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.

Studies on small-sided games (SSG) in team sports have increased in recent decades. However, the literature concerning this training strategy in volleyball is sparse. This study aims to summarize and analyse the scientific evidence on SSG in volleyball. For this purpose, electronic searches were...

Background: Stretching has garnered significant attention in sports sciences, resulting in numerous studies. However, there is no comprehensive overview on investigation of stretching in healthy athletes.Objectives: To perform a systematic scoping review with an evidence gap map of stretching studies in healthy athletes, identify current gaps in the literature, and provide stakeholders with priorities for future research.Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 and PRISMA-ScR guidelines were followed. We included studies comprising healthy athletes exposed to acute and/or chronic stretching interventions. Six databases were searched (CINAHL, EMBASE, PubMed, Scopus, SPORTDiscus, and Web of Science) until 1 January 2023. The relevant data were narratively synthesized; quantitative data summaries were provided for key data items. An evidence gap map was developed to offer an overview of the existing research and relevant gaps.Results: Of ~ 220,000 screened records, we included 300 trials involving 7080 athletes [mostly males (~ 65% versus ~ 20% female, and ~ 15% unreported) under 36 years of age; tiers 2 and 3 of the Participant Classification Framework] across 43 sports. Sports requiring extreme range of motion (e.g., gymnastics) were underrepresented. Most trials assessed the acute effects of stretching, with chronic effects being scrutinized in less than 20% of trials. Chronic interventions averaged 7.4 ± 5.1 weeks and never exceeded 6 months. Most trials (~ 85%) implemented stretching within the warm-up, with other application timings (e.g., post-exercise) being under-researched. Most trials examined static active stretching (62.3%), followed by dynamic stretching (38.3%) and proprioceptive neuromuscular facilitation (PNF) stretching (12.0%), with scarce research on alternative methods (e.g., ballistic stretching). Comparators were mostly limited to passive controls, with ~ 25% of trials including active controls (e.g., strength training). The lower limbs were primarily targeted by interventions (~ 75%). Reporting of dose was heterogeneous in style (e.g., 10 repetitions versus 10 s for dynamic stretching) and completeness of information (i.e., with disparities in the comprehensiveness of the provided information). Most trials (~ 90%) reported performance-related outcomes (mainly strength/power and range of motion); sport-specific outcomes were collected in less than 15% of trials. Biomechanical, physiological, and neural/psychological outcomes were assessed sparsely and heterogeneously; only five trials investigated injury-related outcomes.Conclusions: There is room for improvement, with many areas of research on stretching being underexplored and others currently too heterogeneous for reliable comparisons between studies. There is limited representation of elite-level athletes (~ 5% tier 4 and no tier 5) and underpowered sample sizes (≤ 20 participants). Research was biased toward adult male athletes of sports not requiring extreme ranges of motion, and mostly assessed the acute effects of static active stretching and dynamic stretching during the warm-up. Dose–response relationships remain largely underexplored. Outcomes were mostly limited to general performance testing. Injury prevention and other effects of stretching remain poorly investigated. These relevant research gaps should be prioritized by funding policies.Registration: OSF project (https://osf.io/6auyj/) and registration (https://osf.io/gu8ya).

Match Analysis has provided valuable insights for understanding performance. Social Network Analysis has delivered powerful contributions, especially with its most recent game-centred approaches, coupled with the weighting of direct and indirect connections between game actions. The goal was to expand upon these applications, analysing whether different networks associate with distinct levels of play efficacy applied to high-level men’s volleyball. Eigenvector Centrality was used, and one network was created for each level of play efficacy: 0 (error), 1 (continuity) and 2 (scoring a point). The sample was composed by 1,618 game actions (9 matches from the 2015 World League Finals) of six functional game complexes (i.e. K0 to KV). Results showed similar networks for all efficacy levels, suggesting that game patterns may not be as relevant for discriminating efficacy as individual actions, and/or that the chosen variables were not sensitive enough to differentiate system dynamics. Overall, actions performed under non-ideal conditions were most central in almost all game complexes, which suggests that playing off-system is the rule instead of the exception. It is therefore important to promote tasks that stimulate playing under non-ideal conditions and expose the attackers to stressful conditions (e.g. cohesive blocks), potentially providing transfer from training to competing.

Change is pervasive, but emerging patterns are occasionally detectable through analysis of systemic behaviors. Match analysis uses these patterns in order to reduce the degree of improvisation and to optimize the training process. However, it is possible that certain game phases elude systematic patterning. In this vein, our aim was to analyze the case of attack coverage in men’s volleyball, as we suspected it would elude systematic patterning and has received negligible attention in scientific research. We analyzed the occurrence of attack coverage in 4544 plays of the 2011 Volleyball World League. A Chi-square test with residual adjusted values was applied to explore significant associations between variables. A Monte Carlo correction was applied, as some cells had n<5. Effect sizes were determined using Cramer’s V. Overall, attack coverage occurred in 3.89% of ball possessions, and 23 distinct structures emerged. These structures lacked significant associations with the game complex, setting zone, and effect of attack coverage. Conversely, attack coverage structures showed significant associations with the attack zone and tempo, with very strong effect sizes (V=0.472 and V=0.521, respectively). As certain attack zones are deeply associated with attack tempo, it is apparent that quicker attack plays affect attack coverage structuring, promoting the formation of less complex structures. Ultimately, attack coverage structures seem to depend on momentary constraints, thereby rendering rigid systematization impracticable. Still, we contended that a principle-based approach might be suitable. This invites researchers to rethink how to interpret game regularities.

Purpose: To compare the tactical-technical behavior of volleyball players according to the manipulation of court size in small-sided games. Method: We analyzed the tactical-technical behaviors of 16 male players (12.2 ± 0.5 years and 1.2 ± 0.8 years of practice) using the components of the GPAI instrument (Adjustment, efficiency, decision-making and effectiveness) validated for volleyball. To this end, we examined 1614 transition actions (defense, setting and attack) from games played in four court dimensions (3.0 × 3.0m, 4.0 × 4.0m, 4.6 × 4.6m and 5.2 × 5.2m). Altogether 96 Volleyball games were analyzed. Results: The smaller area per player favored technical skills development, specifically defensive and offensive ones. On the other hand, a larger area per player promoted higher scores in decision-making and effectiveness. Conclusion: Our findings indicate that the players’ behaviors in small-sided games depend on the game configurations, since modifications in the tactical-technical behavior emanate from the game ecology. Thus, the court size manipulation is a powerful pedagogical tool that deeply relates to learning outcomes. Coaches must consider such constraints in the teaching-learning process, since small-sided games manipulations should be linked with clear learning goals.