Wow. I highly recommend reading the Dawes pdf, it's illuminating:

Expert doctors coded [variables from] biopsies of patients with Hodgkin's disease and then made an overall rating of the severity of the process. The overall rating did not predict the survival time of the 193 patients, all of whom died. (The correlations of survival time with ratings was virtually 0, some in the wrong direction). The variables that the doctors coded, however, did predict survival time when they were used in a multiple regression model.

In summary, proper linear models work for a very simple reason. People are good at picking out the right predictor variables ... People are bad at integrating information from diverse and incomparable sources. Proper linear models are good at such integration ...

He then goes on to show that improper linear models still beat human judgment. If your reaction to the top-level post wasn't endorsement of statistical methods for these problems, this pdf is a bunch more evidence that you can use to update your beliefs about statistical methods of prediction.