This research develops a machine learning model to predict the yearly wages of Major League Soccer (MLS) players based on their performance statistics. By analyzing the “MLS season player stats and salaries” dataset that includes metrics such as goals, assists, minutes played, passing accuracy, defensive actions, and more, the study aims to identify the key performance indicators most strongly linked to salary outcomes. The model, trained on 2022 data, utilized feature selection methods to isolate the most relevant statistics, and various regression algorithms like linear and ridge were tested to determine the most accurate predictive model. Performance was evaluated using metrics like Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The findings reveal that metrics such as goals scored, goals per game, and successful dribbles have a strong correlation with player wages, while factors like defensive actions and passing accuracy are less impactful. In general, attacking outputs and positions were stronger predictors of high salary than other statistics.This research highlights the potential of machine learning in sports economics, offering teams and agents a valuable tool for making data-driven decisions in player contracts and negotiations. The study also underscores the importance of data analytics in understanding wage determinants in professional soccer. Future work could expand this approach to other leagues and incorporate additional factors like player age, experience, and marketability.
Dhruv Panchagatti (2025) studied this question.