Using Recruiting Rankings And Returning Team Measurements To Predict College Football Team Success

This paper proposes and compares a set of models of college football team performance for teams in major conferences during the years of 2006 – 2018. The outcome measure of team performance is the team’s standardized Sagarin Ranking at the end of the season after the postseason bowl games and, in recent years, playoff games are complete. Potential predictor variables include several variables taken from the team recruiting rankings at the website www.rivals.com, and other attributes of the team compiled from an annual college football prediction magazine. Models considered include models screened via traditional forward, backward, and stepwise model selection methods, as well as a regression tree model. These candidate models are first compared using a cross-validation technique where each individual season is used successively as a test data set, and the predictive accuracy of the candidate models are compared after these successive comparisons. We find that the model chosen via stepwise selection performs the best in this cross-validation comparison but that other models have comparable error rates. We further consider refinements of the forward selection model when quadratic terms and a piecewise approach is taken for two predictors, and compare the prediction error rates for these models using the same cross-validation technique. Our findings from these analyses suggest that teams with higher recruiting rankings are predicted to perform better in a given season, but that other factors about the team are also significant predictors of performance.