Inter-player Variability Within the Same Positional Status in High-level Men's Volleyball
DOI:
https://doi.org/10.47197/retos.v46.93624Keywords:
performance analysis, match analysis, variability, team sports, game patternsAbstract
In sports, there may be multiple players for the same positional status (e.g., in volleyball, there are two outside hitters, one near the setter and the other away from the setter), and there may be relevant differences within the same positional status. We analyzed inter-player variability within the same positional status in high-level men’s volleyball, through Social Network Analysis (through Gephi© 0.9.2 software). Attack actions of the outside hitters near (OHN) and away (OHA) from the setters were analyzed in ten matches from the 2019 Volleyball Nations League Finals (278 plays). Two Eigenvector Centrality networks were created. Results: (a) in side-out under non-ideal setting conditions, OHNs preferred the strong attack while OHAs alternated between the strong attack and the tip; (b) after a prior action, OHNs attacked via exploration of the block while OHAs preferred the tip; (c) after consecutive errors, OHNs play more in the opponent’s error; (d) after a previous defense action, OHNs preferred the strong attack and exploration of the block while OHAs preferred the strong attack; (e) in transition, OHNs were solicited under non-ideal setting conditions while OHAs were solicited in ideal and non-ideal conditions. Our findings demonstrate variability between players of the same team and having the same positional status. This allows coaches to understand the key differences of players with the same position, and thus better assign the sub-functions. Researchers should be cautious of aggregating data from players of different positional status, and even from players within the same positional status
References
Afonso, J., Mesquita, I., & Palao, J. (2017). Relationship between the use of commit-block and the numbers of blockers and block effectiveness. International Journal of Performance Analysis in Sport, 5(2), 36–45. https://doi.org/10.1080/24748668.2005.11868326
Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social Networks, 29(4), 555–564. https://doi.org/10.1016/j.socnet.2007.04.002
Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71. https://doi.org/10.1016/j.socnet.2004.11.008
Butterworth, A., O’Donoghue, P., & Cropley, B. (2013). Performance profiling in sports coaching: A review. International Journal of Performance Analysis in Sport, 13(3), 572–593. https://doi.org/10.1080/24748668.2013.11868672
Castelão, D. P., Garganta, J., Afonso, J., & Da Costa, I. T. (2015). Análise sequencial de comportamentos ofensivos desempenhados por seleções nacionais de futebol de alto rendimento. Revista Brasileira de Ciencias Do Esporte, 37(3), 230–236. https://doi.org/10.1016/j.rbce.2015.05.001
Clemente, F. M., Sarmento, H., & Aquino, R. (2020). Player position relationships with centrality in the passing network of world cup soccer teams: Win/loss match comparisons. Chaos, Solitons and Fractals, 133(109625). https://doi.org/10.1016/j.chaos.2020.109625
Costa, G., Afonso, J., Barbosa, R. V., Coutinho, P., & Mesquita, I. (2014). Predictors of attack efficacy and attack type in high-level brazilian women’s volleyball. Kinesiology, 46(2), 242–248.
Ferreira, A., Volossovitch, A., & Sampaio, J. (2014). Towards the game critical moments in basketball: A grounded theory approach. International Journal of Performance Analysis in Sport, 14(2), 428–442. https://doi.org/10.1080/24748668.2014.11868732
Fleiss, J., Levin, B., & Paik, M. C. (2013). Statistical methods for rates and proportions. Hoboken: John Wiley & Sons.
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. https://doi.org/10.1080/24748668.2014.11868752
Gonçalves, B. V., Figueira, B. E., Maçãs, V., & Sampaio, J. (2014). Effect of player position on movement behaviour, physical and physiological performances during an 11-a-side football game. Journal of Sports Sciences, 32(2), 191–199. https://doi.org/10.1080/02640414.2013.816761
Hughes, M., & Franks, I. (2008). The Essentials of Performance Analysis. In Routledge.
Hurst, M., Loureiro, M., Valongo, B., Laporta, L., Nikolaidis, T. 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. https://doi.org/10.1080/24748668.2016.11868917
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), 1–14. 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., Igor, A., Medeiros, A., Vargas, N., Conti, G. De, Costa, T., & Afonso, J. (2021). Coexistence of Distinct Performance Models in High-Level Women’s Volleyball. Journal of Human Kinetics, 78(April), 161–173. https://doi.org/10.2478/hukin-2021-0048
Lima, R., Palao, J. M., Moreira, M., & Clemente, F. M. (2019). Variations of technical actions and efficacy of national teams’ volleyball attackers according to their sex and playing positions. International Journal of Performance Analysis in Sport, 19(4), 491–502. https://doi.org/10.1080/24748668.2019.1625658
Liu, H., Gómez, M. A., Gonçalves, B., & Sampaio, J. (2016). Technical performance and match-to-match variation in elite football teams. Journal of Sports Sciences, 34(6), 509–518. https://doi.org/10.1080/02640414.2015.1117121
Marcelino, R., Mesquita, I., & Sampaio, J. (2011). Effects of quality of opposition and match status on technical and tactical performances in elite volleyball. Journal of Sports Sciences, 29(7), 733–741. https://doi.org/10.1080/02640414.2011.552516
Martins, J., Afonso, J., Mendes, A., Santos, L., & Mesquita, I. (2022). Inter-team variability in game play under critical game scenarios: a study in high-level men’s volleyball using social network analysis (Variabilidad entre equipos en el juego bajo escenarios críticos de juego: un estudio en voleibol masculino de alto ni. Retos, 43(1), 1095–1105. https://doi.org/10.47197/RETOS.V43I0.90505
Martins, J. B., Afonso, J., Coutinho, P., Fernandes, R., & Mesquita, I. (2021). The Attack in Volleyball from the Perspective of Social Network Analysis : Refining Match Analysis through Interconnectivity and Composite of Variables. Montenegrin Journal of Sports Science and Medicine, 10(1), 45–54. https://doi.org/10.26773/mjssm.210307
Mclean, S., Salmon, P. M., Gorman, A. D., Stevens, N. J., & Solomon, C. (2018). A social network analysis of the goal scoring passing networks of the 2016 European Football Championships. Human Movement Science, 57. https://doi.org/10.1016/j.humov.2017.10.001
Méndez, C., Gonçalves, B., Santos, J., Ribeiro, J. N., & Travassos, B. (2019). Attacking profiles of the best ranked teams from elite futsal leagues. Frontiers in Psychology, 10(1370), 403–414. https://doi.org/10.3389/fpsyg.2019.01370
Mesquita, I., Palao, J., 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.
Millán-Sánchez, A., Morante Rábago, J. C., & Ureña, A. (2017). Differences in the success of the attack between outside and opposite hitters in high level men’s volleyball. Journal of Human Sport and Exercise, 12(2), 251–256. https://doi.org/10.14198/jhse.2017.122.01
Moura, F. A., Santana, J. E., Vieira, N. A., Santiago, P. R. P., & Cunha, S. A. (2015). Analysis of Soccer Players’ Positional Variability during the 2012 UEFA European Championship: A Case Study. Journal of Human Kinetics, 47(1), 225–236. https://doi.org/10.1515/hukin-2015-0078
Project, D. (2019). Instruction Manual Data Volley 4. Bologna: Data Project.
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), 1689–1696. https://doi.org/10.1007/s40279-017-0695-1
Stamm, R., Stamm, M., Torilo, D., Thomson, K., & Jairus, A. (2016). Comparative analysis of the elements of attack and defence in men’s and women’s games in the Estonian volleyball highest league. Papers on Anthropology, 25(1), 37. https://doi.org/10.12697/poa.2016.25.1.04
Tabachnick, B., & Fidell, L. (2007). Using multivariate statistics. Boston: Pearson.
Vargas, J., Loureiro, M., Nikolaidis, P. T., Knechtle, B., Laporta, L., Marcelino, R., & Afonso, J. (2018). Rethinking Monolithic Pathways to Success and Talent Identification: The Case of the Women’s Japanese Volleyball Team and Why Height is Not Everything. Journal of Human Kinetics, 64(1), 233–245. https://doi.org/10.1515/hukin-2017-0197
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. https://doi.org/10.1080/16138171.2017.1318198
Yi, Q., Gómez, M. Á., Liu, H., & Sampaio, J. (2019). Variation of match statistics and football teams’ match performance in the group stage of the UEFA champions league from 2010 to 2017. Kinesiology, 51(2), 170–181. https://doi.org/10.26582/k.51.2.4
Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social Networks, 29(4), 555–564. https://doi.org/10.1016/j.socnet.2007.04.002
Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71. https://doi.org/10.1016/j.socnet.2004.11.008
Butterworth, A., O’Donoghue, P., & Cropley, B. (2013). Performance profiling in sports coaching: A review. International Journal of Performance Analysis in Sport, 13(3), 572–593. https://doi.org/10.1080/24748668.2013.11868672
Castelão, D. P., Garganta, J., Afonso, J., & Da Costa, I. T. (2015). Análise sequencial de comportamentos ofensivos desempenhados por seleções nacionais de futebol de alto rendimento. Revista Brasileira de Ciencias Do Esporte, 37(3), 230–236. https://doi.org/10.1016/j.rbce.2015.05.001
Clemente, F. M., Sarmento, H., & Aquino, R. (2020). Player position relationships with centrality in the passing network of world cup soccer teams: Win/loss match comparisons. Chaos, Solitons and Fractals, 133(109625). https://doi.org/10.1016/j.chaos.2020.109625
Costa, G., Afonso, J., Barbosa, R. V., Coutinho, P., & Mesquita, I. (2014). Predictors of attack efficacy and attack type in high-level brazilian women’s volleyball. Kinesiology, 46(2), 242–248.
Ferreira, A., Volossovitch, A., & Sampaio, J. (2014). Towards the game critical moments in basketball: A grounded theory approach. International Journal of Performance Analysis in Sport, 14(2), 428–442. https://doi.org/10.1080/24748668.2014.11868732
Fleiss, J., Levin, B., & Paik, M. C. (2013). Statistical methods for rates and proportions. Hoboken: John Wiley & Sons.
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. https://doi.org/10.1080/24748668.2014.11868752
Gonçalves, B. V., Figueira, B. E., Maçãs, V., & Sampaio, J. (2014). Effect of player position on movement behaviour, physical and physiological performances during an 11-a-side football game. Journal of Sports Sciences, 32(2), 191–199. https://doi.org/10.1080/02640414.2013.816761
Hughes, M., & Franks, I. (2008). The Essentials of Performance Analysis. In Routledge.
Hurst, M., Loureiro, M., Valongo, B., Laporta, L., Nikolaidis, T. 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. https://doi.org/10.1080/24748668.2016.11868917
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), 1–14. 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., Igor, A., Medeiros, A., Vargas, N., Conti, G. De, Costa, T., & Afonso, J. (2021). Coexistence of Distinct Performance Models in High-Level Women’s Volleyball. Journal of Human Kinetics, 78(April), 161–173. https://doi.org/10.2478/hukin-2021-0048
Lima, R., Palao, J. M., Moreira, M., & Clemente, F. M. (2019). Variations of technical actions and efficacy of national teams’ volleyball attackers according to their sex and playing positions. International Journal of Performance Analysis in Sport, 19(4), 491–502. https://doi.org/10.1080/24748668.2019.1625658
Liu, H., Gómez, M. A., Gonçalves, B., & Sampaio, J. (2016). Technical performance and match-to-match variation in elite football teams. Journal of Sports Sciences, 34(6), 509–518. https://doi.org/10.1080/02640414.2015.1117121
Marcelino, R., Mesquita, I., & Sampaio, J. (2011). Effects of quality of opposition and match status on technical and tactical performances in elite volleyball. Journal of Sports Sciences, 29(7), 733–741. https://doi.org/10.1080/02640414.2011.552516
Martins, J., Afonso, J., Mendes, A., Santos, L., & Mesquita, I. (2022). Inter-team variability in game play under critical game scenarios: a study in high-level men’s volleyball using social network analysis (Variabilidad entre equipos en el juego bajo escenarios críticos de juego: un estudio en voleibol masculino de alto ni. Retos, 43(1), 1095–1105. https://doi.org/10.47197/RETOS.V43I0.90505
Martins, J. B., Afonso, J., Coutinho, P., Fernandes, R., & Mesquita, I. (2021). The Attack in Volleyball from the Perspective of Social Network Analysis : Refining Match Analysis through Interconnectivity and Composite of Variables. Montenegrin Journal of Sports Science and Medicine, 10(1), 45–54. https://doi.org/10.26773/mjssm.210307
Mclean, S., Salmon, P. M., Gorman, A. D., Stevens, N. J., & Solomon, C. (2018). A social network analysis of the goal scoring passing networks of the 2016 European Football Championships. Human Movement Science, 57. https://doi.org/10.1016/j.humov.2017.10.001
Méndez, C., Gonçalves, B., Santos, J., Ribeiro, J. N., & Travassos, B. (2019). Attacking profiles of the best ranked teams from elite futsal leagues. Frontiers in Psychology, 10(1370), 403–414. https://doi.org/10.3389/fpsyg.2019.01370
Mesquita, I., Palao, J., 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.
Millán-Sánchez, A., Morante Rábago, J. C., & Ureña, A. (2017). Differences in the success of the attack between outside and opposite hitters in high level men’s volleyball. Journal of Human Sport and Exercise, 12(2), 251–256. https://doi.org/10.14198/jhse.2017.122.01
Moura, F. A., Santana, J. E., Vieira, N. A., Santiago, P. R. P., & Cunha, S. A. (2015). Analysis of Soccer Players’ Positional Variability during the 2012 UEFA European Championship: A Case Study. Journal of Human Kinetics, 47(1), 225–236. https://doi.org/10.1515/hukin-2015-0078
Project, D. (2019). Instruction Manual Data Volley 4. Bologna: Data Project.
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), 1689–1696. https://doi.org/10.1007/s40279-017-0695-1
Stamm, R., Stamm, M., Torilo, D., Thomson, K., & Jairus, A. (2016). Comparative analysis of the elements of attack and defence in men’s and women’s games in the Estonian volleyball highest league. Papers on Anthropology, 25(1), 37. https://doi.org/10.12697/poa.2016.25.1.04
Tabachnick, B., & Fidell, L. (2007). Using multivariate statistics. Boston: Pearson.
Vargas, J., Loureiro, M., Nikolaidis, P. T., Knechtle, B., Laporta, L., Marcelino, R., & Afonso, J. (2018). Rethinking Monolithic Pathways to Success and Talent Identification: The Case of the Women’s Japanese Volleyball Team and Why Height is Not Everything. Journal of Human Kinetics, 64(1), 233–245. https://doi.org/10.1515/hukin-2017-0197
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. https://doi.org/10.1080/16138171.2017.1318198
Yi, Q., Gómez, M. Á., Liu, H., & Sampaio, J. (2019). Variation of match statistics and football teams’ match performance in the group stage of the UEFA champions league from 2010 to 2017. Kinesiology, 51(2), 170–181. https://doi.org/10.26582/k.51.2.4
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2022-09-28
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Martins, J. B., Afonso, J., Mendes, A., Santos, L., & Mesquita, I. (2022). Inter-player Variability Within the Same Positional Status in High-level Men’s Volleyball. Retos, 46, 129–137. https://doi.org/10.47197/retos.v46.93624
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