Cover Image

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

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 e...

Full description

Saved in:
Bibliographic Details
Main Authors: Aina Munirah, Ab Rasid, Rabiu Muazu, Musa, Abdul Majeed, Anwar P. P., Maliki, Ahmad Bisyri Husin Musawi, Mohamad Razali, Abdullah, Mohd Azraai, Mohd Razman, Noor Azuan, Abu Osman
Format: Article
Language:English
Published: Public Library of Science 2024
Subjects:
TJ Mechanical engineering and machinery
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/41132/1/Physical%20fitness%20and%20motor%20ability%20parameters%20as%20predictors%20for%20skateboarding%20performance.pdf
http://umpir.ump.edu.my/id/eprint/41132/
https://doi.org/10.1371/journal.pone.0296467
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.41132
record_format eprints
spelling my.ump.umpir.411322024-05-07T06:45:18Z http://umpir.ump.edu.my/id/eprint/41132/ Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis Aina Munirah, Ab Rasid Rabiu Muazu, Musa Abdul Majeed, Anwar P. P. Maliki, Ahmad Bisyri Husin Musawi Mohamad Razali, Abdullah Mohd Azraai, Mohd Razman Noor Azuan, Abu Osman TJ Mechanical engineering and machinery TS Manufactures 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. Public Library of Science 2024 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/41132/1/Physical%20fitness%20and%20motor%20ability%20parameters%20as%20predictors%20for%20skateboarding%20performance.pdf 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) https://doi.org/10.1371/journal.pone.0296467 10.1371/journal.pone.0296467
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TJ Mechanical engineering and machinery
TS Manufactures
spellingShingle TJ Mechanical engineering and machinery
TS Manufactures
Aina Munirah, Ab Rasid
Rabiu Muazu, Musa
Abdul Majeed, Anwar P. P.
Maliki, Ahmad Bisyri Husin Musawi
Mohamad Razali, Abdullah
Mohd Azraai, Mohd Razman
Noor Azuan, Abu Osman
Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis
description 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.
format Article
author Aina Munirah, Ab Rasid
Rabiu Muazu, Musa
Abdul Majeed, Anwar P. P.
Maliki, Ahmad Bisyri Husin Musawi
Mohamad Razali, Abdullah
Mohd Azraai, Mohd Razman
Noor Azuan, Abu Osman
author_facet Aina Munirah, Ab Rasid
Rabiu Muazu, Musa
Abdul Majeed, Anwar P. P.
Maliki, Ahmad Bisyri Husin Musawi
Mohamad Razali, Abdullah
Mohd Azraai, Mohd Razman
Noor Azuan, Abu Osman
author_sort Aina Munirah, Ab Rasid
title Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis
title_short Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis
title_full Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis
title_fullStr Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis
title_full_unstemmed Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis
title_sort physical fitness and motor ability parameters as predictors for skateboarding performance: a logistic regression modelling analysis
publisher Public Library of Science
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41132/1/Physical%20fitness%20and%20motor%20ability%20parameters%20as%20predictors%20for%20skateboarding%20performance.pdf
http://umpir.ump.edu.my/id/eprint/41132/
https://doi.org/10.1371/journal.pone.0296467
_version_ 1822924307971440640
score 13.235362

Similar Items