75 Pacing behavior development in adolescent swimmers 4 why the previous study did find a difference in pacing behavior development between the performance levels. Limitations and future directions Although the models created in the present study provide novel insights into the relationship between age, experience and pacing behavior, the models do not account for all the variance in a swimmers’ pacing behavior. Pacing is a complex, psychophysiological process and even when the task characteristics are set, it is influenced by a multitude of factors relating to the individual (i.e., physical maturity, cognitive development, muscle fiber type distribution) and environment (i.e., coaching culture, training opportunities) (Edwards & Polman, 2012; Menting et al., 2019b; Renfree & Casado, 2018; Mallet et al., 2021). The absence of these factors has potentially led to the lower explained variance of the models. For example, there was no effect for age or performance level on pacing behavior in female swimmers competing in the 100m event. In males, the effect of age and performance group was also more pronounced in the 200m event compared to the 100m event. It could be that 100m freestyle performance is predominantly driven by the development of physical characteristics, such as muscle fiber type distribution, whereas in the 200m event the distribution of effort is a larger determination factor in the outcome of the race. However, another reason might be that the 100m freestyle is often contested by both 50m and 200m specialists. The energetic system requirements between the 50m and 200m freestyle events differ significantly and therefore swimmers who compete in these events are adapted to physiologically very different tasks (Almeida et al., 2020), therefore exhibiting a different pacing behavior. The coming together of these two types of specialized swimmers might have impacted the results of the present study. It should be pointed out that previous studies have evidenced that swimming performance is impacted by velocity in free swimming sections, but also by turns and underwater phases (Simbaña Escobar et al., 2018). Quantification using 25m or even 5m and 10m sections has previously been demonstrated to reveal more detailed definitions of impact of these factors on a swimmers’ performance (Dormehl & Osborough, 2015; Simbaña Escobar et al., 2018). However, these data have to be gathered using camera set-ups and specialized software, which drastically decreases practicality and would have reduced the sample size greatly. In the end, the present study aimed to create models which could provide insight into the relation between age, experience and future performance level, not precisely predict each individual swimmers’ pacing behavior. The large sample size, consisting of swimmers from five continents, and the strong longitudinal nature of the data are of key importance to the rigidity of the present study’s design, not in the first place because more large scale longitudinal studies on pacing behavior development are needed (Elferink-Gemser & Hettinga, 2017; Menting et al., 2022). Consequently, the decision was made to use publicly available 50m split times. The choice for this approach does allow for future studies, using more detailed quantifications of pacing behavior and the inclusion of more individual
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