarizes the data from three aspects of data types, main tasks, visualization techniques, and visual analysis with the use of Tableau software. The objective of this paper is to identify the playing pattern for Liverpool FC during Jurgen Klopp’s era. To identify the playing pattern, this paper will display the diagram of the passing networks from the goals created in the match. Besides, networks and graph theory using Social Network Visualizer is to investigate social structures from the passes data that created goals from an open play. It describes networked systems in terms of nodes and the links between them. The playing pattern may thus be determined by examining the degree of centrality, degree of prestige, and betweenness centrality from nodes and linkages. This paper introduces a visual analysis of competitive football, using the social network from passes to construct degree centrality, and finally discusses the playing pattern for Liverpool FC. For this paper, collecting and flexibly presenting large and complex data is the main concern to increase the understanding of the analysis. In summary, it was feasible to draw the conclusion that network metrics can give sport analysts knowledge that is complimentary to traditional notational analysis by offering a novel visualisation and comprehension of team members' behaviour as well as by characterising particular play patterns. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ISSN:2195271X
DOI:10.1007/978-981-99-0297-2_7