A Logistic Regression/Markov Chain Model for American College Football
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 sys...
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| Format: | Article |
| Language: | English |
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2017-12-01
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| Series: | International Journal of Computer Science in Sport |
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| Online Access: | https://doi.org/10.1515/ijcss-2017-0014 |
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| author | Kolbush J. Sokol J. |
| author_facet | Kolbush J. Sokol J. |
| author_sort | Kolbush J. |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-072a128c3f274e3eb3d97e4356b01ecf |
| institution | DOAJ |
| issn | 1684-4769 |
| language | English |
| publishDate | 2017-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Computer Science in Sport |
| spelling | doaj-art-072a128c3f274e3eb3d97e4356b01ecf2025-08-20T02:50:26ZengSciendoInternational Journal of Computer Science in Sport1684-47692017-12-0116318519610.1515/ijcss-2017-0014A Logistic Regression/Markov Chain Model for American College FootballKolbush J.0Sokol J.1H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of TechnologyH. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of TechnologyKvam 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.https://doi.org/10.1515/ijcss-2017-0014logistic regresionmarkov chainamerican college footballcommon gamemargin of victory |
| spellingShingle | Kolbush J. Sokol J. A Logistic Regression/Markov Chain Model for American College Football International Journal of Computer Science in Sport logistic regresion markov chain american college football common game margin of victory |
| title | A Logistic Regression/Markov Chain Model for American College Football |
| title_full | A Logistic Regression/Markov Chain Model for American College Football |
| title_fullStr | A Logistic Regression/Markov Chain Model for American College Football |
| title_full_unstemmed | A Logistic Regression/Markov Chain Model for American College Football |
| title_short | A Logistic Regression/Markov Chain Model for American College Football |
| title_sort | logistic regression markov chain model for american college football |
| topic | logistic regresion markov chain american college football common game margin of victory |
| url | https://doi.org/10.1515/ijcss-2017-0014 |
| work_keys_str_mv | AT kolbushj alogisticregressionmarkovchainmodelforamericancollegefootball AT sokolj alogisticregressionmarkovchainmodelforamericancollegefootball AT kolbushj logisticregressionmarkovchainmodelforamericancollegefootball AT sokolj logisticregressionmarkovchainmodelforamericancollegefootball |