The main objective was to know the elite coaches’ opinions regarding the relevance, definition, and importance of volleyball contextual variables to measure individual performance in competition. After performing a literature review, an instrument to gather the opinion of the world’s elite volleyball coaches was elaborated by four volleyball specialists. The sample of experts consisted in 20 world’s elite volleyball coaches who met at least three experience years in first division or national teams. The instrument collected experts’ information on the contextual variables in relation to relevance, definition, and importance. Cronbach’s α and Aiken’s V coefficient were used to test the reliability and content validity of the contextual variables, respectively. To compare the importance of the contextual variables U de Mann-Whitney and Kruskal-Wallis tests were used. Results showed that opposition level, set period, score difference, results of the previous set, competitive load variables, high level, final periods ≥20, and high load categories were relevant (Aikens V > 0.70). In addition, high level, final period ≥20 and ≥+10, and high load categories were significantly more important (p < 0.05). We conclude that, according to the elite coaches, the contextual variables should not be analyzed separately. Future studies should consider contextual variables dynamically.

In this study, we proposed novel metrics for evaluating volleyball technical performance in relation to the action context. To assess each player’s relative participation, we also introduced relative contextual coefficients. We analyzed 20 games played by the world’s top eight teams during the 2019 FIVB Women’s Club World Championship, using Data Volley software and Python programming. We evaluated inter- and intra-observer reliability and used binomial logistic regression models to estimate each variable’s probability of having contributed to the team’s set win. We calculated estimated confidence intervals, standard errors, and Wald values; and we employed Akaike’s and Bayesian criteria to evaluate the model’s goodness of fit. We identified optimal cut-off points using receiver operating characteristic curves, and we found that the proposed contextual evaluation coefficients prevented overestimation of a player’s technical performance in uneven situations. We addressed the issue in which the winning team may be the one that scored the fewest points, and we were able to predict a team’s victory with confidence. The proposed coefficients made multiple technical and contextual aspects of the game easily accessible and comprehensible, offering significant beneficial implications for coaches and players.

The aim of the present study was to examine the impact of maturation and relative age on the anthropometric variables and physical performance of young elite volleyball players according to sex and playing positions. The sample included 207 girls (13.59 ± 1.74 years) and 59 boys (14.30 ± 1.48 years) who were selected to participate in the 2020–2025 Spanish National Volleyball Programme. Maturity status was estimated using a non-invasive method (percentage of predicted adult height). Relative age was calculated based on date of birth and expressed as decimal age (0–0.99). The physical tests carried out were spike jump reach, vertical jump, 3 × 9, and strength–endurance–agility–coordination (FRAC) tests. The results showed that there was no impact of the interaction between maturity status and relative age on physical performance, except in the 3 × 9 test by boys in the wing-spiker position. Moreover, maturity status had a greater influence on physical test performance than that of relative age. Specifically, maturation served as a statistically significant positive predictor of height in the SJR test for girls who were all-around players, explaining 71.58% of the variance. In addition, an advanced maturity status correlated with better physical performance outcomes, especially in the all-around player and wing-spiker playing positions in boys and the middle-blocker and all-around player playing positions in girls. Coaches and stakeholders should implement strategies to reduce bias, especially regarding maturation, with the aim of retaining the most physically talented late-maturing players, considering differences by playing position and sex.

This study aimed to compare four team classification methods—hierarchical clustering (HC), final ranking (FR), quartiles (Q), and median split (MS)—to analyze the Home Advantage (HA) in handball. The objective was to evaluate the effectiveness of each method in segmenting teams based on performance level, with particular attention to sex-based differences. Data were collected from the first divisions of the Spanish men's and women's handball leagues over ten seasons (2013–2024), excluding the suspended season due to COVID-19. HA was calculated as the percentage of points earned at home out of total season points. Teams were grouped into high-level teams (HLT), medium-level teams (MLT), and low-level teams (LLT) according to their win percentage using each of the four methods. Each classification method presented distinct strengths and limitations. HC allowed flexible, data-driven grouping and revealed subtle differences between teams, but was sensitive to sample variation. FR, commonly used in sports performance analysis, offered intuitive understanding but lacked statistical granularity. Q provided a detailed segmentation suitable for heterogeneous leagues but sometimes obscured the practical interpretation. MS was simple and efficient, but it risked oversimplifying performance differences. Results showed that HC and Q were more effective at identifying performance patterns, especially when analyzing differences by sex. However, no single method proved to be universally superior. The choice of grouping strategy should be aligned with the specific objectives of the analysis. These findings provide methodological guidance for researchers and practitioners in sport science, with implications for tactical planning, talent identification, and competition analysis in team sports.

The aim of this study was to determine the impact of different contextual variables on winning volleyball sets. The variables were selected based on their significance as determined by expert coaches. The sample consisted of 1,849 sets, representing all matches played in both categories during the 2022 and 2023 Volleyball Nations League and the 2021 Olympic Games. To analyse the variables, multivariate logistic regressions and Markov chains were applied. The results showed that opponent level explained 21.6% of the variability found; being especially relevant when playing against opponents separated by two competitive levels. Winning the previous set increased the chances of winning the next set by 7.83%. Leading the score at the end of both set periods enhanced the likelihood of winning the set, reaching 87.12% when finishing ahead in both periods. Moreover, at the end of the second period, each additional point increased the likelihood of winning the set by 1.54%. These results signify an advancement in comprehending the impact of contextual variables on winning high-level volleyball sets.