Lorenzo Laporta1, Beatriz Valongo2, José Afonso2, Isabel Mesquita2
1Universidade Regional Integrada do Alto Uruguai e das Missões, Santiago, Brazil
2University of Porto, Faculty of Sport, Centre for Research, Formation, Innovation and Intervention in Sport, Porto, Portugal
Game-Centred Study Using Eigenvector Centrality in High-Level Women’s Volleyball: Play Efficacy is Independent of Game Patterns… Or is it?
Monten. J. Sports Sci. Med. 2021, 10(1), 19-24 | DOI: 10.26773/mjssm.210303
In sports, it is often assumed that distinct game patterns may influence the outcome of the play differently. However, a few articles about men’s volleyball have suggested that play efficacy may rely more on the quality of individual attack actions, and not on game patterns. Therefore, the goal of this paper was to scrutinize if and how game patterns influence play efficacy in high-level women’s volleyball. Eigenvector Centrality was assessed to integrate direct and indirect relationships between games actions. Thirteen matches from the women’s World Grand Prix’2015 were analysed (46 sets; 2,016 plays). Actions were categorized according to game complex (K0 to KV) and three levels of the efficacy of each play: error, continuity, and point. The results showed that play efficacy was independent of game patterns (the central pattern was non-ideal setting conditions in all complexes and preference for using slow attacks in the extremities of the net). There were, however, some regularities for each game complex. For example, while in KI to KIII, Zone 4 was the most used attack zone, in KIV and KV there was a complete inversion to Zone 2. Moreover, results revealed that women’s volleyball games are more predictable in relation to the play space (attack zones) while increasing the risk through enhanced game speed (attack tempo), in comparison with what studies in men’s volleyball have shown. Future studies should consider situational variables (e.g., match status, home vs away matches), and individual players’ actions should be considered in order to understand their relationships with team patterns better.
performance analysis, network analysis, efficacy, game complexes, game patterns
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Afonso, J., & Mesquita, I. (2011). Determinants of block cohesiveness and attack efficacy in high-level women’s volleyball. European Journal of Sport Science, 11 (1), 69-75.
Bonacich, P., & Lloyd, P. (2001). Eigenvector-like measures of centrality for asymmetric relations. Social Networks, 23 (3), 191-201.
Costa, G. D. C., Ceccato, J. S., de Oliveira, A. S., de Britto Evangelista, B. F., de Oliveira Castro, H., & Ugrinowitsch, H. (2016). Men’s Volleyball Height Level: Association between Game Actions on the Side-Out |Voleibol Masculino de Alto Nível: associação entre as ações de jogo no side-out|. Journal of Physical Education, 27 (1).
Costa, G., Afonso, J., Brant, E., & Mesquita, I. (2012). Differences in game patterns between male and female youth volleyball. Kinesiology, 44 (1), 60-66.
Cotta, C., Mora, A. M., Merelo, J. J., & Merelo-Molina, C. (2013). A network analysis of the 2010 FIFA world cup champion team play. Journal of Systems Science and Complexity, 26 (1), 21-42.
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. https://doi.org/10.1371/journal.pone.0010937
Fleiss, J. L., Levin, B., & Paik, M. C. (2013). Statistical methods for rates and proportions: John Wiley & Sons.
Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1 (3), 215-239.
Hurst, M., Loureiro, M., Valongo, B., Laporta, L., Nikolaidis, P. T., & Afonso, J. (2016). Systemic Mapping of High-Level Women’s Volleyball using Social Network Analysis: The Case of Serve (K0),
Side-out (KI), Side-out Transition (KII) and Transition (KIII). International Journal of Performance Analysis in Sport, 16 (2).
Kountouris, P., Drikos, S., Aggelonidis, I., Laios, A., & Kyprianou, M. (2015). Evidence for Differences in Men’s and Women’s Volleyball Games Based on Skills Effectiveness in Four Consecutive Olympic Tournaments. Comprehensive Psychology, 4, 30.50. CP. 34.39.
Laporta, L., Afonso, J., & Mesquita, I. (2018a). Interaction network analysis of the six game complexes in high-level volleyball through the use of Eigenvector Centrality. PLoS ONE, 13 (9), e0203348. https://doi.org/10.1371/journal.pone.0203348
Laporta, L., Afonso, J., & Mesquita, I. (2018b). The need for weighting indirect connections between game variables: Social Network Analysis and eigenvector centrality applied to high-level men’s volleyball. International Journal of Performance Analysis in Sport, 18 (6), 1067-1077. https://doi.org/10.1080/24748668.2018.1553094
Laporta, L., Afonso, J., Valongo, B., & Mesquita, I. (2019). Using social network analysis to assess play efficacy according to game patterns: a game-centred approach in high-level men’s volleyball. International Journal of Performance Analysis in Sport, 19 (5), 866-877. https://doi.org/10.1080/24748668.2019.1669007
Laporta, L., Nikolaidis, P., Thomas, L., & Afonso, J. (2015). The Importance of Loosely Systematized Game Phases in Sports: The Case of Attack Coverage Systems in High-Level Women’s Volleyball. Montenegrin Journal of Sports Science and Medicine, 4 (1), 19-24.
Loureiro, M., Hurst, M., Valongo, B., Nikolaidis, P., Laporta, L., & Afonso, J. (2017). A Comprehensive Mapping of High-Level Men’s Volleyball Gameplay through Social Network Analysis: Analysing Serve, Side-Out, Side-Out Transition and Transition. Montenegrin Journal of Sports Science and Medicine, 6 (2), 35-41.
Marcelino, R., Mesquita, I., & Afonso, J. (2008). The weight of terminal actions in volleyball. Contributions of the spike, serve and block for the teams’ rankings in the World League 2005. International Journal of Performance Analysis in Sport, 8 (2), 1-7.
Marcelino, R., Sampaio, J., & Mesquita, I. (2011). Investigação centrada na análise do jogo: da modelação estática à modelação dinâmica.[Research on the game analysis: from static to dynamic modeling]. Revista Portuguesa de Ciências do Desporto, 11 (1), 481-499.
Mesquita, I., & Graça, A. (2002). Probing the strategic knowledge of an elite volleyball setter: a case study. International Journal of Volleyball Research, 5 (1), 13-17.
Mesquita, I., Palao, J. M., Marcelino, J., & Afonso, J. (2013). Performance Analysis in indoor volleyball and beach volleyball (T. McGarry, P. O’Donoghue, & J. Sampaio Eds.). Oxon: Routledge.
O’Donoghue, P. (2009). Research methods for sports performance analysis. New York: Routledge.
Palao, J. M., Manzanares, P., & Ortega, E. (2009). Techniques used and efficacy of volleyball skills in relation to gender. International Journal of Performance Analysis in Sport, 9 (2), 281-293.
Queiroga, M., Matias, C., Greco, P., Graça, A., & Mesquita, I. (2005). The dimension of the high-level setter’s tactical strategic knowledge: Study with setters of Brazilian national teams. Brazilian Journal of Physical Education, Special Edition, 111-119.
Quiroga, M. E., García-Manso, J. M., Rodríguez-Ruiz, D., Sarmiento, S., De Saa, Y., & Moreno, M. P. (2010). Relation between in-game role and service characteristics in elite women’s volleyball. The Journal of Strength & Conditioning Research, 24 (9), 2316-2321.
Silva, M., Marcelino, R., Lacerda, D., & João, P. V. (2016). Match Analysis in Volleyball: a systematic review. Montenegrin Journal of Sports Science and Medicine, 5 (1), 35-46.
Wäsche, H., Dickson, G., Woll, A., & Brandes, U. (2017). Social network analysis in sport research: an emerging paradigm. European Journal for Sport and Society, 14 (2), 138-165.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge University Press.
Yamamoto, Y., & Yokoyama, K. (2011). Common and unique network dynamics in football games. PLoS ONE, 6 (12), e29638.