I just don't really understand the point of these models. If you glance at the swing state polls, it's pretty easy to reach the conclusion that Biden will probably win but Trump has a chance. These models, even if they're well-designed, are just a very snazzy and attention-grabbing way of saying the same thing. They produce a percentage instead of saying "probably", but even they downplay that (538's headline prediction is just "Biden is favored to win the election") because they know the precise number is pretty meaningless and that most people aren't very good at interpreting probabilities anyway.

Probabilities are generally only useful when you're dealing with repeated events, since that way you can actually check whether the probabilities are accurate and you can use them to build up predictions about what will happen on a larger scale. Like if I run a hospital and I know that the next patient who comes in has a 30% chance of having covid, there's not much I can do with that information since I need to be prepared either way. But if I know that 1000 people come into my hospital every day and each one has a 30% chance of having covid, I can use that to work out what resources will be needed for covid care, and I can check at the end of the day whether the 30% figure was correct.

Also an annoying thing with the 538 model is that they build in loads and loads of tiny adjustments just to give themselves stuff to talk about, and so that they can answer "yes!" when people pester them with "OK, but does your model take into account this weird thing I just thought of?" Many of these adjustments will have minimal impact on anything, but they make it harder to understand the model, make it more likely that there will be some kind of bug in it, and also I think this gives people unrealistic expectations about what actual useful models of things are like.