Social Network Analysis and Data Visualization of Football Performance Preceded to the Goal Scored

Social network analysis reveals a significant set of essential information on the behaviour of the players and teams: such as passing sequences between players in the attacking third position of attacking performance. Despite their usefulness, network metrics related to expected goal values for socc...

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Published in:Lecture Notes in Bioengineering
Main Author: M. Fauzi M.S.; Imran K.; 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-s2.0-85161389352&doi=10.1007%2f978-981-99-0297-2_6&partnerID=40&md5=470773c490d4d0c8e9f4189763ad3d16
id 2-s2.0-85161389352
spelling 2-s2.0-85161389352
M. Fauzi M.S.; Imran K.; Mohamed Z.
Social Network Analysis and Data Visualization of Football Performance Preceded to the Goal Scored
2023
Lecture Notes in Bioengineering


10.1007/978-981-99-0297-2_6
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161389352&doi=10.1007%2f978-981-99-0297-2_6&partnerID=40&md5=470773c490d4d0c8e9f4189763ad3d16
Social network analysis reveals a significant set of essential information on the behaviour of the players and teams: such as passing sequences between players in the attacking third position of attacking performance. Despite their usefulness, network metrics related to expected goal values for soccer analysis are minimal. Thus, the study compared network approaches that led to Chelsea FC's goal and playing style in the English Premier League in season 21/22. Moreover, ‘expected goal values’ (xG) show the probability of the goal scored, which can be related to the goals scored passing network. The study used centrality in network analysis such as degree prestige, degree centrality, and betweenness centrality to find the significant contributor of the player's position independently during the match that led to the goal scoring and did not consider the playing style of Chelsea FC. Furthermore, the xG values of shots in every game were visualized using Tableau software. A set of adjacency metrics were computed using the highlight videos of goals for every match, and the results of network analysis found that the most received the ball from their teammates were left wing-back and right wing-back, defensive midfielders and right wing-back have the highest degree centrality and attacking midfielder and left wing-back have the highest betweenness centrality. Furthermore, the statistical data from the visualization such as cross percentage, passing percentage, shots and xG percentage can be used to enhance the team performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Springer Science and Business Media Deutschland GmbH
2195271X
English
Conference paper

author M. Fauzi M.S.; Imran K.; Mohamed Z.
spellingShingle M. Fauzi M.S.; Imran K.; Mohamed Z.
Social Network Analysis and Data Visualization of Football Performance Preceded to the Goal Scored
author_facet M. Fauzi M.S.; Imran K.; Mohamed Z.
author_sort M. Fauzi M.S.; Imran K.; Mohamed Z.
title Social Network Analysis and Data Visualization of Football Performance Preceded to the Goal Scored
title_short Social Network Analysis and Data Visualization of Football Performance Preceded to the Goal Scored
title_full Social Network Analysis and Data Visualization of Football Performance Preceded to the Goal Scored
title_fullStr Social Network Analysis and Data Visualization of Football Performance Preceded to the Goal Scored
title_full_unstemmed Social Network Analysis and Data Visualization of Football Performance Preceded to the Goal Scored
title_sort Social Network Analysis and Data Visualization of Football Performance Preceded to the Goal Scored
publishDate 2023
container_title Lecture Notes in Bioengineering
container_volume
container_issue
doi_str_mv 10.1007/978-981-99-0297-2_6
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161389352&doi=10.1007%2f978-981-99-0297-2_6&partnerID=40&md5=470773c490d4d0c8e9f4189763ad3d16
description Social network analysis reveals a significant set of essential information on the behaviour of the players and teams: such as passing sequences between players in the attacking third position of attacking performance. Despite their usefulness, network metrics related to expected goal values for soccer analysis are minimal. Thus, the study compared network approaches that led to Chelsea FC's goal and playing style in the English Premier League in season 21/22. Moreover, ‘expected goal values’ (xG) show the probability of the goal scored, which can be related to the goals scored passing network. The study used centrality in network analysis such as degree prestige, degree centrality, and betweenness centrality to find the significant contributor of the player's position independently during the match that led to the goal scoring and did not consider the playing style of Chelsea FC. Furthermore, the xG values of shots in every game were visualized using Tableau software. A set of adjacency metrics were computed using the highlight videos of goals for every match, and the results of network analysis found that the most received the ball from their teammates were left wing-back and right wing-back, defensive midfielders and right wing-back have the highest degree centrality and attacking midfielder and left wing-back have the highest betweenness centrality. Furthermore, the statistical data from the visualization such as cross percentage, passing percentage, shots and xG percentage can be used to enhance the team performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
publisher Springer Science and Business Media Deutschland GmbH
issn 2195271X
language English
format Conference paper
accesstype
record_format scopus
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