153 Growing up and reaching for the top: A longitudinal study of talented swimmers 7 Data selection In cases where swimmers had multiple data points within a season, the swimmers’ season best rST, rMSV along with the corresponding rSI, CMJ and anthropometric scores were selected for further analyses (see Appendix B for number of measurements by performance level group and age category). Any other data were excluded, minimizing the impact of variations in achievements within a season. The median number of between-season observations was n = 2 in males and females. Defining Performance Level Groups A higher- and lower-level performance group were defined according to performance trajectories of international elite swimmers, representing a performance level similar to the top 50 swimmers worldwide of the past 5 years (2017-2022 with the exception of 2020, see Post et al., 2020a). Following the approach adopted in previous studies (Stoter et al., 2019; Post et al. (2020b), the maximum season best rST by age category, sex and swim event of these international elite swimmers was used as performance benchmark (%WR, see Appendix C). Swimmers whose season best rST at late junior age (males aged 16; females aged 15) fell within the corresponding performance benchmark were categorized as high-performing late juniors and considered to be on track to reach the elite level (16 males; 10 females). Conversely, swimmers who did not meet the performance benchmark were classified as lower-performing late juniors and considered to be off track to reach the elite level (31 males; 33 females). To illustrate, consider a 16-year-old male swimmer competing in the 100m freestyle. If his season best rST is 115.1%, he would be classified in the high-level performance group since it falls within the performance benchmark for 16-year-old males in the 100m freestyle, which is set at 116.3%. However, if his season best rST is 117.8%, he would be classified in the lower-level performance group as it exceeds the corresponding performance benchmark Statistics All data were analyzed for males and females separately, using R (R Core Team, 2021). Data were initially screened on outliers (using box plots), normality (using QQ-plots) and homogeneity of variance (using Levene’s test). Outliers (16 in males; 19 in females) were acknowledged as a natural occurrence within the population and, consequently, were not removed from the dataset. Normality was violated in males (rST at early and late junior age) and females (height, rMSV and rST at early and late junior age). Homogeneity of
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