Nate Silver’s forecast would have done better had he gotten a few wrong.

I really like 538 and they have done a great job. I have one minor quibble with how they represent their forecasts and their results. 538’s forecasts are *not* binary predictions, they are probability estimates. Meaning, that of all the races he projects to be at 70% probability, the winner should be the leading candidate 70% of the time – not 100% of the time. But 538 and many others are saying that he got 50/50 states right, and that confuses the picture a lot. What does it mean to call Florida “correctly” when the model gave Obama a 50.1 chance of winning?

The example I used when explaining it to some friends was that if a weatherman, for 10 straight days, said there was a 55% chance of rain, and it rained every day – you could say that the weatherman correctly predicted the weather every day, but the correct interpretation is that his estimates were too low.

The code below simply takes 538’s estimates right before the election, with the probability figure being that which 538 assigned to the eventual winner. The code simply assumes that 538’s probability estimates were exactly correct, and shows the distribution of states that would be in error in a run of 10,000 simulations. As you can see, if 538’s estimates were exactly correct, it’s significantly more likely that he would have gotten 1, 2, or 3 states wrong than none at all.

This is not to say that his estimates *weren’t* spot on – there is a good chance they were – I only want to point out that there is a little bit more going on than saying that 538 got 50/50 states “right”.