[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119071-en":3,"doc-seo-119071-105":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},119071,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine Learning for Soccer Match Result Prediction - Chapter 1 - Overview","Machine learning is widely used to predict soccer match outcomes, motivated by a growing research community and the need for reliable, actionable predictions in competitive leagues. This chapter surveys available datasets, model families, and feature engineering choices, then evaluates performance considerations specific to match-result prediction. Findings highlight gradient-boosted trees such as CatBoost with soccer-focused ratings as strong baselines, while deeper comparisons with Random Forest and deep learning are still needed. The chapter also argues for improved interpretability and richer rating systems using player/team context plus spatiotemporal tracking and event data.","Chapter 1  \nMachine Learning for Soccer Match Result Prediction  \nRory Bunker, Calvin Yeung, and Keisuke Fujii  \nAbstract Machine learning has become a common approach to predicting the outcomes of soccer matches, and the body of literature in this domain has grown substantially in the past decade and a half. This chapter discusses available datasets, the types of models and features, and ways of evaluating model performance in this application domain. The aim of this chapter is to give a broad overview of the current state and potential future developments in machine learning for soccer match results prediction, as a resource for those interested in conducting future studies in the area. Our main findings are that while gradient-boosted tree models such as CatBoost, applied to soccer-specific ratings such as pi-ratings, are currently the best-performing models on datasets containing only goals as the match features, there needs to be amore thorough comparison of the performance of deep learning models and Random Forest on a range of datasets with different types of features. Furthermore, new rating systems using both player-and team-level information and incorporating additional information from, e.g., spatiotemporal tracking and event data, could be investigated further. Finally, the interpretability of match result prediction models needs to be enhanced for them to be more useful for team management.  \nRory Bunker  \nGraduate School of Informatics, Nagoya University, Furo-cho, Chikusa ward, Nagoya, 464-8601,  \nJapan, e-mail: [rory.bunker@g.sp.m.is.nagoya-u.ac.jp](rory.bunker@g.sp.m.is.nagoya-u.ac.jp)  \nCalvin Yeung  \nGraduate School of Informatics, Nagoya University, Furo-cho, Chikusa ward, Nagoya, 464-8601,  \nJapan, e-mail: [yeung.chikwong@g.sp.m.is.nagoya-u.ac.jp](yeung.chikwong@g.sp.m.is.nagoya-u.ac.jp)  \nKeisuke Fujii  \nGraduate School of Informatics, Nagoya University, Furo-cho, Chikusa ward, Nagoya, 464-8601,  \nJapan, e-mail: [fujii@i.nagoya-u.ac.jp](fujii@i.nagoya-u.ac.jp)  \n1.1 Introduction  \nPredicting the results of professional soccer1 matches is a challenging problem due to draws being a common outcome in the sport, as well as its low-scoring nature and often highly competitive leagues. Nonetheless, given the global popularity of the sport, both in terms of spectatorship and player numbers, it is a topic that is of interest to many groups including fans, bookmakers and bettors, as well as coaches, players, and performance analysts. While bettors and bookmakers require models that are highly accurate, coaches/management and sports performance analysts also require models that are interpretable, so that the most relevant match features (performance indicators [63]) for winning can be identified and improved upon in future matches. Once a predicted result for a specific match is obtained, an additional problem is to decide whether to actually bet on the match. While this is an important question [38], it is not the focus of the current chapter.  \nA large number of papers have been published that are related to machine learning (ML) for soccer match result prediction, particularly over the past decade [18] . Traditionally, however, statistical models were used to forecast soccer match results. Stefani [105] used least-squares regression to calculate team ratings, which could be updated on a weekly basis, to predict match results based on the difference in ratings between teams. Some early papers fit distributions to the number of goals scored by each team in a match. Maher [81] used an independent Poisson distribution to obtain the attacking and defensive ratings of teams. Dixon & Coles [37] modified this model to handle incomplete data and data from different divisions, and to allow for temporal variations in the performance of teams. Goals distributions have also been fit using, e.g., the dependent Poisson, negative binomial, and extreme value distributions [51, 9, 10, 91] . Indeed, statistical models such as the B","cbCaikP6UthPa4UM","https://ap.wps.com/l/cbCaikP6UthPa4UM","pdf",393243,1,41,"English","en",105,"# Introduction\n## Problem background and requirements\n# Related approaches\n## Statistical models\n## Machine learning and probabilistic graphical models\n## Rating systems\n## Hybrid approaches","[{\"question\":\"Why is predicting soccer match results a challenging task?\",\"answer\":\"Because draws are common, matches are typically low-scoring, and many leagues are highly competitive.\"},{\"question\":\"What datasets, models, and features does the chapter discuss?\",\"answer\":\"It covers available datasets, model types and the kinds of features used for soccer match-result prediction, along with how model performance is evaluated.\"},{\"question\":\"Which modeling approach currently performs best when features are limited to goals?\",\"answer\":\"Gradient-boosted tree models such as CatBoost, applied to soccer-specific ratings like pi-ratings, are reported as best-performing under goal-only feature setups.\"}]","Machine Learning for Soccer Match Result Prediction - Chapter 1 - Overview | PDF",1785722187,103,{"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},"machine-learning-for-soccer-match-result-prediction-chapter-1-overview","",{"@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/machine-learning-for-soccer-match-result-prediction-chapter-1-overview/119071/",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","2026-08-03",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},"Why is predicting soccer match results a challenging task?","Question",{"text":75,"@type":76},"Because draws are common, matches are typically low-scoring, and many leagues are highly competitive.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What datasets, models, and features does the chapter discuss?",{"text":80,"@type":76},"It covers available datasets, model types and the kinds of features used for soccer match-result prediction, along with how model performance is evaluated.",{"name":82,"@type":73,"acceptedAnswer":83},"Which modeling approach currently performs best when features are limited to goals?",{"text":84,"@type":76},"Gradient-boosted tree models such as CatBoost, applied to soccer-specific ratings like pi-ratings, are reported as best-performing under goal-only feature setups.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]