[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128474-en":3,"doc-seo-128474-105":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},128474,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Sports Analytics - Performance Prediction - Master of Science (MSc) in Data Science Thesis","The dissertation is prepared as part of the MSc in Data Science at the International Hellenic University, focusing on how sports analytics can support football clubs through actionable insights. It analyzes large volumes of player and match statistics, then applies feature engineering to structure the data for modeling. The work aims to predict a football player’s next-season performance by estimating the number of goals using prior seasons’ data and three machine learning algorithms. Experiments are implemented in Python and evaluated through thorough comparisons to identify the best-performing model.","Sports Analytics  \nPerformance Prediction  \nNikolaos Giannakoulas  \nSID: 3308210013  \nSchool of Science and Technology  \nA thesis submitted for the degree of Master of Science (MSc) in Data Science  \nJanuary 2023  \nThessaloniki – Greece  \nSports Analytics  \nPerformance Prediction  \nNikolaos Giannakoulas  \nSID: 3308210013  \nSupervisor:  \nSupervising Committee Members:  \nAssoc. Prof. Christos Tjortjis Dr. Paraskevas Koukaras  \nDr. Christos Berberidis  \nSchool of Science and Technology  \nA thesis submitted for the degree of Master of Science (MSc) in Data Science  \nJanuary 2023  \nThessaloniki – Greece  \nAbstract  \nThis dissertation was written as part of the MSc in Data Science at the International Hellenic University.  \nSports Analytics is a rapidly growing field. It is experiencing great development, and its applications are very useful to sports clubs. These clubs can collect information about the players and the game generally and then extract important insights that will help them improve.  \nThere is a wide variety of daily football statistics. Thousands of football games happen every week, resulting in the production of numerous statistics. Sports analytics’responsibility is to collect those statistics, analyze them and then provide conclusions to football clubs.  \nThe goal of this dissertation is to predict the performance of a player in the field of football. The purpose of the dissertations is to predict, as accurately as possible the number of goals a football player will achieve next season based on his previous years’performances.  \nFootball players’ data were collected from valid online sources and then analyzed [1] . After that, feature engineering was implemented to transform the collected data into the desired form. Then, three machine learning algorithms were used for predictions.  \nThis dissertation is separated into two parts. The first part is theoretical, and it includes previous works on the field of performance prediction, not only in football but in other sports too, like basketball, volleyball and tennis.  \nFor the second part, the practical one, we paid attention to the conducted experiments. The results were examined thoroughly and compared to understand them and determine which model had better performance. For this purpose, python was used for coding, specifically PyCharm.  \nAcknowledgments  \nAt this point, I would like to thank my supervisor, Professor Christos Tjortjis, for his helpful advice throughout the dissertation. He gave me the appropriate feedback and he was very supportive whenever I had questions. In addition, I would like to thank PhD candidate Georgios Papageorgiou who was always helping me with efficient comments and suggestions to problems that emerged.  \nMoreover, I would like to thank my family for their support during those difficult and stressful months. They were really supporting me during this journey.  \nNikolaos Giannakoulas  \n07-01-2023  \nContents  \nAbstract ...................................................................................................................................... 3  \nAcknowledgments ................................................................................................................... 4  \nContents ..................................................................................................................................... 5  \nList of Figures ........................................................................................................................... 7  \nList of Tables ............................................................................................................................. 8  \n1 Introduction ............................................................................................................................ 9  \n2 Context .................................................................................................................................. 11  \n2.1 Machine Learning ...............","cbCaifsGMsWIRW8X","https://ap.wps.com/l/cbCaifsGMsWIRW8X","pdf",1625410,2,1,60,"English","en",105,"# Abstract\n# Acknowledgments\n# List of Figures\n# List of Tables\n# 1 Introduction\n# 2 Context\n## 2.1 Machine Learning\n## 2.2 Supervised Learning\n## 2.3 Unsupervised Learning\n## 2.4 Reinforcement Learning\n## 2.5 Data Mining\n## 2.6 Sports Analytics\n## 2.7 Modeling\n# 3 Literature Review","[{\"question\":\"What is the main goal of this dissertation?\",\"answer\":\"To predict a football player’s performance in the field, specifically estimating the number of goals for the next season based on previous performances.\"},{\"question\":\"How are the football player data and inputs prepared for modeling?\",\"answer\":\"Player data are collected from valid online sources, then feature engineering transforms the collected data into the required form for machine learning.\"},{\"question\":\"Which methods are used to generate performance predictions and how are results evaluated?\",\"answer\":\"Three machine learning algorithms are trained for predictions, and Python-based experiments are conducted. Results are thoroughly examined and compared to determine which model performs best.\"}]","Sports Analytics - Performance Prediction - Master of Science (MSc) in Data Science Thesis | PDF",1786001267,151,{"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},"sports-analytics-performance-prediction-master-of-science-msc-in-data-science-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/sports-analytics-performance-prediction-master-of-science-msc-in-data-science-thesis/128474/",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","2026-08-23","2026-08-06",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 goal of this dissertation?","Question",{"text":76,"@type":77},"To predict a football player’s performance in the field, specifically estimating the number of goals for the next season based on previous performances.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are the football player data and inputs prepared for modeling?",{"text":81,"@type":77},"Player data are collected from valid online sources, then feature engineering transforms the collected data into the required form for machine learning.",{"name":83,"@type":74,"acceptedAnswer":84},"Which methods are used to generate performance predictions and how are results evaluated?",{"text":85,"@type":77},"Three machine learning algorithms are trained for predictions, and Python-based experiments are conducted. 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