Using social network analysis to assess play efficacy according to game patterns: a game-centred approach in high-level men’s volleyball

The Decision-Making of High-Level Volleyball Setters in the 2021-2022 Volleyball Men's Superliga: Does the Opponent Matter?

Our primary objective in this study was to investigate the offensive strategies employed in the attack phase of men's volleyball, specifically focusing on side-out as stratified by the type of confrontation that was determined by the opponent's team performance. We analyzed 5524 attacking actions during 22 games of the 12 teams that participated in the Volleyball Men's Superliga (season 2021-2022). Based on their final rankings in the championship, we classified these teams into three tiers: high-performance, intermediate-performance, and low-performance. Subsequently, we examined the dynamics of these matches using Social Network Analysis. We found that the opponent teams' performance levels did not influence the game dynamics. Notably, the eigenvector values were prominently higher for Attack Zones 2 and 4, wherein the middle-blocker jumped to attack close to the setter across all networks. Thus, setters opted for traditional and low-risk strategies to minimize errors, disregarding available information about the skill level of the opposing team, making their offensive tactics predictable.

Inter e intra-variability of the best ranked teams: A network analysis in male high-level volleyball

The present research objective was to analyze the offensive phase from Complex I in high-level male volleyball teams in a macro- and micro-level view, through the inter e intra-team variability analysis of eight best teams of the 2018 Men's Volleyball World Championship over the social network analysis and eigenvector centrality. The sample consisted of 22 matches and 2,743 offensive actions, resulting in 8 sub-networks with 368 nodes and 6221 edges. The results showed from macro view the variables that presented highest centrality values were Attack Zone 4 (range 0.56-0.90), Attack Tempo 2 (0.65-0.87), Power Attack (0.62-0.94), No Touch Block (0.61-1), Attack Effect Continuity (0.59-0.94), and Middle Blocker Centralized (0.60-0.95). In a micro view, Reception Effect, Play Position, Reception Zone, and Block Composition showed high variability in each sub-network. The intra- and inter-team variability presented the importance of to respect each team idiosyncrasies and to consider the different approaches to the game and success.

Sport Performance Analysis with a Focus on Racket Sports: A Review

Athletes, both professional and amateur, are always looking for ways to improve their performance. With the introduction and increasing availability of modern technologies and smart devices arose the need to measure and analyze performance, but likewise, the use of these innovations as a competitive advantage also arose. Scientific publications reflect the wide range of available approaches and technologies, as well as the growing interest in various sports. As a result, we concentrated on a systematic review of publications that presented performance analysis tools and methods in all sports, with a final focus on racket sports. Clarivate Analytics’ Web of Science (WoS) and Elsevier Inc.’s SCOPUS databases were searched for 1147 studies that conducted performance analysis and sports research and were published in English. The data in the systematic review are current, up until 18 May 2021. A general review was performed on 759 items, and then 65 racket sports publications were thoroughly scrutinized. We concentrated on performance data, data collection and analysis tools, performance analysis methods, and software. We also talked about performance prediction. In performance research, we have identified specific approaches for specific sports as well as key countries. We are also considering expanding performance analysis in to E-sports in the future.

Effects of Congested Fixture on Men's Volleyball Load Demands: Interactions with Sets Played

The purpose of this study was to compare the external load, internal load, and technical efficacy between the first and the second matches (M1 and M2) occurring in congested fixtures (two matches in two days) using the number of sets as a moderating factor. An observational analytic research design was adopted. Data from official volleyball matches were collected during the first competitive period of the championship, comprising 14 competitive games within 10 weeks. Ten male elite volleyball athletes (age: 21.7 ± 4.19 years of age; experience: 6.2 ± 3.8 years; body mass: 85.7 ± 8.69 kg; height: 192.4 ± 6.25 cm; BMI: 23.1 ± 1.40 kg/m2) participated in this study. Players were monitored for external load (number of jumps and height of jumps) and internal load (using the rate of perceived exertion-RPE). Additionally, notational analysis collected information about attack efficacy and receptions made during matches. The mixed ANOVA revealed no significant interaction between time (M1 vs. M2) and number of sets for number of jumps per minute (p = 0.235; ηp2 = 0.114), mean jump height (p = 0.076; ηp2 = 0.193), RPE (p = 0.261; ηp2 = 0.106), attack efficacy (p = 0.346; ηp2 = 0.085), Positive reception (p = 0.980; ηp2 = 0.002) and Perfect reception (p = 0.762; ηp2 = 0.022). In conclusion, congested fixtures do not seem to affect the performance of volleyball players negatively.

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

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.