By way of Rafa Irizarry from Simply Statistics, a plot of Nate Silver’s probabilities for Barack Obama winning a state versus the percentage of vote in each state, as of midnight EST.
I guess that’s pretty (100%) good. Looks like the folks at Princeton didn’t do half bad either. It’s a win for Obama and a win for statistics. Well, good statistics, at least. (Looking at you, University of Colorado.)
Update: Drew Linzer at Emory and the Huffington Post Pollster also did well. All in all, it was a good night for statistics.
University of Colorado shouldn’t feel too bad about their prediction for the election being dead wrong. There was a couple of psychics that predicted a Romney win, too! LOL
We did pretty well too, like 100%…! Pollster at Huffington Post (me) and Drew Linzer (votomatic).
Doesn’t Silver project vote percentage as well as probability? Wouldn’t a better comparison be between Silver’s projected vote percentage and the actual vote percentage? That would be apples to apples then.
I would imagine that would fall pretty tightly along a straight line.
An alternate view (analysis & cool data viz) comparing pretty much what @boooeee posited here:
Pulling data from Nate’s blog (he lists all 51 forecasts on the right side), I was able to make a list. For example, in Alabama, he listed Obama as getting 36.7% of the vote and Romney getting 62.8% with a margin of error of 3.8%. Which means, come election day we expect Obama to get between 32.9%-40.5% of the vote and Romney should get between 59%-66.6 (with 95% confidence)
Next we pull the actual results. I grabbed data from uselections.org and sure enough in Alabama, Obama recieved 38.56% of the vote and Romney got 60.52%. Both fall within the margin of error, congratulations statistics.
When it’s all said and done, Nate Silver correctly forecasted 48 of the 51 election results and that’s great! We expected 2.5 states to be outside the margin of error and 3 were. He could not have been more accurate. If he had gotten 51 of 51 states correct, the forecasts would be more wrong because these are estimates with 95% confidence.