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.
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