Complex I in male elite volleyball: an interactional analysis according to reception location

Rocha, Augusto Cézar Rodrigues; Laporta, Lorenzo; Lira, Claudio André Barbosa de; Modenesi, Henrique; Figueiredo, Lucas Savassi

doi:10.1080/24748668.2021.2003961

Cited by 5 publications

(12 citation statements)

References 45 publications

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“…The reception micro-analysis confirms the game trend existing in high-level volleyball, in which it is necessary that the serve restricts the offensive construction [ 18 , 46 ]. Therefore, directing the serve in more distant zones of the net and in the net outside hitter showed a decrease in the offensive structuring, as well as reducing the reception effect [ 22 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

“…These results corroborate the literature, since the blocks faced are mostly single or double [ 61 , 62 ]. On the other hand, the need to avoid the block touching in attacked ball is notorious, since this touch reduces the attack speed, allowing more time for the defense reaction and subsequent counterattack construction [ 22 , 43 ]. In addition, the blocking strategy of reacting to the ball prevents the attack from overlapping, since, in conditions of organized attack, it is possible to use 4 attackers in the current offensive structure of high-level teams [ 55 ].…”

Section: Discussionmentioning

confidence: 99%

“…Although the results are applicable and represent the game type played by the world best teams, the present research has limitations, as the fact that we do not analyze the inter- and intra-team variability according to the confrontation carried out, restricts the extrapolation power of the results, since different strategies can be used game by game, as well as the team situation in the competition can interfere in the adopted game pattern. Therefore, we suggest that future studies analyze the tactical behavior of teams in each game, seeking to find patterns that specify team performance, since the elite men’s teams have a tendency to play in-system in KI situations [ 14 , 15 , 24 ], it is necessary for coaches carry out training in- and off-system [ 22 ], being important to prepare the teams for the competition and develop the ability to respond to the constraints imposed by the game’s ecology, considering the critical game scenarios [ 38 , 66 ].…”

Section: Discussionmentioning

confidence: 99%

“…Studies about men’s high-level volleyball based on SNA reveal that most teams play in-system in KI, and at the same time, present in- and off-system situations in the offensive construction in KII [ 10 , 14 , 15 , 38 ]. In addition, when considering only in-system situations in KI, the reception zones further away from the network (Z5, Z6, Z7, Z8, Z9 and Z1) presented higher centrality values with the other game actions, suggesting greater diversity in the game type played [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…Research on volleyball in Complex I considered that reception predicts the setting efficacy [9,16] and the attack success (point) [17][18][19], and when the reception is not effective (decreased attack options) the chances increase of lost the match [20,21]. Reception also influences the setting location revealing the importance of subsequent actions [22]. There are high possibilities of in-system situations occurring in this complex [23,24], with more powerful attacks occur after fast settings resulting in attack points [25].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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

Nascimento,

Laporta,

Pedrosa

et al. 2023

Percept Mot Skills

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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.

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Modeling the gameplay actions of elite volleyball players and teams based on statistical match reports

Iermakov,

Yermakova,

Prusik

2023

Pedagogy of Health

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Background and Study Aim. In modern sports analysis statistical modeling of gameplay actions based on match data is becoming a key tool for optimizing training processes and tactical preparation. The aim of the research is to create models of volleyball players' actions based on statistical reports of the 2022 World Championship matches. Materials and methods. The study used statistical data on the World Volleyball Championship matches among men. The data was extracted from open internet sources and converted into tables in CSV format. These tables were processed in the PyCharm programming environment using Python code. The pandas library was used for data analysis and statistical operations, and 'scikit-learn' for machine learning. Results. Models are presented that best predict the results for teams and volleyball players. Important features for teams have been identified, indicating the successful execution of game elements for the team. The regression equations for the team represent a linear combination of various gameplay metrics that affect the total number of points the team scores in a match. They also emphasize the importance of action elements. Linear regression equations predict the total number of points a volleyball player scores based on various statistical indicators. Conclusions. It is recommended to use statistical modeling to optimize training and tactical strategies based on key gameplay metrics. Linear regression equations can assist in evaluating the effectiveness of a player and team. Regular data updates will ensure the relevance of models for better match preparation. Consideration should be given to the possibilities of implementing analytical tools based on the developed models into training programs to optimize the team's preparation for future matches.

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Complex I in male elite volleyball: an interactional analysis according to reception location

Cited by 5 publications

References 45 publications

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

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

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

Modeling the gameplay actions of elite volleyball players and teams based on statistical match reports

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