I think money is a little bit overrated in politics. Or let me clarify that. I think it's overrated for high-profile races where the amount of earned media attention far outpaces the amount of paid media attention, and where voters have seen so many ads they kind of get blurry-eyed and tune them out. If I were donating money, I'd focus more on those races where there's not wall-to-wall media coverage.
Yeah. Democrats have raised more money from individual donors by a wide margin.
Republicans have raised far more from PACs, party money, corporate money, to the extent that outweighs Democrats' lead. However, based on FEC rules, if you buy ads using individual donations, you get more favorable rates. Look, money is always nice, but I'd rather have $3.50 gas and less money if I were Trump than $4.50 and more money.
Nate, where does this podcast find you?
I'm in New York, in lower Manhattan.
Oh, nice. Let's bust right into it. We're recording in late October and the midterms are about two weeks out. Any last-minute takes? I realize that's difficult. We're asking you to, you know, the future — the easiest way to predict the future is to make it, and we got 14 days to make it, but recognizing you deserve some grace. Sitting here on October the 22nd, what are your predictions or what are you seeing with respect to the midterms?
So, I'd be precise and say that we make probabilistic forecasts and not predictions. Polls can be off in either direction. But as of when we're recording this in late October, Democrats — I'll just use round numbers — have around a 75% chance to win the Senate. That's been trending upwards since Labor Day, and about 66% — it's up to three in four — to take the House. Now, that's what our model says, and I think it's a good model. You know, one should keep in mind that in our model, Hillary Clinton's chances were also around 70% in 2016; other models had her higher, so we're often more cautious. But, I mean, I put it like this: every sign except one would point toward it being a good Democratic year.
And those signs include President Trump having some of the worst approval ratings ever at a midterm. They include, you know, gas being $4.30 a gallon, and as of when we're recording this, may be going up again. They include the long history of the president's party losing a bunch of seats at the midterms. They include lots of signs of Democratic enthusiasm in the early vote. Democrats are turning out their irregular voters. We'll see if Republicans are able to do that. The only thing going against Democrats is that the most important races are being conducted in very red territory.
So, you know, if Democrats were to win in purple North Carolina and blue Maine — although Susan Collins has been tough to defeat in the past — that would net them only two seats when they need four. So they have to win a state or multiple states, in fact, that Trump won by double digits in 2024: Texas, Ohio, Iowa, Alaska, Kansas is the main part of the menu, maybe Nebraska a little bit. And so it's kind of like a math problem in some sense, right? Will that wave crest high enough to inundate relatively red terrain? Our model says three in four times yes, one in four times no. The House is different. The House — you'd have to have a profound, surprising polling error, bigger than we saw in 2016 or the other Trump elections. You know, statistically you can have tails in the distribution that make that possible, but that would be quite surprising.
Do you see in the data any what feels like tsunamis forming, or trends that are just so powerful that people aren't expecting them and that might result in a surprise?
I think, you know, in the Senate battleground, Republicans are already in a fairly defensive posture, and they're investing in states like Kansas and South Carolina and Nebraska that are kind of on the edge of the plausible frontier. I think in the House you might get some candidates who have been in a bunch of purple or slightly red cycles who are not prepared for a very high Democratic turnout or a very depressed Republican turnout. I mean, that would be what would worry me if I were President Trump, right? Trump benefited in 2024 from irregular voters turning out. We saw, for example, in South Texas, a big working-class Hispanic vote, right? We saw young men of all races and ethnicities kind of voting for Trump for the first time in 2024. Those are not groups that typically turn out very much at midterms. And this kind of magical power that Trump has to get voters to walk on glass to vote for him, who might be irregular voters otherwise, has never really carried over to the midterms or to other types of elections in the Trump era. Combined with the fact that you have some less-than-compelling Republican candidates. You know, Ken Paxton, I think, won't motivate the same type of passion from voters that Trump might.
People are talking about a blue wave, and then other people say it's not a wave, it's a riptide. And when you see some of these self-inflicted injuries like the one we're seeing or we saw with Vivek Ramaswamy in Ohio, do you think it's a case of we have actually pretty good Democratic candidates on the ballot this time, or is it a blue tsunami or a red riptide in your view?
I think the riptide argument is cope. I mean, the polls can be wrong, but the polls are pretty clear that Democrats are — if the polls are right, Democrats will have a pretty good year. I keep in mind, too, that we had a previous midterm under Trump in 2018, and Democrats won the popular vote for the US House by eight and a half points. They flipped 40 or 41 or 42, something, House seats. They lost a couple of Senate seats because they were conducted in very red territory like North Dakota where they had incumbents. So you could argue that's an ominous precedent for the Senate. But we kind of know that, like, when you have a lame-duck second-term president, when consumers are very concerned about the cost of living and gas in particular — I mean, anytime, you know, it's a catchphrase, but like anytime you drive by a gas station, it's kind of an advertisement for the Democratic Party, basically. You know, waves are normal in midterm elections. There are exceptions: after September 11th, you didn't have any appreciable Republican or Democratic gains at all, right? But you know, eight times out of 10 or nine times out of 10, you have some type of wave. And the question is how big it is. And this one looks like slightly above average, but with a range of uncertainty from — I don't know what between a riptide and a wave is, right? You know, some like rollicking Miami Beach waves, right? Versus a total tsunami that wipes out Republicans in districts where they're not prepared for it.
Yeah. And has early voting told us anything?
So, I'm sometimes a skeptic of early voting data. The thing that the most intelligent people looking at early voting look at is: who are people who don't usually vote? Are they turning out? Right? Because we actually have data from voter files on have you voted in zero, one, two, or three of the past three elections, counting midterms. And again, it's late October. As of late October, that data has been quite good for Democrats, where among irregular voters, there's much higher propensity among Democrats, particularly among Black voters, for example. Yeah, I mean, the way this kind of turns out really badly for Republicans is if Democrats treat it like a presidential election and Republicans treat it like a midterm, right? And their turnout lags. Now, I will say it doesn't matter when you vote; it just matters that you do vote. But it is true that if Republicans don't have a strong turnout on election night, then Democrats have banked the lead in some of the more important states.
And as we're watching the results come in on the 3rd, are there any races that people can look at as sort of a bellwether for whether or not this is going to be the blue wave people are predicting?
Yeah, look, it's always important to keep in mind which states report their results first and close their polls first. There is one swing district in Kentucky. Kentucky is the first to close its polls. North Carolina counts votes very quickly. Roy Cooper, the Democrat, is expected to flip that seat. If that seat were not trending Cooper relatively early, that could be an ominous sign for Democrats. A quick call would be a good sign for Democrats. Florida counts its votes very quickly. Florida sometimes marches to its own drummer, has not been the best state for Democrats recently. Obviously, it's kind of the reverse of North Carolina, right? If the Senate race there in Florida, which is not expected to be all that close, were close, then that would be a very ominous sign for Republicans.
Let me back up. Describe your kind of approach to polling. You do sort of a composite, right, of several different polls, and talk a little bit about how polling has evolved, including the prediction markets.
Yeah. So, I'm not a pollster. I'm a modeler, or forecaster slash journalist, or whatever you want to call me.
I think if you was a data analyst, would that be?
That's probably roughly right.
So yeah, we take polls. The polls are weighted based on various factors, based on methodological practices, based on past reliability. You know, recent polls are weighted more heavily. Polls with larger sample sizes are weighted more heavily. But we also incorporate other types of data, right? In particular, what we call the fundamentals, quote unquote. So it's kind of saying how has a state or district voted in previous elections. We look at incumbency, although there's no longer much of an incumbency bonus. We look at fundraising. You know, the trickiest part is figuring out, in some sense, not what the polls say, but how wrong the polls might be, right? The stated margin of error in polls is not as large as the real margin of error, because like sometimes you're not reaching the correct electorate, and in fact pollsters have very different guesses about who will actually turn up on election day. And then furthermore, you can't treat all these races as independent. If there's a polling error in Kansas, probably a similar state like Iowa, you have the same direction of the polling error. The conditions there are pretty similar. And so our model is trying to figure out all the different correlations, and that's how you get to 75%. Right? If you treated every race as an independent coin flip, it would be 90%. But when you account for the fact that you can have these correlated errors, either regionally or nationally, like you saw in a couple of — all three of the Trump presidential years, that's kind of where the GOP hopes of salvaging the situation in the Senate come in.
How do you apply this? Everyone's obviously obsessed with the elections right now. What are the other sectors or things you apply this type of methodology to?