## 1 Introduction  
Soccer, or football as it is known in many countries, is one of the most popular and financially lucrative sports in the world [1, 2]. For instance, the broadcast and commercial revenues to clubs in the Premier League for the 2022/23 season alone reached nearly GBP3 billion [3]. This substantial financial investment and the intense competition among clubs necessitate a focus on improving team performance through innovative methods. As the sport continues to grow globally, the need for advanced strategies and technologies to support team performance and player development also increases. Recent advances in sports analytics have led to the development of various novel metrics that are transforming the way clubs approach team management and player performance. These metrics, such as expected goals (\(xG\)), have become integral to clubs' decision-making processes [4]. However, a comprehensive approach to player and team assessment is still lacking, as existing metrics may not provide a complete view of a player's contributions or a team's effectiveness. This gap exists partly due to the absence of crucial features and the inherent limitations of \(xG\)[5]. It is crucial for analysts to recognize the significance of context. Relying solely on one specific metric for statistical analysis, especially when assessing games individually, can be detrimental. While those utilizing \(xG\) may appreciate its advantages, it is equally important to acknowledge its limitations. Although the data used in any model play a critical role, how that data are interpreted is equally vital. A comprehensive understanding that encompasses both the strengths and weaknesses of \(xG\) is necessary for its fullest potential [6]. In addition to expected goals (\(xG\)), actual goals (\(aG\)) provide a crucial measure of performance, as they reflect the outcomes of goal-scoring opportunities. By incorporating both \(xG\) and \(aG\), we gain a clearer and more comprehensive understanding of a player's effectiveness. In this work, we propose a hybrid approach that predicts both \(xG\) and \(aG\) to address the limitations of relying solely on \(xG\), offering a more realistic evaluation of player performance. For a more detailed assessment, this paper considers expected goals (\(xG\)) and actual goals (\(aG\)) on a per-game basis. Therefore, we use the terms expected goals per game (\(xGg\)) and actual goals per game (\(aGg\)) to represent these metrics. \(xGg\) estimates the number of goals a player or team is expected to score in each game based on the quality of chances created, while \(aGg\) reflects the actual number of goals scored in a game. This distinction helps provide a clearer evaluation of performance and efficiency. This approach involves constructing models that integrate significant features to improve the accuracy of player and team performance predictions. By leveraging these models, we can offer insights into players' goal-scoring opportunities and team strategies. Additionally, our study explores and compares \(xGg\) across six prominent European football leagues: the English Premier League (EPL), German Bundesliga, Spanish La Liga, Italian Serie A, French Ligue 1, and Russian Premier League (RPL). Through our analysis, we identify the top goal scorers from each league and evaluate their performance in relation to the proposed models. The main contributions of this paper are as follows:  
* It proposes a hybrid approach that combines expected goals per game (\(xGg\)) and actual goals per game (\(aGg\)) to enhance player performance prediction. * It introduces a new method for feature construction in player performance modeling. * It compares various machine learning models to evaluate their effectiveness in predicting \(xGg\) and \(aGg\), identifying the best algorithms for player performance analysis. * It categorizes players based on \(xGg\) and \(aGg\) to provide more detailed insights into player performance and effectiveness.