From Tension to Triumph: Design and Implementation of an Innovative Algorithmic Metric for Quantifying Individual Performance in Women Volleyball’s Critical Moments

Abstract

This study introduces the critical individual contribution coefficient (CR-ICC), a novel metric that evaluates player effectiveness in critical moments of the game. We analyzed 16,631 technical actions from the top eight teams across 77 sets of the 2019 FIVB Women’s Club World Championship, ensuring data quality through inter- and intra-observer reliability. Traditional variables such as points scored, attack and reception efficiency, and balance were examined. Python programming was utilized to calculate the values of CR-ICC, which consider the contextual variables of set period, score difference, competitive load, and opponent’s level. Akaike’s and Bayesian information criteria, along with Nagelkerke’s coefficient of determination, were employed. Binomial logistic regression and receiver operating characteristic curves estimated the probability of victory associated with each variable. Interactive dashboards were developed, enabling dynamic analysis and data visualization. Statistically significant differences were observed in all variables (p p < 0.05), at both the team and individual player levels. At the team level, points scored, attack efficiency, and balance exhibited the highest predictive abilities, with CR-ICC also demonstrating a strong predicting ability. The proposed CR-ICC has remarkable potential as a strategic asset for coaches, enabling the identification of players who excel in critical moments of the game.

Keywords

• player effectiveness
• data analysis
• chocking
• set victory prediction
• Python programming
• interactive dashboards