{"id":166737,"date":"2014-02-01T00:00:00","date_gmt":"2014-02-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/knowing-what-we-dont-know-in-ncaa-football-ratings-understanding-and-using-structured-uncertainty\/"},"modified":"2020-09-28T05:30:51","modified_gmt":"2020-09-28T12:30:51","slug":"knowing-what-we-dont-know-in-ncaa-football-ratings-understanding-and-using-structured-uncertainty","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/knowing-what-we-dont-know-in-ncaa-football-ratings-understanding-and-using-structured-uncertainty\/","title":{"rendered":"Knowing what we don&#8217;t know in NCAA Football ratings: Understanding and using structured uncertainty"},"content":{"rendered":"<div class=\"asset-content\">\n<p>There is a great deal of uncertainty in the skills of teams in NCAA football, which makes ranking teams and choosing postseason matchups difficult. Despite this, standard approaches (e.g., the BCS system) estimate a single ranking of teams and use it to make decisions about postseason matchups. In this work, we argue for embracing uncertainty in rating NCAA football teams. Specifically, we (1) develop a statistical model that infers uncertainty in and correlations between team skills based on game outcomes, (2) make proposals for how to communicate the inferred uncertainty, and (3) show how to make decisions about postseason matchups that are principled in the face of the uncertainty. We apply our method to 14 years of NCAA football data and show that it produces interesting recommendations for postseason matchups, and that there are general lessons to be learned about choosing postseason matchups based on our analysis.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>There is a great deal of uncertainty in the skills of teams in NCAA football, which makes ranking teams and choosing postseason matchups difficult. Despite this, standard approaches (e.g., the BCS system) estimate a single ranking of teams and use it to make decisions about postseason matchups. In this work, we argue for embracing uncertainty [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Daniel Tarlow","user_id":"31695"},{"type":"user_nicename","value":"Thore Graepel","user_id":"34034"},{"type":"user_nicename","value":"Tom Minka","user_id":"32943"}],"msr_publishername":"MIT Press","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"MIT Sloan Sports Analytics Conference","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2014-1-1","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"http:\/\/www.cs.toronto.edu\/~dtarlow\/NCAAF.pdf","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-166737","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"MIT Press","msr_edition":"","msr_affiliation":"","msr_published_date":"2014-1-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"http:\/\/www.cs.toronto.edu\/~dtarlow\/NCAAF.pdf","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"http:\/\/www.cs.toronto.edu\/~dtarlow\/NCAAF.pdf","label_id":"243109","label":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":0,"url":"http:\/\/www.cs.toronto.edu\/~dtarlow\/NCAAF.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"Daniel Tarlow","user_id":31695,"rest_url":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Daniel Tarlow"},{"type":"user_nicename","value":"Thore Graepel","user_id":34034,"rest_url":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Thore Graepel"},{"type":"user_nicename","value":"Tom Minka","user_id":32943,"rest_url":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Tom Minka"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[169917,169873],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":169917,"post_title":"Infer.NET","post_name":"infernet","post_type":"msr-project","post_date":"2008-10-15 01:55:31","post_modified":"2023-04-06 09:14:43","post_status":"publish","permalink":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/project\/infernet\/","post_excerpt":"Infer.NET is a .NET library for machine learning. It provides state-of-the-art algorithms for probabilistic inference from data. Various Bayesian models such as Bayes Point Machine classifiers, TrueSkill matchmaking, hidden Markov models, and Bayesian networks can be implemented using Infer.NET. Infer.NET is open source software under the MIT license. For more information about Infer.NET including documentation and examples please see the main Infer.NET page.","_links":{"self":[{"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/169917"}]}},{"ID":169873,"post_title":"TrueSkill\u2122 Ranking System","post_name":"trueskill-ranking-system","post_type":"msr-project","post_date":"2005-11-18 07:02:09","post_modified":"2024-06-20 05:32:08","post_status":"publish","permalink":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/project\/trueskill-ranking-system\/","post_excerpt":"The TrueSkill ranking system is a skill based ranking system for\u00a0Xbox Live developed at Microsoft Research. The purpose of a ranking system is to both identify and track the skills of gamers in a game (mode) in order to be able to match them into competitive matches. TrueSkill has been used to rank and match players in many different games, from Halo 3 to Forza Motorsport 7. 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