A comparative analysis of data mining methods in predicting NCAA bowl outcomes
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.
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- 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.
- Fainmesser, Itay & Fershtman, Chaim & Gandal, Neil, 2005. "A Consistent Weighted Ranking Scheme with an Application to NCAA College Football Rankings," CEPR Discussion Papers 5239, C.E.P.R. Discussion Papers.
- 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.
- Stekler, H.O. & Sendor, David & Verlander, Richard, 2010.
"Issues in sports forecasting,"
International Journal of Forecasting,
Elsevier, vol. 26(3), pages 606-621, July.
- 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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