## 4 Discussion  
We investigated relationships between swimming performance and a set of demographic measures, training background and duration, anthropometry and easily applicable training tests in swimmers. We employed an XAI model with SHAP values to interpret the importance of these relationships. The model interpretation showed that the swimming  
Figure 7: Hierarchical average global impact on swimming performance models for elite and non-elite (**A** and **B** bar graphs, respectively) expressed by Shapley additive explanation (SHAP) values. training duration, lower-body impulse from the countermovement jump, and fat mass were the principal variables explaining differences in swimming performance. However, when exploring separately the cohorts of elite or non-elite swimmers, the differences in swimming performance were principally related to impulse generated during the countermovement jump. The utility of the employed XAI approach permits simple physical tests and swimming training background to predict/explain differences in swimming performance. Artificial neural networks do not force the variables into a linear or non-linear relationship and are multidimensional, with the benefit of reducing the possibility of misinterpreting how that variable interacts with the outcome [15; 16]. On this basis, neural networks are an attractive alternative to statistical methods for computing quantitative measurements of confidence and correlation [6; 19]. Moreover, when regressions are used, the input variables are deemed independent of each other, which may not always be the case in real-life settings. This shortcoming of regression analysis can increase the error of estimation and diminish the strength of the relationships and interpretations. In contrast, an artificial neural network works in an unsupervised and unbiased way [6], getting closer to the real-world context. The selection of the variables included in predictive models normally depends on the determinant factors of the underlying event. In swimming, the selection should follow the concepts of kinematic, kinetic, and energetic performance determinants (e.g., [33; 34; 35]) and studies detailing predictive models (e.g., [10; 35; 36]). However, even variables that present contradictory results in the literature, given the inevitable heterogeneity in methodologies and subjects, should also be explored using artificial models [26]. If a variable needs to be removed from a model, it will be identified by the high predictive error during the model training. The redundant information problem is easily solved by the model that uses that information to control the model, instead of diminishing its predictive capability. Machine learning approaches have been proposed as a beneficial approach to modelling and interpreting human (athletic) performances [17; 21; 37]. In our results, the duration of swimming training, a simple characteristic easily measured and understood by coaches and swimmers, was the highest contributor to swimming performance when both competitive levels were analysed together. The XAI model showed that the second most important variable was lower-body countermovement jump impulse, which only presented the fifth highest correlation value. Height of the swimmer presents a positive correlation but, in the model, had a negative effect on swimming performance, showing that this measure should be combined with other anthropometric variables, particularly arm span and hand length, in seeking a mechanical advantage. Individual linear correlation analysis must be used with caution when interpreting the relations of the variables included in the neural network to avoid contentious interpretations [10], eliminating the main quality of the artificial intelligence of not forcing one type of relationship between the input and output, and missing the local explanations provide by XAI approaches such as SHAP values [15; 17; 21].