Why I Think Kamala's Odds are Better Than What the Models Say
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)Hi everyone! This is the first edition of my revamped subscriber Q&A series. In case you missed the announcement last week, the high level of activity in Ettingerchat has made it logistically infeasible for me to continue to solicit questions for these articles on there. Now, the place to ask me questions will be the comment sections of the Q&A articles themselves, like this one. Be sure to subscribe to place your questions for me down in the comment section here, read the full piece, and fully participate in The Most Active Chat On Substack™. Now, let’s get started with the first question.
Younger Sheldon asks: Without being a Bayesian dork like nasty Nate, what odds do you give Kamala in November?
First off: there’s nothing wrong with being a Bayesian dork. Some of my best friends are Bayesian dorks. Bayesian dorks can do great things, Nate Silver’s model included. I’m not a fan of every decision he makes, but I do think his models have real value. I subscribe to it, and I make sure to check it regularly. For as much as I may disagree with his policy of including fake GQP pollsters, his takes are still worth considering, and his models do a great job at illustrating what he thinks.
The keyword, however, is that one word: takes. It’s easy to take models produced by guys like Silver, look at them as wholesale prediction models, and stress over every fluctuation that occurs in his odds. I think that’s what a lot of people are doing right now, and I also think it’s causing a lot of unnecessary heartburn. At the end of the day, and especially at this point in the race, prediction models are influenced far more by the assumptions the model makers have about the race than anything else, polling included. I don’t think there’s anything wrong with that, but that doesn’t mean I agree with Silver’s model when it says the race is a pure tossup. I think that Kamala is the distinct favorite right now, and it’s because I think about the election in a fundamentally different way than models do. Here’s why.
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