| Literature DB >> 35897334 |
Jung-Piao Tsao1, Chia-Che Liu2, Bi-Fon Chang3.
Abstract
In this study, we sought to develop a testing system to scientifically identify tennis talent. This testing system will provide helpful information for players who intend to pursue a professional tennis career. The experimental subjects were 18 college students consisting of 10 tennis players (including 4 soft tennis) and 8 basketball players (all males). The subjects were tested on their vertical jump, 60 m shuttle runs, and shoulder joint mobility to identify tennis talent. To statistically analyze the data, an R package was used to conduct a principal component analysis of the athletic performance indicators of the samples, and the samples were further classified via agglomerative hierarchical clustering. This study found that tennis players required more flexibility than basketball players. Regarding the differences between male and female soft tennis players, the unclassified results showed that there was a significant difference in explosive power. However, there was no significant difference in flexibility between genders. The research methods and results of this study can be used as a reference for others to build a system for identifying athletic performance characteristics in the future, and it is expected that the implementation of this system can provide sports coaches with more information for talent selection and improve the accuracy of their judgments, allowing athletes to play to their strengths.Entities:
Keywords: adaptive learning; talent identification; tennis
Mesh:
Year: 2022 PMID: 35897334 PMCID: PMC9329992 DOI: 10.3390/ijerph19158963
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Figure 1Principal component analysis results with indicator distribution.
Indicators significantly correlated to Dim1.
| Dim1 | Correlation | |
|---|---|---|
| SJ 3 | 0.8281 | |
| CMJ 2 | 0.8179 | |
| SJ 2 | 0.7781 | |
| CMJ 1 | 0.672 | 0.0023 |
| CMJ 3 | 0.6644 | 0.0026 |
| SJ 1 | 0.6484 | 0.0036 |
| DJR | 0.6448 | 0.0039 |
| DJL | 0.5815 | 0.0114 |
| Height | 0.3368 | 0.1718 |
| Weight | 0.222 | 0.3756 |
| BMI | 0.0161 | 0.9496 |
| RJNT 2 | −0.0308 | 0.9034 |
| LJNT 2 | −0.1746 | 0.4883 |
| LJNT 3 | −0.2159 | 0.3895 |
| RJNT 3 | −0.3322 | 0.1780 |
| LJNT 1 | −0.4287 | 0.0759 |
| RJNT 1 | −0.5564 | 0.0165 |
| 60 M | −0.8369 |
Indicators significantly correlated to Dim2.
| Dim2 | Correlation | |
|---|---|---|
| LJNT 2 | 0.7955 | |
| RJNT 2 | 0.7551 | 0.0003 |
| SJ 2 | 0.5269 | 0.0247 |
| CMJ 3 | 0.5018 | 0.0338 |
| CMJ 2 | 0.2693 | 0.2799 |
| 60M | 0.1956 | 0.4367 |
| LJNT 3 | 0.1877 | 0.4559 |
| LJNT 1 | 0.1717 | 0.4958 |
| RJNT 1 | 0.0599 | 0.8134 |
| DJR | −0.0081 | 0.9746 |
| DJL | −0.0388 | 0.8784 |
| RJNT 3 | −0.0769 | 0.7617 |
| CMJ 1 | −0.0773 | 0.7604 |
| SJ 1 | −0.4892 | 0.0394 |
| BMI | −0.6092 | 0.0073 |
| height | −0.7465 | 0.0003 |
| weight | −0.8768 |
Figure 2Aggregate hierarchical clustering results for the performance of 18 samples.
Comparison of hierarchical clustering results.
| Hierarchical Subgroup | Group 1 | Group 2 | Group 3 | |
|---|---|---|---|---|
| Number of athletes | Tennis | 6 | ||
| Soft Tennis | 4 | |||
| Basketball | 8 |
Figure 3Confidence interval estimate for each group.