融合XGBoost与SHAP模型的足球运动员身价预测及特征分析方法

摘要/Abstract

摘要： 随着足球运动全球化程度的不断提升,全球转会市场愈发庞大,然而针对影响转会交易最关键的因素球员身价的深入模型及应用研究还较为缺乏。以国际足球联合会FIFA的官方球员数据库为研究对象,首先,在区分不同球员位置的前提下,运用Box-Cox变换、F-Score特征选择等方法对原始数据集进行特征处理;其次,通过XGBoost构建球员身价预测模型,并与Random Forest,Adaboost,GBDT,SVR等主流机器学习算法进行10折交叉验证实验对比,证明了XGBoost模型在R2,MAE,RMSE这3项指标上的性能优势;最后,在身价预测模型的基础上,融合SHAP框架分析不同位置影响球员身价的重要因素,为球员身价评估、身价对比分析、球员训练策略制定等场景提供决策支持。

关键词:

机器学习,

球员身价预测,

训练策略,

XGBoost算法,

SHAP值

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.

Key words:

Machine learning,

Player’s value prediction,

Training strategy,

XGBoost algorithm,

SHAP value

中图分类号:

TP391

引用本文

廖彬, 王志宁, 李敏, 孙瑞娜. 融合XGBoost与SHAP模型的足球运动员身价预测及特征分析方法[J]. 计算机科学, 2022, 49(12): 195-204. https://doi.org/10.11896/jsjkx.210600029

LIAO Bin, WANG Zhi-ning, LI Min, SUN Rui-na. Integrating XGBoost and SHAP Model for Football Player Value Prediction and Characteristic Analysis[J]. Computer Science, 2022, 49(12): 195-204. https://doi.org/10.11896/jsjkx.210600029

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参考文献

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