[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127722-en":3,"doc-seo-127722-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127722,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning and Multivariate Statistical Tools for Football Analytics - doctoral thesis","This doctoral thesis focuses on studying, implementing, and applying machine learning and multivariate statistics techniques in the emerging field of sports analytics, specifically in football. It combines commonly used procedures with new methods to address research questions in sports performance and the economic dimension of the game, aiming to support decision-making. Using the free statistical software R and open data, the work contributes to understanding how machine learning and multivariate models behave in analytical sports prediction.","Machine Learning and Multivariate Statistical tools for Football Analytics  \n19 de junio de 2023  \nAutora: Pilar Malagón-Selma  \nDirectores: Ana Debón Aucejo Alberto J. Ferrer Riquelme  \n“Yo te alabo, Padre, Señor del Cielo y de la tierra, porque ocultaste estas cosas a los sabios y prudentes y las revelaste a los pequeños”. Lc 10, 21  \nAknoledgements Agradecimientos  \nA Dios, por esta tesis que no es más mía que suya.  \nA mi marido, por ser el bastón que me mantiene erguida, la roca de mi descansoy el sol en mis días nublados.  \nA mi padre y mi madre, por su amor incondicional y por educarme en la cultura del trabajo y del esfuerzo.  \nA mi yaya, por su amor eterno.  \nA mis hermanos, amigos y comunidad, por la motivación, el ánimo y el sustentodado a lo largo de estos años.  \nTo Prof. Maurizio, for accepting me as a visiting researcher in your group. Also, thanks to my colleagues Matteo, Riccardo and Mattia during these months in Brescia. Most of the work compiled here would have been impossible without your friendship, welcome and company.  \nA mis directores de tesis Dr. Alberto J. Ferrer y Dra. Ana Debón por sudedicación, colaboración y confianza durante este largo proceso de aprendizaje.  \nA Rafael Nadal, por ser una fuente de motivación e inspiración y por mostrar que el sacrificio y el trabajo duro tienen su recompensa.  \nAbstract  \nThis doctoral thesis focuses on studying, implementing, and applying machine learning and multivariate statistics techniques in the emerging field of sports analytics, specifically in football. Commonly used procedures and new methods are applied to solve research questions in different areas of football analytics, both in the field of sports performance and in the economic field. The methodologies used in this thesis enrich the techniques used so far to obtain a global vision of the behaviour of football teams and are intended to help the decision-making process. In addition, the methodology was implemented using the free statistical software R and open data, which allows for reproducibility of the results.  \nThis doctoral thesis aims to contribute to the understanding of the behaviour of machine learning and multivariate models for analytical sports prediction, comparing their predictive capacity and studying the variables that most influence the predictive results of these models. Thus, since football is a game of chance where luck plays an important role, this document proposes methodologies that help to study, understand, and model the objective part of this sport. This thesis is structured into five blocks, differentiating each according to the database used to achieve the proposed objectives.  \nThe first block describes the most common study areas in football analyticsand classifies them according to the available data. This part contains an exhaustive study of football analytics state of the art. Thus, part of the existing literature is compiled based on the objectives achieved, with a review of the  \nstatistical methods applied. These methods are the pillars on which the new procedures proposed here are based.  \nThe second block consists of two chapters that study the behaviour of teams concerning the ranking at the end of the season: top (qualifying for the Champions League or Europa League), middle, or bottom (relegating to a lower division) . Several machine learning and multivariate statistical techniques are proposed to predict the teams' position at the season's end. Once the prediction has been made, the model with the best predictive accuracy is selected to study the game actions that most discriminate between positions. In addition, the advantages of our proposed techniques compared to the classical methods used so far are analysed. The database used for the analysis comprises quantitative variables that store cumulative information on the game actions performed by the teams throughout the 2018/2019 season.  \nThe third block consists of a single chapter in which a web scraping code is de","cbCaina5f6k7KPOz","https://ap.wps.com/l/cbCaina5f6k7KPOz","pdf",7780282,2,1,188,"English","en",105,"# Abstract\n## Study scope and objectives\n## Block 1: football analytics state of the art and data classification\n## Block 2: predicting end-of-season team rankings\n## Block 3: web scraping and match outcome prediction\n## Block 4: economic football and transfer fee indicator modeling\n## Block 5: key findings and future research lines","[{\"question\":\"What is the main focus of the thesis?\",\"answer\":\"The thesis studies, implements, and applies machine learning and multivariate statistics for football analytics. It targets both sports performance and the economic aspects of the game.\"},{\"question\":\"How are reproducible results ensured?\",\"answer\":\"The methodology is implemented using free statistical software R and open data, enabling reproducibility of the results.\"},{\"question\":\"How is the thesis structured?\",\"answer\":\"The thesis is organized into five blocks, each differentiated by the database used to achieve specific objectives. These blocks cover state-of-the-art review, ranking prediction, match outcome modeling, transfer-fee indicators, and concluding research directions.\"}]","Machine Learning and Multivariate Statistical Tools for Football Analytics - doctoral thesis | PDF",1785941228,474,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-and-multivariate-statistical-tools-for-football-analytics-doctoral-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-and-multivariate-statistical-tools-for-football-analytics-doctoral-thesis/127722/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main focus of the thesis?","Question",{"text":76,"@type":77},"The thesis studies, implements, and applies machine learning and multivariate statistics for football analytics. It targets both sports performance and the economic aspects of the game.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are reproducible results ensured?",{"text":81,"@type":77},"The methodology is implemented using free statistical software R and open data, enabling reproducibility of the results.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the thesis structured?",{"text":85,"@type":77},"The thesis is organized into five blocks, each differentiated by the database used to achieve specific objectives. 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