Network centrality analysis to determine the tactical leader of a sports team

S, Koh; Yamamoto, Takumi; Miyao, Masahiko; Katsuta, Takashi; Kono, Ichiro

doi:10.1080/24748668.2017.1402283

Cited by 25 publications

(27 citation statements)

References 17 publications

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“…This complex correlation is crucial for coaches to understand because it allows them to promote game scenarios based on previous actions or complexes (Paulo et al, 2018). This instrument has the potential to promote the use of Social Network Analysis in match analysis because it allows for a greater reading of the complexity and dynamics of the game (Passos et al, 2011;Ribeiro et al, 2017;Sasaki, Yamamoto, Miyao, Katsuta, & Kono, 2017), based on its systemic review. As for avenues to explore in future studies, Social Network Analysis and Eigenvector Centrality should be incorporated into research that addresses contextual variables (punctual difference, set/game moment and intra-and inter-set relationships).…”

Section: Complex Variable Eigenvector Centrality Valuesmentioning

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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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: Complex Variable Eigenvector Centrality Valuesmentioning

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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“…However, to our knowledge, this metric is still underused in performance analysis of sports [ 19 , 22 , 23 ]. One exception is a study by Sasaki et al [ 24 ], which used Network Centrality Analysis (Density and Eigenvector) to determine the tactical leader of high-level rugby teams (2015 Rugby World Cup) and to analyze the impact of defensive actions on the outcome of the game. This revealed the existence of decisive relational structures where the highest turnover performance would contribute to the winning game, and that certain individuals play key roles in the game (e.g., “fly-half").…”

Section: Introductionmentioning

confidence: 99%

Interaction network analysis of the six game complexes in high-level volleyball through the use of Eigenvector Centrality

Laporta¹,

Afonso

Mesquita

2018

PLoS ONE

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Social Network Analysis establishes a network system and provides information about the relationships (edges) between system components (nodes). Although nodes usually correspond to actors within the network (e.g., the players), it is possible to stipulate game actions as nodes, thus creating a network of the flow of game actions. In this study, Eigenvector Centrality (a form of weighted centrality that considers n-order connections) was used to identify differences in the centrality of distinct game actions within each of the six game complexes of volleyball. Thirteen matches (46 sets, 2,049 rallies) of the final round of the 2015 FIVB's World Grand Prix (Women) were analyzed. Results showed that analyzing actions as actors (i.e., nodes) offers a clear and comprehensive understanding of game flow and poses an interesting alternative to mainstream research where players are considered nodes. Functional differences between the six game complexes were highlighted, denoting the validity of such division. Out-of-system playing (i.e., having to set the attack under non-ideal conditions, e.g., in KI, KII, KIII and KIV), emerged as a regularity of the game and should be translated into the training process.

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“…However, and regardless of the specific methodological approach, research on MA has focused on average performance models ( Clemente et al, 2014 ; Laporta et al, 2015a , 2018a ; Sasaki et al, 2017 ). Despite the tremendous contributions from studies such as these, the more comprehensive look at the different performance models will depend on the research question, in which a whole-sport approach or a single-team approach has different purposes and is complementary.…”

Section: Introductionmentioning

confidence: 99%

“…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 ).…”

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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Network centrality analysis to determine the tactical leader of a sports team

Cited by 25 publications

References 17 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

Interaction network analysis of the six game complexes in high-level volleyball through the use of Eigenvector Centrality

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

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