In a previous posting, I looked at the limits of prediction and concluded that the best performance we could hope for from a predictor would be in the 70-80% range for correct predictions. Today I happened to run across "The Prediction Tracker" which (amongst other things) tracks the performance of various college basketball rating systems as predictors of game performance. For the season that just ended, the best computer predictor belonged to Jon Doktor, and managed a "% Correct" measure of 73%. All of the predictors tracked by that site cluster in the lower end of the 70-80% range. Given that they include early season games, that's fairly solid performance. It's also interesting to note that (1) none of the predictors managed even a 1% advantage betting against the spread, and (2) all of them had MOV errors in the 9-10 point range. (Most of these predictors use margin of victory, so we would expect them to perform better on MOV than systems like RPI which use only win-loss.)
I got to the Prediction Tracker via the TeamRankings.com blog, which has a 4 part series discussing their rating systems starting here. The discussion lacks any concrete details on the algorithms but covers some interesting ground and is worth a look.