## 3 Methodology
### Data Preprocessing  
By resolving errors and deleting unnecessary variables, data cleaning is an essential step in maintaining dataset dependability. The original dataset for this study contained information on all participants and contained 34 variables. A criterion was defined to include only pertinent data, excluding records that had minimal impact on the study, and irrelevant variables were deleted to maintain analytical integrity.  
1. **Irrelevant variables**: The numerical data collected from the sources include several variables irrelevant to the analysis, such as expected goals (the number of goals a  
Figure 1: Methodology Approach and Structure.  
player is likely to score in the current season by analyzing his previous season's performance), nationality, and the number of yellow and red cards acquired by player throughout the season. These variables need to be removed before conducting the analysis. The process of feature selection in this study was guided by a combination of domain expertise and statistical analysis to ensure that only the most relevant features were included in our models. Initially, features were chosen based on their known impact on player performance and injury risk from existing sports science literature. This foundational selection was further refined through exploratory data analysis, where correlation matrices and preliminary regression models helped identify features with significant predictive power and minimal collinearity. 2. **Minutes played**: The data include players who played as little as one minute of a match in the season. This will result in the data analysis being irrelevant and misleading. To address this, we set a parameter to include only those data that qualify for data analysis. We set the parameter to a minimum of 1140 min (setting an average of 30 min for each match played, so 38 matches imply a total of 38 multiplied by 30, which is 1140 min). This minimum threshold was chosen because it represents a substantial portion of the match time to contribute meaningfully to team dynamics while allowing for the inclusion of players who may not start every match but are regular contributors. This method not only reduces the risk of analyzing data that might not truly reflect a player's impact but also helps to eliminate incomplete records from the dataset. Minutes played = Total number of matches in the season \(\times\) Minimum standard set = 38 \(\times\) 30 = 1140 min 3. **Player position**: Due to the different roles that midfielders and forwards play on the field, the research will focus on these two groups of players when predicting performance. The central planners of attacking moves are the midfielders (MF) and forwards (FW) players, who create opportunities for goals through assists and their own goals. They are the perfect subjects for this research because their performance measures are, by nature, focused on attack and creativity. Unlike goalkeepers or defenders, whose contributions are assessed differently, analyzing MF and FW players' offensive prowess offers deeper insights into the fundamental elements of football performance that directly impact a team's ability to win. This tactical decision enables us to focus on the players whose actions impact goal-scoring and offensive playmaking most.