This is technically true (random quote from blog commenter, but one which reflects a lot of educated-people-who-know-about-stats opinion on the Silver model):
Silver’s analysis (which I happen to accept) won’t be contradicted (or proven) in any way by tomorrow’s outcome. Either result is accounted for in the model. People seem not to understand that.
However, it’s a silly thing to say. If you craft a model in such a way that you are publicly on record as saying that one candidate in a two-horse race has a 90% chance of winning, and he loses, then you will find it very hard to avoid looking like a tit, even if your stats were absolutely correct and the result is just a one-in-ten piece of bad luck for your model.
The only way in which you could plausibly avoid the tail-risk of looking like a tit would be to focus a sizeable part of your commentary on that tail-risk, why your model shouldn’t be taken as an out-and-out prediction, and why you might be wrong, rather than focusing on the reasons that you think are underlying the 90%-likely outcome.
Mr Silver has gone very strongly for the “focusing on the underlying reasons” option, presumably because he’d much take a 90% chance of being The Awesome Pollster Who Correctly Tipped The Election with a 10% chance of being That Tit, than a 100% chance of being That Boring Wonk Who Explained Why We Shouldn’t Pay Too Much Attention To His Numbers.
Which is entirely rational, given the risk/reward matrix he faces, but does mean that anyone who suggests we should refrain from calling him That Tit if the 10% scenario comes through is missing the point.