Analysis of volleyball matches based on normalized indicators of competitive activity

Abstract

Background and Study Aim. The analysis of volleyball matches plays a key role in understanding game dynamics, evaluating tactical effectiveness, and optimizing player training. The aim of this study is to identify key patterns and trends that influence the performance of volleyball players and teams.

Materials and Methods. Data from the European Volleyball Federation's European Golden League – Men, 2024, were used for the analysis. The sample included competitive performance indicators of 210 players from 12 teams, categorized by playing position: Setter, Middle Blocker, Opposite, Outside Spiker, and Libero. The data were normalized to calculate key metrics, including total points scored, serve efficiency, reception efficiency, attack efficiency, and blocking efficiency. Statistical analysis involved calculating means, medians, and standard deviations, as well as conducting correlation and cluster analyses. Correlations were assessed using Spearman’s method, and visualization was performed using heatmaps. Cluster analysis was conducted using the K-means method to group players by position. Regression analysis was applied to determine relationships between performance indicators and player effectiveness.

Results. The analysis showed that the use of normalized data allowed for the identification of the best players in each team, except for the Libero and Setter positions. The average total points scored by team leaders was 76.4 ± 12.3, while attack efficiency reached 0.82 ± 0.15. Correlation analysis revealed a positive correlation between Ace and Ace per Set (r = 0.983) for players in the Setter and Outside Spiker positions, as well as between Total Points and Total Points (r = 0.976) for Middle Blockers. Cluster analysis identified three groups of players: consistent performers (Total Points in the range of 35–45), high-performing leaders (Total Points > 60 and Total Points > 80), and players in supporting roles (key performance indicators below 30). Regression analysis established equations demonstrating the influence of the number of sets played and attack efficiency on player performance.

Conclusions. The obtained results demonstrate the significance of normalized indicators for assessing the effectiveness of gameplay actions in volleyball. Correlation and cluster analyses identified key patterns, such as strong relationships between scoring actions (e.g., aces and attack points) and their impact on overall team performance. The regression equations provide an analytical basis for predicting player performance based on their game metrics. These findings can be utilized to optimize tactical preparation and develop individualized training programs aimed at enhancing team effectiveness.