Building a model for predicting the ultimate winner of a US presidential election is particularly difficult, because you are dealing with noisy input data and nonlinear effects, i.e. just a few thousand votes in a few key states can completely flip the outcome. If you then have poorly calibrated polls with a large margin of error, there is really nothing much you can do.
replyOn the other hand, it does raise the question how valuable the 538 models for something like this really are if the outcome is a coin flip anyway.
Exactly, and correlated errors, where a polling error in one state predicts similar errors across the board.
replyI disagree that it's all pointless though. Most basically it's smart for campaigns to have a good model and let that inform strategy where appropriate. Since the president is a big deal other people's decisions are also impacted, and in the long run it pays to have good predictions of those chances. Also, the outcome sometimes is fairly certain and that isn't always easy to see.
Regularly referring to that ~30% spread as "one polling error" made this a lot more understandable than most statistics for many people.
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