[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125601-en":3,"doc-seo-125601-105":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},125601,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine Learning in Sports Industry - Discover promising athletes for the future of Handball","Technology and data-driven methods increasingly shape modern sports industries through systematic collection and processing. Visual and analytical data interpretation supports coaches, managers, and scouting decisions across disciplines, including handball. This project work introduces predictive analytics to identify promising handball athletes using collected variables, despite limited data from the Federação de Andebol de Portugal. It includes data collection and pre-preparation, visual presentations (bar charts and maps), and machine learning models to forecast player potential.","Master Degree Program in  \nData Science and Advanced Analytics  \nMDSAA  \nMachine Learning in Sports Industry  \nDiscover promising athletes for the future of Handball  \nMiguel Coutinho Nunes  \nProject Work  \npresented as partial requirement for obtaining the Master Degree Program in Data Science and Advanced Analytics  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nMACHINE LEARNING IN SPORTS INDUSTRY: DISCOVER PROMISING ATHLETES FOR THE FUTURE OF HANDBALL  \nby  \nMiguel Coutinho Nunes  \nProject Work presented as partial requirement for obtaining the Master’s degree in Advanced Analytics, with a Specialization in Business Analytics  \nSupervisor: Vítor Manuel Pereira Duarte dos Santos, PhD  \nAdvisor: Ricardo Andorinho  \nNovember 2022  \nDEDICATION  \nDedico inteiramente esta tese à minha família e namorada que sempre me apoiaram econtribuíram para que conseguisse atingir esta etapa de minha vida, sendo uma das mais importantes. Obrigado a todos!  \nAKNOWLEDGMENT  \nGostaria de começar por agradecer o apoio e colaboração do Prof. Dr. Vítor Duarte dos Santos na orientação e disponibilidade prestada ao longo do desenvolvimento da tese. A suainfluência e encorajamento foram essenciais para que este projeto fosse finalizado.  \nQuero também agradecer à Federação Portuguesa de Andebol e seus representantes, Ricardo Andorinho e João Sousa, por providenciarem os dados, esclarecerem dúvidas e contribuírem com ideias para que conseguisse avançar com o estudo.  \nNão poderia deixar de mencionar o agradecimento pela ajuda do meu amigo Rui Sousa na criação do programa em VBA para pré-preparação dos dados e não menos importante, um reconhecimento especial pelo apoio da minha namorada, Madalena Cabaço, ao incentivarme nos momentos finais de entrega.  \nABSTRACT  \nNowadays, technology applied in industries has increased day by day. As a result, different ways of collecting and processing data have been investigated. Machine Learning is one of those ways where, in addition to its applicability in all sectors, it has been increasingly explored in different sports. Furthermore, the analysis of data at a visual level helps in the interpretation and understanding of them.  \nThese types of procedures always seek to support in the decision-making work done by coaches, managers and scouting. It can be inherent to any sport and handball is obviously included.  \nThis specific investigation addresses methods to create advantages for Handball, introducing predictive analytics. The discovery of promising athletes based on collected variables is oneof the biggest challenges in this sport and although the data provided by the Federação de Andebol de Portugal are limited, this study demonstrates a 'direction' of how it can be done based on a single variable.  \nIn addition to working in the collection and pre-preparation of sports data, examples of visual presentations such as vertical/horizontal bar graphs and maps are exposed. Finally, Machine Learning algorithms with and without default parameters are used to predict if the player is promising. From this perspective, it can be concluded that, based on formation years, models score is slightly better for Support Vector Machines algorithms despite the proximity of the results. It is important to point out that relevant conclusions were also drawn from the graphs.  \nKEYWORDS  \nMachine Learning; Promising forecasting; Predictive Analysis; Sports industry; Performance metrics; Handball  \nINDEX  \n1. Introduction ........................................................................................................................ 1  \n1.1. Context......................................................................................................................... 1  \n1.2. Motivation .......................................","cbCaigs1XHKiRIaX","https://ap.wps.com/l/cbCaigs1XHKiRIaX","pdf",4641181,1,101,"English","en",105,"# 1. Introduction\n## 1.1. Context\n## 1.2. Motivation\n## 1.3. Objectives\n## 1.4. Study importance and relevance\n# 2. Literature review\n## 2.1. Handball\n## 2.2. Study tools and concepts\n## 2.3. 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