Musa, Rabiu Muazu and Majeed, Anwar P. P. Abdul and Suhaimi, Muhammad Zuhaili and Abdullah, Mohamad Razali and Mohd Razman, Mohd Azraai and Abdelhakim, Deboucha and Abu Osman, Noor Azuan (2023) Identification of high-performance volleyball players from anthropometric variables and psychological readiness: A machine-learning approach. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 237 (4). pp. 317-324. ISSN 1754-3371, DOI https://doi.org/10.1177/17543371211045451.
Full text not available from this repository.Abstract
Modern indoor volleyball has evolved into a high-level strength sport and is seen as one of the most popular open-skilled team sports. The nature of the sport as an open-based skill requires players to have a high degree of both psychological skill and physical ability to cope with the sport's externally and internally induced pace. The purposes of this study were to examine the essential basic anthropometric variables, as well as competition and practice psychological readiness, that could provide a performance edge and identify high and low-performance players based on the parameters. The anthropometric variables of height, weight, and age were assessed, while the test for performance strategies instrument was used to evaluate competition and practice psychological readiness skills of the players. The players' performances were analyzed in real-time during a volleyball tournament. The Louvain clustering algorithm was used to determine the performance class of the players with reference to the variables evaluated. A total of 45 players were ascertained as high-performance volleyball players (HVP), while 20 players were deemed as low-performance volleyball players (LVP) via the clustering analysis technique. The logistic regression classifier was used to classify the performance of the players. Nonetheless, owing to the skewed representation between the HVP and LVP during the training of the model, the Synthetic Minority Oversampling TEchnique (SMOTE) was employed to artificially increase the minority class dataset to avoid the overfitting notion upon classification. It was shown from the study that, through the machine learning pipeline developed, an excellent identification of the HVP and LVP could be attained. The findings could be invaluable to coaches and other relevant stakeholders in team preparation and the selection of high-performance players in volleyball.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Anthropometric index; Psychological readiness; Indoor volleyball; High-performance players; Logistic regression; SMOTE |
| Subjects: | T Technology > TJ Mechanical engineering and machinery |
| Divisions: | Faculty of Engineering > Department of Biomedical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 10 Nov 2025 06:31 |
| Last Modified: | 10 Nov 2025 06:31 |
| URI: | http://eprints.um.edu.my/id/eprint/49562 |
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