Political News from the Prediction Markets
Intrade has some interesting things to say to political junkies. According to the prediction market, the hot VP options right now are:
Republican:
These folks are notable because they are ranking in the double digits, as of yet no one is more likely to win than the field (someone yet to be named). But unless some dramatic news story erupts (a certainty in politics), support will probably gravitate around one of these early market leaders.
Another important piece of political information you can get from these markets is perhaps more interesting: what will the electoral college breakdown be for the election?
If the markets are perfectly accurate (an unlikely scenario), then the electoral count will be 306 Obama over McCain's 232. That's unlikely because a lot of these states are really just too close to call. That leads into the next thing the markets can tell us: what are the potential battleground states? The following states are the closest, and they are states the Republicans would be wise to fight for:
- New Hampshire (4 electoral votes): 50.1% Dem, 46% Rep
- New Mexico (5): 60.1% Dem, 34% Rep
- Ohio (20): 60.5% Dem, 39% Rep
- Virginia (13): 45.5% Dem, 45% Rep
Meanwhile, they would have to keep the Democrats from picking up:
- Missouri (11): 39% Dem, 55% Rep, or
- Nevada (5): 48.5% Dem, 50.5% Rep
Of course, it's almost a sure thing for Obama if he just wins any one of the above 6 states. The probability of that happening is somewhere over 95%, if those odds above are remotely accurate. So maybe he can snag Jeremiah Wright as VP, and it wouldn't even matter.
Sources: Intrade State By State Presidential Election; Electoral College Calculators found here or here.
UPDATE: These statistical trends are to be expected for correlated probabilities. This is to say that if McCain wins one of these states, he has a better chance of winning the others (a rise in his nation-wide popularity due to a naton-wide media event, say). I foolishly overlooked this explanation, which explains all the data. I was tipped off on this in a kind email from the folks at the Freakonomics Blog. Thanks, Justin Wolfers.
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