Integrating XGBoost and SHAP Model for Football Player Value Prediction and Characteristic Analysis
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...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2022-12-01
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| Series: | Jisuanji kexue |
| Subjects: | |
| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-12-195.pdf |
| _version_ | 1797845161360752640 |
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| author | LIAO Bin, WANG Zhi-ning, LI Min, SUN Rui-na |
| author_facet | LIAO Bin, WANG Zhi-ning, LI Min, SUN Rui-na |
| author_sort | LIAO Bin, WANG Zhi-ning, LI Min, SUN Rui-na |
| collection | DOAJ |
| description | 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 <i>R</i><sup>2</sup>,<i>MAE </i>and <i>RMSE</i>.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. |
| first_indexed | 2024-04-09T17:34:05Z |
| format | Article |
| id | doaj.art-d91062f404c44cc3b0a6d427c1a0fe0f |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-04-09T17:34:05Z |
| publishDate | 2022-12-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-d91062f404c44cc3b0a6d427c1a0fe0f2023-04-18T02:32:59ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2022-12-01491219520410.11896/jsjkx.210600029Integrating XGBoost and SHAP Model for Football Player Value Prediction and Characteristic AnalysisLIAO Bin, WANG Zhi-ning, LI Min, SUN Rui-na01 College of Big Data Statistics,Guizhou University of Finance and Economics,Guiyang 550025,China ;2 College of Statistics and Data Science,Xinjiang University of Finance and Economics,Urumqi 830012,China ;3 Institute of Information Engineering,Chinese Academy of Sciences,Beijing 100093,China ;4 School of Networks Security,University of Chinese Academy of Sciences,Beijing 100049,ChinaWith 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 <i>R</i><sup>2</sup>,<i>MAE </i>and <i>RMSE</i>.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.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-12-195.pdfmachine learning|player’s value prediction|training strategy|xgboost algorithm|shap value |
| spellingShingle | LIAO Bin, WANG Zhi-ning, LI Min, SUN Rui-na Integrating XGBoost and SHAP Model for Football Player Value Prediction and Characteristic Analysis Jisuanji kexue machine learning|player’s value prediction|training strategy|xgboost algorithm|shap value |
| title | Integrating XGBoost and SHAP Model for Football Player Value Prediction and Characteristic Analysis |
| title_full | Integrating XGBoost and SHAP Model for Football Player Value Prediction and Characteristic Analysis |
| title_fullStr | Integrating XGBoost and SHAP Model for Football Player Value Prediction and Characteristic Analysis |
| title_full_unstemmed | Integrating XGBoost and SHAP Model for Football Player Value Prediction and Characteristic Analysis |
| title_short | Integrating XGBoost and SHAP Model for Football Player Value Prediction and Characteristic Analysis |
| title_sort | integrating xgboost and shap model for football player value prediction and characteristic analysis |
| topic | machine learning|player’s value prediction|training strategy|xgboost algorithm|shap value |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2022-49-12-195.pdf |
| work_keys_str_mv | AT liaobinwangzhiningliminsunruina integratingxgboostandshapmodelforfootballplayervaluepredictionandcharacteristicanalysis |