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)

Abstract

Competitive sports are growing in popularity at an exponential rate, with training becoming an almost overwhelming process, demanding an understanding and awareness of the effects of a great number of variables on sport performance. Thus, systemic approaches have emerged as essential for understanding the complex dynamics of performance. In this vein, Social Network Analysis (SNA) acquires particular relevance in comprehending the relationships established between different nodes. Therefore, the purpose of the present study was to analyze performance in high-level women’s volleyball using SNA. A systematic mapping of four game complexes of the volleyball game was carried out using Gephi©. The analyzed complexes were: serve (K0), side-out (KI), side-out transition (KII) and transition (KIII). A total of 8 matches from the first Group Stage of the Women’s World Grand Prix 2015 were viewed (1,264 rallies), and eigenvector centrality values were calculated. Results showed that most variables presented categories with relatively close eigenvector values, pointing to a diversified distribution of events. However, some categories did exhibit substantially distinct eigenvector centralities. Based on the findings of the present study, it was concluded that it was crucial to develop ways to enhance teams’ abilities to play off-system, as it was the most common situation in female high-level volleyball. It was also shown that this enhancement should be carried out by providing diversity to the teams’ options, as this diversity would create more uncertainty in the opponent and therefore, a higher chance of success.