<abstract xmlns="http://www.w3.org/1999/xhtml"><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.</p></abstract>

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.

A Logistic Regression/Markov Chain Model for American College Football

H. Milton Stewart School of Industrial and Systems Engineering

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"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...

A Logistic Regression/Markov Chain Model for American College Football

Online veröffentlicht: 29. Dez. 2017

Seitenbereich: 185 - 196

DOI: https://doi.org/10.1515/ijcss-2017-0014

Schlüsselwörter
logistic regresion, Markov chain, American college football, common game, margin of victory

© 2017 Kolbush, J. et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Logistic Regression/Markov Chain Model for American College Football

Online veröffentlicht: 29. Dez. 2017

Seitenbereich: 185 - 196

DOI: https://doi.org/10.1515/ijcss-2017-0014

Schlüsselwörterlogistic regresion, Markov chain, American college football, common game, margin of victory

© 2017 Kolbush, J. et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
logistic regresion, Markov chain, American college football, common game, margin of victory