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.
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http://dx.doi.org/10.3390/ijerph19158963 | DOI Listing |
Eur J Appl Physiol
January 2025
School of Physical Education and Sport Science, National and Kapodistrian University of Athens, Athens, Greece.
Osteogenesis with impact-loading exercise is often assessed by the extra bone growth induced in the loaded arm of tennis players. We used PRISMA to explore % bone mineral content (BMC) and area (BA) asymmetry in players 8-30 years according to weekly training hours, age, sex, maturity, and bone segment. Proper statistics for 70 groups were extracted by two reviewers from 18 eligible studies of low risk of bias (< 35, STROBE) and good quality (> 70%).
View Article and Find Full Text PDFJ ISAKOS
January 2025
Fortius Clinic, London, United Kingdom; Department of Bioengineering, Imperial College, London, United Kingdom.
Introduction: Arthrodesis of the first metatarsophalangeal joint (MTPJ) is a reliable procedure in alleviating pain and restoring function. However, there is limited published literature of the outcomes of this procedure and the ability to return to sport in elite athletes. This study aims to assess the outcomes of first MTPJ arthrodesis in the elite athlete population and their ability to return to professional sport.
View Article and Find Full Text PDFJSES Int
November 2024
LAM - Motion Lab, University of Liège, Liège, Belgium.
Background: Musculoskeletal adaptations are common in overhead athletes. As they also are involved in injury prevention, there has been an increase in their evaluation through shoulder screening over the last years. However, for some evaluations, and especially for functional testing, there is a lack of normative values, which limits the interpretation of the values measured.
View Article and Find Full Text PDFJ Hand Ther
January 2025
Department of Physical Therapy, University of Tennessee at Chattanooga, Chattanooga, TN. Electronic address:
Background: Epicondylalgia is a common overuse injury in tennis. However, little is known regarding epicondylalgia in pickleball.
Purpose: This study examined the prevalence of positive epicondylalgia tests in recreational pickleball players and the relationship between positive tests and player characteristics.
Eur J Sport Sci
February 2025
School of Human Sciences (Exercise and Sport Science), The University of Western Australia, Perth, Australia.
End-range movements are among the most demanding but least understood in the sport of tennis. Using male Hawk-Eye data from match-play during the 2021-2023 Australian Open tournaments, we evaluated the speed, deceleration, acceleration, and shot quality characteristics of these types of movement in men's Grand Slam tennis. Lateral end-range movements that incorporated a change of direction (CoD) were identified for analysis using k-means (end-range) and random forest (CoD) machine learning models.
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