Aina Munirah, Ab Rasid and Rabiu Muazu, Musa and Abdul Majeed, Anwar P. P. and Maliki, Ahmad Bisyri Husin Musawi and Mohamad Razali, Abdullah and Mohd Azraai, Mohd Razman and Noor Azuan, Abu Osman (2024) Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis. PLoS ONE, 19 (e0296467). pp. 1-16. ISSN 1932-6203. (Published)
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Abstract
The identification and prediction of athletic talent are pivotal in the development of successful sporting careers. Traditional subjective assessment methods have proven unreliable due to their inherent subjectivity, prompting the rise of data-driven techniques favoured for their objectivity. This evolution in statistical analysis facilitates the extraction of pertinent athlete information, enabling the recognition of their potential for excellence in their respective sporting careers. In the current study, we applied a logistic regression-based machine learning pipeline (LR) to identify potential skateboarding athletes from a combination of fitness and motor skills performance variables. Forty-five skateboarders recruited from a variety of skateboarding parks were evaluated on various skateboarding tricks while their fitness and motor skills abilities that consist of stork stance test, dynamic balance, sit ups, plank test, standing broad jump, as well as vertical jump, were evaluated. The performances of the skateboarders were clustered and the LR model was developed to classify the classes of the skateboarders. The cluster analysis identified two groups of skateboarders: high and low potential skateboarders. The LR model achieved 90% of mean accuracy specifying excellent prediction of the skateboarder classes. Further sensitivity analysis revealed that static and dynamic balance, lower body strength, and endurance were the most important factors that contributed to the model’s performance. These factors are therefore essential for successful performance in skateboarding. The application of machine learning in talent prediction can greatly assist coaches and other relevant stakeholders in making informed decisions regarding athlete performance.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | logistic regression analysis; machine learning; muscle strength; regression model |
| Subjects: | T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures |
| Faculty/Division: | Faculty of Manufacturing and Mechatronic Engineering Technology |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 07 May 2024 06:45 |
| Last Modified: | 07 May 2024 06:45 |
| URI: | http://umpir.ump.edu.my/id/eprint/41132 |
| Download Statistic: | View Download Statistics |
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