[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126925-en":3,"doc-seo-126925-105":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},126925,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Using Machine Learning Algorithms to Predict Outcomes of Chess Games Using Player Data","Chess is a two-player game with three possible outcomes: White wins, Black wins, or a draw. The study tests multiple machine learning methods using an online dataset from “The Week In Chess” comprising about 2.5 million games to classify game results. Inputs focus on player-level and opening-level statistics: White’s win/draw rates, Black’s win/draw rates, and the opening’s win/draw rates. Classifier performance is compared across the full feature set and various feature subsets to determine the best achievable accuracy.","Rochester Institute of Technology  \nRIT Digital Institutional Repository  \nTheses  \n6-7-2024  \nUsing Machine Learning Algorithms to Predict Outcomes of Chess Games Using Player Data  \nSofia DeCredico[sld4732@rit.edu](sld4732@rit.edu)  \nFollow this and additional works at: [https://repository.rit.edu/theses](https://repository.rit.edu/theses)  \nRecommended Citation  \nDeCredico, Sofia, \"Using Machine Learning Algorithms to Predict Outcomes of Chess Games Using Player Data\" (2024) . Thesis. Rochester Institute of Technology. Accessed from  \nThis Thesis is brought to you for free and open access by the RIT Libraries. For more information, please contact [repository@rit.edu](repository@rit.edu).  \nA Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Masters of Science in Applied and Computational Mathematics  \nUsing Machine Learning Algorithms to Predict Outcomes of Chess Games Using  \nPlayer Data  \nBy: Sofia DeCredico  \nRochester Institute of Technology College of Science Mathematics Rochester, NY  \nJune 7, 2024  \nThesis Committee Members:  \n1. Dr. Nathan Cahill  \n2. Dr. Matthew Coppenbarger  \n3. Dr. Ernest Fokoue  \nAbstract  \nChess is a two-player game, popular with a wide variety of people, ranging from people who play casually every once in a while with their family to professional players who make their living through playing and teaching. There are three outcomes that a chess game could have: player 1 (White) wins, player 2 (Black) wins, or both players draw. Using an online database called “The Week In Chess” which contains information about 2.5 million chess games, a variety of machine learning methods are tested to predict the outcomes of chess games. The features investigated as inputs for classification are: White’s Win Rate, White’s Draw Rate, Black’s Win Rate, Black’s’ Draw Rate, Opening’s Win Rate and Opening’s Draw Rate. Then, the results of the classifiers using the full set of features and various subsets of features are compared in order to try and determine the best possible accuracy in classifying the game outcome.  \nIntroduction  \nChess is a well known game played by people of varying skill level, from people who know the basic rules of how the pieces move but don’t have much of a strategy, to people who make their living playing and coaching and who spend many hours most days playing and studying the game in an effort to improve and play chess at the professional level. Every time a game is played, two people sit down at the chess board across the table from each other. Player one (“White”) plays the white pieces and player two (“Black”) plays the black pieces. The first phase of a chess game is called the “opening”, which is followed by the middle game and then the endgame. Where the opening ends and the middle game starts is a blurry line, however openings are set moves that players play to start the chess game and they are known by names. The game will end in one of three results, White can win, Black could win, or the game could end in a tie which is called a Draw.  \nThis research will use different machine learning algorithms to predict, or classify, the outcomes of chess games without using the position evaluation. Features used for classification include: White’s Win Rate, White’s Draw Rate, Black’s Win Rate, Black’s’ Draw Rate, Opening’s Win Rate and Opening’s Draw Rate. We will explore the effects of using different subsets of features as inputs on the accuracy.  \nCurrently there are many available chess engines online that people can use to evaluate their chess games. One of the more well-known chess engines is Stockfish [1] . These engines can calculate the “position evaluation” at any particular point in the game. The position evaluation is a number that indicates the degree to which the current configuration of pieces (the“position”) is favorable to each player. A positive position evaluation favors White, a negative position evaluation favors Black, and a position evalu","cbCaiiVwolR0xDrM","https://ap.wps.com/l/cbCaiiVwolR0xDrM","pdf",1186084,1,40,"English","en",105,"# Abstract\n# Introduction\n## Chess basics and game phases\n## Outcome prediction approach without position evaluation\n## Chess engines and position evaluation\n## Prior work on outcome prediction\n## Comparison of modeling methods","[{\"question\":\"What outcomes does the research aim to predict for chess games?\",\"answer\":\"The research targets three results: White wins, Black wins, or a draw.\"},{\"question\":\"Which dataset is used to train and test the machine learning models?\",\"answer\":\"The study uses an online database called “The Week In Chess” containing information on roughly 2.5 million chess games.\"},{\"question\":\"What features are used as inputs for classifying the game outcome?\",\"answer\":\"Features include White’s win rate and draw rate, Black’s win rate and draw rate, and opening-level win and draw rates.\"}]","Using Machine Learning Algorithms to Predict Outcomes of Chess Games Using Player Data | PDF",1785935689,101,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"using-machine-learning-algorithms-to-predict-outcomes-of-chess-games-using-player-data","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/using-machine-learning-algorithms-to-predict-outcomes-of-chess-games-using-player-data/126925/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What outcomes does the research aim to predict for chess games?","Question",{"text":75,"@type":76},"The research targets three results: White wins, Black wins, or a draw.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset is used to train and test the machine learning models?",{"text":80,"@type":76},"The study uses an online database called “The Week In Chess” containing information on roughly 2.5 million chess games.",{"name":82,"@type":73,"acceptedAnswer":83},"What features are used as inputs for classifying the game outcome?",{"text":84,"@type":76},"Features include White’s win rate and draw rate, Black’s win rate and draw rate, and opening-level win and draw rates.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":21,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]