I've come across a rumour that some study showed that the performance of predictive models depends more on the expertise of the data analyst with the chosen method than on the choice of the method.
In other words, the claim is that it is more important that the data analyst is familiar with the chosen method than how "appropriate" the method would seem for the problem from a more theoretical standpoint.
This was mentioned in the context of chemometrics, which involves typically problems of many variates (100s - 1000s), multiple collinearity, and of course, too few samples. Prediction may have been classification or regression.
My personal experience suggests that this is plausible, but a study was mentioned (I asked the person who mentioned that by email after a quick but unsuccessful search, but never received any answer). However, also with a more elaborate search, I was not able to track down any papers.
Is anyone aware of such findings? If not, what does the personal experience of Big Guys here say?