Το έργο με τίτλο Introducing the technical individual contribution coefficient: a metric for evaluating performance in elite volleyball από τον/τους δημιουργό/ούς López-Serrano Carlos, Zakynthinaki Maria, Mon-López Daniel, Molina-Martín Juan José διατίθεται με την άδεια Creative Commons Αναφορά Δημιουργού-Μή Εμπορική Χρήση-Όχι Παράγωγα Έργα 4.0 Διεθνές
Βιβλιογραφική Αναφορά
C. López-Serrano, M. S. Zakynthinaki, D. Mon-López and J. J. Molina-Martín, “Introducing the technical individual contribution coefficient: a metric for evaluating performance in elite volleyball,” Int. J. Perform. Anal. Sport, 2023, doi: 10.1080/24748668.2023.2278380.
https://doi.org/10.1080/24748668.2023.2278380
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 utilized to predict set outcomesbased 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 utilized make it easier to understand multiple comparisons and to identify ways for improving performance.