One last topic: the role of intuition when building an election model. To the largest extent possible, when I build election models, I try to do it “blindfolded,” by which I mean I make as many decisions as possible about the structure of the model before seeing what the model would say about the current year’s election. That’s not to say we don’t kick the tires on a few things at the end, but it’s pretty minimal, and it’s mostly to look at bugs and edge cases rather than to change our underlying assumptions. The process is designed to limit the role my priors play when building a model.

Sometimes, though, when we do our first real model run, the results come close to my intuition anyway. But this year they didn’t. I was pretty sure we’d have Biden with at least a 75 percent chance of winning and perhaps as high as a 90 percent chance. Instead, our initial tests had Biden with about a 70 percent chance, and he stayed there until we launched the model.

Why was my intuition wrong? I suspect because it was conditioned on recent elections where polls were fairly stable — and where the races were also mostly close, making Biden’s 8-point lead look humongous by comparison. If I had vividly remembered Dukakis blowing his big lead in 1988, when I was 10 years old, maybe my priors would have been different.

But as I said earlier, I’m not necessarily sure we can expect the polls to be quite so stable this time around. And when you actually check how accurate summer polling has been historically, it yields some pretty wide margins of error.