## 1 Introduction  
The numerous unique chains of dynamic interactions between players during soccer games can be reduced to different performance indicators, to allow more insight into game performance [1]. A performance indicator is a selection, or combination, of action variables aiming to define some or all aspects of performance, and can be used to assess the performances of an individual, a team or the elements of a team [2]. Generally, sports performance judgments are prone to bias [3], as good outcomes are attributed to internal causes and bad outcomes to external causes [4]. Better insights into performance indicators could therefore not only aid the decision-making process of coaches and players with regard to training and game preparation, but also help other stakeholders related to the team, such as scouting and management, to correctly evaluate team and player performances [5].  
Previously, several performance indicators have already been linked to performance in soccer, such as ball possession [6, 7, 8, 9], number of passes [10, 11], number of shots [6, 8, 10, 12], number of shots on target [6, 8, 12], entries into the penalty box [9, 13] and successfulness in duels [9]. Previous studies investigating performance indicators in soccer, however, often suffer from limitations and/or methodological problems, such as small sample sizes and univariate analyses of the observed variables [6]. If presented in isolation, a single set of indicators representing the performance of an individual or a team can give a distorted impression of a performance by ignoring other, more or less important, variables [2], which likely explains differences between previous studies [6]. It is therefore important that variables are not considered in isolation when relating performance indicators to game performance.  
Technical innovations have led to an increasing availability of different performance indicators. Advanced metrics can currently be calculated in soccer using tracking data [14]. As tracking data results in millions of data points per season [15], calculating these metrics challenges data management and analytical methods of analysts [16]. Advanced metrics based on tracking data are also often provided by professional sports data companies, but obtaining these metrics is usually not free. So although metrics based on tracking data may better capture the complex nature of soccer [14], obtaining these metrics may at the moment not be feasible because of pratical and/or financial reasons for many teams. Furthermore, although there has been an increased interest into the utilization of tracking data by academics, practitioners still tend to use more traditional performance indicators [17].  
In contemporary soccer, there is a wealth of different performance indicators, describing technical, tactical and physical performances, as well as contextual information. In order to provide to-the-point information to the coaching staff, a selection of the most important performance indicators can be helpful. Therefore, this study aimed to identify the strongest predictive variables of winning and losing in soccer using a broad range of variables. Machine learning is used instead of inferential statistics, as machine learning is better at handling large amounts of input variables [18]. Based on previous research, it can be hypothesized that shot-related variables such as shots and shots on target are closely related to performance in soccer; however, it is also expected that other indicators will be amongst the most important predictors.