Manuel Loureiro1, Marta Hurst1, Beatriz Valongo1, Pantelis Nikolaidis2, Lorenzo Laporta1, José Afonso1
1University of Porto, Faculty of Sports, Centre of Research, Education, Innovation and Intervention in Sport, Porto, Portugal
2Hellenic Army Academy, Department of Physical and Cultural Education, Athens, Greece
A Comprehensive Mapping of High-Level Men’s Volleyball Gameplay through Social Network Analysis: Analysing Serve, Side-Out, Side-Out Transition and Transition
Monten. J. Sports Sci. Med. 2017, 6(2), 35-41 | DOI: 10.26773/mjssm.2017.09.005
A deeper understanding of the factors behind performance and their interactions is essential to promote better training practices. Notwithstanding, the focus often relies on the outcomes of players’ actions (e.g., efficacy rates), whereas the nature and variations of particular classes of actions remain largely unexplored. Our purpose was to conduct a systemic analysis of categorical game variables and their interactions using Social Network Analysis. Game actions were counted as nodes and their interactions as edges. Eigenvector centrality values were calculated for each node. Eight matches of the Men’s World Cup 2015 were analysed, composing a total of 27 sets (1,209 rallies). Four game complexes were considered: Complex 0 (Serve), Complex I (Side-out), Complex II (Side-out transition) and Complex III (Transition). Results showed that teams frequently play in-system when in Complex I (i.e. under ideal conditions), but present reduced variation with regard to attack zones and tempos, whereas in Complex II teams most often play out-of-system. Based on these findings, it was concluded that practicing with non-ideal conditions is paramount for good performance in Complex II. Furthermore, most literature combines Complex II and Complex III as a single unit (counter-attack); however, our results reinforce the notion that these two game complexes differ and should be analysed separately.
Performance Analysis, Systemic Analysis, Social Networks, Game Logic
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