What do Nate Silver, Darryl Holman, Drew Linzer, and Sam Wang all have in common? They all use statistical methods to forecast elections, especially presidential ones. Their models all tend to say the same thing: the odds are pretty good that Obama is going to win. Yet they often make different predictions about the number of electoral votes that, say, Obama will get, and about the probability that Obama would win if an election were held right now.

For example, as of right now, Silver predicts 294 electoral votes to Obama with 3 to 1 odds of an Obama win. Holman predicts an average 299 electoral votes with 9 to 1 odds of an Obama win. Wang predicts a median 291 electoral votes, also with 9 to 1 odds of an Obama win. Linzer predicts a whopping 332 electoral votes and doesn't report the probability of an Obama win.

I contacted each of those men to request access to their electoral vote probability distributions. So far, Sam Wang and Darryl Holman have accepted. Drew Linzer declined. Nate Silver hasn't answered, likely because his mailbox is chock full of fan and hate mail.

Wang and Holman now both offer their histogram of electoral vote probabilities on their respective web pages. I went and grabbed these discrete probability distributions and did what a good, albeit naive model averager would do: I averaged the probability distributions to come up with a summary probability distribution (which, by the way, still sums to one).

This method makes sense because, basically, these guys are estimating 538 parameters, and I'm simply averaging those 538 parameters across the models to which I currently have access because I currently have no reason to think they are much different in predictive power (although later on the method could be extended to include weights).

From the aggregated electoral vote distribution, I calculated the mean, median, 2.5th percentile, and 97.5th percentile of the number of electoral votes (EV) to Obama. I also calculated the probability that Obama will get 270 EV or more, winning him the election.

Mean EV: 296

Median EV: 294

95% Confidence interval: 261, 337

Probability Obama wins: over 90%

So 9 to 1 odds Obama wins. Something like 294 or 296 electoral votes.

I'd love to see what happens if I put Nate Silver into the equation. Obviously, it will drag the distribution down. I might look into modeling weights at that point, too, because both Holman and Wang predicted the electoral votes better than Silver, and I believe Wang did a slightly better job than Holman, although I forget.

*This was originally posted at Malark-O-Meter.*

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