<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper first analyzes the basic law of the formation process of volleyball skills, puts forward the four-stage theory based on this, and then gives the elements of modern volleyball players’ competitive ability. Then, the evaluation system of volleyball players’ competitive level is constructed, the weight of the index is calculated, and the data is analyzed by grey correlation analysis, entropy method and other algorithms. Finally, data mining algorithms such as least square support vector regression and long and short memory neural networks are used to mine different kinds of data to achieve the purpose of risk prediction and timely release of early warning information. Finally, the women’s volleyball team of B City is taken as an example to evaluate and test. The results show that the top four weights of the secondary indexes are running height, field of vision, serving skill score and height, and their weights are 0.5157, 0.4862, 0.4431 and 0.4248, respectively. It shows that volleyball players can improve their muscle reaction speed by actively developing the explosive power of their lower limbs so as to improve the mastery of volleyball skills and enhance the level of volleyball competition.</p>
</abstract>

This paper first analyzes the basic law of the formation process of volleyball skills, puts forward the four-stage theory based on this, and then gives the elements of modern volleyball players’ competitive ability. Then, the evaluation system of volleyball players’ competitive level is constructed, the weight of the index is calculated, and the data is analyzed by grey correlation analysis, entropy method and other algorithms. Finally, data mining algorithms such as least square support vector regression and long and short memory neural networks are used to mine different kinds of data to achieve the purpose of risk prediction and timely release of early warning information. Finally, the women’s volleyball team of B City is taken as an example to evaluate and test. The results show that the top four weights of the secondary indexes are running height, field of vision, serving skill score and height, and their weights are 0.5157, 0.4862, 0.4431 and 0.4248, respectively. It shows that volleyball players can improve their muscle reaction speed by actively developing the explosive power of their lower limbs so as to improve the mastery of volleyball skills and enhance the level of volleyball competition.

Volleyball players’ mastery of sports skills and improvement of competitive level based on big data analysis

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper first analyzes the basic law of the formation process of volleyball skills, puts forward the four-stage theory based on this, and then gives the...

Volleyball players’ mastery of sports skills and improvement of competitive level based on big data analysis

Online veröffentlicht: 11. Nov. 2023

Seitenbereich: -

Eingereicht: 19. Feb. 2023

Akzeptiert: 22. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01103

Schlüsselwörter
Motor skills, Competitive ability, Grey correlation analysis, Entropy method, Index weight

© 2023 Xiaoshen Yan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Volleyball players’ mastery of sports skills and improvement of competitive level based on big data analysis

Online veröffentlicht: 11. Nov. 2023

Seitenbereich: -

Eingereicht: 19. Feb. 2023

Akzeptiert: 22. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01103

SchlüsselwörterMotor skills, Competitive ability, Grey correlation analysis, Entropy method, Index weight

© 2023 Xiaoshen Yan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Motor skills, Competitive ability, Grey correlation analysis, Entropy method, Index weight