Machine learning analysis for predicting performance in female volleyball players in India Implications for talent identification and player development strategies

Main Article Content

Swamynathan Sanjaykumar
https://orcid.org/0000-0001-9945-2223
Subhashree Natarajan
https://orcid.org/0000-0002-2329-5935
Ponnusamy Yoga Lakshmi
Yuliya Kalmykova
https://orcid.org/0000-0002-6227-8046
Joseph Lobo
https://orcid.org/0000-0002-2553-467X
Ratko Pavlović
Edi Setiawan
https://orcid.org/0000-0001-7711-002X

Abstract

Talent identification and player development are crucial aspects of sports management, particularly in volleyball, where understanding players' performance predictors is essential. The primary objective is to investigate the relationships between players' demographic and physical attributes and their on-court performance, providing valuable insights for talent identification and player development strategies. The dataset comprises demographic and physical attributes alongside performance metrics of college-level female volleyball players in India. Data were meticulously collected from various institutions participating in volleyball tournaments across India. Three machine learning algorithms—linear regression, random forest regression, and XGBoost regression—were trained using the pre-processed dataset. Standard regression evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), and R-squared (R2) score were used to assess model performance. Random forest regression emerged as the top-performing ML technique, achieving a prediction accuracy of 94.18%, followed by XGBoost regression with 92.76%. Height, muscle mass, and bone mass exhibited strong positive correlations with performance prediction, emphasizing their significance. This study highlights ML techniques' potential, particularly random forest regression, in improving talent identification and performance prediction in college-level female volleyball players in India.

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How to Cite
Sanjaykumar, S., Natarajan, S., Lakshmi, P. Y., Kalmykova, Y., Lobo, J., Pavlović, R., & Setiawan, E. (2024). Machine learning analysis for predicting performance in female volleyball players in India: Implications for talent identification and player development strategies. Journal of Human Sport and Exercise , 20(1), 207-215. https://doi.org/10.55860/cn2vdj44
Section
Performance Analysis of Sport
Author Biographies

Swamynathan Sanjaykumar, SRM Institute of Science and Technology

Department of Physical Education and Sports Sciences. Faculty of Science and Humanities.

Subhashree Natarajan, RV University

School of Business.

Ponnusamy Yoga Lakshmi, SRM Institute of Science and Technology

Department of Computer Science. Faculty of Science and Humanities.

Yuliya Kalmykova, Kharkiv State Academy of Physical Culture

Department of Physical Therapy.

Joseph Lobo, Bulacan State University

College of Sports Exercise and Recreation.

Ratko Pavlović, University of East Sarajevo

Faculty of Physical Education and Sport.

Edi Setiawan, University Suryakancana

Faculty of Teacher Training and Education. Department of Physical Education, Health and Recreation.

How to Cite

Sanjaykumar, S., Natarajan, S., Lakshmi, P. Y., Kalmykova, Y., Lobo, J., Pavlović, R., & Setiawan, E. (2024). Machine learning analysis for predicting performance in female volleyball players in India: Implications for talent identification and player development strategies. Journal of Human Sport and Exercise , 20(1), 207-215. https://doi.org/10.55860/cn2vdj44

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