{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T00:16:04Z","timestamp":1774743364720,"version":"3.50.1"},"reference-count":16,"publisher":"Walter de Gruyter GmbH","issue":"3","license":[{"start":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T00:00:00Z","timestamp":1512086400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Kvam and Sokol developed a successful logistic regression\/Markov chain (LRMC) model for ranking college basketball teams part of Division I of the National Colligate Athletic Association (NCAA). In their 2006 publication, they illustrated that the LRMC model is one of the most successful ranking systems in predicting the outcome of the NCAA Division I Basketball Tournament. However, it cannot directly be extended to college football because of the lack of home-and-home matchups that LRMC exploits in performing its Logistic Regression. We present a common-opponents-based approach that allows us to perform a Logistic Regression and thus create a football LRMC (F-LRMC) model. This approach compares the margin of victory of home teams to their winning percentage in games played against common-opponents with the away team. Computational results show that F-LRMC is among the best of the many ranking systems tracked by Massey's College Football Ranking Composite.<\/jats:p>","DOI":"10.1515\/ijcss-2017-0014","type":"journal-article","created":{"date-parts":[[2018,1,6]],"date-time":"2018-01-06T22:16:12Z","timestamp":1515276972000},"page":"185-196","source":"Crossref","is-referenced-by-count":8,"title":["A Logistic Regression\/Markov Chain Model for American College Football"],"prefix":"10.1515","volume":"16","author":[{"given":"J.","family":"Kolbush","sequence":"first","affiliation":[{"name":"H. Milton Stewart School of Industrial and Systems Engineering , Georgia Institute of Technology"}]},{"given":"J.","family":"Sokol","sequence":"additional","affiliation":[{"name":"H. Milton Stewart School of Industrial and Systems Engineering , Georgia Institute of Technology"}]}],"member":"374","published-online":{"date-parts":[[2017,12,29]]},"reference":[{"key":"2021040710030515853_j_ijcss-2017-0014_ref_001_w2aab3b7b2b1b6b1ab1ab1Aa","unstructured":"BCS computer rankings. (2012). Retrieved December 28, 2016, from http:\/\/www.bcsfootball.org\/news\/story?id=4765872"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_002_w2aab3b7b2b1b6b1ab1ab2Aa","unstructured":"Beck, T. (2002). About the PerformanZ ratings. Retrieved December 28, 2016, from http:\/\/tbeck.freeshell.org\/fb\/descript.txt"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_003_w2aab3b7b2b1b6b1ab1ab3Aa","doi-asserted-by":"crossref","unstructured":"Brown, M. and J. Sokol (2010). An Improved LRMC Method for NCAA Basketball Prediction. Journal of Quantitative Analysis in Sports, 6 (3), Article 4.","DOI":"10.2202\/1559-0410.1202"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_004_w2aab3b7b2b1b6b1ab1ab4Aa","unstructured":"Forman, S. (n.d.) Sports Reference. Retrieved from http:\/\/www.sports-reference.com\/cfb\/"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_005_w2aab3b7b2b1b6b1ab1ab5Aa","unstructured":"Hamdy, O., Shichen, Z., Osman, T., Salheen, M. A., & Eid, Y. Y. (2016). Applying a Hybrid Model of Markov Chain and Logistic Regression to Identify Future Urban Sprawl in Abouelreesh, Aswan: A Case Study. Geosciences, 6(4), 1-17. doi:10.3390\/geosciences604004310.3390\/geosciences6040043"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_006_w2aab3b7b2b1b6b1ab1ab6Aa","unstructured":"Kambour, E. (2003). PPT. Edward Kambour. Retrieved from http:\/\/www.kambour.net\/football.ppt"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_007_w2aab3b7b2b1b6b1ab1ab7Aa","doi-asserted-by":"crossref","unstructured":"Knottenbelt, W. J., Spanias, D., & Madurska, A. M. (2012). A common-opponent stochastic model for predicting the outcome of professional tennis matches. Computers & Mathematics with Applications, 64(12), 3820-3827.10.1016\/j.camwa.2012.03.005","DOI":"10.1016\/j.camwa.2012.03.005"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_008_w2aab3b7b2b1b6b1ab1ab8Aa","doi-asserted-by":"crossref","unstructured":"Kvam, P. and J.S. Sokol (2006). A logistic regression\/Markov chain model for NCAA basketball. Naval Research Logistics, 53, 788-803.","DOI":"10.1002\/nav.20170"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_009_w2aab3b7b2b1b6b1ab1ab9Aa","unstructured":"Liu, Y., Dai, L., & Xiong, H. (2015). Simulation of urban expansion patterns by integrating auto-logistic regression, Markov chain and cellular automata models. Journal Of Environmental Planning & Management, 58(6), 1113-1136. doi:10.1080\/09640568.2014.91661210.1080\/09640568.2014.916612"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_010_w2aab3b7b2b1b6b1ab1ac10Aa","unstructured":"Massey, K. (n.d.). Massey Ratings. Retrieved from http:\/\/www.masseyratings.com\/"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_011_w2aab3b7b2b1b6b1ab1ac11Aa","unstructured":"Maclay, L.A. (n.d.). Retrieved February 14, 2017 from https:\/\/bracketology.engr.wisc.edu\/ncaa-bb-rankings\/"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_012_w2aab3b7b2b1b6b1ab1ac12Aa","unstructured":"NCAA College Football Polls - ESPN. (n.d.). Retrieved November 10, 2016, from http:\/\/www.espn.com\/college-football\/rankings"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_013_w2aab3b7b2b1b6b1ab1ac13Aa","unstructured":"New Formula for Football Championship Announced. (1998). Retrieved April 26, 2012, from http:\/\/www.umterps.com\/sports\/m-footbl\/spec-rel\/061098aaa.html"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_014_w2aab3b7b2b1b6b1ab1ac14Aa","unstructured":"Palm, J. (2013). Sagarin changes formula, finally removes \u2018Margin of Victory.\u2019 Retrieved April 26, 2017, from http:\/\/www.cbssports.com\/college-football\/news\/sagarinchanges-formula-finally-removes-margin-of-victory\/"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_015_w2aab3b7b2b1b6b1ab1ac15Aa","unstructured":"Selection Committee Protocol. (2015). Retrieved January 10, 2017, from http:\/\/www.collegefootballplayoff.com\/selection-committee-protocol"},{"key":"2021040710030515853_j_ijcss-2017-0014_ref_016_w2aab3b7b2b1b6b1ab1ac16Aa","unstructured":"Trono, J. (2012). Bowl Game Predictions. Retrieved October 3, 2016, from http:\/\/academics.smcvt.edu\/jtrono\/OAF_BCS\/Compare.html"}],"container-title":["International Journal of Computer Science in Sport"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/content.sciendo.com\/view\/journals\/ijcss\/16\/3\/article-p185.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.sciendo.com\/article\/10.1515\/ijcss-2017-0014","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T00:34:46Z","timestamp":1617842086000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.sciendo.com\/article\/10.1515\/ijcss-2017-0014"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,1]]},"references-count":16,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2017,12,29]]},"published-print":{"date-parts":[[2017,12,1]]}},"alternative-id":["10.1515\/ijcss-2017-0014"],"URL":"https:\/\/doi.org\/10.1515\/ijcss-2017-0014","relation":{},"ISSN":["1684-4769"],"issn-type":[{"value":"1684-4769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,1]]}}}