Journal of Sports and Motor Development and Learning

Scientific Journal

Document Type : Research Paper I Open Access I Released under CC BY-NC 4.0 license

Authors

¹ Department of Physical Education and Sports Sciences, Faculty of Psychology and Educational Sciences, Yazd University, Yazd, Iran.

² Department of Physical Education, Technical and Vocational University (TVU), Tehran, Iran.

³ Department of Behavioral and Cognitive Sciences in Sports, Faculty of Sport Sciences and Health, University of Tehran, Tehran, Iran.

https://doi.org/10.22059/jsmdl.2025.387595.1817

Abstract

Introduction: Sports talent identification (STI) is a complex process that benefits from insights across various scientific fields to enhance its credibility. Nowadays, artificial intelligence (AI) has proven effective in prediction and classification tasks. Given that talent identification revolves around accurately predicting and classifying individuals, leveraging AI can be truly transformative. This study aimed to explore how artificial intelligence can be used for STI.
Methods: Despite the diversity of sports disciplines, four categories were selected: ball sports, racket sports, martial arts, and aquatic sports. The expert opinion method was used to prioritize the selected indicators, which included anthropometric characteristics, physical abilities, perceptual‑motor skills, fitness‑related abilities, and psychological factors. A total of 310 participants (boys and girls aged 6 to 16 years) were evaluated and labeled based on their scores. To assess the relationship between each component and the labels, independent t‑tests were conducted, resulting in the selection of six key components for each category. Finally, a perceptron 6‑1‑1 neural network was used to evaluate the accuracy and validity of the STI results.
Results: The neural network results showed that the classification accuracy for ball sports, racket sports, martial arts, aquatic sports, and other sports was 97.9%, 97.9%, 87.2%, 91.5%, and 80.8%, respectively, which represent high and desirable accuracy levels.
Conclusion: Finally, it can be concluded that identifying the principal components specific to each sport category and designing an artificial neural network can help researchers and coaches recognize the important indicators for each sport and use them for sports talent identification in their respective fields.

Keywords

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Journal of Sports and Motor Development and Learning

Volume 18, Issue 1
April 2026

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Receive Date: 25 December 2024
Revise Date: 02 March 2025
Accept Date: 04 March 2025

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