[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120749-en":3,"doc-seo-120749-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120749,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Prediction of football players’ position - using Data Mining and Machine Learning techniques","This thesis implements Data Mining and Machine Learning techniques to predict the best position of footballers on the pitch. The dataset is built from professional player characteristics available through FIFA22 videogame data. Because positions correspond to multiple class levels, classification methods are used for analysis and prediction, including Multinomial Logistic Regression, Linear Discriminant Analysis, and regularization such as Ridge and Lasso. Machine Learning models include Decision Tree, Random Forest, k-Nearest Neighbour, Naive Bayes, and Support Vector Machine, with response-class reduction and a performance/accuracy vs. computational cost comparison.","Università degli Studi di Padova  \nDepartment of Mathematics “Tullio Levi-Civita”  \nMaster Degree in Computer Science  \nPrediction of football players’ position  \nusing Data Mining and Machine Learning  \ntechniques  \nMaster degree thesis  \nSupervisor Graduate student  \nProf. Annamaria Guolo Alberto Gobbo  \nUniversity of Padua  \nAcademic Year 2022-23  \nAlberto Gobbo: Prediction offootball players’position using Data Mining and Machine Learning techniques, Master Degree thesis in Computer Science, Academic Year 2022-23, © February 2023 .  \n“Coloro che fanno sforzi continui sono sempre pieni di speranza. Abbracciate i vostri sogni e inseguiteli.  \nGli eroi quotidiani sono quelli che danno sempre il massimo nella vita.”  \nRoberto Baggio  \niv  \nAbstract  \nThis thesis focuses on the implementation of Data Mining and Machine Learning techniques for predicting the best position of footballers in the pitch.  \nThe dataset used to this aim has been created starting from the characteristics about professionals football players available from FIFA22 videogame data.  \nSince the position held by footballers in the pitch has different levels, classification instruments have been used for data analysis and predictions. Data Mining techniques, including Multinomial Logistic Regression, Discriminant Analysis, and regularization methods, such as Ridge Regression and Lasso, have been adopted for the aim of discovering relationships between the predictors. Machine Learning techniques used mainly for predictions purposes include Decision Tree, Random Forest, k-Nearest Neighbour, Naive Bayes, and Support Vector Machine. In addition to that, the reduction of the response variable classes is considered to check possible improvements on the best Data Mining and Machine Learning techniques found. A comparison between the methods in terms of performance, accuracy of the results and computational cost concludes the analysis.  \nvi  \nContents  \n1 Introduction 1  \n1.1 The problem ................................ 1  \n1.2 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2  \n1.3 Programming languages and tools ...................... 4  \n1.3.1 Programming languages ...................... 4  \n1.3.2 Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5  \n1.4 Personal motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6  \n1. 5 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6  \n2 The FIFA dataset 7  \n2.1 The source of data: [SoFIFA.com ...................... 8](SoFIFA.com ...................... 8)  \n2.2 List of FIFA players’ personal data ...................... 10  \n2.3 List of FIFA players’ attributes ....................... 10  \n2.3.1 Outfield players’ attributes ..................... 11  \n2.3.2 Goalkeeper’s attributes ....................... 15  \n2.4 List of FIFA players’ special attributes .................... 16  \n2.5 List of FIFA players’ positions ........................ 17  \n2.6 Creating the dataset ............................. 22  \n2.6.1 FirstPageWebScraping(url) method ................. 23  \n2.6.2 NextPagesWebScraping(url, 60) method .............. 24  \n2.6.3 ManipulateAndCleanData() method ................ 25  \n2.6.4 GenerateCSVDataset(cleaned_dataset) method .......... 25  \n2.6.5 Precautions for a safe web scraping ................. 26  \n2.7 Attributes names in the cleaned dataset . . . . . . . . . . . . . . . . . . . 26  \n3 Preliminary data analysis of FIFA dataset 29  \n3.1 Pre-processing of dataset .......................... 29  \n3.2 Graphical evaluation ............................ 31  \n3.2.1 Response variable distribution ................... 31  \n3.2.2 Response variable against categorical covariates ........... 32  \n3.2.3 Response variable against quantitative covariates .......... 33  \n3.2.4 Possible interactions between covariates . . . . . . . . . . . . . . 39  \n3.3 Correlation matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43  \n4 Da","cbCaiu9Z9Mp6Qns6","https://ap.wps.com/l/cbCaiu9Z9Mp6Qns6","pdf",6849041,1,188,"English","en",105,"# Introduction\n## The problem\n## Related works\n## Programming languages and tools\n## Personal motivations\n## Thesis structure\n# The FIFA dataset\n## The source of data: SoFIFA.com\n## Creating the dataset\n# Preliminary data analysis of FIFA dataset\n## Pre-processing of dataset\n## Graphical evaluation\n## Correlation matrix\n# Data Mining Techniques\n## Multinomial Logistic Regression\n## Linear Discriminant Analysis\n## Regularization methods\n# Data Mining Results\n## Dataset split and metrics\n## Predictions by model family\n# Machine Learning Techniques\n## Bagging\n## Decision Tree\n## Random Forest\n## K-Nearest Neighbour","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To predict the most suitable position of football players on the pitch using Data Mining and Machine Learning techniques.\"},{\"question\":\"How is the dataset created for the prediction task?\",\"answer\":\"The dataset is generated from professional player characteristics available in FIFA22 videogame data, using web-scraping and data-cleaning procedures.\"},{\"question\":\"Which models are used and how are they compared?\",\"answer\":\"The study uses Multinomial Logistic Regression, Discriminant Analysis, Ridge/Lasso, and Machine Learning models such as Decision Tree, Random Forest, k-Nearest Neighbour, Naive Bayes, and Support Vector Machine, with a comparison based on performance, accuracy, and computational cost.\"}]","Prediction of football players’ position - using Data Mining and Machine Learning techniques | PDF",1785731836,474,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"prediction-of-football-players-position-using-data-mining-and-machine-learning-techniques","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-football-players-position-using-data-mining-and-machine-learning-techniques/120749/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the thesis?","Question",{"text":75,"@type":76},"To predict the most suitable position of football players on the pitch using Data Mining and Machine Learning techniques.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset created for the prediction task?",{"text":80,"@type":76},"The dataset is generated from professional player characteristics available in FIFA22 videogame data, using web-scraping and data-cleaning procedures.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models are used and how are they compared?",{"text":84,"@type":76},"The study uses Multinomial Logistic Regression, Discriminant Analysis, Ridge/Lasso, and Machine Learning models such as Decision Tree, Random Forest, k-Nearest Neighbour, Naive Bayes, and Support Vector Machine, with a comparison based on performance, accuracy, and computational cost.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]