## 1 Introduction  
Jumping performance is considered an overall indicator of gymnastics ability, demanding high power outputs [1], which are possible through specialized power training complemented with high force production [2]. In Acrobatic Gymnastics (ACRO), gymnasts work in pairs or groups to perform balance (static positions) and dynamic elements, characterized by flight from throws, boosts, pitches, and catches. Therefore, each gymnast plays a distinct role in achieving the requirements defined by the Code of Points: base and top gymnasts [3]. Bases are generally older and present larger morphological measurements compared to top gymnasts, with no differences in the muscular component [4]. Therefore, the use of progressive loads and training planification according to the gymnasts' age are key aspects in ACRO [5]. In a typical training session, top gymnasts work on specific balance positions, while bases perform strength training to learn the appropriate techniques to provide a stable platform for tops to balance on, launch from, and land on [6]. Both perform trampoline training and individual elements, divided into balance, flexibility, agility, and floor categories [3]. Two studies investigated the differences between roles in ACRO, focusing on the unipedal balance [7] and the anthropometric profile [4]. One study evaluated the jumping performance of 5-8-year-old girls (role unknown) training ACRO twice a week [8]. Recently, the differences in the jumping skills of bases, tops, and rhythmic gymnnasts were investigated [9], and the results showed that the specificity of the role and gymnastics discipline influenced the gymnasts' jumping performance. To maximize the individual ballistic performance, it is important to consider the force-velocity (F-V) profile, which represents the balance between force and velocity qualities [10; 11], and is considered a relevant parameter for the jumping ability assessment [12]. However, as the athlete's level and training background increase, the difficulty of producing training adaptations to maximal power activities, such as vertical jumps, also increases [2]. There is also a greater specialization as gymnasts progress in age [7]. Evaluating to what extent this applies to the F-V profile and investigate the variables that explain variations between roles would improve coaches' knowledge of each specific role. Nevertheless, the multifactorial nature of the variables that influence sports performance requires an evaluation method with an extraordinary ability to deal with complex nonlinear problems, such as the multilayer neural network system, with strong self-learning, self-adaptability, and fault tolerance [13]. Unlike classic statistical models and correlative methods, neural networks consist of multiple indirect interconnections between input and output variables and nonlinear mathematical equations and statistical techniques to successively minimize the variance between actual and predicted outputs, yielding a model that can be applied to an independent data set [14]. This approach offers better optimization possibilities for predicting sports results, athlete recruitment, and selection processes than the widely applied regression models [15]. Two key factors of this tool are its explainability and interpretability, focusing on the results but also on the data's inherent patterns and the ability of the algorithms to explain them [16]. As an efficient tool for studying and disseminating data populations into groups, this approach has been applied in team sports such as football [16], body composition research [14], and sports psychology [13], but no studies have applied this method to gymnastics. Accordingly, this work aims to investigate a hierarchy of variables that influence the F-V profile of base and top gymnasts, using a deep artificial neural network model. We hypothesized that the selected variables should explain variations in the F-V profile of acrobatic gymnasts.