Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis

Aina Munirah Ab Rasid, Rabiu Muazu Musa, Anwar P.P. Abdul Majeed, Ahmad Bisyri Husin Musawi Maliki, Mohamad Razali Abdullah, Mohd Azraai Mohd Razmaan, Noor Azuan Abu Osman

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)e0296467
JournalPLoS ONE
Issue number2
Publication statusPublished - 2024


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