## 2 Background and Literature Review
### Understanding Expected Goals (\(xG\))  
Soccer has undergone a significant transformation in recent years, with an increasing emphasis on tactical battles between coaches. To navigate these strategic complexities, metrics that evaluate the performance of teams and players and assess their contributions have become essential. One of the most prominent metrics in modern soccer is \(xG\), which plays a pivotal role in tactical discussions.  
Moreover, \(xG\) is a statistical metric that provides insight into the quality of scoring opportunities for both individual players and teams. This measurement evaluates how likely a player or team is to score based on the quality of their chances. As a vital tool in analyzing player efficiency and team strategies, \(xG\) offers a deeper understanding of the game beyond traditional goal-scoring metrics [22]. Many data analytics companies use their own approaches to calculating \(xG\), often considering factors such as the distance to the goal post, angle of the shot, body part used to take the shot, and the type of assist or preceding action, all based on historical player data [23]. Furthermore \(xG\) assigns a value to each shot, ranging from 0.00 to 1.00, to reflect the probability of it resulting in a goal [22]. For instance, a shot with an \(xG\) of 0.01 suggests a 1% probability of scoring, indicating that a goal would be expected once in every 100 attempts--a low-chance scenario. Conversely, a shot with an \(xG\) of 0.99 implies it would be scored 99 times out of 100 by an average player, representing an almost certain goal. The \(xG\) value can vary significantly based on the position and angle of the shot. For example, there is a large disparity in the \(xG\) value between shots taken from inside the penalty box versus those from outside the box. Shots from within the box generally have a higher \(xG\) due to their proximity to the goal and better angles. Using \(xG\), these situations can be quantified numerically. For example, if a shooting opportunity is assigned an \(xG\) of 0.1, it indicates that a player is expected to score one goal from every ten shots in that specific scenario. If a player has 15 shooting chances from inside the box in a single match, their expected goal total would be 15 times 0.1, resulting in 1.5 \(xG\). This means the player's expected goal value can fluctuate depending on the quality and frequency of the chances they receive [24]. However, relying solely on \(xG\) does not provide a complete picture of team and player contributions. When analyzing a single match, \(xG\) can sometimes present a skewed interpretation of team performance. For example, if Team A accumulates 3 \(xG\) while team B records only 1 \(xG\), it might appear that Team A dominated the game. However, the reality could be different; Team B might have scored three early goals and then chosen to defend their lead, allowing Team A more opportunities to attack. In such a case, Team A may have generated better chances, but Team B's early success would have changed the dynamics of the game. This illustrates the potential discrepancy between what \(xG\) suggests and the actual course of events [25].