Using Network Metrics in Soccer: A Macro-Analysis

• Authors: Filipe Manuel Clemente , Micael Santos Couceiro , Fernando Manuel Lourenço Martins , Rui Sousa Mendes
• Languages of publication: EN

Abstracts

EN

The aim of this study was to propose a set of network methods to measure the specific properties of a team. These metrics were organised at macro-analysis levels. The interactions between teammates were collected and then processed following the analysis levels herein announced. Overall, 577 offensive plays were analysed from five matches. The network density showed an ambiguous relationship among the team, mainly during the 2nd half. The mean values of density for all matches were 0.48 in the 1st half, 0.32 in the 2nd half and 0.34 for the whole match. The heterogeneity coefficient for the overall matches rounded to 0.47 and it was also observed that this increased in all matches in the 2nd half. The centralisation values showed that there was no ‘star topology’. The results suggest that each node (i.e., each player) had nearly the same connectivity, mainly in the 1st half. Nevertheless, the values increased in the 2nd half, showing a decreasing participation of all players at the same level. Briefly, these metrics showed that it is possible to identify how players connect with each other and the kind and strength of the connections between them. In summary, it may be concluded that network metrics can be a powerful tool to help coaches understand team’s specific properties and support decision-making to improve the sports training process based on match analysis.

Keywords

EN

game analysis; soccer; network; metrics

• Publisher: De Gruyter Open
• Journal: Journal of Human Kinetics
• Year: 2015
• Volume: 45
• Issue: 1
• Pages: 123-134

Dates

published

1 - 3 - 2015

online

7 - 4 - 2015

Contributors

author

Filipe Manuel Clemente

• Polytechnic Institute of Coimbra, Coimbra College of Education, Department of Education, Portugal
• Faculty of Sport Sciences and Physical Education – University of Coimbra, Portugal

author

Micael Santos Couceiro

• Ingeniarius, Lda., Coimbra, Portugal

author

Fernando Manuel Lourenço Martins

• Polytechnic Institute of Coimbra, Coimbra College of Education, Department of Education, Portugal
• Instituto de Telecomunicações (IT), Covilhã, Portugal

author

Rui Sousa Mendes

• Polytechnic Institute of Coimbra, Coimbra College of Education, Department of Education, Portugal

References

• Albert R, Jeong H, Barabasi AL. Error and attack tolerance of complex networks. Nature, 2010; 406: 378-382
• Balkundi P, Harrison D. Ties, leaders, and time in teams: strong inference about network structure’s effects on team viability and performance. Acad Manage J, 2006; 49: 49-68[Crossref]
• Bourbousson J, Poizat G, Saury J, Seve C. Team Coordination in Basketball: Description of the Cognitive Connections Among Teammates. J Appl Sport Psychol, 2010; 22: 150-166[Crossref][WoS]
• Clemente FM, Couceiro MS, Martins FM, Mendes R. An Online Tactical Metrics Applied to Football Game. Res J Appl Sci Eng Technol, 2013; 5: 1700-1719
• Clemente FM, Couceiro MS, Martins FML, Mendes RS. Using network metrics to investigate football team players’ connections: A pilot study. Motriz, 2014; 20: 262-271
• Cotta C, Mora AM, Merelo JJ, Merelo-Molina C. A network analysis of the 2010 FIFA World Cup champion team play. J Syst Sci Complex, 2013; 26: 21-42[WoS]
• Couceiro MS, Clemente FM, Martins FM. Towards the Evaluation of Research Groups based on Scientific Co-authorship Networks: The RoboCorp Case Study. Arab Gulf J Sci Res, 2013; 31: 36-52
• Cummings JN, Cross R. Structural properties of work groups and their consequences for performance. Soc Networks, 2003; 25: 197-210[Crossref]
• Duarte R, Araújo D, Correia V, Davids K. Sports Teams as Superorganisms: Implications of Sociobiological Models of Behaviour for Research and Practice in Team Sports Performance Analysis. Sports Med, 2012; 42: 633-642[Crossref][WoS]
• Duch J, Waitzman JS, Amaral LA. Quantifying the Performance of Individual Players in a Team Activity. PLoS ONE, 2010; 5: e10937[Crossref][WoS]
• Estrada E. Edge adjacency relationships in molecular graphs containing heteroatoms: A new topological index related to molecular volume. J Chem Inf Comp Sci, 1995; 35: 701-707[Crossref]
• Fiduccia CM, Mattheyses RM. A Linear-Time Heuristic for Improving Network Partitions. In 19th Design Automation Conference, IEEE. Schenectady, NY, 175-181; 1982
• Fortunato S. Community detection in graphs. Phys Rep, 2010; 486: 75-174
• Grehaigne JF, Bouthier D, David B. Dynamic-system analysis of opponent relationships in collective actions in soccer. J Sport Sci, 1997; 15: 137-149[Crossref]
• Grund TU. Network structure and team performance: The case of English Premier League soccer teams. Soc Networks, 2012; 34: 682-690[Crossref]
• Grunz A, Memmert D, Perl J. Analysis and Simulation of Actions in Games by Means of Special Self- Organizing Maps. Int J Comp Sci Sport, 2009; 8: 22-36
• Grunz A, Memmert D, Perl J. Tactical pattern recognition in football games by means of special selforganizing maps. Hum Movement Sci, 2012; 31: 334-343[Crossref]
• Hespanha JP. An efficient MATLAB Algorithm for Graph Partitioning. University of California; 2004
• Horvath S. Weighted Network Analysis: Applications in Genomics and Systems Biology. New York: Springer; 2011
• Lago-Peñas C, Dellal A. Ball possession strategies in elite soccer according to the evolution of the matchscore: the influence of situational variables. J Hum Kinet, 2010; 25: 93-100
• Lim C, Bohacek S, Hespanha J, Obraczka K. Hierarchical Max-Flow Routing. Global Telecommunications Conference - GLOBECOM '05 IEEE. Los Angeles, CA; 2005
• Malta P, Travassos B. Characterization of the defense-attack transition of a soccer team. Motricidade, 2014; 10: 27-37
• Memmert D, Perl J. Game creativity analysis using neural networks. J Sport Sci, 2009; 27: 139-149[Crossref][WoS]
• 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. J Sci Med Sport, 2011; 14: 170-176[Crossref][PubMed]
• Peña JL, Touchette H. A network theory analysis of football strategies. In Clanet C (Ed.), Sports Physics: Proc. 2012 Euromech Physics of Sports Conference. Palaiseau, Editions de l'Ecole Polytechnique, 517-528; 2012
• Salas E, Dickinson TL, Converse SA, Tannenbaum SI. Toward an understanding of team performance and training. In Teams: Their training and performance. Eds Swezey RW, Salas E. Norwood, NJ: Ablex, 3-29; 1992
• Wasserman S, Faust K. Social network analysis: Methods and applications. New York, USA: Cambridge university press; 1994
• Watts DJ. A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences of the United States of America, 2002; 99: 5766-5771[WoS]
• Wu M. wgPlot-Weighted Graph Plot. MatLab Central File Exchange. Obtido em 10 de January de 2012, de http://www.mathworks.com/matlabcentral/fileexchange/24035; 2009

Identifiers

DOI

10.1515/hukin-2015-0013