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ML predictive models are trained separately for each player to forecast fantasy scoring and a lineup optimizer is built to maximize total Fantasy Points across selected dates. Experiments use PyCaret with four finalized per-player models from datasets combining advanced and basic statistics. Training relies on historical seasons 2010-11 to 2020-21, adding extra seasons when players appear in fewer than 100 games, and results indicate accurate player prediction and strong lineup performance.",{"@graph":63,"@context":119},[64,80,102],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/data-mining-in-sports-daily-nba-player-performance-prediction/566117/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/data-mining-in-sports-daily-nba-player-performance-prediction/566117.png","ImageObject",442,249,{"name":88,"@type":89},"Terk","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-27","2026-10-13",true,{"@type":98,"interactionType":99,"userInteractionCount":101},"InteractionCounter",{"@type":100},"ViewAction",8,{"@type":103,"mainEntity":104},"FAQPage",[105,111,115],{"name":106,"@type":107,"acceptedAnswer":108},"What is the dissertation’s main goal for NBA data?","Question",{"text":109,"@type":110},"To predict each NBA player’s daily performance in terms of Fantasy Points and use those predictions to optimize fantasy lineups for selected game dates.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"How are the predictive models created and evaluated?",{"text":114,"@type":110},"The work develops four finalized ML models per selected player using PyCaret, trained on historical season data and datasets built from advanced and basic statistics.",{"name":116,"@type":107,"acceptedAnswer":117},"What lineup optimization approach is used?",{"text":118,"@type":110},"A lineup optimizer is constructed with restrictions applied, aiming to maximize the sum of Fantasy Points for the selected players on the chosen dates.","https://schema.org",{"og:url":78,"og:type":121,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":123,"canonical":78},"index,follow",{"doc_id":125,"site_id":56},566117,1792083963,{"code":4,"msg":5,"data":128},{"doc_id":125,"user_id":129,"nickname":88,"user_avatar":130,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":101,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":136,"language":137,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":61,"update_tm":141,"read_time":142},1099525198933,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Data Mining in Sports: Daily NBA Player Performance  \nPrediction  \nGeorgios Papageorgiou  \nSID: 3308200022  \nSCHOOL OF SCIENCE & TECHNOLOGY  \nA thesis submitted for the degree of  \nMaster of Science (MSc) in Data Science  \nJANUARY 2022  \nTHESSALONIKI – GREECE  \nData Mining in Sports: Daily NBA Player Performance  \nPrediction  \nGeorgios Papageorgiou  \nSID: 3308200022  \nSupervisor: Assoc. Prof. Christos Tjortjis  \nSCHOOL OF SCIENCE & TECHNOLOGY  \nA thesis submitted for the degree of  \nMaster of Science (MSc) in Data Science  \nJANUARY 2022  \nTHESSALONIKI – GREECE  \nAbstract  \nThis dissertation was written as a part ofthe MSc in Data Science at the International Hellenic University.  \nThis dissertation is related to the NBA League and its players; it focuses on Daily performance prediction in terms of Fantasy Points for each player and Lineup Optimization for betting purposes in Fantasy Tournaments. The primary purpose of this dissertation is to explore, develop and evaluate ML predictive models, each one focused separately on each player for Daily Player's Performance Prediction in terms of Fantasy Points. In adittion, tries to develop and evaluate a Lineup Optimizer focused on total Fantasy Points for a range of Dates.  \nIn this project tried to experiment with Pycaret library. Therefore, we develop four finalized models for each selected player. We used two primary datasets, with advanced statistics and only basic statistics. Also, the models are developed with historical data from Season 2010-11 to Season 2020-21, and in historical data from last seasons (2018-19, 2019-20, 2020-21) while in cases that the player does not participate in at least 100 games, additional season's data is included. Furthermore, in the next stage of this project, using the predictions, we developed a Lineup Optimizer with restrictions applied, focused on maximizing the sum of NBA Fantasy Points of our selected players. Results show that we can accurately predict the performance of each selected player in terms of Fantasy Points and build a well-performing Lineup for selected game dates.  \nAcknowledgements  \nAt this point, I would like to truly thank my Supervisor, Professor Christos Tjortjis, for his guidance in every point of this project, providing me with essential assessment and crucial suggestions. In addition, I would like to extend thanks to Ph.D candidate Vangelis Sarlis for his expert assistance at any issue that occurred, providing me immediate feedback anytime I needed it.  \nGeorgios Papageorgiou  \n7-1-2022  \nContents  \nABSTRACT.................................................................................................................... 3  \nCONTENTS .................................................................................................................... 1  \nLIST OF FIGURES........................................................................................................ 3  \nLIST OF TABLES ......................................................................................................... 4  \nLIST OF APPENDICES ............................................................................................... 5  \n1 INTRODUCTION ..................................................................................................... 6  \n2 LITERATURE REVIEW.......................................................................................... 8  \n2.1 HISTORICAL BACKGROUND ............................................................................... 8  \n2.1.1 Baseball................................................................................................ 8  \n2.1.2 Football ............................................................................................... 10  \n2.1.3 Backetball (NBA) .............................................................................. 12  \n2.2 RELATED WORK .............................................................................................. 13  \n2.2.1 Introduction .","cbCairTaYjou3Zpg","https://ap.wps.com/l/cbCairTaYjou3Zpg","pdf",1670844,82,"English","# Abstract\n# Acknowledgements\n# 1 Introduction\n# 2 Literature Review\n## 2.1 Historical Background\n## 2.2 Related Work\n# 3 Main Research Topics\n## 3.1 Data Mining\n## 3.2 Machine Learning\n## 3.3 Sports Analytics\n# 4 Methodology\n## 4.1 Process Description\n# 5 Modeling\n## 5.1 PyCaret","[{\"question\":\"What is the dissertation’s main goal for NBA data?\",\"answer\":\"To predict each NBA player’s daily performance in terms of Fantasy Points and use those predictions to optimize fantasy lineups for selected game dates.\"},{\"question\":\"How are the predictive models created and evaluated?\",\"answer\":\"The work develops four finalized ML models per selected player using PyCaret, trained on historical season data and datasets built from advanced and basic statistics.\"},{\"question\":\"What lineup optimization approach is used?\",\"answer\":\"A lineup optimizer is constructed with restrictions applied, aiming to maximize the sum of Fantasy Points for the selected players on the chosen dates.\"}]","Data Mining in Sports - Daily NBA Player Performance Prediction | PDF",1791861056,29]