As with baseball, football, and basketball (and I’m sure other sports too), the standard of political analytics is just so much higher than it was, decades ago.

I was talking with Gustavo just the other day about Red State Blue State, and how that work was motivated by confusion following the 2000 election emanating from pundits of the left, right, and center. Back then I felt the compulsion to write a whole damn book to explain what was really going on. I even came up with an entirely new (to the best of my knowledge) concept, “second-order availability bias,” to explain how the journalists could’ve gotten things so wrong.

The concept of “second-order availability bias” never caught on, to say the least: it appears only once in the easily-accessed published literature:

So maybe it’s not such a useful psychological concept. What’s relevant here, though, is that the pundits were getting it so wrong, and with such a consensus, that I felt the need to refute them.

Nowadays, things are different. There aren’t so many all-purpose pundits like David Brooks—people who know essentially nothing and have no real interest in learning but present themselves as infallible experts—, and those who remain don’t have such a platform. Also, with partisan polarization, commentary has become fragmented, and we rarely see much of a consensus among pundits across the political polarization. It’s just not on the table.

Meanwhile, political analytics has become more and more impressive, with important contributions being made by academics, journalists, and political professionals. There’s still disagreement (as here) and some difficulties of communication (as here), but in the past two decades the level has gone up so much: the best analytics has become much more impressive, and what might be called replacement-level analytics has become much better too. I don’t think my own sophistication has increased much at all, and that’s one reason why I’m now less likely to crunch the numbers myself (as I did in the early morning hours of 5 Nov 2008) and more likely to just link to the analyses of others (as with Yair’s report on 2024).

Just today I came across two excellent examples online from journalist colleagues of mine.

Elliott Morris, “I re-analyzed the raw data from Wisconsin’s primary polls. Here’s what actually went wrong,” which features this split-the-difference summary that warms my Bayesian heart:
• Most of the miss in polls in Wisconsin is attributable to faulty demographic targets (too many young people). This inflated Hong’s vote margin by somewhere between 5 and 10 points.
• My best guess is that the race moved 6-10 points toward Crowley after pollsters released their final surveys.
• Non-ignorable non-response within demographic categories likely further inflated Hong’s vote margin by 2-5 points.
Morris goes through lots of details too. I haven’t tried to check any of this, but it seems reasonable. We’ve been saying for a long time that primary elections are hard to predict, but some polls are off by much worse than others, and it’s instructive to look into exactly how this can happen.

Beyond the details and the direct interest of this post to political organizations and pollsters, I appreciate Elliott’s work here because he goes beyond statistical generalities (“regression to the mean,” “sometimes you get a draw from the tail of the distribution,” etc.). This is an important statistical point: the “error term” is only an error term until you drill down, look at more data, and figure out what is going on. It’s so common for researchers to just take their numbers and not think about where they came from (as here)—and, indeed, academics and pundits alike can be rewarded for that sort of asinine don’t-look-carefully-at-the-data attitude. So it’s good to see Elliott demonstrating how it’s possible to do better—if you’re willing to put in the work.

Nate Silver and Eli Mckown-Dawson, “FLIPR 2026 midterm election forecast,” which leads Nate to summarize that “[Michigan Senate candidate] El-Sayed would be an underdog in an election held today and is an underdog in our “Lite” (polls-only) version. The fancy versions look at the fundamentals and are more convinced he’ll come back.”

What I really like about this is how “workflow” it feels. What I’m talking about here is how they fit two different models that are doing two different things, they learn something from the comparison, and then they track this back to their data. This sort of thing isn’t in the textbooks (well, it wasn’t until now) but it’s so important to good applied statistical work. So I love to see it here.

The point about these two posts, one by Morris and one by Silver and Mckown-Dawson, is not that they’re so amazing. I mean, yeah, they’re great, but the real point is how professional they are.

I remember Bill James once wrote, in reaction to the unexpected playoff heroics of Bucky Dent or Ray Knight or somebody like that, that, sure, it’s cool when someone steps up and does the unexpected, but what’s more impressive are those Eddie Murray types who can consistently deliver the expected. Because then you can design a game plan around them and not just have to hope for a miracle.

Morris, Silver, Mckown-Dawson, and others doing what’s now expected, doing it well, and demonstrating modern principles of statistical workflow . . . That’s impressive.