## 1 Introduction  
Artificial neural networks can be employed to form a simplified model of the brain cells' cooperation in performing a desired function in human movement based on recognition and patterns classification. This type of network uses a series of non-linear interconnected mathematical equations to calculate an output variable (e.g., swimming performance) based on independent input variables [1, 2, 3]. These models can be used for different tasks (e.g., classification, data noise reduction, and prediction [4]), with interpretation and translation important for implementation in practical settings. Artificial neural network complexity and difficulty in understanding the interaction between input variables (the so-called \"blackbox\" effect [5]), for example the hidden layers where computations performed on the input data extract increasingly abstract features and patterns, have limited applications in sports performance prediction, particularly in swimming [6; 7]. However, deep learning models have been used for assessing key movement points and swimming techniques [8; 9]. Studies that used these models in swimming prediction typically combined them with correlation and/or regression analysis for data interpretation [10; 11; 12], potentially limiting the accuracy of the artificial neural networks. Nevertheless, swimming performance prediction using simpler models (e.g., linear and non-linear regression) remains commonplace given their easy-to-interpret results [13; 14]. However, their lack of generalisation power and the likelihood of substantial prediction errors in the interactions between the inputs should be considered. In this context, the explainable AI (XAI) has begun to be implemented. XAI is a branch of artificial intelligence dedicated to developing models that can offer clear and interpretable explanations for their predictions and decisions [15; 16]. Among the most widely used techniques in this field, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) stand out for human performance modelling; however, the latter only present local (individual) explanations [17]. SHAP values are based on cooperative game theory and offer a measure of feature importance with both local and global accuracy [15; 17]. Other XAI approaches can be found in the literature, with their usefulness dependent on the objective and type of AI used [18]. Artificial neural network interpretation can be as simple as the outputs obtained from correlations and regressions models in combination with the SHAP due to their additive interpretation [15; 19; 20]. SHAP values are a useful visualisation tool that make a deep machine learning model explainable and interpretable by visualizing its output in terms understandable to human intuition [16; 19]. XAI with SHAP values permits more accurate and interpretable predictive models (presenting lower error than traditional statistical methods), with better generalisation and representation of non-linear system behaviours like human athletic performance [6; 11; 19; 20; 21]. This approach has begun to be applied in team-sports-related research [17; 22] but to our knowledge only one technical study has applied the SHAP interpretation method to swimming performance prediction, specifically backstroke-to-breaststroke turn performance by age group [2], while another study used SHAP to predict teamwork effects on individual effort expenditure in swimming relays [21]. Given the multifactorial nature of swimming performance, and that a predicting model should be as practical as possible [10], the input data selection should be derived from the literature (biomechanical, physiological, psychological, genetical, and contextual [23; 24]) and integrate data easy to collect and apply in the training context [25; 26].