Contextualizing Evaluation of Performance in Volleyball: Introducing Contextual Individual Contribution Coefficients to Assess Technical Actions

Abstract

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.