Joao Bernardo Martins1, José Afonso1, Patrícia Coutinho1, Ricardo Fernandes1,2, Isabel Mesquita1

1University of Porto, Faculty of Sport, Centre for Research, Education, Innovation and Intervention in Sport, Porto, Portugal
2University of Porto, Porto Biomechanics Laboratory, Porto, Portugal

The Attack in Volleyball from the Perspective of Social Network Analysis: Refining Match Analysis through Interconnectivity and Composite of Variables

Monten. J. Sports Sci. Med. 2021, 10(1), 45-54 | DOI: 10.26773/mjssm.210307


This study aimed to develop an instrument for analysing the attack in high-level volleyball considering the refined variables adjacent to the attack action, the interconnection between direct and indirect actions, the impact of the previous action, and the formation of composite variables. The game complexes were approached as interacting subsystems. The primary goal was to understand the influence of game actions adjacent to the attack. Three matches of a National Women’s 1st Division 2018/2019 (nine sets, 415 plays) were analysed, considering all game complexes (except attack coverage due to reduced occurrence). An Eigenvector Centrality network with 420 nodes and 7367 edges was created. The networks showed that ideal setting conditions, and strong attacks by the outside and opposite hitters without having received a perfect ball, were central in side-out. In transition, we highlight ideal setting conditions, preferences of the outside hitter, quick attacks in Z4, and high balls in Z2. This study is distinct because it considers different aspects related to the systemic review of the game by using composite variables and the actions prior to the attack. Of these results, we highlight that players attacked with slower tempos for the double action of receive-attack, and these were either preferably directed to the parallel or explored the block. Moreover, for the double defence-attack actions, attackers sought the soft spike in Z2, Z4, and Z8; and when two consecutive individual errors occurred, the players did not err but instead continued to attack to force the opponent’s error.


performance analysis, game analysis, social network analysis, eigenvector centrality, attack, volleyball

View full article
(PDF – 1318KB)


Afonso, J., & Mesquita, I. (2011). Determinants of block cohesiveness and attack efficacy in high-level women’s volleyball. European Journal of Sport Science, 11, 69−75.

Afonso, J., Esteves, F., Araújo, R., Thomas, L., & Mesquita, I. (2012). Tactical determinants of setting zone in elite men’s volleyball. Journal of Sports Science & Medicine, 11 (1), 64–70.

Afonso, J., Laporta, L., & Mesquita, I. (2017). A importância de diferenciar o KII do KIII no voleibol feminino de alto nível. Revista Portuguesa de Ciências do Desporto, 140−147.

Afonso, J., Mesquita, I., Marcelino, J., & Silva, J. (2010). Analysis of the setter’s tactical action in high-performance women’s volleyball. Kinesiology, 42 (1), 82−89.

Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social Networks, 29 (4), 555−564.

Borgatti, S. (2005). Centrality and network flow. Social Networks, 27, 55–71.

Conejero, M., Claver, F., González-Silva, J., Fernández-Echeverría, C., & Moreno, M. (2017). Analysis of performance in game actions in volleyball, according to the classification. Revista Portuguesa de Ciências do Desporto, 17, 196−204.

Costa, G., Afonso, J., Barbosa, R., Coutinho, P., & Mesquita, I. (2014). Predictors of attack efficacy and attack type in high-level Brazilian women’s volleyball. Kinesiology, 46, 242−248.

Drikos, S., & Tsoukos, A. (2018). Data benchmarking through a longitudinal study in high-level men’s volleyball. International Journal of Performance Analysis in Sport, 18 (3), 470−480.

Fernández-Echeverría, C., Mesquita, I., González-Silva, J., Claver, F., & Perla Moreno, M. (2017). Match analysis within the coaching process: A critical tool to improve coach efficacy. International Journal of Performance Analysis in Sport, 17 (1−2), 149−163.

Fleiss, J. Levin, B., & Paik, M. (2013). Statistical methods for rates and proportions. John Willey & Sons.

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

Hodges, N., & Franks, I. (2008). The provision of information. In M. D. Hughes, and I. M. Franks (Eds.), Essentials of performance analysis: An introduction (pp.21−39). London: Routledge.

Hughes, M. (2004). Performance analysis: A 2004 perspective. International Journal of Performance Analysis in Sports, 4 (1), 103−109.

Hurst, M., Loureiro, M., Valongo, B., Laporta, L., Nikolaidis, P., & 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), 695−710.

Instruction Manual (2018). Data Volley 4, Data Project.

Laporta, L. (2014). A cobertura de ataque em voleibol de alto nível feminino e masculino. Estruturas e regularidades emergentes do jogo (Unpublished master dissertation). Portugal: Faculty of Sport, University of Porto.

Laporta, L., Afonso, J., & Mesquita, I. (2018a). 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.

Laporta, L., Afonso, J., & Mesquita, I. (2018b). Interaction network analysis of the six game complexes in high-level volleyball through the use of Eigenvector Centrality. PLoS ONE, 13 (9), e0203348.

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.

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., Afonso, J., Moraes, J. C., & Mesquita, I. (2014). Determinants of attack players in high-level men’s volleyball. Kinesiology, 46 (2), 234–241.

McLean, S., Salmon, P. M., Gorman, A. D., Stevens, N. J., & Solomon, C. (2018). Full length article: A social network analysis of the goal scoring passing networks of the 2016 European football championships. Human Movement Science, 57, 400–408.

Mesquita, I., Palao, J. M., Marcelino, R., & Afonso, J. (2013). Performance analysis in indoor volleyball and beach volleyball. In T. McGarry, P. O Donoghue, & J. Sampaio (Eds.), Handbook of sports performance analysis (pp. 367–379). London: Routledge.

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.

Paulo, A., Zaal, F. T. J. M., Seifert, L., Fonseca, S., & Araújo, D. (2018). Predicting volleyball serve-reception at group level. Journal of Sports Sciences, 36(22), 2621–2630. 02640414.2018.1473098

Ribeiro, J., Silva, P., Duarte, R., Davids, K., & Garganta, J. (2017). Team sports performance analysed through the lens of social network theory: Implications for research and practice. Sports Medicine, 47 (9), 1–8.

Sasaki, K., Yamamoto, T., Miyao, M., Katsuta, T., & Kono, I. (2017). Network centrality analysis to determine the tactical leader of a sports team. International Journal of Performance Analysis in Sport, 17(6), 822–831.

Tabachnick, B., & Fidell, L. (2007). Using multivariate statistics. Boston: Pearson.

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.