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

Dursun Delen, Douglas Cogdell, Nihat Kasap

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


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.

Original languageEnglish
Pages (from-to)543-552
Number of pages10
JournalInternational Journal of Forecasting
Issue number2
StatePublished - 1 Apr 2012
Externally publishedYes


  • Classification
  • College football
  • Knowledge discovery
  • Machine learning
  • Prediction
  • Regression


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