Player performance in an intense sport such as basketball is known to be related to attributes such as speed, agility, and power. This study presents a comparative analysis of associations between anthropometric assessment and physical performance in different age groups of elite youth basketball players, while simultaneously identifying the predictors for speed and agility in these players. U14 (n = 44), U15 (n = 45), and U16 (n = 51) players were tested for anthropometry, lower-body power, speed, and agility. U16 players were found to be taller, heavier, more muscular than U14 and U15 players. In addition, the U16 group showed better performance in all performance tests. Age had a significant positive correlation with countermovement (CMJ) and drop jump (DJ) performance in U14 players, and a significant negative correlation with 15m and 20m sprint times in the U15 group. CMJ and DJ emerged as the most significant predictors for sprint and agility variables, respectively. Body fat percentage was found to be a significant predictor for the speed and agility tests in all age groups, but a negative lower-body power predictor. Therefore, besides all sport-specific and fitness tests, it is essential to place emphasis on the percentage of body fat when designing players’ individualized training programs, and during team selection.

The aim of the research was to implement an athletic program to improve the explosive force in order to optimize physical fitness at the level of elite football-tennis players and evaluate the progress made through specific tests using the Opto Jump. The research included 10 elite European and world-class players, on whom an experimental program was applied in order to improve the explosive force of the limbs in conditions of speed, endurance, and dynamic balance. Study tests: five vertical jumps on the spot, on the left/right leg; five back and forth jumps on the left/right leg; five left/right side jumps on the left/right leg; vertical jumps on both legs 60 s; BFS vertical jumps. For each test, the following parameters specific to the explosive force were statistically analyzed: contact time (s); flight time (s); jump height (cm), jump power (w/kg); RSI—Reactive Strength Index, defined as Height (m/s). In the study, the average value of the parameters specific to the jumps performed in each test was taken into account. During the study, the tests were performed and processed on the Opto Jump device and software. In all tests of the experiment monitored through Opto Jump, significant progress was made in the final test compared to the initial one, which demonstrates the efficiency of the physical training program implemented for the development of explosive force, with an impact on the sports performance of elite players. The most relevant results obtained for the left leg regarding the improvement of the explosive force of the lower limbs materialized in the jump height parameter was in the test of five vertical jumps on one leg on the spot, and for the right leg in the tests of: five back and forth jumps and five left/right side jumps. The most significant advances in the study were in the tests, in descending order of their weight: 60 s vertical jumps on both legs; five back-and-forth jumps and five left/right side jumps, five vertical jumps on one leg standing, and BFS vertical jumps.

Player’s performance in an intense sport such as basketball is known to be related to attributes like speed, agility, and power. This study presents a comparative analysis of associations between anthropometric assessment and physical performance in different age-group elite youth basketball players, while simultaneously identifying the predictors for speed and agility in these players. U14 (n=44), U15 (n=45) and U16 (n=51) players were tested for anthropometry, lower body power, speed, and agility. U16 players were found to be taller, heavier, more muscular than U14 and U15 players. Also, the U16 group showed better performance in all performance tests. Age had a significant positive correlation with countermovement (CMJ) and drop jump (DJ) performance in U14 players, and a significant negative correlation with 10m and 20m sprint times in U15 group. CMJ and DJ emerged as the most significant predictors for sprint and agility variables, respectively. Body fat percentage was found to be a significant predictor for the speed and agility tests in all age groups, but a negative lower-body power predictor. Therefore, besides all sport-specific and fitness tests, it is essential to place emphasis on the percentage of body fat when designing players’ individualized training programs, and during team selection.

The present study aimed to assess the capacity of stability and motor impact in the development of the balance of student athletes by reducing the support surface on the ball in the up plank position, by using three categories of balls of equal size, but with different elasticity and weight. In this study, the second aim was to investigate the differences in maintaining static balance, on different balls, between those who are practicing individual sports or team sports. The total study sample include 48 students, active athletes (45.8% of individual sports and 54.2% of team sports), age X ± SD 18.74 ± 1.94 years. The research included two test sessions (initial and final) applied in two stages. The static balance tests were performed by measuring the time maintaining the up plank position with two and three points of support on the three balls, with different characteristics of elasticity and ranges of deformation: medical ball, handball ball and fitness ball. The results of the study showed that the superior initial and final results were recorded on the fitness ball, and the inferior results on the medicine ball. The upper difference was recorded at the up plank position with two support points (arms, legs) on the fitness ball, at 4980 s, and the lowest in the same test on the medical ball, at 3420 s. The largest difference was recorded at the up plank position with three support points on the handball ball, at 7.082 s, and the lowest in the same test on the medical ball, at 3.093 s. The subjects of the study perceived that the most difficult position to execute was the up plank position on the medical ball with two support points, with 43.8%, and the easiest stability was registered in the up plank position on the fitness ball with three points of support, of 37.5%. The relevance of the research results from the possibility of using different balls in conditions of positioning and body posture with a diminished support base in order to improve physical fitness.

Purpose: This study investigated the effects of different small-sided game (SSG) formats and pitch sizes on GPS-derived acceleration and deceleration event counts as indicators of external movement demands in elite youth soccer players. Methods: Twenty male elite youth soccer players (age: 19 ± 1.24 years) performed 4v1 and 6v2 SSG formats across multiple pitch dimensions (4v1: 5×5, 6×6, 7×7, and 8×8 m; 6v2: 6×6, 7×7, 8×8, 9×9, and 10×10 m). Movement data were collected from players positioned in the middle using 10 Hz GPS devices (MinimaxX v4.0, Catapult Innovations, Melbourne, Australia). Acceleration and deceleration counts were analyzed according to game format, pitch size, and bout duration (15 s and 30 s) using repeated-measures ANOVA and Holm-adjusted post-hoc comparisons. Results: Significant main effects of game format were observed across all acceleration and deceleration event-count variables (p < 0.001), with large effect sizes (partial η² = 0.632–0.837). In common pitch dimensions (6×6–8×8 m), the 4v1 format consistently elicited higher event counts than the 6v2 format. For example, during the 30 s condition at 6×6 m, 4v1 produced higher acceleration Zone 1 event counts than 6v2 (mean difference = 1.80; p < 0.001; Cohen’s dz = 2.24). Within the 4v1 format, the smallest pitch size (5×5 m) generated the highest acceleration and deceleration event counts, whereas in the 6v2 format the largest pitch size (10×10 m) elicited the greatest event counts. Conclusion: These findings suggest that the interaction between player configuration and spatial constraints substantially influences GPS-derived acceleration and deceleration event counts during SSGs. From a practical perspective, 4v1 SSGs performed in reduced spaces may provide an effective training stimulus for increasing acceleration and deceleration event counts. However, these outcomes should be interpreted as external movement-demand indicators and not as direct measures of quickness, agility, or perceptual-cognitive performance.

Background: The purpose of this study is to develop a valid and reliable measurement tool to assess sleep quality and sleep perceptions in the context of exercise among adults who engage in regular exercise. Methods: In line with this aim, the Exercise-Context Sleep Quality Scale (ECSQS) was developed, and exploratory and confirmatory factor analyses were conducted on two independent samples. It was determined that the scale has a structure consisting of a total of 12 items and three factors (Perception of Exercise-Related Sleep, Sleep Hygiene and Environmental Factors, general Sleep Adequacy). The results of the exploratory factor analysis showed that the total explained variance was 56.8%, which is above the thresholds recommended for construct validity in the social sciences. Results: The findings of the confirmatory factor analysis indicated that the three-factor model exhibited a good level of fit to the data according to the χ²/df ratio, RMR, RMSEA and fit indices such as GFI, AGFI, NFI, IFI, TLI and CFI. In reliability analyses, Cronbach’s alpha coefficient was 0.83 for the first factor, and 0.65 and 0.64 for the second and third factors, respectively; for the total scale, Cronbach’s alpha and McDonald’s omega values were found to be 0.77. Discriminant validity was supported by the Fornell–Larcker criterion, while convergent validity received only partial support because AVE values were below 0.50. Composite reliability (CR) values were acceptable. Item discrimination analyses showed that all items had item–total correlations above 0.30, and that Cohen’s d effect sizes in comparisons of the lower and upper 27% groups were mostly at “large” or “very large” levels; these results indicate that the scale strongly distinguishes between individuals with low and high levels of sleep quality in the context of exercise. Conclusions: The findings demonstrate that the ECSQS can validly and sufficiently reliably measure sleep quality in regular exercisers through a three-dimensional structure, and that, as one of the first Turkish sleep quality scales specific to the exercise context, it fills an important measurement gap in the sports sciences and sleep science literature.