Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 10, Issue 1


Published
on

March 17, 2025


Pages


DOI

Article

The practice of music in volleyball players’ physical recovery: incorporating data-driven analysis

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Authors

Dan Lv Affiliation:
School of Physical Education, Inner Mongolia University, Hohhot, Inner Mongolia, 012000, China.
, Li Zhou Affiliation:
School of Art and New Media, Shijiazhuang Institute of Technology, 050228, Shijiazhuang, Hebei, 050228, China.
, Wei Li Affiliation:
School of Art and New Media, Shijiazhuang Institute of Technology, 050228, Shijiazhuang, Hebei, 050228, China.
and Yan Wang Affiliation:
School of Art and New Media, Shijiazhuang Institute of Technology, 050228, Shijiazhuang, Hebei, 050228, China.


Abstract

This paper uses a method called “data-driven meta-analysis” to look at the effects of music on physical recovery in a planned way. It does this by looking at statistical tests for publication bias, mean effect sizes, and moderated effects. Music therapy is used as a positive auxiliary treatment recovery means in the experiment and application of volleyball players’ sports physical recovery, and the practical effect of music on physical recovery is analyzed using comparative tests. The results showed that 85.5% of the volleyball players preferred to listen to some slow and soothing music during their fatigue state. The mean effect size of music was g = 0.422, P < 0.001, indicating that it can significantly enhance the physical performance of athletes. All the scores of fatigue showed a significant decrease (P < 0.01) after the music therapy intervention. The experimental results of this paper can provide useful help in recovering volleyball players’ physical performance.


Keywords

Meta-analysis, Data-driven, Mean effect size, Physical performance, Volleyball players, 68M10


Citation

Lv, D., Zhou, L., Li, W., & Wang, Y. (2025). The practice of music in volleyball players’ physical recovery: Incorporating data-driven analysis. Applied Mathematics and Nonlinear Sciences, 10(1). https://doi.org/10.2478/amns-2025-0261

Published by: Engineering Journals

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