"Even the quote you cite suggests that the total amount of variance accounted for was about 19%, which isn't too shabby by any means, but is also very crude whe

"Even the quote you cite suggests that the total amount of variance accounted for was about 19%, which isn't too shabby by any means, but is also very crude when you think about implications for real-world consequences." This is 19% after range restriction. They're making the same point that the graph of quartiles does: even after you set an extremely high bar, differences in the test score are still predicting quite a bit of variance despite all the other possible diluting factors like geography/family/test-error/personality/interest/wealth/opportunity... If anything, it shows that institutions aren't being 'crude' enough - if they were using the test scores optimally and extracting all of the signal, the variance would be 0%.