  This study introduces an advanced machine learning method for predicting
soccer players' market values, combining ensemble models and the Shapley
Additive Explanations (SHAP) for interpretability. Utilizing data from about
12,000 players from Sofifa, the Boruta algorithm streamlined feature selection.
The Gradient Boosting Decision Tree (GBDT) model excelled in predictive
accuracy, with an R-squared of 0.901 and a Root Mean Squared Error (RMSE) of
3,221,632.175. Player attributes in skills, fitness, and cognitive areas
significantly influenced market value. These insights aid sports industry
stakeholders in player valuation. However, the study has limitations, like
underestimating superstar players' values and needing larger datasets. Future
research directions include enhancing the model's applicability and exploring
value prediction in various contexts.
