[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119315-en":3,"doc-seo-119315-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119315,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning-Based Prediction of Football Match Statistics","This bachelor thesis develops a machine learning-based approach for predicting football match statistics beyond match outcomes. It collects open datasets from at least four leagues, reviews existing methods for statistical prediction, and proposes a dedicated predictive method. The work compares results against at least four baseline models and reports findings in both textual and graphical forms to support clearer visualization of key match metrics such as cards, corners, and shots.","Assignment of bachelor’s thesis  \nTitle: Machine Learning-Based Prediction of Football Match Statistics  \nStudent: Ondřej Herman  \nSupervisor: Rodrigo Augusto da Silva Alves, Ph. D.  \nStudy program: Informatics  \nBranch / specialization: Knowledge Engineering  \nDepartment: Department of Applied Mathematics  \nValidity: until the end of summer semester 2023/2024  \nInstructions  \nFootball is the most popular sport in the world, with 1.5 billion people having watched the 2022 World Cup final, according to the International Federation of Football (FIFA). With the growing interest in sports betting on football, various predictive models have emerged in related literature for match outcome prediction. However, little exploration has been done on predicting statistics of a football match, such as the number of yellow cards, corners, or shots. These statistics are not only essential for predicting match outcomes but also for better understanding teams from a technical standpoint. This bachelor thesis aims to develop a machine learning-based method for predicting football match statistics. Specifically, the thesis will cover the following objectives:  \n1) Collect open datasets of football statistics from at least four different leagues.  \n2) Conduct a literature review of existing methods for predicting match statistics.  \n3) Propose a machine learning-based method for predicting football match statistics.  \n4) Compare the proposed method's results with at least four baseline models.  \n5)Present the results in both textual and graphical formats to improve data visualization methods for football match statistics.  \nOverall, this thesis aims to contribute to the development of machine learning-based methods for predicting football match statistics, which can aid in improving match analysis and decision-making for teams, analysts, and betting enthusiasts.  \nElectronically approved by Ing. Magda Friedjungová, Ph.D. on 27 February 2023 in Prague.  \nBachelor’s thesis  \nMACHINE LEARNING-BASED PREDICTION OF FOOTBALL MATCH STATISTICS  \nOndˇrej Herman  \nFaculty of Information Technology Katedra aplikovan´e matematiky  \nSupervisor: Rodrigo Augusto da Silva Alves, Ph.D. May 11, 2023  \nCzech Technical University in Prague Faculty of Information Technology  \n© 2023 Ondˇrej Herman. All rights reserved.  \nThis thesis is school work as defined by Copyright Act of the Czech Republic. It has been submitted at Czech Technical University in Prague, Faculty of Information Technology. The thesis is protected by the  \nCopyright Act and its usage without author’s permission is prohibited (with exceptions defined by the Copyright Act) .  \nCitation of this thesis: Herman Ondˇrej. Machine Learning-Based Prediction of Football Match Statistics. Bachelor’s thesis. Czech Technical University in Prague, Faculty of Information Technology, 2023 .  \nContents  \nAcknowledgments vii  \nDeclaration viii  \nAbstract ix  \nList of abbreviations x  \n1 Introduction 1  \n1. 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n1.2 Football . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2  \n1.2. 1 Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2  \n1.2.2 League . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n1.3 Objectives and Contributions ............................. 3  \n2 Literature Review 5  \n2.1 Over/Under prediction ................................. 5  \n2.2 Predicting the Outcome ................................ 6  \n2.2.1 Predicting outcome based on predicting statistics . . . . . . . . . . . . . . 8  \n3 Methodology 11  \n3.1 Machine Learning Approaches ............................. 11  \n3.2 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12  \n3.2.1 Poisson Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12  \n3.2.2 Ridge Regression . . . . . . . . . . . . . . . . . . . . .","cbCaijKDyryo2N44","https://ap.wps.com/l/cbCaijKDyryo2N44","pdf",1320438,1,55,"English","en",105,"# Introduction\n## Objectives and Contributions\n# Literature Review\n## Over/Under prediction\n## Predicting the Outcome\n# Methodology\n## Machine Learning Approaches\n## Models\n# Data\n## Data Sources\n## Datasets\n## Feature Engineering\n# Experiments\n## Metrics\n## Validation Procedure\n## Experimental Design\n## Results\n# Conclusion","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To build a machine learning-based method that predicts football match statistics such as yellow cards, corners, and shots.\"},{\"question\":\"What data and preparation steps does the thesis require?\",\"answer\":\"The thesis collects open football statistics datasets from at least four different leagues and performs feature engineering, including preparation for matrix factorization.\"},{\"question\":\"How are the proposed results evaluated?\",\"answer\":\"The method is compared with at least four baseline models using metrics such as MAE, RMSE, and R2, with results presented in textual and graphical formats.\"}]","Machine Learning-Based Prediction of Football Match Statistics | 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is the main goal of the thesis?","Question",{"text":76,"@type":77},"To build a machine learning-based method that predicts football match statistics such as yellow cards, corners, and shots.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data and preparation steps does the thesis require?",{"text":81,"@type":77},"The thesis collects open football statistics datasets from at least four different leagues and performs feature engineering, including preparation for matrix factorization.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the proposed results evaluated?",{"text":85,"@type":77},"The method is compared with at least four baseline models using metrics such as MAE, RMSE, and R2, with results presented in textual and graphical 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