## 1 Introduction  
Performance analysis, i.e., the recording and exploring of events occurring during competitions, is not only an important tool for coaches but also a subject of extensive research [1,2]. The goal of performance analysis is to provide feedback, create new knowledge, and assist in decision-making, with the ultimate aim of improving the performance of athletes and teams [3]. In recent years, the advancement of technology has offered researchers a large volume of data, while Artificial Intelligence (AI) techniques have provided new possibilities for handling these data [4, 5, 6, 7]. These developments have helped investigate complex questions. For example, securing a top position in the French football championship (Ligue 1), which is one of the positions that qualify for the UEFA Champions League (UCL), is a very strong motivation for the teams, as the UCL brings prestige and financial rewards to clubs [8]. Each season, UCL brings together the best teams from across Europe in a highly competitive tournament, which is considered to be the crown jewel of club football [9]. Additionally, UEFA distributes huge amounts of money to the teams participating in UCL [10]. The factors that contribute to such high rankings in various leagues remain complex and multifaceted. The literature on football analytics is extensive, with numerous studies attempting to decode the elements of successful team performance [11]. Most researchers have employed various statistical techniques to predict teams' rankings. For instance, most authors have used analyses of variance (ANOVA, MANOVA) to find differences in performance indicators between predefined clusters of teams based on their position in the ranking table [12, 13, 14]. Additionally, some combined analyses of variance with correlation analyses (Pearson or Spearman) found correlations between the points collected by the team in the final ranking and the performance indicators [15, 16, 17, 18]. However, some authors used more complex multivariate statistical analyses to also check for the interactions between various performance indicators. For example, [PERSON] and [PERSON] [19] used ordinary least squares (OLS); [PERSON] et al. [20] used multiple linear regression, and [PERSON] et al. [21] used a generalized linear model. From the above studies, it is seen that several factors can lead a team to success by occupying one of the first positions in the standings of a league. For example, [PERSON] et al. [13], [PERSON] et al. [16], and [PERSON] et al. [21] found a high ball possession percentage to be one of the most important, while [PERSON] et al. [14] drug deeper and found that when possession was in the opponent's half, then the chances of success were even greater. Additionally, an element necessary for ball possession, i.e., passing, has been found important for success by many researchers [13, 16, 19, 21]. However, the number of long passes has the opposite effect [22]. Other research has shown that crosses [12, 21] and through balls [21] could increase a team's probability of success since they could increase the number of entry passes in the penalty area [14]. Finally, regarding the effect of running variables, most research shows that only runs in possession of the ball are associated with success and not total runs during the match [15, 18, 20]. On the other hand, in recent years, machine learning (ML) techniques have increasingly been used to solve problems that concern researchers in the field of sports, especially football [23, 24, 25]. Additionally, many researchers have used ML methods that could also provide interpretability of the results [26]. For example, the SHapley Additive Explanations (SHAP) model has been used in many studies in the field of football. Specifically, most researchers have used it in outcome prediction studies [27, 28, 29] as well as in injury risk prediction [30, 31, 32]. It has also been used in more specific topics, such as the prediction of defensive success [33] or entry into the attack zone [34].