Determining Essential Fitness and Motor Skill Parameters for Talent Identification in 12-Year-Old Girls: A Comparison of Machine Learning-Based Feature Extraction Techniques

Authors

  • Rabiu Muazu Musa Centre for Fundamental and Continuing Education, Department of Credited Co-Curriculum, Universiti Malaysia Terengganu, Malaysia
  • Siti Musliha Mat-Rasid Faculty of Sports Science and Coaching, Sultan Idris Education University, Tanjong Malim, Perak, Malaysia
  • Jeffrey Fook Lee Low Faculty of Sports Science and Coaching, Sultan Idris Education University, Tanjong Malim, Perak, Malaysia
  • Gunathevan Elumalai Faculty of Sports Science and Coaching, Sultan Idris Education University, Tanjong Malim, Perak, Malaysia
  • Mohd. Izwan Shahril Faculty of Sports Science and Coaching, Sultan Idris Education University, Tanjong Malim, Perak, Malaysia
  • Mohamad Azri Ismail Ahmad Faculty of Sports Science and Coaching, Sultan Idris Education University, Tanjong Malim, Perak, Malaysia
  • Norlaila Azura Kosni Faculty of Sport Science & Recreation, Universiti Teknologi MARA, Pahang Branch, Pahang, Malaysia

Keywords:

Fitness, Machine Learning, Motor Skill, Talent Identification

Abstract

A successful talent identification program relies on the assessment and mapping of relevant parameters for the evaluation of an athlete’s ability. Physical fitness and motor-skill parameters are performance variables that contribute to success in a variety of sports. Although these parameters are prerequisites for any sporting event, their requirements may differ depending on the athletes’ level and age. The present investigation endeavors to identify essential physical fitness and motor skill-related parameters for talent identification in 12-year-old girls. A total of 236 girls were recruited from different schools in Malaysia and completed a standard test for physical fitness and motor skills that constituted of 30-meter run, step test, 1-minute curl up, hand grip, agility T-test, stork balance stand test, and standing broad jump test. Two different feature extraction techniques viz. Symmetrical Uncert Attribute Evaluation (SymmU) and Recursive Feature Elimination (RFE) were employed to determine the important parameters that could be considered for talent identification in 12-year-old girls. Both the SymmU and RFE demonstrated four sets of parameters worthy of consideration. There was agreement in the selection of three parameters across the two techniques (sit and reach, agility, and stork balance stance test). However, SymmU revealed the standing broad jump as the fourth essential parameter, while RFE indicated a 1-minute curl up. Overall, these tests are shown to be essential for consideration during talent identification programs for 12-year-old girls. The identified parameters provide actionable knowledge for coaches and sports organizations, fostering the development of the next generation of elite female athletes.

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References

S. M. Mat-Rasid et al., “The Relative Age Effect on Physical Fitness of School Children,” International Journal of Human Movement and Sports Sciences, vol. 10, no. 5, pp. 1098–1106, Oct. 2022, doi: https://doi.org/10.13189/saj.2022.100528.

A. M. Williams and T. Reilly, “Talent identification and development in soccer,” Journal of sports sciences, vol. 18, no. 9, pp. 657–67, 2000, doi: https://doi.org/10.1080/02640410050120041.

I. Balyi, R. Way, and C. Higgs, Long-Term Athlete Development. Human Kinetics, pp. 27–46, 2013.

R. Vaeyens, M. Lenoir, A. M. Williams, and R. M. Philippaerts, “Talent Identification and Development Programmes in Sport,” Sports Medicine, vol. 38, no. 9, pp. 703–714, 2008, doi: https://doi.org/10.2165/00007256-200838090-00001.

T. A. Haugen, E. Tønnessen, and S. K. Seiler, “The Difference Is in the Start: Impact of Timing and Start Procedure on Sprint Running Performance,” Journal of Strength and Conditioning Research, vol. 26, no. 2, pp. 473–479, Feb. 2012, doi: https://doi.org/10.1519/jsc.0b013e318226030b.

R. M. R., A. N., M. A. B. H. M., M. R. M., K. L. A., and J. H., “Unsupervised Pattern Recognition of Physical Fitness Related Performance Parameters among Terengganu Youth Female Field Hockey Players,” International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 1, p. 100, Feb. 2017, doi: https://doi.org/10.18517/ijaseit.7.1.1145.

R. M. Malina, “Early Sport Specialization,” Current Sports Medicine Reports, vol. 9, no. 6, pp. 364–371, Nov. 2010. doi: https://doi.org/10.1249/JSR.0b013e3181fe3166

A. Nadzmi et al., “Development of Motor Performance Index: A Preliminary Study Among 7 Years Old Malaysian Kids,” Lecture notes in bioengineering, pp. 143–156, Jan. 2023, doi: https://doi.org/10.1007/978-981-99-0297-2_11.

A. F. M. Rozi et al., “The Development of Malaysian Body Somatotype Model using Comprehensive Multivariate Techniques,” International Journal of Recent Technology and Engineering, vol. 8, no. 2S7, pp. 264–267, Sep. 2019, doi: https://doi.org/10.35940/ijrte.b1053.0782s719.

J. Pion, A. Hohmann, T. Liu, M. Lenoir, and V. Segers, “Predictive models reduce talent development costs in female gymnastics,” Journal of Sports Sciences, vol. 35, no. 8, pp. 806–811, Jun. 2016, doi: https://doi.org/10.1080/02640414.2016.1192669.

F. Louzada, A. C. Maiorano, and A. Ara, “iSports: A web-oriented expert system for talent identification in soccer,” Expert Systems with Applications, vol. 44, pp. 400–412, Feb. 2016, doi: https://doi.org/10.1016/j.eswa.2015.09.007.

K. Till et al., “Identifying Talent in Youth Sport: A Novel Methodology Using Higher-Dimensional Analysis,” PLOS ONE, vol. 11, no. 5, p. e0155047, May 2016, doi: https://doi.org/10.1371/journal.pone.0155047.

G. A. Whitaker, R. Silva, D. Edwards, and I. Kosmidis, “A Bayesian approach for determining player abilities in football,” Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 70, no. 1, pp. 174–201, Nov. 2020, doi: https://doi.org/10.1111/rssc.12454.

D. Barron, G. Ball, M. Robins, and C. Sunderland, “Identifying playing talent in professional football using artificial neural networks,” Journal of Sports Sciences, vol. 38, no. 11–12, pp. 1211–1220, Jan. 2020, doi: https://doi.org/10.1080/02640414.2019.1708036.

N. Chmait, S. Robertson, H. Westerbeek, R. Eime, C. Sellitto, and M. Reid, “Tennis superstars: The relationship between star status and demand for tickets,” Sport Management Review, vol. 23, no. 2, Apr. 2019, doi: https://doi.org/10.1016/j.smr.2019.03.006.

J. G. Claudino, D. de O. Capanema, T. V. de Souza, J. C. Serrão, A. C. Machado Pereira, and G. P. Nassis, “Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review,” Sports Medicine - Open, vol. 5, no. 1, Jul. 2019, doi: https://doi.org/10.1186/s40798-019-0202-3.

R. S. Lloyd et al., “Long-Term Athletic Development, Part 2,” Journal of Strength and Conditioning Research, vol. 29, no. 5, pp. 1451–1464, May 2015, doi: https://doi.org/10.1519/01.jsc.0000465424.75389.56.

I. Kononenko, E. Šimec, and M. Robnik-Šikonja, “Overcoming the myopia of inductive learning algorithms with RELIEFF.” Applied Intelligence, vol. 7, no. 1, pp. 39–55, 1997, doi: https://doi.org/10.1023/a:1008280620621.

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene Selection for Cancer Classification Using Support Vector Machines,” Machine Learning, vol. 46, no. 1/3, pp. 389–422, 2002, doi: https://doi.org/10.1023/a:1012487302797.

Rabiu Muazu Musa, A. Majeed, Norlaila Azura Kosni, and Mohamad Razali Abdullah, Machine Learning in Team Sports. 2020. doi: https://doi.org/10.1007/978-981-15-3219-1.

Mat-Rasid, S. M. et al., “Applied Multidimensional Analysis for Assessing Youth Performance in Sports Talent Identification Program,” International Journal of Recent Technology and Engineering, vol. 8, no. 2S7, pp. 207–211, Sep. 2019, doi: https://doi.org/10.35940/ijrte.b1051.0782s719.

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Published

24.03.2024

How to Cite

Muazu Musa, R. ., Mat-Rasid, S. M. ., Lee Low, J. F. ., Elumalai, G. ., Shahril, M. I. ., Ismail Ahmad, M. A. ., & Kosni, N. A. . (2024). Determining Essential Fitness and Motor Skill Parameters for Talent Identification in 12-Year-Old Girls: A Comparison of Machine Learning-Based Feature Extraction Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 180–184. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5239

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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