Introducing the technical individual contribution coefficient: a metric for evaluating performance in elite volleyball

ABSTRACT

This study introduces a new metric, the Technical Individual Contribution Coefficient, that enables the quantification of the individual technical performance in elite volleyball, from the practical perspective of coaches. Additionally, three Relative Individual Contribution Coefficients provide complimentary information on the players’ relative participation. Data from 20 matches of eight teams during the 2019 Club World Championship were provided by Data Volley software. The numerical evaluation of the players’ actions was based on experts’ ratings, and all calculations were carried out using Python programming. Binomial logistic regression and the areas calculated under the receiver operating characteristic curves were utilised to predict set outcomes based on team variables. For individual analysis, Spearman’s rho correlations and multiple descriptive analyses were conducted, and dynamic visualisations in Power BI were employed to enhance interpretation. The proposed coefficients efficiently predict both absolute and relative technical performance, across all game actions. This novel metric offers a comprehensive tool for performance evaluation and has significant potential to benefit not only fans and the media, but also coaches and team managers in their decision-making process for player selection. The dynamic visualisations utilised make it easier to understand multiple comparisons and to identify ways for improving performance.

KEYWORDS:

• Sport analytics
• volleyball
• team sport
• individual assessment
• data analysis
• python programming

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Design: All authors contributed. Methodology: M.Z and C.L.S. Data collection: C.L.S. Data analyses: M.Z and C.L.S. JJ.M.M, C.L.S, and M.Z drafted the manuscript and all the authors edited and revised the manuscript. All the authors approved the final version.

Data availability statement

The data that support the findings of this study are openly available in figshare at doi.org/10.6084/m9.figshare.22189657