Inter-player variability in game patterns in high-level women's volleyball: A study with Outside Hitters (Near vs. Away) using Social Network Analysis. [Variabilidad entre jugadores en los patrones de juego en el voleibol femenino de alto nivel: Un estudio con bateadores externos (Cerca versus Lejos) usando Análisis de Redes Sociales].

João Bernardo Martins, Isabel Mesquita, Ademilson Mendes, Letícia Santos, José Afonso

Resumen


Abstract

A wide body of research on team sports has focused on positional status based differences, providing information on inter-player variability according to the functional roles within the game. However, research addressing inter-player variability within the same positional/function status is scarce. The present article presents an analysis of inter-player variability within the same positional status during critical moments, in high-level women's volleyball, using Social Network Analysis. Attack actions of the outside hitters near (OHN) and away (OHA) from the setter were analysed in ten matches from the 2019 Volleyball Nations League Finals (268 plays). Two independent Eigenvector Centrality networks were created, one for OHN and another for OHA. Main results: (a) in side-out with ideal setting conditions, the OHA used more tips and exploration of the block than the OHN; under non-ideal setting conditions, the OHN had slower attack tempos than the OHA; (b) OHA used tip and directed attacks after error situations while OHN was typically not requested after error situations; (c) in transition, OHN typically attacked after having performed a previous action, performing a dual task within each ball possession, while OHA only attacked when there was no prior action; (d) there were also inter-positional similarities, with both OHN and OHA preferring a strong attack in ideal conditions during KI and KIV, and slower tempos in transition in non-ideal conditions. Conclusions: Even within the same positional status, there seems to be subtle, but relevant inter-player variability. Consequently, coaches should devote careful attention when assigning players to positional.

Resumen

Un amplio conjunto de investigaciones sobre deportes de equipo se ha centrado en las diferencias basadas en el estado posicional, proporcionando información sobre la variabilidad entre jugadores de acuerdo con los roles funcionales dentro del juego.  Sin embargo, la investigación que aborda la variabilidad entre jugadores dentro del mismo estado posicional/de función es escasa. El presente artículo presenta un análisis de la variabilidad entre jugadores dentro del mismo estado posicional durante momentos críticos, en el voleibol femenino de alto nivel, utilizando Análisis de Redes Sociales. Las acciones de ataque de los bateadores externos cerca (OHN) y fuera (OHA) del colocador fueron analizadas en diez partidos de las Finales de la Liga de Naciones de Voleibol de 2019 (268 jugadas). Se crearon dos redes independientes de centralidad de autovector, una para OHN y otra para OHA. Resultados principales: (a) en KI de ajuste ideales, la OHA utilizó más consejos y exploración del bloque que el OHN; en condiciones de ajuste no ideales, el OHN tenía tempos de ataque más lentos que el OHA; (b) OHA utilizó ataques de propina y dirigidos después de situaciones de error, mientras que OHN normalmente no se solicitó después de situaciones de error; (c)  en transición, OHN típicamente atacado después de haber realizado una acción anterior, realizando una doble tarea dentro de cada posesión de la balón, mientras que OHA sólo atacó cuando no había acción previa; (d) también hubo similitudes inter-posicionales, con OHN y OHA prefiriendo un ataque fuerte en condiciones ideales durante KI y KIV, y tempos más lentos en transición en condiciones no ideales. Conclusiones:  Incluso dentro del mismo estado posicional, parece haber una variabilidad entre jugadores sutil, pero relevante. Por lo tanto, los entrenadores deben dedicar una atención cuidadosa al asignar jugadores a los estados posicionales.

https://doi.org/10.5232/ricyde2021.06503

References/referencias

Afonso, J.; Laporta, L., & Mesquita, I. (2018). A importância de diferenciar o KII do KIII no Voleibol feminino de alto nível. Revista Portuguesa de Ciências Do Desporto, 2017(S1A), 140–147.
https://doi.org/10.5628/rpcd.17.s1a.140

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), 1-11.
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 i high-level brazilian women’s volleyball. Kinesiology, 46(2), 242-248.

de Ribaupierre, A., & Lecerf, T. (2018). On the importance of intraindividual variability in cognitive development. Journal of Intelligence, 6(2), 1-8.
https://doi.org/10.3390/jintelligence6020017

Ferreira, A.; Volossovitch, A.; Gomes, F., & Infante, J. (2010). Dynamics of coach’s game practical knowledge in basketball. International Journal of Sport Psychology, 41(4), 68-69.

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

Hodges, N., & Franks, I. (2008). The Provision of Information. In M. Hughes & I. Franks (Eds.), Essentials of Performance Analysis: An Introduction (pp. 21-39). London: Routledge.

Hileno, R., & Buscà, B. (2012). Observational tool for analyzing attack coverage in volleyball. Revista Internacional de Medicina y Ciencias de La Actividad Física y El Deporte, 12(47), 557–570.

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.; Valongo, B.; Afonso, J., & Mesquita, I. (2021). Game-Centred Study Using Eigenvector Centrality in High-Level Women’s Volleyball: Play Efficacy is Independent of Game Patterns … Or is it? Montenegrin Journal of Sports Science and Medicine, 10(1), 19-24.
https://doi.org/10.26773/mjssm.210303

Larkin, P.; Mesagno, C.; Berry, J., & Spittle, M. (2018). Exploration of the perceptual-cognitive processes that contribute to in-game decision-making of Australian football umpires. International Journal of Sport and Exercise Psychology, 16(2), 112-124.
https://doi.org/10.1080/1612197X.2016.1167760

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. 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, 400-408.
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

Papadimitriou, K.; Pashali, E.; Sermaki, I.; Mellas, S., & Papas, M. (2017). The effect of the opponents’ serve on the offensive actions of Greek setters in volleyball games. International Journal of Performance Analysis in Sport, 4(1), 23-33.
https://doi.org/10.1080/24748668.2004.11868288

Palao, J.; Santos, J., & Ureña, A. (2004). Effect of the Setter’s Position on the Block in Volleyball. Internationa Journal of Volleyball Research, 6(1), 2–5.

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.
https://doi.org/10.1016/j.jsams.2010.10.459

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

Seabra, F., & Dantas, L. E. P. B. T. (2006). Space definition for match analysis in soccer. International Journal of Performance Analysis in Sport, 6(2), 97-113.
https://doi.org/10.1080/24748668.2006.11868376

Ramos, A.; Afonso, J., & Martins, M. (2016). Freeball and downball: what we know and what remains to be researched so far. EFDeportes, 20(213), 1-9.
https://doi.org/10.13140/RG.2.2.30931.78889

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.

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

 


Palabras clave/key words


performance analysis; match analysis; team sports; análisis de rendimiento; análisis de partidos; deportes de equipo; voleibol; variabilidad.

Texto completo/Full Text:

PDF (English) PDF




------------------------ 0 -------------------------

RICYDE. Revista Internacional de Ciencias del Deporte
logopublisher_168


Publisher: Ramón Cantó Alcaraz
ISSN:1885-3137 - Periodicidad Trimestral / Quarterly
Creative Commons License