In fact he says a good amount of this himself. I think some of his readers impute a greater level of "scientificness" to his numbers than he himself claims. He's had many posts throughout the fall explaining where his model is based on some assumptions that could turn out to be incorrect, and key parameters fit based on relatively limited data. For example, an important one is how you translate current poll leads to likelihood of winning on election day, i.e. why does an x% lead a week before the election give you y% chance of winning? His method is to look at the empirical distribution of poll misses in the 11 elections 1968-2008, make some normality assumptions, and use that to estimate poll->results mapping, which serves as a single estimate of a whole bunch of miscellaneous sources of error (likelihood the polls are systematically biased this year, likelihood of a last-minute change, etc.). But of course that's a small number of data points, and not IID ones, either, all of which he acknowledges. All he really claims is that this model is a reasonable attempt to integrate the available data.