We start by generating the sharpest possible snapshot, based on state polls. State polls are more accurate than national polls, which at this late date are a source of unnecessary uncertainty.
For each state, my code calculates a median and its standard error, which together give a probability. This is done for each of 56 contests: the 50 states, the District of Columbia, and five Congressional districts that have a special rule. Then a compounding procedure is used to calculate the exact distribution of all possibilities, from 0 to 538 electoral votes, without need for simulation. The median of that is the snapshot of where conditions appear to be today.
Note that in 2008 and 2012, this type of snapshot gave the electoral vote count very accurately – closer than FiveThirtyEight, in fact.
This approach has multiple advantages, not least of which is that*it automatically sorts out uncorrelated and correlated changes between states. As the snapshot changes from day to day, unrelated fluctuations between states (such as random sampling error) get averaged out. At the same time, if a change is correlated among states, the whole snapshot moves.
The snapshot gets converted to a Meta-Margin, which is defined as how far all polls would have to move, in the same direction, to create a perfect toss-up. The Meta-Margin is great because it has units that we can all understand: a percentage lead. At the moment, the Meta-Margin is Clinton +2.6%.
Now, if we want to know what the statistical properties of the Meta-Margin are, we can just follow it over time:
This variation over time automatically tells us the effects of correlated error among all states. Uncorrelated error is cancelled by aggregation under the assumption of independence; what is left is correlated variation.
To turn the Meta-Margin into a hard probability, I had to estimate the likely error on the Meta-Margin. For the home stretch, the likely-error fomula in my code assumed an Election Eve error of*0.8% on average, following a t-distribution (parameter=3 d.f.). The t-distribution is a way of allowing for “longer-tail” outcomes than the usual bell-shaped curve.
So…there’s only one parameter to estimate. Again, hooray! However, estimating it was an exercise in judgment, to put it mildly. Here are some examples of how the win probability would be affected by various assumptions about final error:
As you can see, a less aggressive approach to estimating the home-stretch error would have given a Clinton win probability of 91-93%. That is about as low as the PEC approach could ever plausibly get.
I have also included the prediction if polls are assumed to be off by 5% in either direction on average. It is at this point that we finally get to a win probability that is as uncertain as the FiveThirtyEight approach. However, a 5% across-the-board error in state polls, going against the front-runner, has no precedent in data that I can see.
Bottom line:*Using the Princeton Election Consortium’s methods, even the most conservative assumptions lead to a Clinton win probability of 91%.