Players’ Value Prediction Based on Machine Learning Method

Abstract It is important to predict football players’ value, especially during transfer period. This paper uses the player information and value data of the game FIFA 18 as data source. It is able to realize the prediction of its players’ best positions and values. After reducing the dimensionality of the value prediction model, a cluster analysis on the player’s position is introduced, and then grid search method is adopted to adjust the Xgboost parameters, Finally, Xgboost method is used to predict the player’s worth. The experimental results show that certain accuracy is achieved, but t there is still room for improvement in the accuracy of prediction. Discussions based on experiment results are made.