# Predictors of Speed and Agility in Youth Male Basketball Players
## 4 Discussion  
This research aimed to identify the anthropometric factors predicting the speed and agility profiling in under 14 (U14), under 15 (U15), and under 16 (U16) basketball players. In addition, we aimed to investigate the correlation of physical performance tests with the players' anthropometric profiles. As we omitted biological age, the study may have overlooked potential variations in physical development within the age groups studied, which limits the understanding of how maturation influences the observed performance differences and correlations. The relative age effect also could confound the results, as a player's competitive advantage may be due to their relative maturity within the cohort. This would allow for the identification of age-related patterns, potential maturation effects, and the influence of biological age on performance outcomes. Moreover, considering biological age in talent identification and training program design can aid in the appropriate placement and development of young athletes, accounting for individual differences in growth and maturation. Besides obvious differences in anthropometric variables, we found significant between age group differences in all performance variables. Interestingly, no PBF differences were found between age groups. Further, although the U14 and U16 groups differed in all the measured variables, the U14 and U15 groups differed in only two variables (height and body mass), and the U15 and U16 groups differed in three variables (10 and 20 m sprints and \(t\)-test). This is understandable considering the fact that great biological and morphological transformations and variations occur in adolescence [29]. In fact, similar inconsistencies have been found in numerous earlier studies. For example, [PERSON] et al. (2012) [10] found similar trends in mixed young basketball and football players aged 12-15 years. In their study, they divided participants according to their chronological age (12, 13, 14, and 15 years). No differences in speed and agility test results were found between 12 and 13, and 14 and 15, whilst 13 and 14 differed in all performance variables. Within age group correlation showed that chronological age correlated with all performance variables in U14 and sprint performance in the U15 group. No significant correlations were found in the U16 group, implying that the phenomenon known as relative age effect [30] is present in U15 and younger age groups. This implies that a specific biological development stabilization takes place, and it is crucial to take this stabilization into account during testing and scouting. Further, body height and weight correlation with performance tasks was found to be weak and insignificant (<0.35), whereas PBF showed large and constant correlation with power and sprint tests. In each age group, sprint and power performance exhibited a large to nearly perfect correlation, with correlation coefficients ranging 0.475-0.969 (irrespective of the direction). Similar results were found previously in young [10,31] and professional basketball players [6]. The results indicate that CMJ and DJ are significantly correlated with sprint and agility performance in all groups; however, while CMJ showed a stronger correlation with these results than DJ in U14 and U15, it was opposite in the U16 age category. A potential reason could be the difference in biological age, which could give more comprehensive understanding of the relationships between the maturity of anthropometric factors, physical performance, and CNS developmental processes that are more related to success in DJ. Additionally, more mature players have higher hormonal levels, stronger muscle structure, and possess rapid force development that could be advantageous during DJ [32,33]  
It is possible that the findings could be influenced by several factors, including the age and maturity of the participants, as well as the specific training methods used.