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

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

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Article ID: iaor20121983
Volume: 28
Issue: 2
Start Page Number: 543
End Page Number: 552
Publication Date: Apr 2012
Journal: International Journal of Forecasting
Authors: , ,
Keywords: datamining, forecasting: applications, statistics: inference
Abstract:

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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