Are they? Sounds like they're both swing states, pretty close to 50-50, so which state you're from doesn't have a big effect on what your politics are likely to be. Which is exactly what the R^2 tells us. Where's the paradox?
In other countries 45/55 could easily be a swing state.
For example the 2014 Scottish independence referendum was decided 45/55 and that is usually held to be a fairly close result, at least close enough that it hasn’t ended the question.
So the real story here is voter polarization. The reason that Texas isn’t a swing state is because there’s a big hard core of immoveable voters at either end. So the actual population of swing voters is small.
I genuinely don't think that outside of a small minority who drank the Trump kool-aid the political views of the country at large have really changed. The biggest shift is that my friends who were and still are run-of-the-mill "Republicans" don't feel particularly represented right now, and I get it.
With the murmurs of Biden maybe stepping down I think if the Democrats nominated a Moderate Republican they would win in a landslide. It's nuts how much I didn't appreciate having two genuinely good candidates in 2008/2012.
No, it isn't. Your "ten point gap" is something like a 5% delta. That's below the confidence level of some polls.
Exactly. I propose that the paradox is in first-past-the-post voting, a 5% swing leads to a 100% change in representation. How can that be?
> The most commonly-used example of a ranked-choice system is the familiar plurality voting rule, which gives one "point" (vote) to the candidate ranked first, and zero points to all others (making additional marks unnecessary).
Additionally, rated choice voting systems are not subject to Arrow's theorem.
Also, traditional R squared with binary variables, or maybe categorical variables, never made much sense to me. The "meaning" of the variance I don't think is quite the same. You generally have to nonlinearly transform (e.g., logit) any linear model quantities into something else to put it on an observed variable scale.