No abstract

Critical scenarios are highly relevant to match analysis because they contribute to a better understanding of performance and provide essential information about team evolution. The goal of this study was to investigate inter-team variability in high-level men's volleyball during critical game scenarios (i.e., non-ideal setting conditions). Ten matches of the Men’s 2019 Volleyball Nations League Finals (Russia, USA, Poland, Brazil, Iran, France) were analyzed (n = 649 plays). Six independent Eigenvector Centrality networks were created (632 nodes; 3507 edges) using Social Network Analysis. When playing under critical scenarios the top two ranked teams differed in side-out attack. Specifically, the USA presented quick attacks, mainly in zone 4, using both the strong attack and exploration of the block. Conversely, Russia presented a game with high attack tempos and strong attacks. The USA and Russia also differed from Poland and Brazil in their approach to the game, the latter two teams using a varied attack (between strong, exploited, and directed attacks). After one error in attack, most teams presented a game style characterized by strong attacks, although Russia played using exploration of the block. The study shows teams competing at the same competitive level have differences in game patterns. The variability in approaches to the attack in critical scenarios (e.g., under non-ideal setting conditions and/or after consecutive attack errors) revealed that teams find different solutions for similar problems. Findings imply that match analysis should focus on exploring inter-team differences in gameplay while being cautious when interpreting aggregate data.  Resumen. Los escenarios críticos son muy relevantes para el análisis de partidos porque contribuyen a una mejor comprensión del rendimiento y proporcionan información esencial sobre la evolución del equipo. El objetivo de este estudio fue investigar la variabilidad entre equipos en el voleibol masculino de alto nivel durante escenarios críticos de juego (principalmente en condiciones de colocación no ideales). Se analizaron diez partidos de las Finales de la Liga de Naciones de Voleibol Masculino 2019 (Rusia, Estados Unidos, Polonia, Brasil, Irán, Francia) (n=649 jugadas). Se crearon seis redes de centralidad de autovector independientes (632 nodos; 3507 bordes) utilizando el análisis de redes sociales. Cuando se jugaba en escenarios críticos, los dos mejores equipos clasificados diferían en ataque lateral. Específicamente, los Estados Unidos presentaron ataques rápidos, principalmente en la zona 4, utilizando tanto el fuerte ataque como la exploración del bloqueo. Por el contrario, Rusia presentó un juego con altos ritmos de ataque y ataques fuertes. Los Estados Unidos y Rusia también se diferenciaron de Polonia y Brasil en su enfoque del juego, los dos últimos equipos utilizando un ataque variado (entre ataques fuertes, explotados y dirigidos). Después de un error en ataque, la mayoría de los equipos presentaron un estilo de juego caracterizado por ataques fuertes, aunque Rusia jugó utilizando la exploración del bloque. El estudio muestra que los equipos que compiten al mismo nivel competitivo tienen diferencias en los patrones de juego. La variabilidad en los enfoques del ataque en escenarios críticos (en condiciones de colocación no ideales y/o después de errores de ataque consecutivos) reveló que los equipos encuentran diferentes soluciones para problemas similares. Los hallazgos implican que el análisis de partidos debe centrarse en explorar las diferencias entre equipos en el juego y, al mismo tiempo, ser cauteloso al interpretar los datos agregados.

In sports, game scenarios can be critical or non-critical, potentially presenting very distinct implications for the game flow. However, defining a critical scenario is not an easy task. Although there is some research on game scenarios, game situations, game moments, and critical moments, through this systematic review we intend to fill a gap in the knowledge of critical scenarios in order to structure the existing knowledge and pinpoint current limitations. the search was conducted in July of 2020 in Web of Science, Scopus, PubMed, SciELO, and, through a manual search, in Google Scholar. the eligibility criteria included original research with quantitative and/or qualitative analysis of critical game scenarios or game moments. the participants were humans of any gender, age group, health status, competitive level, or expertise. Risk of bias assessment involved 14 criteria in the evaluation of the studies. the study synthesis methods followed a qualitative synthesis of the main results of each study in the final sample. Of the 279 researched articles, only 4 met the inclusion criteria, i.e. only 4 provided data concerning critical game scenarios. their contributions are discussed in detail, as is the open research windows for the future. Overall, there is clearly a need for more research specifically addressing this topic, with a huge gap between theoretical relevance and actual investigation.

A wide body of research on team sports has focused on positional status based differences, providing information on inter-player variability according to the functional roles within the game. However, research addressing inter-player variability within the same positional/function status is scarce. The present article presents an analysis of inter-player variability within the same positional status during critical moments, in high-level women's volleyball, using Social Network Analysis. Attack actions of the outside hitters near (OHN) and away (OHA) from the setter were analysed in ten matches from the 2019 Volleyball Nations League Finals (268 plays). Two independent Eigenvector Centrality networks were created, one for OHN and another for OHA. Main results: (a) in side-out with ideal setting conditions, the OHA used more tips and exploration of the block than the OHN; under non-ideal setting conditions, the OHN had slower attack tempos than the OHA; (b) OHA used tip and directed attacks after error situations while OHN was typically not requested after error situations; (c) in transition, OHN typically attacked after having performed a previous action, performing a dual task within each ball possession, while OHA only attacked when there was no prior action; (d) there were also inter-positional similarities, with both OHN and OHA preferring a strong attack in ideal conditions during KI and KIV, and slower tempos in transition in non-ideal conditions. Conclusions: Even within the same positional status, there seems to be subtle, but relevant inter-player variability. Consequently, coaches should devote careful attention when assigning players to positional.

In sports, there may be multiple players for the same positional status (e.g., in volleyball, there are two outside hitters, one near the setter and the other away from the setter), and there may be relevant differences within the same positional status. We analyzed inter-player variability within the same positional status in high-level men’s volleyball, through Social Network Analysis (through Gephi© 0.9.2 software). Attack actions of the outside hitters near (OHN) and away (OHA) from the setters were analyzed in ten matches from the 2019 Volleyball Nations League Finals (278 plays). Two Eigenvector Centrality networks were created. Results: (a) in side-out under non-ideal setting conditions, OHNs preferred the strong attack while OHAs alternated between the strong attack and the tip; (b) after a prior action, OHNs attacked via exploration of the block while OHAs preferred the tip; (c) after consecutive errors, OHNs play more in the opponent’s error; (d) after a previous defense action, OHNs preferred the strong attack and exploration of the block while OHAs preferred the strong attack; (e) in transition, OHNs were solicited under non-ideal setting conditions while OHAs were solicited in ideal and non-ideal conditions. Our findings demonstrate variability between players of the same team and having the same positional status. This allows coaches to understand the key differences of players with the same position, and thus better assign the sub-functions. Researchers should be cautious of aggregating data from players of different positional status, and even from players within the same positional status