Explainable artificial intelligence model for identifying Market Value in Professional Soccer Players

TL;DR

This paper presents an explainable machine learning approach using ensemble models and SHAP to accurately predict soccer players' market values, providing interpretability and actionable insights for stakeholders.

Contribution

It introduces a novel combination of ensemble models and SHAP for interpretable soccer player valuation, with high predictive accuracy and feature importance analysis.

Findings

Gradient Boosting Decision Tree achieved R-squared of 0.901

Player skills, fitness, and cognition significantly influence market value

Model highlights key attributes affecting player valuation

Abstract

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.