Motor Behavior

Identifying Self-Organization Maps in the Iranian National Soccer Team from the Perspective of Social Networking Theory: An Analysis of the offensive Performance in the 2018 World Cup Matches

Document Type : Research Paper

Authors

Mohsen Mohammadi 1
Ali Sharifnezhad 2
Abbas Bahram 3

1 Ph.D. Student in Motor Behavior, Kharazmi University

2 Assistant Professor of Sport Bio Mechanic, Sport Sciences Research Institute

3 Professor of Motor Behavior, Kharazmi University

https://doi.org/10.22089/mbj.2018.6279.1710
Abstract
The purpose of this study was to use a new method based on social networking techniques to identify self-organizing maps in football teams. The research approach was observational. Based on the data extracted from the FIFA site, after each game, network indicators were organized at two levels of micro and macro, and three official matches of the national football team of Iran were analyzed in the 2018 World Cup. Based on Available Data The network indicators were organized at two levels of micro and macro, and three official matches of the Iranian national team were analyzed in the 2018 World Cup. A total of 517 offensive phases were analyzed in three games. For each attacking phase, a series of proximity matrices was created, which analyzed the density, clustering and game center based on the relative position of the player in the field at two levels using the Nod XL software. The results provided explanations for the tactical performance and features of the Iranian national team networks in these games, and low levels of density and clustering coefficients indicated weak tactical solutions in the offensive phase. The result is a comprehensive explanation for the tactical team performance. Iran nationalized in these games. In general, the findings of this study provide operational strategies that can be used to examine the structure of the team network and its indicators in football teams, and to educate the instructors in understanding the characteristics of teamwork, improving decision making during the match, and providing a protocol Appropriate training exercises.

Keywords

Social Networks
Self-Organized Maps
Team Synergy
Complex Systems
Graph Theory
Subjects
  1. Rice E, Yoshioka-Maxwell A. Social network analysis as a toolkit for the science of social work. sswr. 2015; 6(3):369-83.
  2. Araújo D, Bourbousson J. Theoretical perspectives on interpersonal coordination for team behavior. Interpersonal coordination and performance in social systems. 2016:17-32.
  3. Ribeiro J, Silva P, Duarte R, Davids K, Garganta J. Team sports performance analysed through the lens of social network theory: implications for research and practice. Sports Med. 2017;47(9):1689-96.
  4. Araújo D, Silva P, Davids K. Capturing group tactical behaviors in expert team players. Rotledge Handbook of Sport Expertise: Routledge. 2015.209-217
  5. Passos P, Araújo D, Volossovitch A. Performance analysis in team sports: Taylor & Francis; New York; 2016. 4-20
  6. Silva P, Garganta J, Araújo D, Davids K, Aguiar P. Shared Knowledge or Shared Affordances? Insights from an Ecological Dynamics Approach to Team Coordination in Sports. Sports Med. 2013;43(9):765-72.
  7. Araújo D, Davids K. Team synergies in sport: theory and measures. fpsyg. 2016;7:1449.
  8. Steiner S, Macquet A-C, Seiler R. An Integrative Perspective on Interpersonal Coordination in Interactive Team Sports. fpsyg. 2017;8:1440.
  9. Warner S, Bowers MT, Dixon MA. Team dynamics: A social network perspective. JSM. 2012;26(1):53-66.
  10. Den Hartigh RJ, Van Dijk MW, Steenbeek HW, Van Geert PL. A dynamic network model to explain the development of excellent human performance. fpsyg. 2016;7:532.
  11. Cummings JN, Cross R. Structural properties of work groups and their consequences for performance. Social networks. 2003;25(3):197-210.
  12. Gaston ME, DesJardins M. The effect of network structure on dynamic team formation in multi‐agent systems. Computational Intelligence. 2008;24(2):57-122.
  13. Balkundi P, Harrison DA. Ties, leaders, and time in teams: Strong inference about network structure’s effects on team viability and performance. Academy of Management Journal. 2006;49(1):49-68.
  14. Grund TU. Network structure and team performance: The case of English Premier League soccer teams. Social Networks. 2012;34(4):682-90.
  15. Clemente FM, Martins FML, Couceiro MS, Mendes RS, Figueiredo AJ. A network approach to characterize the teammates’ interactions on football: A single match analysis. Cuadernos de Psicología del Deporte. 2014;14(3):141-8.
  16. Lusher D, Robins G, Kremer P. The application of social network analysis to team sports. Measurement in physical education and exercise science. 2010;14(4):211-24.
  17. Gama J, Passos P, Davids K, Relvas H, Ribeiro J, Vaz V, et al. Network analysis and intra-team activity in attacking phases of professional football. International Journal of Performance Analysis in Sport. 2014;14(3):692-708.
  18. Passos P, Davids K, Araújo D, Paz N, Minguéns J, Mendes J. Networks as a novel tool for studying team ball sports as complex social systems. jsams. 2011;14(2):     170-6.
  19. Passos P, Davids K, Chow JY. Interpersonal Coordination and Performance in Social Systems: Routledge; New York; 2016: 126-40.
Motor Behavior
Volume 11, Issue 37
Fall 2019
Autumn 2019
Pages 69-86

  • Receive Date 18 August 2018
  • Revise Date 18 November 2018
  • Accept Date 02 December 2018

Home

Reviewers

Submit Manuscript

Publication Ethics

news

Journal Metrics

Contact Us