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A comparative analysis of data mining methods in predicting NCAA bowl outcomes


  • Delen, Dursun
  • Cogdell, Douglas
  • Kasap, Nihat


Predicting the outcome of a college football game is an interesting and challenging problem. Most previous studies have concentrated on ranking the bowl-eligible teams according to their perceived strengths, and using these rankings to predict the winner of a specific bowl game. In this study, using eight years of data and three popular data mining techniques (namely artificial neural networks, decision trees and support vector machines), we have developed both classification- and regression-type models in order to assess the predictive abilities of different methodologies (classification versus regression-based classification) and techniques. In the end, the results showed that the classification-type models predict the game outcomes better than regression-based classification models, and of the three classification techniques, decision trees produced the best results, with better than an 85% prediction accuracy on the 10-fold holdout sample. The sensitivity analysis on trained models revealed that the non-conference team winning percentage and average margin of victory are the two most important variables among the 28 that were used in this study.

Suggested Citation

  • Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
  • Handle: RePEc:eee:intfor:v:28:y:2012:i:2:p:543-552 DOI: 10.1016/j.ijforecast.2011.05.002

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    References listed on IDEAS

    1. Itay Fainmesser & Chaim Fershtman & Neil Gandal, 2009. "A Consistent Weighted Ranking Scheme With an Application to NCAA College Football Rankings," Journal of Sports Economics, , vol. 10(6), pages 582-600, December.
    2. Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
      • Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, Research Program on Forecasting.
    3. West Brady T & Lamsal Madhur, 2008. "A New Application of Linear Modeling in the Prediction of College Football Bowl Outcomes and the Development of Team Ratings," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(3), pages 1-21, July.
    4. Song, ChiUng & Boulier, Bryan L. & Stekler, Herman O., 2007. "The comparative accuracy of judgmental and model forecasts of American football games," International Journal of Forecasting, Elsevier, vol. 23(3), pages 405-413.
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    2. Carlos Serrano-Cinca & Yolanda Fuertes-Callén & Begoña Gutiérrez-Nieto & Beatriz Cuellar-Fernández, 2014. "Path modelling to bankruptcy: causes and symptoms of the banking crisis," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3798-3811, November.
    3. Graham James & Mayberry John, 2014. "Measures of tactical efficiency in water polo," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(1), pages 67-79, January.
    4. repec:spr:qualqt:v:51:y:2017:i:5:d:10.1007_s11135-016-0375-5 is not listed on IDEAS
    5. Koen W. De Bock, 2017. "The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles," Post-Print hal-01588059, HAL.
    6. repec:spr:reaccs:v:22:y:2017:i:3:d:10.1007_s11142-017-9407-1 is not listed on IDEAS
    7. Keller, Jonas & von der Gracht, Heiko A., 2014. "The influence of information and communication technology (ICT) on future foresight processes — Results from a Delphi survey," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 81-92.


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