[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-328005-105":59,"doc-detail-328005-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","predicting-drafted-deck-strength-for-magic-the-gathering","Predicting Drafted Deck Strength for \"Magic: the Gathering\"","","Many real-world games lack fixed, compact rule sets because gameplay emerges from interactions among many evolving pieces. Magic: the Gathering (MTG) exemplifies this, since cards themselves reshape rules, constraints, and long-term outcomes while the available card pool changes over time. This paper studies MTG Draft, where eight players select 39–45 cards from semi-random packs under partial information to form a 40-card deck, enabling tractable study of synergy-driven decision making.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-drafted-deck-strength-for-magic-the-gathering/328005/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predicting-drafted-deck-strength-for-magic-the-gathering/328005.png","ImageObject",300,407,{"name":92,"@type":93},"River Wang","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the paper address in MTG Draft?","Question",{"text":113,"@type":114},"It targets outcome prediction for MTG Draft when the draft environment and card interactions are complex and evolve with available cards.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is MTG Draft formalized in the study?",{"text":118,"@type":114},"Eight players make 39–45 sequential selections from semi-random packs, under partial information, to construct a 40-card deck.",{"name":120,"@type":111,"acceptedAnswer":121},"What model contribution does the paper propose?",{"text":122,"@type":114},"It proposes an encoder-based model that generates set-contextualized card embeddings to encode the draft decision sequence and improve over linear baselines.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},328005,1790536627,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":19,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":52,"preview_page_count":4},1099514067438,"https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542","Predicting Drafted Deck Strength for“Magic: the Gathering”  \nTomas Rigaux  \nGrad. School of Informatics Kyoto University Kyoto, Japan [tomas@rigaux.com](tomas@rigaux.com)  \nHisashi Kashima  \nGrad. School of Informatics Kyoto University Kyoto, Japan [kashima@i.kyoto-u.ac.jp](kashima@i.kyoto-u.ac.jp)  \narXiv :2607 .04782v 1 [ cs .LG] 6 Jul 2026  \nAbstract—Many real-world games do not admit a fixed, compact rule set: instead, their dynamics are defined by interactions among a large and often evolving collection of game pieces, making general-purpose policy learning impractical. Magic: the Gathering (MTG) exemplifies this setting, where the cards themselves define and alter gameplay rules, strategic constraints, and long-term outcomes, while the pool of available cards is everchanging. We study Draft, a constrained deck-building format of MTG in which eight players make 39–45 sequential selections from semi-random packs to construct a 40-card deck under partial information. By isolating the card selection process from gameplay, Draft provides a tractable yet non-trivial setting for studying decision-making driven by combinatorial card synergies. We propose an encoder-based model that produces setcontextualized card embeddings to encode the draft decision sequence, with a consistent improvement over linear baselineson large-scale real-world data, establishing a first learned benchmark for outcome prediction in MTG Draft. Our code is available at [github.com/akulen/MtGDraftEncoder](github.com/akulen/MtGDraftEncoder).  \nIndex Terms—Machine Learning, Card Games, Magic: the Gathering, Neural Networks, Game AI, Transformers.  \nI. INTRODUCTION  \nComplex games have long served as useful benchmarks for evaluating the capabilities of learning agents, as they offer clearly defined rules and measurable objectives. Collectible card games extend the scope as they allow for the rule set to be altered. The game pieces are a growing collection of cards, each with the ability to introduce novel mechanics, modify existing rules, or combine with previous cards to recontextualize their utility. As a result, learning a single, global policy that fully captures the game is impractical: mastery requires continual adaptation to new content and the ability to encode generic game pieces and how they change the game rules.  \nIn this work, we focus on Magic: The Gathering (MTG), a game with thousands of new cards released every year, and some of the most complex rules in games. We restrict our attention to the Draft format, a variant we describe in Section II, full of highly non-local decisions: the value of a card depends not only on its intrinsic properties, but also on previously selected cards, the previously seen cards that  \nThis work was supported by JST BOOST, Grant Number JPMJBS2407 and JST CREST, Grant Number JPMJCR21D1 .  \ncould wheel back, and signals from passed cards giving noisy information about the draft choices made by the other drafters. We use the outcome of games subsequent to the Draft as an evaluation metric, making our approach outcome-driven.  \nLimited work exists on applying machine learning methods to MTG in general, mostly limited to predicting human pick choices. While such approaches provide useful signals, they limit themselves to descriptive modeling of player behavior. In contrast, our goal is to directly predict draft outcomes: given a partial or complete draft sequence, we estimate the expected win-rate of the resulting deck in subsequent games.  \nWe summarize our contributions as follows:  \n1) A procedure to transform raw card features into contextualized embeddings given a draft environment.  \n2) DraftEncoder, an encoder-based architecture designed to model drafting as a sequence of combinatorial decisions, updating card embeddings dynamically as the draft progresses, in order to reason about both local card quality and global deck composition.  \n3) An evaluation of our model and linear baselines on large-scale real-world ","cbCaij4lC67Fq831","https://ap.wps.com/l/cbCaij4lC67Fq831","pdf",476616,"English","# Introduction\n## Drafting process and decision sequence\n# Magic: the Gathering (MTG)\n## MTG overview and Draft format","[{\"question\":\"What problem does the paper address in MTG Draft?\",\"answer\":\"It targets outcome prediction for MTG Draft when the draft environment and card interactions are complex and evolve with available cards.\"},{\"question\":\"How is MTG Draft formalized in the study?\",\"answer\":\"Eight players make 39–45 sequential selections from semi-random packs, under partial information, to construct a 40-card deck.\"},{\"question\":\"What model contribution does the paper propose?\",\"answer\":\"It proposes an encoder-based model that generates set-contextualized card embeddings to encode the draft decision sequence and improve over linear baselines.\"}]","Predicting Drafted Deck Strength for \"Magic: the Gathering\" | PDF",1789978474]