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

López Serrano, Carlos ORCID: https://orcid.org/0000-0002-3661-8705, Zakynthinaki, María S., Mon López, Daniel ORCID: https://orcid.org/0000-0001-8333-1304 and Molina Martín, Juan José ORCID: https://orcid.org/0000-0003-2458-5974 (2024). Introducing the technical individual contribution coefficient: a metric for evaluating performance in elite volleyball. "International Journal of Performance Analysis in Sport", v. 24 (n. 3); pp. 204-217. ISSN 1474-8185. https://doi.org/10.1080/24748668.2023.2278380.

Descripción

Título: Introducing the technical individual contribution coefficient: a metric for evaluating performance in elite volleyball
Autor/es:
Tipo de Documento: Artículo
Título de Revista/Publicación: International Journal of Performance Analysis in Sport
Fecha: 1 Enero 2024
ISSN: 1474-8185
Volumen: 24
Número: 3
Materias:
Palabras Clave Informales: Data Analysis; Efficiency; Individual Assessment; Python Programming; Team Sport; Volleyball; Data Analysis; Level; Sport Analytics
Escuela: Facultad de Ciencias de la Actividad Física y del Deporte (INEF) (UPM)
Departamento: Deportes
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 86874
Identificador DC: https://oa.upm.es/86874/
Identificador OAI: oai:oa.upm.es:86874
URL Portal Científico: https://portalcientifico.upm.es/es/ipublic/item/10115053
Identificador DOI: 10.1080/24748668.2023.2278380
Depositado por: iMarina Portal Científico
Depositado el: 29 Ene 2025 08:29
Ultima Modificación: 29 Ene 2025 08:29