Integrating XGBoost and SHAP Model for Football Player Value Prediction and Characteristic Analysis

Abstract

With the increasing globalization of football,the global player transfer market is becoming more and more prosperous.However,as the most important factor affecting player transfer transaction,the player’s transfer value lacks in-depth model and application research.In this paper,the FIFA’s official player database is taken as the research object.Firstly,on the premise of distinguishing different player positions,Box-Cox transformation,F-Score feature selection,etc.are used to perform feature processing on the original data set.Secondly,the player value prediction model is constructed by XGBoost,and compared with the main machine learning algorithms such as random forest,AdaBoost,GBDT and SVR for 10-fold cross validation experiments.Experimental results prove that the XGBoost model has a performance advantage over the existing models on the indicators of R2,MAE and RMSE.Finally,on the basis of constructing the value prediction model,this paper integrates the SHAP framework to analyze the important factors affecting the players’ value score in different positions,and provides decision support for some scenarios,such as player’s value score evaluation,comparative analysis,and training strategy formulation,etc.

Keywords

• machine learning|player’s value prediction|training strategy|xgboost algorithm|shap value