2023/11/08 by Chunyang Huang, Huang, Chunyang, Shaoliang Zhang +1
Decision Sciences · Economics, Econometrics and Finance · #Computational Finance (q-fin.CP) #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Sports Analytics and Performance #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2311.04599
openalex publication_date 2023/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.