As Professor Wang explains,
"To calculate this snapshot, we (a) use recent polls for each state (3 polls or 7 days, whichever is greater) to calculate the probability that one candidate is ahead, (b) calculate the exact distribution of all 2^51 = 2.3 quadrillion outcomes, measured in terms of electoral votes (EV), and (c) take the median of the distribution to get the expected EV count."
Princeton actually does two types of predictions, one called the "Random Drift" and the other the "Bayesian prediction."
The first assumes that opinion is equally likely to move in either direction; under the Random Drift theory, Obama's odds of winning are "only" 89%.
Under the Bayesian model, the assumption is that the Meta-Margin "is more likely to move towards its average (Obama +3.1+/-1.3%) than away from it. This has been the case in past elections."
Professor Wang thinks the 97% odds under the Bayesian model is more accurate, but provides both:
In my view, the [Bayesian] prediction is the correct probability. But if the prior seems like an unwarranted assumption to you, then use the Random Drift probability instead. This is a more conservative estimate. Anyway, over the coming days these probabilities will converge to the same value.
So he thinks 89% is conservative, but 97% is more likely.
I like those odds.
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