Social Network Analysis of Football Communications by Finding Motifs

Document Type : Original Article

Authors

1 Master of Information Technology Engineering, Tarbiat Modares University, Tehran, Iran

2 Assistant Professor of Information Technology Engineering, Tarbiat Modares University, Tehran, Iran

3 Master of Computer Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

Statistics, extraction, analysis are vital in sports science. Information technology and data science will significantly increase the quality of research and decisions of sports clubs and organizations. Currently, many coaches and sports institutions use analytics and statistics that are calculated manually. Sports science shows that winning a match depends on different factors.
The purpose of the research is to improve team performance by analyzing social networks, communication networks (such as players' passes and transactions during the match), and analyzing repetitive areas. These results are done by analyzing the data collected from 4 matches of the Persepolis team, including three matches from the first half of the Iranian Premier League in 2018-1399 and a Persepolis match against Al-Sharjah. This research examines the issue from two interconnected aspects: 1- Examining the performance of players individually and as part of a social network. 2- explore the communication network between players and land areas. This analysis uses the innovative method of identifying and classifying motifs.

Keywords

References

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AmirHossein Ahmadi received his B.Sc degree in Buin Zahra Technical University, Qazvin, Iran in 2019 and Currently, he is an M.Sc. student in Information Technology at Tarbiat Modares University. His research interests include data mining, machine learning, network science and programming.

 Babak Teimourpour received his B.Sc degree in Industrial Engineering, Sharif University, Tehran, Iran in1996 and received his M.Sc. degree in Socio- Economic Systems Engineering, Institute for Research on Planning and Developement, Tehran, Iran in 1998. Also he received his Ph.D. in Industrial Engineering at Tarbiat Modares University, Tehran, Iran in 2010. His research interests include data mining, social network analysis.

Mahtab Mahbood received her B.Sc degree in Buin Zahra Technical University, Qazvin, Iran in 2020 and Currently, She is an M.Sc. student in Computer Engineering at Amirkabir University of Technology (Tehran Polytechnic) . Her research interests include network analysis and machine learning.

 

 

International Journal of Web Research
Volume 5, Issue 2
Summer-Autumn
December 2022
Pages 39-46

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