{"71350":{"#nid":"71350","#data":{"type":"news","title":"Computer System Consistently Makes Most Accurate NCAA Picks","body":[{"value":"\u003Cp\u003ESports professionals and fans get pretty emotional about their picks for the NCAA basketball tournament each year, and that emotion often clouds their judgment.\n\u003C\/p\u003E\n\u003Cp\u003EBut three engineering professors at the Georgia Institute of Technology have created a computer ranking system, called LRMC, that consistently predicts NCAA basketball rankings more accurately than the AP poll of sportswriters and the ESPN\/USA Today poll of coaches, formulas (the Ratings Percentage Index), other computer models (the Massey ratings and the Sagarin ratings), and even the tournament seeds themselves.\n\u003C\/p\u003E\n\u003Cp\u003EAfter correctly picking all four of this year\u0027s finalists (and Kansas as this year\u0027s champion), the LRMC method has now identified 30 of the last 36 Final Four participants (83 percent accuracy over the past nine years of NCAA tournaments) as one of the top two teams in their region. Over the same nine-year stretch, the seedings and polls have correctly identified only 23, and the RPI indentified 21.\n\u003C\/p\u003E\n\u003Cp\u003ELRMC predicted Kansas as champion, despite UNC, UCLA and Memphis being the top three ranked teams by most systems.\n\u003C\/p\u003E\n\u003Cp\u003ELRMC (Logistic Regression Markov Chain) is a college basketball rankings system designed to use only basic scoreboard data, including which teams played, which team had home court advantage and the margin of victory. It was originally designed by Joel Sokol and Paul Kvam and has been maintained and improved by Sokol and George Nemhauser, all three optimization and statistics professors in the Stewart School of Industrial and Systems Engineering at Georgia Tech.\n\u003C\/p\u003E\n\u003Cp\u003E\u0022As fans, we only get to see most tournament teams two or three times at most during the season, so our gut feelings about a team are really colored by how well or poorly they played the few times we\u0027ve been watching,\u0022 said Sokol. \u0022On the other hand, our system objectively measures each team\u0027s performance in every game it plays, and mathematically balances all of those outcomes to determine an overall ranking.\u0022\n\u003C\/p\u003E\n\u003Cp\u003ELRMC seems to have a particular knack for predicting good bubble teams and identifying the top teams. In addition to correctly picking the Final Four, LRMC also correctly identified several over-rated and under-rated teams as potential upsets. First-round losers Drake (5-seed, LRMC #30), Vanderbilt (4-seed, LRMC #38), and Connecticut (4-seed, LRMC #26), as well as second-round loser Georgetown (2-seed, LRMC #12), were all picked by LRMC as significantly over-rated teams.  \n\u003C\/p\u003E\n\u003Cp\u003EOn the other hand, teams like West Virginia (7-seed, LRMC #17), which defeated second-seeded Duke, and Kansas State (11-seed, LRMC #19), which defeated sixth-seeded USC, were correctly identified by LRMC as under-rated teams that could pull off one or more upsets.  \n\u003C\/p\u003E\n\u003Cp\u003EBut LRMC isn\u0027t perfect - it picked Clemson as under-rated (upset in the first round) and Davidson wasn\u0027t identified as under-rated by any major ranking method, including LRMC.\n\u003C\/p\u003E\n\u003Cp\u003ELRMC differs from other computer rankings systems in two important ways. When determining the value of home court advantage, LRMC considers how much playing at home helps a team win rather than how many points playing on a home court is worth. \n\u003C\/p\u003E\n\u003Cp\u003EGeorgia Tech researchers have also been able to show that very close games are often \u0027toss-ups,\u0027 meaning the better team barely wins more than half the time. So, they determined that winning a close game shouldn\u0027t be worth as much as winning easily, and losing a close game shouldn\u0027t hurt a team\u0027s ranking as much as losing badly. LRMC\u0027s ranking methodology takes this into account.\n\u003C\/p\u003E\n\u003Cp\u003ESimilar to other rankings systems, LRMC also uses the quality of each team\u0027s results and the strength of each team\u0027s schedule to rank teams.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"Three engineering professors at Georgia Tech have created a computer ranking system, called LRMC, that consistently predicts NCAA basketball rankings more accurately than polls, formulas, other computer models and even the tournament seeds themselves.","format":"limited_html"}],"field_summary_sentence":[{"value":"System accuracy tops other polls and computer systems"}],"uid":"27281","created_gmt":"2008-04-03 00:00:00","changed_gmt":"2016-10-08 03:01:10","author":"Lisa Grovenstein","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2008-04-08T00:00:00-04:00","iso_date":"2008-04-08T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"related_links":[{"url":"http:\/\/www.isye.gatech.edu\/","title":"Stewart School of Industrial and Systems Engineering"},{"url":"http:\/\/www2.isye.gatech.edu\/people\/faculty\/Joel_Sokol\/lrmc\/lrmc.sort0.html","title":"LRMC Basketball Rankings"}],"groups":[{"id":"1214","name":"News Room"}],"categories":[{"id":"153","name":"Computer Science\/Information Technology and Security"},{"id":"145","name":"Engineering"},{"id":"135","name":"Research"}],"keywords":[{"id":"2142","name":"basketball"},{"id":"2141","name":"brackets"},{"id":"1431","name":"industrial and systems engineering"},{"id":"1158","name":"LRMC"},{"id":"1155","name":"NCAA"},{"id":"2140","name":"Nemhauser"},{"id":"169279","name":"Sokol"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cstrong\u003ELisa Grovenstein\u003C\/strong\u003E\u003Cbr \/\u003ECommunications \u0026amp; Marketing\u003Cbr \/\u003E\u003Ca href=\u0022http:\/\/www.gatech.edu\/contact\/index.html?id=lgrovenste3\u0022\u003EContact Lisa Grovenstein\u003C\/a\u003E\u003Cbr \/\u003E\u003Cstrong\u003E404-894-8835\u003C\/strong\u003E","format":"limited_html"}],"email":["lisa.grovenstein@comm.gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}