The goal of this study is to improve prediction of freshman GPA based on college admission data to better inform the decision as to who to admit to Carnegie Mellon. This analysis assessed the utility of the non-academic data to find a better algorithm for making this prediction. Data for two consecutive entering classes at CMU were used. Both classical and Bayesian approaches were performed here. The classical methods allowed us to better understand the previous criterion of acceptance and to investigate the significance of a difference between students who were admitted and enrolled and the students who were admitted and did not come to CMU. A Bayesian predictive approach was used to identify the cutoff based on admission data for the predictive probability that a students' first semester GPA is greater than 2.0.