  Most existing work on predicting NCAAB matches has been developed in a
statistical context. Trusting the capabilities of ML techniques, particularly
classification learners, to uncover the importance of features and learn their
relationships, we evaluated a number of different paradigms on this task. In
this paper, we summarize our work, pointing out that attributes seem to be more
important than models, and that there seems to be an upper limit to predictive
quality.
