Characterizing different team sports using network analysis

Florian Korte, Martin Lames

Abstract


Team sports are complex dynamic systems based on the frequent interaction of various players. Recently, social network analysis has been introduced to the study of sports dynamics in order to quantify the involvement of individual players in the interplay and to characterize the organizational processes used by teams. Nonetheless, only a limited set of team sports has been assessed to date, and the focus of most studies has been on the application of small sets of network metrics to a single sport. Our study aims at comparing the network patterns of different team sports in order to contribute to the understanding of their underlying nature. It considers three invasion games, namely professional matches from basketball, football and handball. By applying relevant centrality measures and minimum spanning trees a first comparison between the nature of interplay in various team sports is offered as well as a deeper understanding of the role of different tactical positions in each sport. The point guard in basketball, defensive midfielder in football and center in handball are identified as the most central tactical positions. Direct interplay is most balanced in football followed by basketball and handball. A visualization of the basic structure of interplay for each sport is achieved through minimum spanning trees.

Keywords


social network analysis; team sports; interaction matrices; minimum spanning trees

Full Text:

PDF

References


Araújo, D., & Davids, K. (2016). Team Synergies in Sport: Theory and Measures. Frontiers in Psychology, 7, 1449. http://doi.org/10.3389/fpsyg.2016.01449

Bonanno, G., Caldarelli, G., Lillo, F., & Mantegna, R. N. (2003). Topology of correlation-based minimal spanning trees in real and model markets. Physical Review E, 68(4), 046130.

Bourbousson, J., Poizat, G., Saury, J., & Seve, C. (2010). Team coordination in basketball: Description of the cognitive connections among teammates. Journal of Applied Sport Psychology, 22(2), 150-166.

Cardinale, M., Whiteley, R., Hosny, A. A., & Popovic, N. (2016). Activity Profiles and Positional Differences of Handball Players During the World Championships in Qatar 2015. International journal of sports physiology and performance, 1-23.

Clemente, F. M., Martins, F. M. L., Kalamaras, D., & Mendes, R. S. (2015a). Network analysis in basketball: Inspecting the prominent players using centrality metrics. Journal of Physical Education and Sport, 15(2), 212.

Clemente, F. M., Martins, F. M. L., Wong, P. D., Kalamaras, D., & Mendes, R. S. (2015b). Midfielder as the prominent participant in the building attack: A network analysis of national teams in FIFA World Cup 2014. International Journal of Performance Analysis in Sport, 15(2), 704-722.

Clemente, F. M., & Martins, F. M. L. (2017). Network structure of UEFA Champions League teams: association with classical notational variables and variance between different levels of success. International Journal of Computer Science in Sport, 16(1), 39-50.

Courneya, K. S., & Carron, A. V. (1992). The home advantage in sport competitions: A literature review. Journal of Sport and Exercise Psychology, 14(1), 13-27.

Duch, J., Waitzman, J. S., & Amaral, L. A. N. (2010). Quantifying the performance of individual players in a team activity. PloS one, 5(6), e10937.

Ferguson, C. J. (2009). An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 40(5), 532.

Fewell, J. H., Armbruster, D., Ingraham, J., Petersen, A., & Waters, J. S. (2012). Basketball teams as strategic networks. PloS one, 7(11), e47445.

Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social networks, 1(3), 215-239.

Gama, J., Passos, P., Davids, K., Relvas, H., Ribeiro, J., Vaz, V., & Dias, G. (2014). Network analysis and intra-team activity in attacking phases of professional football. International Journal of Performance Analysis in Sport, 14(3), 692-708.

Glazier, P. S., & Davids, K. (2009). Constraints on the complete optimization of human motion. Sports Medicine, 39(1), 15-28.

Gower, J. C., & Ross, G. J. (1969). Minimum spanning trees and single linkage cluster analysis. Applied statistics, 54-64.

Grund, T. U. (2012). Network structure and team performance: The case of English Premier League soccer teams. Social Networks, 34(4), 682-690.

Gwet, K. (2001). Handbook of inter-rater reliability: How to estimate the level of agreement between two or multiple raters. Gaithersburg, MD: STATAXIS Publishing Company.

Karcher, C., & Buchheit, M. (2014). On-court demands of elite handball, with special reference to playing positions. Sports medicine, 44(6), 797-814.

Lappas, T., Liu, K., & Terzi, E. (2009, June). Finding a team of experts in social networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 467-476). ACM.

Leydesdorff, L. (2007). Betweenness centrality as an indicator of the interdisciplinarity of scientific journals. Journal of the Association for Information Science and Technology, 58(9), 1303-1319.

Li, N., Hou, J. C., & Sha, L. (2005). Design and analysis of an MST-based topology control algorithm. IEEE Transactions on Wireless Communications, 4(3), 1195-1206.

Li, C. T., & Shan, M. K. (2010, August). Team Formation for Generalized Tasks in Expertise Social Networks. In Proceedings of the 2010 IEEE Second International Conference on Social Computing (pp. 9-16). IEEE Computer Society.

Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 11(1), 193-197.

Newman, M. E. (2001). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical review E, 64(1), 016132.

O'Donoghue, P. (2009). Research methods for sports performance analysis. Routledge.

Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social networks, 32(3), 245-251.

Passos, P., Davids, K., Araújo, D., Paz, N., Minguéns, J., & Mendes, J. (2011). Networks as a novel tool for studying team ball sports as complex social systems. Journal of Science and Medicine in Sport, 14(2), 170-176.

Passos, P., Araújo, D., & Volossovitch, A. (2016). Performance analysis in team sports. Taylor & Francis.

Pena, J. L., & Touchette, H. (2012). A network theory analysis of football strategies. arXiv preprint arXiv:1206.6904.

Vilar, L., Araújo, D., Davids, K., & Bar-Yam, Y. (2013). Science of winning soccer: Emergent pattern-forming dynamics in association football. Journal of systems science and complexity, 26(1), 73-84.

Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press.




DOI: https://doi.org/10.15203/CISS_2018.005

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.