TY  - JOUR
PY  - 2024//
TI  - Physical fitness and motor ability parameters as predictors for skateboarding performance: a logistic regression modelling analysis
JO  - PLoS one
A1  - Ab Rasid, Aina Munirah
A1  - Muazu Musa, Rabiu
A1  - Abdul Majeed, Anwar P. P.
A1  - Musawi Maliki, Ahmad Bisyri Husin
A1  - Abdullah, Mohamad Razali
A1  - Mohd Razmaan, Mohd Azraai
A1  - Abu Osman, Noor Azuan
SP  - e0296467
EP  - e0296467
VL  - 19
IS  - 2
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1932-6203
UR  - http://dx.doi.org/10.1371/journal.pone.0296467
ID  - ref1
ER  -