## 2 Background and Literature Review  
### Literature Review  
Data analytics has become a key component in soccer, with most team coaches relying on assistants to help prepare players for games and gather intelligence on opponents. Advanced data modeling techniques are now widely used by professional soccer teams to address challenges such as team selection, personalized training, tactical analysis, and player recruitment. While the use of these advanced techniques is relatively recent, statistical analysis in soccer dates back to the 1950s. [PERSON], the first soccer analyst to use a notational system to record every event in a match, found that a sequence of three or four consecutive passes significantly increased the chances of scoring a goal [7; 8]. Despite soccer's widespread popularity, there has historically been little apparent connection between the sport and statistical data analysis [1; 2]. However, the Expected Goals (\(xG\)) metric bridges this gap by employing statistical methods to model the probability of a shot resulting in a goal [9]. Since [PERSON]'s groundbreaking article introducing \(xG\), this metric has become one of the most prevalent and insightful tools in soccer analytics [10]. Numerous studies have explored the application of \(xG\) models by incorporating different features, primarily focusing on two key variables: the distance from the goal and the angle of the shot [11; 12; 13]. For instance, [PERSON] applied these variables to data from the 2012/13 Premier League and Bundesliga seasons [14]. His approach involved dividing the football pitch into eight zones and analyzing the probability of scoring from shots taken in each zone. [PERSON]'s findings demonstrated that both distance and angle significantly affect the likelihood of scoring a goal. Similarly, [PERSON] examined the impact of distance and angle on shooting outcomes [15]. His model, based on event data from a 14-team professional football league during the 2017/18 season, introduced a probabilistic approach to measure off-ball scoring opportunities (OBSO). This nuanced approach highlights the complexity and potential of \(xG\) modeling in soccer analytics. Another important aspect discussed in the literature is the shot type, which provides contextual information about the shot [16; 17; 18]. Shot types can be divided into two main categories: the part of the body used to take the shot (e.g., left/right foot or head) and the game situation when the shot occurred. [PERSON] and [PERSON] incorporated these features into their model and found that both aspects influence shot outcomes [19]. They observed that shots from free kicks and penalty kicks are more likely to result in goals compared to shots taken in open play with either foot. Conversely, shots from headers tend to be less likely to result in goals. Selecting the right features is crucial for implementing \(xG\) models, as it plays a pivotal role in both past and future research. In addition to shot type and shooting position, other key features identified in previous studies include player ability and home advantage [16; 17; 20; 21]. However, not all earlier studies encompass all the essential features for accurately predicting \(xG\) and assessing soccer players' and teams' performance. In our study, we aim to enhance the overall performance of the model by proposing constructed features. These additions seek to provide a more comprehensive and nuanced understanding of the factors that influence \(xG\) and overall soccer performance. Furthermore, most previous studies have utilized \(xG\) metrics to predict the performance of individual players and soccer teams. However, relying solely on \(xG\) may not provide a comprehensive assessment of player or team performance, as even teams with high \(xG\) metrics may not always achieve the desired results. To illustrate the additional insights provided by \(xG\), consider Manchester United's 0-0 draw with Watford in February 2022 [21].