Tennis, a widely enjoyed sport, motivates athletes and coaches to optimize training for competitive success. This retrospective predictive study examines anthropometric features and statistics of 1990 tennis players in the 2022 season, using 20,040 data points retrospectively obtained from the ATP official source after the end of the season. These data were cross-verified with information from other sources before categorisation to address any discrepancies. Employing various analytical methods, the results emphasize the strategic importance of tournament participation and gameplay for financial gains and higher rankings. Prize money analysis reveals a significant disparity favoring top players. Multivariate Analysis of Variance highlights the need to consider multiple variables for understanding ATP rankings. Multinomial Logistic Regression identifies age, height, and specific service-related metrics as key determinants, with older and taller players more likely to secure top positions. Neural Network models exhibit potential in predicting ATP Rank outcomes, particularly for ATP Rank (500). Our results argue for the use of Artificial Intelligence (AI), specifically Neural Networks, in handling complex interactions and emphasize that AI is a supportive tool in decision-making, requiring careful consideration by experienced individuals. In summary, this study enhances our understanding of ATP ranking factors, providing actionable insights for coaches, players, and stakeholders in the tennis community.

• MeSH
• Adult MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Retrospective Studies MeSH
• Athletes statistics & numerical data   MeSH
• Athletic Performance statistics & numerical data   MeSH
• Machine Learning * MeSH
• Tennis * MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Tennis is a popular and complex sport influenced by various factors. Early training increases the risk of career dropout before peak performance. This study analyzed game statistics of World Junior Tennis Final participants (2012-2016), their career paths and it examined how game statistics impact rankings of top 300 female players, aiming to develop an accurate model using percentage-based variables. Descriptive and inferential statistics, including neural networks, were employed. Four machine learning models with categorical predictors and one response were created. Seven models with up to 18 variables and one ordinal (WTA rank) were also developed. Tournament rankings could be predicted using categorical data, but not subsequent professional rankings. Although effects on rankings among top 300 female players were identified, a reliable predictive model using only percentage-based data was not achieved. AI models provided insights into rankings and performance indicators, revealing a lower dropout rate than reported. Participation in elite junior tournaments is crucial for career development and designing training plans in tennis. Further research should explore game statistics, dropout rates, additional variables, and fine-tuning of AI models to improve predictions and understanding of the sport.

• MeSH
• Humans MeSH
• Neural Networks, Computer MeSH
• Machine Learning MeSH
• Tennis * physiology   MeSH
• Check Tag
• Humans MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Given that serve velocity has been identified as one of the most important components influencing performance in tennis, identifying the factors associated with serve velocity is crucial for coaches and athletes. The aim of this study was to describe the relationship between physical performance, anthropometric characteristics and stroke velocity in youth tennis players. Twenty-seven youth players (male = 16, age = 15.69 ± 1.70 years; female = 11, age = 15.82 ± 1.40 years) underwent an anthropometric and physical performance assessment. On a tennis court, players were assessed for forehand, backhand and serve velocities. Pearson's correlation coefficient revealed that forehand velocity was significantly correlated with height (r = 0.58) and handgrip strength (right hand: r = 0.68; left hand: r = 0.57), whereas backhand velocity was significantly correlated with running time (r = 0.52) and handgrip strength (right hand: r = 0.67; left hand: r = 0.55) in males. Similarly, in males, serve velocity was significantly correlated with height (r = 0.60), running time (r = 0.62) and handgrip strength (right: r = 0.77, left hand: r = 0.71). In females, a significant correlation was only demonstrated between serve velocity and body weight (r = 0.69). These findings highlight that handgrip strength, running time and body height variables are positively associated with stroke velocities in male youth tennis players.

• Keywords
• athletic performance, physical fitness, tennis, youth athlete,
• Publication type
• Journal Article MeSH

Volleyball is an exceedingly popular physical activity in the adolescent population, especially with females. The study objective was to assess the effect of volleyball training and natural ontogenetic development on the somatic parameters of adolescent girls. The study was implemented in a group of 130 female volleyball players (aged 12.3 ± 0.5 - 18.1 ± 0.6 years) along with 283 females from the general population (aged 12.3 ± 0.5 - 18.2 ± 0.5 years). The measured parameters included: body height (cm), body mass (kg), body fat (kg, %), visceral fat (cm2), body water (l), fat free mass (kg) and skeletal muscle mass (kg, %). Starting at the age of 13, the volleyball players had significantly lower body fat ratio and visceral fat values than those in the general population (p < 0.001 in body fat % and p < 0.01 in visceral fat). In volleyball players, the mean body fat (%) values were 17.7 ± 6.6 in 12-year-old players, 16.7 ± 4.9 in 13-year-old players, 18.5 ± 3.9 in 16-year-old players, and 19.3 ± 3.1 in 18-year-old players. In the general population, the mean body fat (%) values were 19.6 ± 6.3 in 12-year-old girls, 21.7 ± 6.4 in 13-year-old girls, 23.4 ± 6.1 in 16-year-old girls, and 25.8 ± 7.0 in 18-year-old girls. The visceral fat (cm2) mean values were 36.4 ± 19.3 in 12-year-old players, 39.2 ± 16.3 in 13-year-old players, 45.7 ± 14.7 in 16-year-old players, and 47.2 ± 12.4 in 18-year-old players. In the general population, the mean visceral fat (cm2) values were 41.4 ± 21.1 in 12-year-old girls, 48.4 ± 21.5 in 13-year-old girls, 58.0 ± 24.7 in 16-year-old girls, and 69.1 ± 43.7 in 18-year-old girls. In volleyball players, lower body fat ratio corresponded with a higher skeletal muscle mass ratio. The differences found in skeletal muscle mass ratio were also significant starting at the age of 13 (p < 0.001). The mean skeletal muscle mass (%) values were 44.1 ± 3.4 in 12-year-old volleyball players, 45.4 ± 2.5 in 13-year-old players, 45.0 ± 2.2 in 16-year-old players, and 44.7 ± 1.8 in 18-year-old players. In the general population, the mean skeletal muscle mass (%) values were 42.8 ± 3.2 in 12-year-old girls, 42. ± 4.1 in 13-year-old girls, 41.9 ± 3.3 in 16-year-old girls, and 40.6 ± 3.7 in 18-year-old girls. Differences in body composition between the individual age groups were similar between the volleyball players and girls in the general population. The results indicate that regular volleyball training influences the body composition of young females however the development of body composition parameters is subject to their ontogenetic development.

• Keywords
• Body composition, General population, Ontogenetic development, Parameter differences, Volleyball players, Volleyball training,
• Publication type
• Journal Article MeSH