The need for weighting indirect connections between game variables: Social Network Analysis and eigenvector centrality applied to high-level men’s volleyball

Laporta, Lorenzo; Afonso, José; Mesquita, Isabel

doi:10.1080/24748668.2018.1553094

Cited by 25 publications

(35 citation statements)

References 26 publications

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“…actions in the flow of the game (Wäsche et al, 2017). Eigenvector Centrality weight both direct and indirect connections between nodes (Laporta et al, 2018a;2018b). The current study aimed to create a more refined instrument for studying the attack in volleyball.…”

Section: Discussionmentioning

confidence: 99%

“…Thus far, the most common measure has been degree centrality (e.g., Gama et al, 2014;McLean, Salmon, Gorman, Stevens, & Solomon, 2018), which calculates the number of direct connections between nodes (Borgatti, 2005). However, recent research in volleyball (Laporta, Afonso, and Mesquita, 2018a;2018b;Laporta, Afonso, Valongo, and Mesquita, 2019) has applied Eigenvector Centrality, which considers the value of a node as the weighted sum of both direct and indirect connections (Bonacich, 2007). Moreover, such studies have begun to consider game actions, and not only players, as nodes (e.g., Hurst et al, 2016;Laporta et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Martins¹,

Afonso²,

Coutinho³

et al. 2021

Monten. J. Sports Sci. Med.

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Martins¹,

Afonso²,

Coutinho³

et al. 2021

Monten. J. Sports Sci. Med.

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“…Although the most widely used measure in SNA is Degree Centrality (Gama et al, 2014;McLean, Salmon, Gorman, Stevens, & Solomon, 2018), Eigenvector Centrality has the advantage of also weighting direct connections based on their indirect connections (Bonacich, 2007). Moreover, while it is common for studies using MA to centre SNA around the behaviours of individual players (Ribeiro, Silva, Duarte, Davids, & Garganta, 2017), it is possible to apply the same tools to analyse relationships between game actions, sequences, and game complexes, as has been successfully implemented in volleyball (Hurst et al, 2016;Laporta, 2018aLaporta, , 2018bLaporta, , 2019, and thus providing a thorough understanding of the game dynamics (Passos, Davids, Araújo, Paz, Minguéns, & Mendes, 2011).…”

Section: Discussionmentioning

confidence: 99%

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].

Martins

Mesquita

Mendes

et al. 2021

Rev. int. cienc. deporte

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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.

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“…Several interesting studies have applied SNA in different sports ( Clemente et al, 2015 ; Dey et al, 2017 ; Praça et al, 2019 ) and to answer different problems ( Fewell et al, 2012 ; Ribeiro et al, 2019 ; Sasaki et al, 2017 ). In recent years, our research team has applied SNA to high-level volleyball ( Hurst et al, 2016 ; Laporta et al, 2018a , 2018b , 2019 ; Loureiro et al, 2017 ). For example, Hurst et al (2016) and Loureiro et al (2017) analysed the interaction of game actions belonging to side-out, side-out transition and transition (KI, KII and KIII) using eigenvector centrality, while Hurst et al (2017) used similar methodology to analyse attack coverage and freeball (KIV and KV).…”

Section: Introductionmentioning

confidence: 99%

“…For example, Hurst et al (2016) and Loureiro et al (2017) analysed the interaction of game actions belonging to side-out, side-out transition and transition (KI, KII and KIII) using eigenvector centrality, while Hurst et al (2017) used similar methodology to analyse attack coverage and freeball (KIV and KV). More recently, Laporta et al ( 2018a , 2018b ) analysed all game complexes in an interconnected manner and weighting both direct and indirect connections. This body of research has provided proof of concept of the usefulness of SNA when applied to understand game dynamics in volleyball, as well as delivered relevant information such as the predominance of off-system playing (i.e., most setting and attacking actions occur under non-ideal conditions).…”

Section: Introductionmentioning

confidence: 99%

Coexistence of Distinct Performance Models in High-Level Women’s Volleyball

Laporta

Medeiros

Vargas

et al. 2021

Journal of Human Kinetics

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In performance analysis, and most notably in match analysis, generalizing game patterns in a sport or competition may result in formulating generic models and neglecting relevant variability in benefit of average or central values. Here, we aimed to understand how different game models can coexist at the same competitive level using social network analysis with degree centrality to obtain systemic mappings for six volleyball matches, one for each of the six national teams playing in the 2014 World Grand Prix Finals, guaranteeing a homogeneous game level and balanced matches. Although the sample was not recent, this was not relevant for our purposes, since we aimed to merely expose a proof of concept. A total of 56 sets and 7,176 ball possessions were analysed through Gephi Software, considering game actions as nodes and the interaction between them as edges. Results supported the coexistence of different performance models at the highest levels of practice, with each of the six teams presenting a very distinct game model. For example, important differences in eigenvector centrality in attack zones (ranging from 0 to 34) and tempos (20 to 38) were found between the six teams, as well as in defensive lines (20 to 39) and block opposition (22 to 37). This further suggests that there may be multiple pathways towards expert performance within any given sport, inviting a re-conceptualization of monolithic talent identification, detection and selection models. Future studies could benefit from standardizing the metrics in function of the number of ball possessions.

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The need for weighting indirect connections between game variables: Social Network Analysis and eigenvector centrality applied to high-level men’s volleyball

Cited by 25 publications

References 26 publications

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

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

Coexistence of Distinct Performance Models in High-Level Women’s Volleyball

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