This is not what voters want the president to be focused on. And this mismatch in priorities has been showing up in the polls for months.

A February poll from Wave Polling and Research (my polling firm) found 69% of U.S. adults agree the Trump administration is “focusing too much on deportations and not focusing enough on fixing the economy, inflation, and the rising cost of living.” That includes 87% of voters who didn’t back Trump in 2024, 77% of his 2024 voters who now say they’ll vote Democratic in the generic ballot — and 32% of Trump’s 2024 voters who are still voting Republican in November.

My take is that this mismatch in priorities will have electoral consequences for Trump and the GOP. Generally speaking, I have noticed American pundits spend a lot of time writing about how policy positioning affects candidate success, and comparatively little time on how perceived prioritization does. But the latter is equally, if not more, important.

Today’s Chart of the Week is about the growing gap between what Trump is working on and what voters are actually asking him to fix, and about the overlooked importance of issue prioritization to most voters.

This post is free to read, but it was not free to produce. If you want to support our independent, data-driven political journalism and get premium posts sent to your inbox 2x a week, become a paying subscriber to Strength In Numbers today. Members also get early access to data products, a private community on Discord for our political data nerds, and an ad-free newsletter experience.

Subscribe now

They’re calling it “Hoover-maxxing”

Where else to begin but by noting the absolutely insane politics of building a giant marble ballroom with money from taxpayers and your billionaire friends while people are struggling to put food on the table. If any other president had tried this — in an election year, no less — he would have been sidelined by his own party’s congressional leaders the next day.

Back in May, our Strength In Numbers/Verasight poll found that 68% of voters opposed spending taxpayer money to build Trump’s ballroom. This included a plurality of Republicans, at 44% opposed to 42% in support of funding construction.

And our August poll shows the cost that focusing on projects like this has had for Americans’ evaluation of the president. Nearly 60% of adults now name prices/inflation as one of their top-three issues facing the country today, 20 points higher than the second issue “jobs and the economy.” And the president’s net approval on prices is at an all-time low, and approaching a net rating of -50.

Generally, the more important voters say an issue is right now, the more voters dislike how the president is handling it. The issues Trump and Republicans are strongest on, border security and crime, are rated as the most important problem by the fewest voters. (The correlation across all 12 issues is r = -0.76.)

And it’s not just my polling showing this. A CNN/SSRS survey found in July that just 27% think Trump has had the right priorities, and 73% say he hasn’t been focusing enough on what matters most to them. That itself was a record in CNN’s polling, which has been negative for the president since last year.

Subscribe now

Voters don’t like when politicians ignore their issues

So what does the empirical political science say about the importance of issue prioritization? In their book “News That Matters,” Shanto Iyengar and Donald Kinder’s classic “priming” experiments found that whichever issues dominate the news become more important to voters in both (a) evaluating the performance of the president and (b) choosing between candidates in future elections.

And in a study of the 1968, 1980 and 1984 presidential elections, Stanford political scientist Jon Krosnick found that voters’ views on policies had a substantially larger effect on their evaluations of candidates — and ultimately their votes — when they considered those policies personally important. Patrick Fournier and four coauthors later found the same pattern looking specifically at incumbent performance: voters put more weight on how the government was handling an issue when they considered that issue important, while performance on issues they considered unimportant had little effect on whether they supported the incumbent.

But you do not need a political science research paper to understand that a president spending all his time on things voters don’t care about — indeed, spending a lot of time and energy on policies that exacerbate their top problems — would be bad. If you're hungry, and your parents don’t feed you, you understandably get upset at your parents.

It is thus not surprising (but still worth emphasizing) that Trump has lost support not only with the average American, but also his own voters, because he has been focused on the wrong issues. An August Navigator Research poll found 20% of 2024 Trump voters now regret their vote, with the war with Iran (45%) and prices (43%) at the top of the list of reasons and healthcare cuts close behind (38%). More than a third of Trump vote regretters said they changed their mind on him because the president was “focusing on the wrong priorities like the White House ballroom.”

As David and I put it in the weekly podcast on Thursday, Trump is doing everything you’d expect from someone who wants to lose an election.

That brings us back to the one thing voters want: They want the president to work on the problems they see as the most pressing. And in Donald Trump’s case, they get a daily reminder that the president is simply not prioritizing those issues. Right now, Americans are preoccupied with prices and jobs, while Trump keeps giving them constant evidence that he is not dealing with them.

Trump’s electoral problem — and a big problem for the Republicans this November — is not merely that voters dislike the president’s policies, but increasingly see him as distracted from the country’s most pressing problems. And they’re right.

Subscribe now

Related Articles

This video is free to watch, but it was not free to produce. If you value this podcast and want to help keep it going, please consider becoming a premium subscriber to Strength In Numbers. Paid subscriptions keep the show ad-free, fund the newsletter, and ensure we have the time it takes to do original data-driven analysis of politics and elections. Paying subscribers also gain the ability to send in questions during our live streams, so you can directly shape the conversations we’re having on the podcast.

Upgrade to support Strength In Numbers

A couple of big takeaways from the conversation:

• A new UMass Amherst poll shows just 84% of Trump 2024 voters sticking with him in a hypothetical do-over election. That compares to 94% of Harris voters staying with Harris. Similarly, Navigator Research finds 18% of Trump’s voters say they regret their vote, chiefly over Iran, the cost of living, and corruption and abuse of power by the White House.

• 2026 is looking like an above-average reaction against the party in power. In Gallup’s latest poll, U.S. adults now lean 49% toward the Democrats and just 39% toward Republicans — the biggest gap for Democrats since the peak of the financial crisis in 2008. Trump and the Republicans have essentially speedrun George W. Bush’s entire second term in 19 months.

• Trump is picking the 20% side of every issue. From tweeting his support for data centers by saying “let Data Reign” to renaming Lake Ontario to lavishly spending on the White House ballroom, Donald Trump is constantly taking positions that are opposed by the vast majority of voters. Now Republican candidates for Congress have started distancing themselves from him. Ralph Alvarado and Andy Barr in deep-red Kentucky both scrubbed mentions of Trump from their campaign websites, and Carlos de la Cruz quietly demoted himself on his site from “Trump conservative” to “proven conservative.”

Thanks to everyone who joined us for the weekly livestream on Substack. We’ll be back next week!

As a reminder, paid subscribers to Strength In Numbers get to participate in our live Q&A! If you missed our video livestream, you can watch it by clicking play on the web version of this post at gelliottmorris.com.

You can also subscribe to us on your favorite podcast app to listen on your own time. And if you do listen via one of those apps, please drop us a five-star rating and review if you feel we’ve earned it — it really helps people discover the show!

Subscribe now

You can read the transcript of our conversation by clicking the headline of this article to take you to the web version of the podcast, then clicking the button just below the byline that looks either like a piece of paper or is labeled “Transcript,” like so:

If you’re a reader of Strength in Numbers and haven’t yet subscribed to David’s newsletter, head to the-downballot.com.

Subscribe to The Downballot

And if you’re coming from David’s site, subscribe here to get the numbers behind the news.

Subscribe to Strength In Numbers

Original polling like the one covered in this article is funded entirely by readers. Become a paid subscriber to support this work and read the full analysis below.

Upgrade to support Strength In Numbers

At its summer meeting on Aug. 15, the Democratic National Committee passed a resolution calling for the abolition of Immigration and Customs Enforcement. Then, right after, it passed a second resolution calling for ICE to be reformed, but not abolished. Chair Ken Martin, quoted in an AP report, explained that the two resolutions are not official platform planks but signals about policy direction — the one thing that unites Democrats, he said, “is people believe that what ICE is doing right now — and CBP and the federal government — should stop.”

This apparent split personality disorder is part of a broader pattern among Democratic lawmakers and voters. One thing Democrats and Democratic-leaning independents agree on, according to our new poll, is that party leadership should “fight” more.

Few, however, have a handle on what that means in concrete terms. Fight who? For what? Do Democratic voters just want Congress to stop Trump, or is the target broader?

So in August we asked voters to tell us, in their own words, what they think a “better Democratic party would look like. This question was part of a battery of questions about the direction and values of the party in general — who it should listen to, what policies it should push forß, etc. (Republicans got a mirrored set about the GOP; their numbers appear below for comparison.)

What I uncovered is that Democrats are split between what they want their party to do now, and what they want it to be focused on in the long term. While Democrats say they want to be focused on resisting Trump in 2026 — much like how an opposition party would operate in other democracies — they also want the party to do a better job delivering for the average person, getting money out of politics, and “being honest.” In addition, 11% of Democrats responded to the open-ended question about party focus indicating they wanted the party to move left, compared to 6% who want it to be more “middle of the road.”

Republican voters also want some big changes, and view dishonesty and a neglect of affordability as chief problems their party should fix.

Subscribe now

Democrats’ 2026 priority is fighting Trump

We showed Democrats a multi-select list of things the party could focus on and let them check as many as they wanted. Overall, 81% picked ending Trump’s corruption and abuse of power, 54% selected affordability, lower costs, and better wages, and 36% said the party should protect the rights of for women, people of color, and LGBTQ+ Americans. (Options are not mutually exclusive.)

The Democratic Party’s voters right now are overwhelmingly focused on Trump.

Comparatively, 74% of Republicans say the party should focus on affordability.

But the topline undersells what’s actually happening here. Respondents could check every box, so this isn’t a horse race between three options. Another way to cut the data is to check which option respondents would pick by itself. This tells us more about prioritization of Trump vs affordability vs other policy.

The results are not close. Overall 49% of Democrats and Democratic leaners picked only one option to the question above. Of those, 76% — or 37% of all Democrats — picked “ending Trump’s corruption and abuse of power” as one thing the party should focus on, and picked nothing else. In contrast, 10% picked affordability alone, and 2% placed minority rights above other priorities.

In total, 31% picked all three, and 12% said the party should focus on both corruption and affordability, but left minority rights off their list.

We can also look at the correlation between options people selected (though there are no surprises here, given the above). Among Democrats who picked affordability as a priority, 79% picked Trump’s corruption too. And among Democrats who picked corruption, only 52% selected affordability too.

In a second question, we asked Democrats to tell us which came closer to their personal view: “Democratic leaders are doing enough to oppose Trump” or “need to show more fight.” Overall, 83% of Democrats said they need to show more fight against Trump, whereas just 9% said they’re doing enough. Eight percent said they didn’t know.

Subscribe now

What Democrats want the party to do, in their own words

It is somewhat unsurprising that Democrats, if given a box labeled “do you want to end Trump’s corruption and abuse of power?”, would check that box. But this doesn’t tell us much about competing priorities for the party’s long-term vision, or what it means to “fight.”

So we asked Democrats to tell us, in their own words, what they think “a better Democratic Party would look like.” This question is similar to one we asked for a Strength In Numbers greatest-hits article last November, which was later replicated by the Democratic polling firm Blue Rose Research. The result is 645 free-text responses from voters describing what their party would look like in its ideal form. I analyzed these responses and placed each into one of nine categories that I derived, with the help of an AI coder, by identifying common themes across them.

When you ask Democrats if they want their party to fight Trump, they say yes. But if you ask them what they want their party to look like, the president loses the top spot. The bar chart below shows the frequency of each of the nine themes I identified among all responses.

The rest of this Deep Dive is for paying subscribers. I write at least one post a week reserved for the members of this community who keep Strength In Numbers running. Upgrade to continue reading and support the time and data collection it takes to do analyses like this one.

Upgrade to support Strength In Numbers

Read more

Upgrade to support Strength In Numbers

Just a couple of links today. My 3-month-old has not been sleeping much, so I haven’t had time to collect as many links as normal. But she did sleep 8 hours straight last night, so I’m feeling punchy in this first section.

As always, thanks for reading this week, and expect more results from our August poll this Tuesday-Wednesday. We were trying to better understand the “Left Tea Party” dynamic in the Democratic Party, so we did a whole battery of questions on what Democrats and Republicans want from party leadership/want the direction of their party to be. That’ll be this week’s Deep Dive.

1. One out of six Trump voters says they wouldn’t vote for him again

In May 2025, I reported on results from my Strength In Numbers/Verasight poll that found Trump would lose around 14% of his 2024 voter base if the election were held again. That alone would have been enough to cost him the election, but we also found a large share of 2024 non-voters said they regretted not voting and would pull the lever for Harris if given the chance:

Friend of SIN Michael Podhorzer covered these numbers in a post (again, let me underscore, *in MAY 2025*) titled “the emerging anti-MAGA majority“. He used the vote-transfers above to simulate the results of the election using counts of registered voters:

We were writing these articles in the midst of a wider conversation at the time spurred by CNN’s Harry Enten, who cherry-picked results of a UMass Amherst poll to claim Trump would win a redo election. Enten erred in that (a) the particular UMass poll he cited at the time was an outlier and (b) he only looked at the Trump -> Harris switchers, and not at Trump voters who might pick a third-party option or not vote at all, and he ignored what non-voters would do. Quoting his TV segment here:

“So if there was a repeat, if folks got to be able to redo their vote that they had back in 2024, would the result be any different? I doubt it would be. I doubt it would be or it would still be extremely close. The bottom line is for all this talk of Trump voters regretting their vote, in the numbers, it really just doesn’t show up.”

This was wrong at the time, and is one of the many clips of pundits covering polls early in the second Trump administration that have aged very badly.

Anyway, I was reminded of all of this Trump regret polling this morning when a new UMass Amherst poll came through my inbox on early release late last night. According to the new poll, only about five in six Trump voters say they’d support him in a redo election (so 1/6 defect). The defectors are unequally split between switching to Harris, a third-party candidate, or not voting at all — but the full results are due out on Monday, so I can’t share the full topline yet.

And this is no outlier. As mentioned, the Strength In Numbers/Verasight poll found the same level of defection in May 2025. The UMass results also broadly align with the numbers in a poll my company, Wave Polling and Research, conducted for a client in February 2026 — and also in a new survey we put out today.

Strength In Numbers has been early in detecting backlash to Trump. That’s because we’re open-minded about the data, not because I have special powers or proprietary data.

In the spring of 2025, the conventional view from U.S. political pundits was that Trump had won a real national mandate for political change. Republicans, in the popular telling, had picked up durable new cultural authority along with it, and the Democrats’ only route back to power was to give up significant ground on economic and social policy. This was the height of popularity, for example, of such wrong-headed arguments as “Democrats shouldn’t talk about immigration.”

New York Magazine said the right was on a path to “cultural domination”:

I spent a good chunk of last spring arguing that this dominant view was incorrect. Trump won a small victory in 2024, the numbers showed, and Trump, the man, was consistently more popular than Trumpism, the political project. The individual planks of his immigration agenda polled particularly poorly compared to his topline number on the issue. A lot of powerful people simply did not realize how unpopular Trump was, and they were paying a price for their ignorance. (In the case of many politicians, a political price; CEOs, a literal one.)

But the thing is… this was obvious. None of those articles took a special questionnaire or blockbuster data source to write. You just had to look at the data with an open mind. The polling data I used to write my original “Trump wouldn’t win a redo election” article wasn’t hidden on my hard drive, but out in public crosstabs for anyone to read. Even the UMass results Enten used to argue Trump would have won a do-over in April 2025 in fact contradicted him if he had just read the full poll!

We weren’t writing about anything hidden, or partisan, or really even early. It was just uncomfortable for people to write these takes when the zeitgeist demanded different.

Final note on this subject: A related problem I see all the time still — in the fall of 2026! — is the refusal to view Trump’s victory in 2024 through the lens of anti-incumbent bias or normal retrospective politics. While a lot of opinion columnists want to pretend, for example, that Trump’s win was because of backlash to “woke one” (see here), this is a completely unempirical view. Like the original Trump Regret article from Enten, it is frankly, obviously wrong to anyone willing to look at the data with an open mind.

Share

2. What Strength In Numbers published last week

Last Sunday’s roundup had a refreshed version of the neighborhood-level model of Trump’s job approval, down to your own neighborhood — there’s exactly one demographic group left in the country that’s net-positive on him:

On Wednesday, the August Strength In Numbers/Verasight poll landed, and Trump lost his last positive issue — he’s now net negative on border security, with strong disapproval outnumbering strong approval by nearly 3-to-1:

On Thursday, I talked to economist Rajiv Sethi about prediction markets — whether they beat pollsters, why they blew the papal conclave, and whether they’re going to eat forecasting and journalism:

And on Friday, I opened up 15 months of our own polling to check two things people keep telling me are wrong with surveys this cycle: that respondents are getting more progressive, and that weighting by past vote is loading up on hardcore Trump voters. Neither one shows up in our data:

If you’re a frequent reader of Strength In Numbers, I’m confident you will get a lot of value out of a paid subscription. You’ll get access to all of my Tuesday Deep Dives, monthly polling crosstabs, our private community of election nerds on Discord, and more.

Upgrade to support Strength In Numbers

3. Even more numbers!

• My colleagues Mary Radcliffe and Cooper Burton wrote at FiftyPlusOne on Thursday that “No, the polling industry is not on the verge of collapse.” Fake polls, new pollsters, and a shift away from live phone surveys, they say, are all nothing new. While recent misfires in primary polls have been large, they have not been atypical.

• Stephen Clermont and Lauren Goldstein, two Change Research pollsters, similarly argued last week that measuring opinion so you can move it has always been part of their job as private/campaign pollsters. They say that polling for strategy does not prove that polling is broken.

  • I don’t do a whole lot of campaign-style polling work personally, so I view this mindset to be significantly different from that of a public pollster, whose job is to measure, not change. Here at Strength In Numbers, we use polling to measure the will of the people and give them a megaphone in Washington. (Kinda my whole philosophy on public opinion and democracy...)

• The Economist/YouGov, August 21–24: Democrats up 6 on the generic ballot among registered voters, 46% to 40% — and up 11 among the registered voters who say they’ll definitely turn out in November. Likely voters, data centers, ‘woke,’ and more

• Pew’s Andrew Mercer found that there is “no easy fix for bogus respondents in online opt-in polls.” Data from survey marketplaces should be treated with a lot of skepticism. Voter file matching and pre-screening (like Verasight does for SIN, off their panel of both web respondents and people recruited from probability sources) are a must.

• Marquette has David Crowley up 49–44 on Tom Tiffany in Wisconsin among likely voters. Crowley is leading Tiffany among likely voters, tied among all registered voters.

• Cook moved three House races in Michigan and Florida toward Democrats this week, including two out of Lean Republican and into Toss Up. Three House Race Ratings in Michigan and Florida Shift in Democrats’ Direction

  • My take is Cook is still significantly behind the curve in the House. I hope to write about this this week.

• And finally, Trump’s approval on handling the economy is at just 32%, with 64% disapproving, among registered voters, my new poll finds. Trump is net negative on handling deportations and border security in this survey.

  

Share

And that’s it for this week! Thanks for reading. Strength In Numbers will be back in your inbox on Tuesday-Wednesday with the weekly deep dive for premium subscribers.

Got more for next week? Email your links or add them to the comments below! Comments are unlocked in the Sunday roundup.

Leave a comment

Subscribe now

From time to time, Strength In Numbers accepts sponsors for free posts sent out to all readers (never paywalled articles). These funds help pay for things like data acquisition, subscriber churn, and staff members, including people who help with the podcast and survey research. This post contains one such sponsorship, clearly denoted after the introductory portion of the article.

Last Friday, I asked whether polls are overestimating Democrats again this election cycle. The answer I gave doesn’t actually answer that question — because the point I made was that nobody can answer the question until Election Day! Trying to predict the direction of polling error for this cycle using the error over the last four cycles has historically been a bad idea; It often would have left your predictions worse off than just taking the polls at face value:

I’m not promising a more concrete answer today. But there are some more things we can learn on this subject by looking at current polls (whereas last week’s column just looked at historical data).

In talking to other pollsters, I’ve heard anxieties expressed about two potential threats to the polls right now. First, the possibility that people taking surveys might be too progressive, which would cause polls to overstate support for Democrats. But second, there’s also the possibility that people who still admit they voted for Trump in 2024 might be much more right-leaning than 2024 Trump voters were as a whole. This would be bad, because most pollsters now balance their samples by past vote, which would increase the weight on these super-Trump voters.

So this week’s Friday column looks under the hood of our monthly Strength In Numbers/Verasight polls. Are the people answering them now predictably more left- or right-leaning than they were a year ago?

Thanks to Strength In Numbers Discord user “Jofish” for suggesting this topic.

This section contains sponsored content.

One of the big lessons from this election cycle so far is that not all polls are created equal. The same is true for the news you see online.

That’s why I’m happy to partner with Ground News, a website and app I use constantly that helps me identify when stories are being pushed by one side of the ideological spectrum. Ground News gathers news articles from over 50,000 sources worldwide and shows you a measure of each outlet’s factuality, ownership, and political bias. You can also filter news by whether an outlet is owned by private equity or a corporate conglomerate — or is an independent outlet, like this Substack!

Ground News is a smart news aggregator to help you deal with the challenges of our modern information ecosystem. The constant news monitoring and feature filtering help me keep my own news diet attuned to facts, not spin. I think these tools would be incredibly useful to my audience — informed readers trying to find the facts in the news. Today, they’re offering readers of Strength In Numbers 40% off their “Vantage plan,” which gives you a personalized feed as well as data on factuality, partisan bias, and funding information for every news outlet.

Get 40% OFF Ground News today

Question 1: Are respondents to polls too progressive?

I can proxy an answer for this question by looking at the trend line in weighted ideological self-placement in our polling data. This is a crude approach in that it leaves open the possibility that a panel as a whole has been biased from the start, though we do not think that is the case for reasons explained here; we can monitor the data for changes over time.

Verasight records where panelists place themselves on a seven-point liberal-to-conservative scale as part of an intake survey respondents take when they are recruited to join Verasight’s panel. Crucially, this question is asked separately from the survey we field each month — it is a question asked previously, so it can be used as a proxy for how liberal/conservative the panel is over time.

As a gut check on the stability of the ideology variable, I trained a machine-learning model to identify respondents who have taken our polls multiple times over the past year by matching people by demographic traits within zip code, and identified roughly 1,000 out of roughly 24,000 respondents who look to have taken our survey multiple times over the last 15 months. (I could have asked Verasight for respondent IDs to link them formally but ran out of time; expect a follow-up article soon.) Over 99% of these respondents report the same ideology in both surveys they took.

So when the ideological mix of our poll shifts, it’s mostly because different people answered the poll, not because people are changing their ideological ID over time. However, we do not find a predictable increase in the liberal-conservative tilt of our panel over the last year. In fact, if anything, the panel has gotten slightly more conservative since June 2025.

The weighted liberal share of respondents went from 34.4% in the first three waves to 33.8% in the most recent three. The very liberal share slipped from 11.0% to 10.9%. Neither trend is distinguishable from zero.

I also checked that the unweighted share of respondents identifying as Democrats has been stable over time, and it has, floating between 44.3 to 45.1 percent over the last 15 months with no discernible trend.

Share

Question 2: Is weighting by past vote increasing support for Republicans?

Buckle in for a bit of survey wonkery on this one.

Most pollsters now balance samples against the results of the 2024 presidential election — which, if they only get, say, 20% of all adults saying they voted Trump in 2024, this group gets up-weighted to the true population proportion (around 38%).

But this creates a potential compositional problem within the category of people who say they voted Trump in 2024: It’s possible that the softer Trump voters have quietly stopped telling pollsters they voted for him last time. If so, the group still claiming a Trump vote would be an increasingly hard-core remnant, and weighting to 2024 would increase their share in the population — and would, in turn, increase the poll’s estimated population proportion for anything correlated with intensity of Trump approval (e.g., the House generic ballot vote).

To see if this was true — if our poll was getting increasingly Trump-y over time — I built a model that is trained on polling data from 2024 (and calibrated to match results of the election at the county level) that predicts how someone with a given age, race, sex, education, income, and zip-level neighborhood characteristics (2024 presidential vote, rural-urban status, percent gay and percent in college) would have voted in 2024. Because the formula is fixed to predict the results in 2024 accurately, it can be used to gauge the political composition of the people responding to our poll.

I can then break out these scores by voters’ recalled past vote, and see if those people who claim to have voted last time look like more intense Trump supporters, given their demographics and geography.

Within the group of people who say they voted for Trump in 2024, the average predicted Trump support fell by 0.4 points from May 2025 through August 2026 — well within the margin of error for both the individual polls (around 2.5% in the average month) and the trend visualized below (about 1.5%). I have removed the Y axis values from this chart because the thing to focus on is the “lack of” trend, not the level.

On balance, Harris voters have become a little less Trump-y, and independents are roughly the same as last year. Overall, the pool of people who respond to our poll is no more Trump- or Harris-leaning now than in early 2025.

We can also bucket by intensity of support for Trump and see if the most Trump-leaning respondents increased their population share. They didn’t: those with a Trump probability above 70% went from 29.9% of the group to 27.7%, and the soft middle from 39.9% to 41.7%.

This suggests the opposite of the hardening story — at least in the Strength In Numbers/Verasight poll.

Now for two caveats. This analysis cannot tell us if the Trump voter pool is getting more Trump-y (a) in other polls or (b) in ways that aren’t captured by demographics or local geography. The polls could still end up “wrong” in November for other reasons; we’ve just ruled out two things that aren’t a problem specifically for the Strength In Numbers/Verasight poll.

Concretely, what I have shown is that the people answering our surveys are not drifting to the left, and the pool of 2024 Trump voters we are still getting to respond looks like the same sort of 2024 Trump voters who were telling us that a year ago.

It is still possible that the people who will admit to a pollster today that they voted for Trump in 2024 are higher-probability Trump/Republican voters. But this is why we use a recall measurement that was asked in the past, before the president’s approval rating slipped to 36%.

These are diagnostics we will keep an eye on in our next three polls before election day.

Subscribe now

Related Articles

Thanks again to Ground News for sponsoring today’s newsletter. Get 40% off their Vantage plan today.

Ground News paid Strength In Numbers for this sponsorship, but they did not have input on any editorial content.

Subscribe now

Share

A couple of big takeaways from the conversation:

• Markets are well calibrated probabilistically, but are not a 1:1 substitute for statistical forecasters. When information is sparse, markets cannot provide highly accurate forecasts of future events. And they can’t provide a standardized method for understanding the weight assigned to different types of information in anticipating outcomes, which limits their use journalistically.

• But markets can be very useful when other models can’t function, such as when Joe Biden withdrew from the 2024 election, and forecasters had to spend a few weeks updating their models. Markets kept producing continuous probability estimates through that uncertainty. Rajiv made the point that prediction markets were able to react to new information during the COVID-19 pandemic when epidemiological models were still getting trained.

• There is an under-studied potential bandwagon effect with the markets. Highly visible odds undoubtedly have an effect on consumers (and voters) psychologically, even if they are hard to detect. Market prices from Kalshi are now contractually embedded in CNN's election coverage — Rajiv and Elliott discussed whether that visibility could itself depress turnout for whichever side is being priced as the underdog. (If you are studying this, reach out to me!)

• No, markets aren't going to replace polls or journalism. Rajiv makes the great point that markets depend on polls, since traders seek out measurements of opinion. And we know that traders even commission their own private polls, like the French "whale" who contracted surveys from the Trafalgar Group in 2024. If public polling actually became scarcer, Rajiv expects markets would get more volatile and less accurate.

Thanks to everyone who joined us for the weekly live stream on Substack, and we’ll be back next week!

As a reminder, paid subscribers to Strength In Numbers get to participate in our live Q&A! If you missed our video livestream, you can watch it by clicking play on the web version of this post at gelliottmorris.com.

You can also subscribe to us on your favorite podcast app to listen on your own time. And if you do listen via one of those apps, please drop us a five-star rating and review if you feel we’ve earned it — it really helps people discover the show!

If you value this work and want to help keep it going, please consider becoming a premium subscriber to Strength In Numbers. Paid subscriptions support this podcast, the newsletter, and the time it takes to do original data-driven analysis of politics and elections. Paying subscribers also gain the ability to send in questions during our live streams, so you can directly shape the conversations we’re having on the podcast.

Upgrade to support Strength In Numbers

Share

You can read the transcript of our conversation by clicking the headline of this article to take you to the web version of the podcast, then clicking the button just below the byline that looks either like a piece of paper or is labeled “Transcript,” like so:

If you’re a reader of Strength in Numbers and haven’t yet subscribed to David’s newsletter, head to the-downballot.com.

Subscribe to The Downballot

And if you’re coming from David’s site, subscribe here to get the numbers behind the news.

Subscribe to Strength In Numbers

Paying subscribers to Strength In Numbers have access to additional visuals and a full archive of crosstabs here, and can suggest questions for future polls in the comments section below!

On Aug. 13, the Department of Homeland Security announced there had been 15 straight months with zero releases of migrants at the southern border, part of a crackdown the administration says has cut daily apprehensions by 94% from the last year of the Biden administration. DHS says the border is secure for the first time in a generation.

Yet voters do not view the president’s handling of the situation positively.

In our new Strength In Numbers/Verasight poll, fielded Aug. 14–19, President Donald Trump’s net approval on border security — his best issue in this tracker for most of the past year and a half — fell to −5, the worst mark we’ve recorded on it and now squarely net-negative. Every one of the 12 issues we test is now underwater, and not one of them improved this month.

The president heads into the home stretch of the midterm elections at nearly a high point for intensity of opposition to him. In our new poll, the gap between Americans who strongly disapprove of him and those who strongly approve is now nearly 3-to-1. The movement against the president is now primarily in his own coalition, with significant softening in the share who say they “strongly support” the job he’s doing over the last two months.

Below, I report full results from our monthly Strength In Numbers/Verasight political tracking survey, including where prices and inflation now stand, how the generic ballot looks, and what voters told us about the direction of the country.

Subscribe now

Headline poll findings

• Job approval: Trump’s job approval this month is 37% approve / 60% disapprove (net −23), down from −21 in July. 49% strongly disapprove of his job as president vs. 17% who strongly approve.

• Intensity: that’s a 32-point gap between strong disapproval and strong approval — nearly 3-to-1 — up from a 22-point, 2-to-1 average across all of 2025. Only 47% of Trump’s own approvers feel strongly about it now, down from 54% last year.

• Every issue is underwater: Trump is now net negative on all 12 issues we test. Border security, the last one standing, fell from +1 in July to −5 — its worst reading yet.

• Prices: net −48, a new record low in our tracker, down 6 points from July’s −42. Just 24% approve; 73% disapprove.

• Generic ballot: Democrats lead 49–43 among registered voters (D+6), equivalent to June’s lead. They’ve now led in all 15 polls we’ve run since May 2025.

• Party trust: Democrats lead on 8 of 12 issues, and by 10 points (46–36) on voters’ own most important problem. The party now nearly matches the Republicans on trust to handle immigration and deportations.

• Direction of the country: 52% of U.S. adults say things are “going poorly” in the country and “major, disruptive changes are needed” — roughly the same as 49% in July. Just 7% say things are going well.

• Top problem: 37% name prices/inflation as the single most important problem facing the country — and it’s the No. 1 problem for Republicans (36%), Democrats (37%), and independents (38%) alike.

Select questions held for future release.

Independent polling like this only works if readers fund it. For $10/month (or $90/year), you’ll get the full crosstabs for every survey, the ability to submit subscriber-only poll questions, and the knowledge that you’re keeping this work free of corporate sponsors and Super PAC influence. Plus, you get access to premium articles like my weekly Deep Dive every Tuesday, other exclusive posts, and a community Discord server for like-minded readers.

Upgrade to support Strength In Numbers

Trump hits new low, now net-negative, on border security

Since Trump’s inauguration in 2024, border security had been the one issue on which his head was above water for most of the life of this tracker, as high as +11 in May 2025 and still positive at +1 in July.

But in August, Trump finally lost his last positive issue, falling to a new low of net −5 in our survey. That’s the worst number since Strength In Numbers began polling in May 2025.

This isn’t the first time Trump has polled underwater or even on every issue we test. That happened in February, March, and May of this year. What’s new is that this is the first survey where the president’s rating is clearly below zero on every issue.

Trump saw some big dips elsewhere as well. His rating on foreign policy fell 7 points; prices and border security by 6; trade 5, and jobs and the economy 4. Only his ratings on education and elections and democracy held flat, and neither of those was in good shape to begin with.

Looking more closely at prices, Trump’s worst issue also got worse this month. Net approval on prices and inflation fell from −42 in July to −48, the lowest reading we’ve recorded on any issue in this tracker, beating the −47 set back in May. Just 24% of U.S. adults approve, and 72% disapprove of how the president is handling prices.

President Trump’s overall net approval also fell 2 points this month. Overall, 37% of adults approve, and 60% disapprove of the job Donald Trump is doing as president, a net of −23, down from −21 in July. That change is well within expected sampling variation, and still short of April’s −26 floor.

Trump’s overall net rating fell by 2 points, while five of these issue nets fell four points or more. Issue ratings are moving faster than the topline number, which may be a sign of erosion that hasn’t yet shown up fully in his approval rating.

Subscribe now

Strong disapproval outnumbers strong approval by nearly 3-to-1

Nearly half the country does not merely disapprove of Trump, but disapproves strongly: 49% of adults strongly disapprove of how he is handling his job as president, against just 17% who strongly approve, a 32-point gap and a ratio of nearly 3-to-1.

That ratio widened sharply through late 2025 and early 2026, and has held near its wider level since. Through 2025, strong disapproval averaged 44.8% against 22.2% strong approval — a gap of roughly 23 points, or 2-to-1. Across 2026, it has run 49.5% against 18.6%, a gap of nearly 31 points.

What has changed most in the last few months is on Trump’s own side: the share of his approvers who feel strongly about his performance fell from 54% in 2025 to under 47% now. As I wrote earlier this month, Trump and Republicans are getting squeezed from both ends; his own voters are cooling on him even as independents move toward Democrats. So his coalition did not just shrink; the remaining supporters have softened.

Subscribe now

Prices are still the country’s top problem

37% of adults in our August data name prices and inflation the single most important problem facing the country, essentially unchanged from 36% in June and July. Elections and democracy are a distant second at 14%, jobs and the economy at 13%, and health care at 8%. When given the choice between three picks, 59% include prices among their top three problems, ahead of jobs (40%), health care (37%) and elections (24%).

The cross-party agreement is the striking finding here. Prices are the top problem for Republicans (36%), Democrats (37%) and independents (38%) at nearly identical rates. On the country’s single biggest problem,m there is remarkable agreement across party lines. The disagreement is about who’s to blame and what to do about it.

Democrats still lead on the issues voters care about most

Democrats lead on partisan trust to handle 8 of the 12 issues we test, including every top-tier priority: health care by 22 points, government funding and social programs by 17, education by 15, elections and democracy by 10, prices by 9, jobs by 7, trade by 7, and foreign policy by 6.

Republicans lead only on lower-salience issues, none of which crack voters’ top four picks for most important problem: border security (R+14), crime (R+4), immigration (R+1) and deportations (R+1). The last 2 are notable for how far they have drifted in the Democratic direction this year.

On whichever issue each respondent named as the country’s most important problem, Democrats lead on handling it (at the respondent level) by 46% to 36%, a 10-point edge that has held steady for months.

Movement from July was small but mostly toward Democrats: health care, crime, prices, jobs, trade and deportations each shifted at least two points their way, while education slipped a point toward Republicans. These are within the margin of error.

Subscribe now

Democrats lead the generic ballot for a 15th straight poll

Democrats lead the generic congressional ballot 49–43 among registered voters, and 48–41 among all adults, in our August Strength In Numbers/Verasight poll. That’s a touch narrower than July’s 50–43, but well within normal variation in our poll. This margin matches June’s result.

Over the last 16 months, Democrats have held an incredibly stable lead in the generic ballot. Across 15 polls since May 2025, Democrats have never trailed, with margins between D+4.5 and D+10.1.

When voters are weighted according to their stated likelihood to vote, the party’s margin expands to D+7.5, with 51.1% of registered likely voters saying they’ll vote for Democrats in their local congressional seats and 43.6% saying they’ll vote for Republicans.

Among only those voters who say they are definitely going to vote in November, Democrats hold a 9-point lead.

Subscribe now

A majority says the country is off on the wrong track

It will be no surprise, given support for the opposition party and anxiety about prices, that Americans are overall in a sour mood about the direction of the party. Among adults, 52% say things are going poorly, and major, disruptive changes are needed to get the country back on track, up from 49% in July (though still below April’s 55% high). Comparatively, 38% say things could be going better, and just 7% say things are going well.

By party, 82% of Democrats, 53% of independents and 21% of Republicans say things are going poorly. A related column I wrote on this a couple of weeks back: Anti-system sentiment, not ideology, is what’s driving Democratic gains right now.

Subscribe now

Reader-suggested questions: financial fragility and federal spending cuts

Each month, subscribers suggest questions, and I pick a few to field. Here are two from this wave:

How long could Americans withstand a loss of income? More than a quarter of Americans, 27%, say they could maintain their current standard of living for less than a month if they lost their income; 44% say three months or less. Just 20% say they could go more than a year.

What government program would you cut first? Given a list of federal spending to cut, 46% pick foreign aid and 22% pick immigration enforcement and deportations. Social Security and Medicare (2%), Medicaid (2%), and medical and scientific research (2%) all rank toward the bottom.

On the broader question of party image: 37% of adults say Democrats care more about people like them, vs. 27% say Republicans do — but 25% say neither party does. The parties overall are viewed negatively; on a 0–100 feeling thermometer, Americans rate the Democratic Party at 45.2 and the Republican Party at 41.5.

I’m holding a few findings from this wave for release later in the week. On Friday’s Chart of the Week, I’ll take a look at issue salience or generational splits within each party’s base — whatever the chartage is more compelling on. And next week, I’ve got results from a battery we ran among each party’s voters that asks about the direction they want their respective party organizations to take. Make sure you’re signed up to get those in your inbox!

If this type of work is useful to you, or you believe in supporting it for the public good, consider becoming a paid member of this Substack. Paying subscribers to Strength In Numbers make it possible for me to spend time on this original, independent, data-driven political journalism that’s not beholden to any corporation or billionaire donor. Support my work and get access to articles like this one for $10/month or $90/year.

Upgrade to support Strength In Numbers

Methods statement:

Verasight collected the data for this survey from Aug. 14–19, 2026. The sample consists of 1,514 United States adults ages 18 and above. The data are weighted to match the June 2026 Current Population Survey on age, race/ethnicity, sex, income, education, region, and metropolitan status, as well as to a running three-year average of partisanship distributions from the Pew Research Center NPORS benchmarking surveys and population benchmarks of the 2024 vote. The margin of sampling error is ±2.6%.

G. Elliott Morris prepared the topline document and this write-up for Strength In Numbers. Strength In Numbers had input on question wording, but all other methodological decisions were made and carried out by Verasight to ensure independence of the data-generating process behind these results. Verasight also reviewed the questionnaire.

You can download a full topline file, key crosstabs, and full methodology statement at the Strength In Numbers/Verasight polling portal. The paywalled section containing full crosstabs and more interactive graphics will be updated Tuesday morning.

If you have any questions about this poll or the release, please email polling[AT]gelliottmorris.com.

Have a suggestion for next month’s poll? Leave it in the comments below!

Related Articles

Upgrade to support Strength In Numbers

Readers, one programming note before the main contents. The August wave of our monthly Strength In Numbers/Verasight poll comes out this week! It contains questions on how much savings people have, what party people think cares most about them, and how Democrats are viewing their party’s efforts fighting Trump, and whether they want it to make ideological compromises to win elections.

As a fun little pre-release bonus for readers, I’ve updated the Trump approval maps below with the August data.

1. Trump’s approval rating in every neighborhood: now even more underwater

In January, I built a very detailed map of Trump’s job approval, broken out by Census “Public Use Microdata Area” — or PUMAs for short. PUMAs are geographic units of roughly 100,000 people on average, which offers a finer resolution than counties in most places (sorry, Wyoming; you actually have more counties than PUMAs).

The model that generates these estimates combines more than 24,000 interviews from the last 18 months of Strength In Numbers/Verasight polling. We place respondents to our poll in individual zip codes across the country, and then predict Trump’s popularity in each using demographic and political data about who lives there.

I have now refreshed this model with the latest numbers. If you thought things looked bad before, you’re in for a surprise. As of now there is only one demographic group in the country that on net supports the president, and then turns into a negative net approval rating in the vast majority of PUMAs.

Here’s the area-level view, which looks decently okay because of the huge stretches of the Mountain West, the Great Plains, Appalachia, and the Deep South that have no people living in them. Green is net-positive approval, geographically speaking.

But switch it to a population cartogram instead, where every PUMA is sized by how many people actually live there, and the population groks easier. Almost every dense population center turns solid orange, with tens to hundreds of PUMAs in each.

This map is so orange because land doesn’t vote, people do. Most of the people in this country currently disapprove of the president. That means most PUMAs are going to at least be light orange.

FiftyPlusOne’s polling average had Trump underwater by 24 points at publication — 36.1% approve to 59.9% disapprove. Disapproval isn’t just a cities-and-suburbs story anymore; it’s creeping into genuinely rural, red-leaning PUMAs too,

Zoom into your own area and see where you land.

And if you want more state- and district-level MRP estimates for policy, check out my new polling company Wave Polling and Research, which uses the underlying model technology behind these maps to make projections of policy support and other opinions on dozens of topics, from hundreds of thousands of interviews in public and private survey data. We are currently looking for partners to expand our data collection to weekly cadence on new topics.

Share

2. What Strength In Numbers published last week

On Tuesday, I took a fresh look at whether Abdul El-Sayed can actually win Michigan’s Senate race, arguing pundits are far more confident than the evidence warrants about which kinds of progressives underperform:

Also Tuesday, over in the Data Lab (free to read), I dug into why it is so hard to know the age composition of the Democratic primary electorate in Wisconsin — but data suggests it’s somewhere in the neighborhood of 9%.

For Thursday’s podcast, I talked with FiftyPlusOne’s head of research, Mary Radcliffe, about a wave of fabricated and undisclosed partisan polls hitting the industry this cycle. I doubt this is the last time we will have to talk about this topic this year.

And Friday’s Chart of the Week checked a Republican pollster’s claim that surveys are systematically overestimating Democrats again this year. Maybe they are, but predicting polling bias from the last cycle’s miss is typically a fool's errand.

If you’re a frequent reader of Strength In Numbers, I’m confident you will get a lot of value out of a paid subscription. You’ll get access to all of my Tuesday Deep Dives, monthly polling crosstabs, our private community of election nerds on Discord, and more.

Become a paying member today

3. Even more numbers!

• Bright Line Watch: expert ratings of U.S. democracy ticked up this summer, the best of Trump’s second term, though still below the low point of Biden’s presidency. A Partial Democratic Rebound?

• Bradley Wascher lays out the Senate math: Democrats need something like a D+9 national environment to flip the majority — which a year ago would have seemed impossible but now looks possible, in fact is ay even odds in our forecast. Democrats Seek Majority in Most Republican Senate Class

• YouGov: 66% of Americans now say “arrogant” describes Trump a lot, and the share calling him a strong leader has sagged from 49% just after his 2024 election to 36% today. How do Americans describe Donald Trump?

• A Reuters/Ipsos poll found 63% of Americans think it’s inappropriate that Trump and his family have profited from crypto ventures since he returned to office — including about three in ten of his own Republicans. Most Americans believe Trump has inappropriately profited since returning to power

• The new Economist/YouGov poll has Democrats up 7 on the generic ballot overall — and up 23 points among the most enthusiastic registered voters. Republican support for Trump has fallen as Democrats hold a big lead among enthusiastic midterms voters

• Another banger fron my colleagues at FiftyPlusOne — rural voters have swung toward Democrats since Trump’s inauguration, leaving the GOP’s current rural advantage just shy of its 14-point edge in the 2018 wave. Rural and suburban voters haven’t looked this Democratic since 2018

• Are betting markets and bad polls making it harder to tell who’s actually winning? Reid Epstein digs into the confusion, using Haley Stevens’s near-miss in Michigan as exhibit A. Why Polling Chaos Could Create Big Problems for American Politics

Share

And that’s it for this week! Thanks for reading. Strength In Numbers will be back in your inbox on Tuesday.

Got more for next week? Email your links or add them to the comments below! Comments are now UNLOCKED on the Sunday roundup.

Leave a comment

While polls often overestimated support for Republicans as well, the average survey overestimated Democrats significantly. For any given Senate poll released in August of 2024, 2022, 2020, or 2018, you had a much better-than-even shot at predicting Republicans would beat it. About 70% of August polls in the past 8 years have overestimated support for Democrats.

Ruffini’s chart is in many ways unsurprising. The window contains both the 2024 and 2020 elections, years we know surveys had too many Democrats in them, and where preferences moved right in the final months. But it also contains 2022 and 2018, when polls did much better. In 2022, polls on Election Day actually overstated support for Republicans, bucking the previous cycles’ trend.

But the big question this piece prompts us to ask — indeed, prompted dozens of you to email me about — is this: so are the polls overestimating Democrats again this year?

The truth is, Ruffini’s argument is right in some ways and wrong in others. He is right to point out that polls early are often “wrong” (only if you view early polls as forecasts of future preferences instead of snapshots of opinion at the time they were taken), and the election forecasting model I built for FiftyPlusOne.news goes to great lengths to treat polls with the proper amount of uncertainty/skepticism.

But the implication or the piece — that polls are likely biased toward Democrats right now — is not warranted by looking at the historical data. Ruffini only has 4 cycles of polls in his analysis. But I have polls going back to 1998, so in this week’s Chart of the Week, I show that bias in polls tends to change randomly from year to year. Like taking the under on low-probability bets, predicting polling error from previous cycles’ misses works until it doesn’t.

This is post is free to read, but if you want to support my independent data-driven political journalism and get premium posts like these sent to your inbox 2x a week, become a paying subscriber to Strength In Numbers today. Members also get early access to data products, a private community on Discord for our political data nerds, and an ad-free newsletter experience.

Upgrade to support Strength In Numbers

If a paid subscription is out of reach, the next best thing you can do is to share this article with a friend. Organic recommendation is the #1 driver of new readers here at Strength In Numbers.

Share

“Unskewing” the polls usually increases error

I have taken FiftyPlusOne’s dataset of all horse-race general election polls since 1998 and filtered it down to polls released by August 31 in each year. Then, I calculated rolling 45-day polling averages for each competitive Senate race in those cycles and compared those polling averages to the November results in each race.

The table below shows the average bias of polls in the average race by year, going back to 1998. It also contains, for each year, (a) the average bias of polls on Election Day and (b) the average bias in polls released in August in the prior 4 election cycles.

As Ruffini found, those August polls have been overestimating Democrats recently. But note (a) that this wasn’t/is not always the case, and (b) polls often improve in predictive performance as we get closer to November.

What’s really striking from this table, though, is how poorly lagged cycle bias predicts future poll misfires.

The lagged average bias of polls in year Y-8:Y got the direction of bias in year Y right in ten of fourteen cycles. But it missed the magnitude by an average of 2.5 points, and the direction about 30% of the time. And the predictive failures one would make using the heuristic “polls will be as biased as they have been recently” are not small ones. In 2014, the four preceding cycles pointed to polls understating Democrats by two points; August polls that year overstated them by seven. You would have created a 9-point error in the wrong direction in your poll or election forecast if you had applied the lagged error to the average survey.

Similarly, in 2016, the 4-cycle lagged average bias suggested the polls were essentially clean, and they turned out to be off by nearly six. Back in 2002. You would have predicted a pro-Democratic polling environment on the historical data, but surveys had a two-and-a-half-point Republican tilt instead.

Plotting the same data above makes the problem inherent in predicting poll bias easy to see. While two variables — observed poll bias and lagged bias — are generally related, they are not highly correlated, and there are some very big differences.

Each election year occupies the point in the graph above corresponding to its cycle’s lagged average bias on the horizontal axis and its observed August bias on the vertical. If past bias predicted future bias, the years would line up along the dashed diagonal. Instead, they scatter.

The fitted line slopes upward at 0.57 — meaning a one-point pro-Democratic tilt in the last four cycles came with about half a point in the next one. But the standard error of that slope is 0.38, which means we cannot statistically distinguish it from zero. Zero is what you would get if the past told you nothing about future polling bias at all.

But the wider shaded band is the real kicker here. That is the 95% prediction interval — the uncertainty interval around predictions of future bias given lagged bias over the past 4 cycles. It is huge. Knowing what polls did in the four previous cycles narrows your expectation for the next one by essentially nothing.

Ruffini’s post is about August polls, but for the record, the serial correlation of poll bias is even worse on Election Day. Bias by Nov tends to even revert somewhat from the previous cycle.

Check out 2022 there, on the far bottom right of the graph. The lagged bias then predicted a 3-point overestimation of Democrats in Senate races, but the bias ended up by a 3-point miss in the opposite direction. If you had adjusted polls for past bias in 2022, you would have massively messed up your election predictions based on those polls.

And this is the finding that really matters — because if you’re going to put your thumb on the scale for a party, you want there to be a close to zero chance of causing a larger error than if you had left the polls alone. We can test this too.

“Unskewing” also introduces high “risk of ruin”

I went back to my historical dataset of polling averages, took the lagged bias values from the first table of this post, added those lagged direction biases to the polls in each year, and then calculated the average bias of the resulting polls. Then I did the same thing, assuming no average bias in the polls.

The chart below shows the distribution of signed error in Senate polling averages under each scenario, and for polls as of August and November.

Note that the adjusted polls do not systematically outperform the unadjusted polls — and in many cases actually do worse. Since polls tend to do well or poorly across the country by similar amounts each cycle, this thumb-on-the-scale adjustment of polls also creates the problem that a forecaster releasing adjusted numbers tends to increase all their errors across all races at the same time.

The 2022 election is a good example of this. If one were to have calculated averages in each Senate race and then subtracted out the bias in polls from 2014-2020 from them, one of have pushed all the polls to the right by exactly 3 percentage points. This would have increased your error in 8 out of 8 key Senate races, and called 4 races for Republicans that polls said Democrats lead in.

If that sounds familiar to you, it’s exactly what RealClearPolitics did in 2022. I wrote about how stupid this was while it was happening here. Back then, I wrote:

These results suggest that if RCP repeated its adjustment procedure for all Senate elections since 2002, they would have ended up with more biased polling averages than if they had stuck with the raw average in 7 of the last 10 election cycles. The only years in which their adjustment would have made their averages more accurate are 2010, 2012 (barely) and 2020

For Senate races in August, adjustment would have improved your forecasts in 9 out of 14 of the last elections and worsened them in 5/14. By Election Day, the results flip, and an adjustment would have cost you in 10/14! In those races, it would have been better for you to leave the polls alone and focus on measuring uncertainty, not predicting bias.

Share

Why 2026 may be different

Zooming out before conclusion, it’s worth a couple of paragraphs to think about the mechanisms of “error” here. Looking back at Ruffini’s chart, why was there such a reliable shift from August to Election Day in 2018-2024?

Well, one thing to say is that early polls are not meant to perfectly predict election outcomes. Voters’ preferences can change over the campaign as they tune in to information and socialize about key races. Some of this “error” in August polls is likely measurement error, I grant that — but it’s also the case that it’s truly just pretty early in the campaign still. Most people are checked out until after Labor Day.

But another plausible reason is that early polls tend to sample adults or registered voters instead of likely voters. But as Election Day approaches, pollsters tend to make the switch to the more accurate voter screen.

In the past few cycles, Democrats were doing better in RV and A polls than in polls of likely voters. So the LV switch produced a natural curve to the right that made it look like Democrats were losing ground — when in reality, their support in early polls was inflating because they sampled the wrong populations.

But, as Ruffini’s own polls have shown, this is not the case for 2026. Democrats are doing about a point better in LV polls vs adult and RV samples this year. So one could even argue we should naturally see the opposite drift this year as pollsters switch to LV screening.

In 2022, you also had Dobbs in the summer, which delayed but did not eventually disrupt the typical midterm dynamic of the party in power losing ground as Election Day approached. There is no Dobbs in 2026 disrupting that cycle, so the data points from that summer may be useless in anticipating potential poll bias.

Still, none of this should be read as me arguing that the polls are all fine and affirmatively won’t be biased come November. Early polls will not be a perfect prediction of the November election results — that much we know pretty much for sure.

This is a good reason not to take the polls at face value and to instead use something like the FiftyPlusOne.news election forecast that (a) simulates uncertainty in polls according to this exact historical distribution of error and (b) combines the polls with other predictors of election outcomes, including “the fundamentals” (past district history, incumbency status in a seat, etc) and race ratings.

In sum, it is undisputed that polls have been trending in a pro-Democratic direction recently. But that does not mean that they are reliably biased in a knowable direction, such that you can subtract the error in advance and arrive at an unskewed result. Ruffini argues that Senate polls are “always worse in the summer.” This is true with his limited post-2018 data, but it is not supported by a full historical study back to 1998.

Adjusting your polls in the way Ruffini’s findings imply you should would have made your forecasts worse in many key races — including in 2022, when early polls forecast Democrats would hold onto their majority in the U.S. Senate, which they ultimately did.

Given the stakes of elections today (and frankly, the reward in making overconfident forecasts months out), it is tempting to try to forecast future polling misfires using those in the recent past. But the historical evidence suggests this is a fool’s errand. After all, predictions are hard, especially about the future.

If you believe in the importance of data-driven coverage of polling and public opinion, the best way to support my work is with a paid subscription to this newsletter.

Upgrade to support Strength In Numbers

Related Articles

Subscribe now

Here are the big takeaways:

• A firm called “Median Strategies” faked polls in California and Wisconsin. The firm told the LA Times it had invented its results as a “social experiment,” then scrubbed its anonymously registered website once reporters started asking for methodological details. Mary explains why FiftyPlusOne never reported on this organization’s “polls” and the screening questions she asks every pollster before she shares their numbers at 50+1.

• A pollster called The Public Sentiment Institute admitted to reassigning respondents’ vote choices to boost numbers for one candidate — who coincidentally paid for the company’s polls, which it didn’t disclose. TPSI says they “leaned” respondents who had selected voting for Jay Collins toward James Fishback — whose campaign, it turns out, had paid them about $1,500 shortly before the polls came out. Collins finished in the mid-20s, and Fishback got 10%.

• Fake polls have always been around. But it’s easier than ever to conduct surveys that are both real and unprofessional or low-quality. A firm called Research 2000 faked Daily Kos’s daily Obama approval tracker for over a year before they got caught. Now, in 2026, AI tools make it trivial for enterprising young coders to fabricate release paperwork and even topline and crosstab data — and anonymous prediction markets create an avenue for payoff. Mary says that social media accounts that collect and republish polls should only publish verified results, and link to their sources (instead of posting screenshots of polls to go viral). Better yet, just go to FiftyPlusOne.news for all your polling needs.

Thanks to everyone who joined us for the weekly live stream on Substack, and we’ll be back next week!

As a reminder, paid subscribers to Strength In Numbers get to participate in our live Q&A! If you missed our video livestream, you can watch it by clicking play on the web version of this post at gelliottmorris.com.

You can also subscribe to us on your favorite podcast app to listen on your own time. And if you do listen via one of those apps, please drop us a five-star rating and review if you feel we’ve earned it — it really helps people discover the show!

If you value this work and want to help keep it going, please consider becoming a premium subscriber to Strength In Numbers. Paid subscriptions support this podcast, the newsletter, and the time it takes to do original data-driven analysis of politics and elections. Paying subscribers also gain the ability to send in questions during our live streams, so you can directly shape the conversations we’re having on the podcast.

Upgrade to support Strength In Numbers

You can read the transcript of our conversation by clicking the headline of this article to take you to the web version of the podcast, then clicking the button just below the byline that looks either like a piece of paper or is labeled “Transcript,” like so:

If you’re a reader of Strength in Numbers and haven’t yet subscribed to David’s newsletter, head to the-downballot.com.

Subscribe to The Downballot

And if you’re coming from David’s site, subscribe here to get the numbers behind the news.

Subscribe to Strength In Numbers

This post is part of The Data Lab, a new section of Strength In Numbers for extra charts, general polling wonkery, and methods work that doesn’t fit into the normal editorial vertical. If you’re the type of person who wants to know how our polling is done or reads election forecasting methodologies, the Data Lab is for you.

Emails for posts on the Data Lab vertical are off by default, but you can easily toggle them on here. If you have a post to contribute to the Data Lab, email me.

Most Data Lab posts are free, but the occasional deeper ones are for paying subscribers, whose support makes all of this possible.

Upgrade to support Strength In Numbers

The folks behind the Marquette University Law School poll have responded to my analysis of last week’s polling miss in the Democratic primary for governor of Wisconsin. As a reminder, the final Marquette poll of the race from July 22-27 had Francesca Hong winning 38% of the vote and David Crowley, who ultimately won with 40% of the vote, at 7%. This is a 33-point miss — a “remarkable difference between poll and outcome,” per the director of the poll, Charles Franklin. In margin terms, this is a 31-point miss.

As a second reminder, my analysis looked at the microdata from a non-profit I did some poll methods work for previously called State Navigate, whose August poll had Hong at 44% and Crowley at 20% — a 20-point miss, about 50% less than Marquette.

The explanation from Marquette of their miss is here. Part of their explanation is also that their poll was taken early, before a lot of late consolidation to Crowley. That’s plausible, but also, 31 points is a lot of consolidation. This is one of the hard things about conducting primary polls in general, though; preferences are very fluid and tend to move a lot very late, especially when candidates are entering and exiting the race.

I don’t want to get into a line-by-line back and forth, but I do want to put their opening here. Marquette’s piece begins as such:

G. Elliot Morris argues that the polls in Wisconsin underestimated David Crowley’s support in the 2026 Democratic primary because they included too many young voters (who mostly supported Francesca Hong) and too few older voters (who mostly supported Crowley).

I think a more accurate reading of my article is that the State Navigate survey was primarily biased because they included too many young voters (with a target for the under-30 population ranging from 9% in their August poll to 17% in July. Additionally, in my article I gave three reasons why the polls were off, not just one:

• 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.

Fair enough, I did say “in polls” there, though the article was mostly about State Navigate. I think this probably made Marquette think I was attacking them and hurt their feelings, though, which was not my intention. But note I did attribute the lion’s share of the “error” to late movement, as does Marquette’s John Johnson:

I would not describe late movement as leading to “error” in our survey, since we were measuring the electorate as of July 22-27 and not predicting what it would be on August 11.

The remainder of Johnson’s article is a good deep dive into why it’s very hard to know what the age composition of the likely total electorate in Wisconsin is. That’s because the state of Wisconsin does not make age of registrant available for commercial voter file vendors, so you get the “vampire” problem State Navigate termed and which I described in the footnotes of my original article, and in this follow-up.

I’m pretty sure Johnson didn’t read the footnotes or follow-up because I did a sensitivity analysis of the age makeup of the Wisconsin electorate that answers his objections outright. I explained that we didn’t know from the WI file what the unknown % exactly was, that polling data provided a benchmark of about 50% - 75% of the entrants with unknown ages being in the 18-30 camp, and then tested what the analysis would show assuming those percentages. I demonstrated that the poll bias was not linearly related to the age composition; that is, even at a high 18-29 voter share, the weighted State Navigate polling data was still a lot better than what they published in August. I’m sure it would make a big difference vs what they published in July,

I explained, for example, that even if you weight the State Navigate data to a 9% target for ages 18-30, you still get a big Hong overestimation (this data is from August 7-8, the week before the election):

Importantly, I also showed that weighting to a rough guess of the ideological composition of Wisconsin Democratic voters, via 2016 primary exit polls, gave a much more accurate picture of the race (that’s the solid line above).

But none of this answers the big question: What share of the Wisconsin likely primary voter universe is made up of under-30s? We will never know for sure, so we are left guessing — and, importantly, weighting polls to those guesses, which is why sensitivity tests of the kind above are so useful and why pollsters should publish them.

For some informed guesswork, Johnson produces this table of estimates of all registered voters and general election voters using data from the Current Population Survey. It shows that 14% of self-described 2024 general election voters in Wisconsin were ages 18-29.

This appears to be slightly less than the benchmark Marquette weighted to (18% in their published tabs). I think this is because their poll weighted to registered voter benchmarks, and then filtered to the population of likely voters based on their stated intent. (This introduces a lot of noise because people are really bad at estimating how likely they are to vote.) I have asked Johnson on Substack to give me the exact weighting targets and will update this paragraph when he responds.

Unfortunately, I’m pretty sure both of these estimates from Johnson are too high. For one thing, a poll weighted to a lower share is more accurate in predicting the winner, even if you adjust for ideology and for late deciders favoring Crowley. (But maybe that’s just because those weights deflate Hong supporters, but still give an unrepresentative look at the electorate. Fair enough.)

The problem with Johnson’s estimates is that he is looking at a different population than I was. For re-weighting the State Navigate data, I was targeting the population of Wisconsin voters who have cast at least one ballot in a Democratic primary for any office since 2016. Johnson is, in contrast, producing an estimate of the statewide population of likely November presidential general election voters. The latter population is going to have a lot more young people in it, because primary voters skew old.

To demonstrate that point, I have loaded up the L2 voter file for Minnesota, which does not have missing data for voters by age, and is demographically one of the most similar states to Wisconsin. According to L2, the statewide share of registered voters in Minnesota who are between the ages of 18 and 30 is 18%. However, the share of likely Democratic primary voters who are between 18 and 30 is only 11%. That’s a 7-point difference. Additionally, about 14% of validated 2024 voters were 18-30 — 3 points higher than the likely primary electorate.

And according to the Census, Minnesota has about 2 points more voters who are 18-30 than Wisconsin does. (Wisconsin voters are older than Minnesota voters.)

If we take Johnson’s estimate that 14% of Wisconsin 2024 voters were ages 18-30 and subtract 3 + 2 from it, we get 9%.

This is the same target I used in the updated analysis I posted last Thursday, which “controls for the vampires.” In that analysis, I produced the following breakdown of error in the State Navigate data, using the 9% 18-30 target:

1. 10 points having too many young people people in the poll / other weirdness in the State Navigate weights

2. At least 5 points of late movement (5 is what we can detect with the recontact) and plausible up to 10

3. About 5 points of non-response bias

Adjusting for the misspecified age composition gets you within the 13 percentage point average error for the margin between two candidates in primary polls. That would indicate the primary polls and Wisconsin were not any worse than average, after adjusting for what looks like maybe a simple mistake or extra extrapolation from Michigan on the part of State Navigate.

But you can eliminate most of the error in 1 and 3 by weighting your primary poll to a reasonable benchmark for ideological composition. We don’t know what the precise ideological composition of any group of voters is, so see Recommendation 3 in my original analysis. Time for some sensitivity analysis, baby!!

For a few more examples, Strength In Numbers subscriber and the former Secretary of State of the state of Oregon, Phil, calculated the following turnout and young voter percentages in recent primaries in the following states:

• NC - 21% overall turnout, 18-29s 7% share

• OR - 42% overall turnout, 18-29s 6% share

• PA - 22% overall turnout, 19-29s 8% share

• OH - 23% overall turnout, 18-29s 7.5% share

He wrote me:

With 35% overall turnout in WI, my educated guess is that the 18-29 year old share of the D electorate might be closer to 10%, plus or minus 2%.

In summary

1. I’m sorry if I hurt Marquette’s feelings.

2. I already conducted a sensitivity analysis on the youth composition of Wisconsin voters, which I should have just published up top in the first article so I didn’t confuse people, and the conclusions about error do not change.

3. Primary polling is very hard, especially in states where we don’t have good voter file data on age composition. People should adjust their expectations for accuracy downward, especially in polls taken weeks before the actual election (as in the case of Marquette).

4. Marquette’s alternate estimate of the age composition of Wisconsin seems too high; this is because they are not adjusting for the fact that likely primary voters tend to be older.

If I were you, I would just put this on repeat:

Primary polling is very hard, especially in states where we don’t have good voter file data on age composition. People should adjust their expectations for accuracy downward, especially in polls taken weeks before the actual election.

Writing for the New York Times last weekend, Nate Cohn argues “It can be hard to predict exactly which progressive candidates are liable to underperform, but Dr. El-Sayed has a lot of the hallmarks of the kind of progressive who has underperformed in the past.” I discuss this piece at length below the fold.

Because of columns from Cohn and others, most Democratic elites have adopted the belief that progressives always underperform moderates. Readers would not be blamed for believing that as well.

But I’m not here today to argue against the idea that moderates do better than progressives (despite the real evidence otherwise). Instead, I’m arguing for uncertainty — for even just a dose of epistemic humility from DC practitioners and media pundits about why voters are voting the way they are. If you take a look at the research on this subject, it becomes clear that we all know much less than the conventional wisdom asserts. And more than that, we know a lot less than pundits say about how voters will behave in the future.

So this piece is partly about El-Sayed in Michigan, and partly about how confident any of us should be in the new conventional wisdom about “electability” and moderation. The evidence on whether ideology costs a candidate votes is thin, contested, and importantly constantly shifting. As Cohn notes accurately in his piece, there is even debate about what counts as “progressive” anymore. As he shows in his examples, it has increasingly little to do with policy preferences.

Putting our Bayesian hats on, the honest accounting of all the evidence is that we can’t be sure of a penalty for ideological “extremism” in any one isolated race — and almost nobody in this argument is accepting that uncertainty.

But before we get into all that, it’s worth taking stock of the current and past polling in the Michigan Senate race. What do they say now about El-Sayed? What did the polls say about Stevens, an unabashed moderate — would she have fared any better?

Then, I dive into Cohn’s big example for how to be a progressive and not appear as one: Tammy Baldwin. The problem here, unfortunately for Cohn, is that Baldwin is seen as the second most liberal Democratic Senator. Ouch.

The rest of this Deep Dive is for paying subscribers. I write at least one post a week reserved for the members of this community who keep Strength In Numbers running. Upgrade to continue reading and support the time and data collection it takes to do analyses like this one.

Upgrade to support Strength In Numbers

What the polls actually say about El-Sayed and Stevens

Read more

This post is part of The Data Lab, a new section of Strength In Numbers for extra charts, general polling wonkery, and methods work that doesn’t fit into the normal editorial vertical. If you’re the type of person who wants to know how our polling is done or reads election forecasting methodologies, the Data Lab is for you.

Emails for posts on the Data Lab vertical are off by default, but you can easily toggle them on here. If you have a post to contribute to the Data Lab, email me.

Most Data Lab posts are free, but the occasional deeper ones are for paying subscribers, whose support makes all of this possible.

Upgrade to support Strength In Numbers

The feedback to my Thursday piece re-analyzing Wisconsin polling microdata has been overwhelming and positive. I have received more emails and texts about this survey than anything I’ve written in a while. I consider this a huge win, not only because I am a millennial/Gen-Z and crave your validation, but because it shows the utility of explaining the ins and outs of polling in public.

I did want to issue a short follow-up post about this. What about the vampires? This post is going in the Data Lab, because it’s for the real sickos out there.

Do not invite the vampires into your poll

The Wisconsin voter file is notorious for having a lot of missing and incorrect data. One of the variables that is particularly errant is the age variable. In their methodology report, for some added context, State Navigate wrote:

“many voters in Wisconsin have a birth year of 1800, and we don’t believe in vampires. These generally are newer voters who skew very young; about half of voters who we polled with a birth year of 1800 say they are in the 18-29 age bracket. Thus, if you were to simply take the voter file’s birth year data, you’ll get a Democratic primary electorate that is majority 65+ and has less than 2% of voters under the age of 30. Absentee requests for the Wisconsin primary (which includes requests for Republican ballots) are 5-6% 18-34. Given that this age group has been notorious for voting late/on Election Day, we find it hard to imagine a Wisconsin Democratic primary electorate that is <8% 18-29. Maybe it is; we don’t know for sure due to the ridiculousness that is the Wisconsin voter file”

Well, I am more than a little wary of using polling data that had too many responses from young people to make any sort of predictions about what age numbers would be on the voter file, so it is another opportunity for sensitivity analysis.

In the plot below, I re-raked the August survey with the under-30 target set by increasingly aggressive assumptions about how old the “vampires” are — from my baseline a priori estimate (unknown ages allocated in proportion to the known ages) up through State Navigate’s estimate (half of unknowns under 30) and past it (70%) — under both the L2C weights and the ideology-free L2B weights.

Notably, stepping up the share of voters who were <30 increases error linearly. Under the L2C weights (which account for ideology and income), the margin goes from Hong +2.4 at my baseline, to Hong +4.9 using State Navigate’s stated floor of an 8%-under-30 electorate, to Hong +6.6 if you assume half of unknowns are under-30, and Hong +8.4 at 70%.

Under the L2B weights (which don’t have ideology in the targets), the same exercise runs from Hong +13.8 up to Hong +19.7, nearly all the way back to the unweighted poll’s +23.8. The reason is simple: the young people who actually answered this survey were overwhelmingly Hong voters (likely too pro-Hong, as the non-response analysis suggests), so every additional young voter you add to the electorate model pushes the poll further from the result.

The adjusted weighted estimates I present — even at a 7-10% under-30 percentage — are still much closer to the result than the original weighted or unweighted numbers. Whatever the true age of Wisconsin’s vampires, the topline takeaways from my original analysis stand: The polls had too many progressives in them, which you can mostly but not entirely solve with weighting — especially if you don’t know the breakdown of the electorate by age! — and a large amount of late movement toward Crowley.

Share

Somebody get the garlic

But about the non-response problem: With the adjusted numbers and new age breaks, the weighted estimates do land farther away from the targets. Does that mean we should conclude there was more non-response among moderates after all?

Well, not entirely. Again, the big problem here is that the Wisconsin voter file can’t produce a good benchmark for age (or other) variables, so pollsters are left flying blind as to what to weight their data to. (The right move here, in my opinion, is to simulate what your poll would show under a bunch of different weights to varying targets, but I will save this for another post.)

To set the stage, here is what the results of the State Navigate poll in August would have been if you allocated undecideds and some minor-candidate voters to Hong and Crowley based on their flows in the July-August recontact study State Navigate conducted. This uses my adjusted weighting model and the maybe-realistic-but-we-don’t-really-know assumption that 50% of people on the Wisconsin voter file with a birth date in the year 1800 are actually under 30 years old (what the polls that had too many young progressives in them indicated).

What is really interesting here is that the base scenario is almost perfect on Hong’s vote share, but underestimates Crowley. That’s likely because there was an additional large swing in his direction after the final August poll wrapped fielding, which polls simply cannot capture. The observed flow to Crowley among supporters of the minor candidates in the recontact study was 1.5:1. I also calculated what the results of the poll would have been if you allocate those voters 2:1 to Crowley vs Hong:

Now, here is the same chart, but if you weight the sample additionally to a benchmark for ideological makeup of the Wisconsin primary electorate. (Remember, the State Navigate poll had a quarter of respondents identifying as Democratic Socialists, which we can be reasonably sure is too high.)

Weighting by ideology recovers about 8 points of vote margin for Crowley.

This means moderates aren’t responding as much to our surveys, right? Well, yes and no. We don’t know if “progressive” is the psychological quality that’s acting on voters to not make them vote. It’s likely something related to one’s ideology: like political engagement. I don’t have the year-vintage breakdown of vote history participation for Wisconsin voters in, say, 2018 (the last time there was a competitive primary in Wisconsin), so I can’t do that math, but maybe someone else could.

But we can use the updated “vampire-adjusted” weights and a reasonable guess at the vote flows in the last week (2:1 to Crowley) and plug into our non-response program (the MUBP — see the original post for an explanation).

The best guess from the authors of the paper I’m basing all this math on is that a reasonable starting point for the impact of non-response is setting phi = 0.5. And you can see why: in the L2Cv weights (which account for ideology), pushing that non-response adjustment up to 100% would have yielded a poll with 20% Hong and 60% Crowley! That’s very bad in the opposite direction of most published polls in Wisconsin.

And to be clear, since the L2Cv weights (solid line) already balance the sample by ideology, the non-response here is something about voting for Hong and Crowley that isn’t proxied by ideological identity in a survey. So you can’t use those trends to say anything about non-response among progressives. That is already taken into account by the level-shift in Hong and Crowley vote at phi = 0.

If, however, you use the L2Bv weights with the non-response dial at the 50% position, you get somewhere around 42% Hong and 40% Crowley. Given they each won about 40% of the vote, that’s a pretty good guess. The residuals here point to about a 5-6 point boost for Hong from non-response.

But again, caution is warranted, since the data for this survey all came from a week before the election. If we had data on election day, the impact of a “non-response” adjustment would be lower. (Technically, I guess, the poll not accounting for future movement in the race is “non-sampling” error, in that it’s error that comes from something other than sampling, but that is different from non-response.)

Share

Polling primaries is very hard

In summary, after adjusting my original analysis for the existence of vampires in Wisconsin, the miss in the original State Navigate polls (+20 points of rHong) breaks down roughly as a result of…

1. 10 points from misspecified age weights

2. At least 5 points of late movement (5 is what we can detect with the recontact

3. About 5 points of non-response

Adjusting for the misspecified age composition gets you within the 13 percentage point average error for the margin between two candidates in primary polls. That would indicate the primary polls and Wisconsin were not any worse than average, after adjusting for what looks like maybe a simple mistake or extra extrapolation from Michigan on the part of State Navigate.

But you can eliminate most of the error in 1 and 3 by weighting your primary poll to a reasonable benchmark for ideological composition. We don’t know what the precise ideological composition of any group of voters is, so see Recommendation 3 in my original analysis. Time for some sensitivity analysis, baby!!

If I were to editorialize a bit, I would say 5 points of ideological non-response in a primary poll is really not bad, all things considered. Especially if it doesn’t impact general election polls or what we know about public opinion writ large.

That’s just to say there will be no standalone Friday column this week. Instead, I’m sharing some graphs that got left out of my Tuesday piece on the anti-system vs left-right divide within the Democratic Party right now.

These charts do a good bit of myth-busting about the role age is playing in the party’s ideological divide right now. Reading news coverage of the recent wins for Abdul El-Sayed in Michigan or DSA candidates in New York and Denver, you’d get the impression that only young Democrats support this new wing of the party. El-Sayed’s win in particular generated headlines crediting his young base for his victory. Look out, it’s a youthhhhhhquaaaaaaake!

But if you look at ratings for key left-leaning figures in our June Strength In Numbers/Verasight poll, age is not so predictive after all. While favorability rating is not the same as primary vote choice, it tells us a lot about the direction of the party and appetite for new, fresh, urgent thinking.

This is post is free to read, but if you want to support my independent data-driven political journalism and get premium posts like these sent to your inbox 2x a week, become a paying subscriber to Strength In Numbers today. Members also get early access to data products, a private community on Discord for our political data nerds, and an ad-free newsletter experience.

Upgrade to support Strength In Numbers

If a paid subscription is out of reach, the next best thing you can do is to share this article with a friend. Organic recommendation is the #1 driver of new readers here at Strength In Numbers.

Share

Older Democrats like Mamdani almost as much as young Democrats

According to our survey, older Democrats rate Zohran Mamdani at about 72 on the 0-100 “feeling thermometer” scale.

Young Democrats are at 76 — a difference the poll can’t distinguish from zero.

Young Democrats, however, are sharply more critical of their party. Mamdani significantly out-polls the party brand and its congressional leadership among young respondents — not because under-30s (a category I sadly recently passed out of) rate lefty Dems better, but because they rate the “establishment” worse:

And it’s not just Mamdani. Young Democrats rate the party’s other left-wing figures about the same as older Democrats do — but they rate the party’s current management much colder. Schumer (34) and Jeffries (44) are the only figures under-30 Democrats put below the midpoint of the scale.

Finally, a point about absolute differences. The age gradient is strong. But if you compare feeling thermometer ratings for Mamdani and AOC vs Schumer and Jeffries, not one group of Democratic voters prefers the leadership:

This may sound pedantic, but there is a big difference between “only young people like left candidates” and “young people are voting left because they are more likely to hate the Democratic Party.” Remember, among self-described moderate and conservative Democrats, Bernie Sanders rates higher than any other current party figure we tested — Obama excepted:

The generational split in the Democratic electorate isn’t really about who likes Mamdani or AOC — Democrats of most ages do, more or less. It’s about who’s fed up with the leaders currently running the party. And that’s an anti-system thing, as much as or more than it is an ideology thing.

If you get value out of data-driven coverage of polling and public opinion, the best way to support this work is with a paid subscription to this newsletter.

Subscribe now

Related Articles

Subscribe now

Here are the big takeaways:

• Hong’s miss is a 90th-percentile polling miss, not proof the industry is broken. The pre-election average had her up about 20 on David Crowley, and she lost by one. In the Senate and governor primaries Elliott and Mary Radcliffe analyzed going back a decade, the average poll misses the margin between the top two by 13 points, and misses of 20-plus happen about 10% of the time. What’s genuinely unusual is the direction: in all eight of the 20-point misses in the database, the polls underestimated the leader — Tammy Duckworth by 20 in the 2016 Illinois Senate primary, Sam Brown in Nevada in 2024 — and the previous record for overestimating an eventual loser was 19 points, in the 2024 Puerto Rico governor’s race. (Lamar Alexander in 2014 and Spencer Cox were both overestimated by 15-plus and won anyway.) Evergreen reminder: polling primaries is hard.

• We should judge candidates by their absolute performance, not performance relative to the polls. If nobody had polled Wisconsin, the only available take Tuesday night would have been “wow, that was close.” Instead, polls set up a prior for everyone that Hong would win big — so when she didn’t, the election gets cast as a “disappointment for the left.” David argues for rooting our grading of candidates by their actual performance. Elliott says you could also use the fundamentals. But be consistent: you can throw out the polls as junk and trash them when they’re wrong, but then you can’t say candidates “underperformed” based on polls.

• At a D+7 national environment, Wisconsin and Minnesota are at worst lean D, and maybe likely. With a generic ballot running D+7 to D+8, Minnesota a couple points bluer than the country and Wisconsin about a point redder, a win by under five points for either Democratic candidate would be a surprise. Especially considering in Wisconsin the Republican nominee for governor, Tom Tiffany, is a Freedom Caucus member, election denier who voted against recognizing Biden’s win days after January 6, one of only 18 House members to oppose the resolution condemning QAnon, and very far right on abortion. Democrats will spend heavily making all of that central to their campaign — while Trump has, at best, a 40-42% approval rating in the state. Maybe this is bluer than people think?

Thanks to everyone who joined us for the weekly live stream on Substack, and we’ll be back next week!

As a reminder, paid subscribers to Strength In Numbers get to participate in our live Q&A!

If you missed our video livestream, you can watch it by clicking play on the web version of this post at gelliottmorris.com.

You can also subscribe to us on your favorite podcast app to listen on your own time. And if you do listen via one of those apps, please drop us a five-star rating and review if you feel we’ve earned it — it really helps people discover the show!

If you value this work and want to help keep it going, please consider becoming a premium subscriber to Strength In Numbers. Paid subscriptions support this podcast, the newsletter, and the time it takes to do original data-driven analysis of politics and elections. Paying subscribers also gain the ability to send in questions during our live streams, so you can directly shape the conversations we’re having on the podcast.

Upgrade to support Strength In Numbers

Share

You can read the transcript of our conversation by clicking the headline of this article to take you to the web version of the podcast, then clicking the button just below the byline that looks either like a piece of paper or is labeled “Transcript,” like so:

If you’re a reader of Strength in Numbers and haven’t yet subscribed to David’s newsletter, head to the-downballot.com.

Subscribe to The Downballot

And if you’re coming from David’s site, subscribe here to get the numbers behind the news.

Subscribe to Strength In Numbers

For a follow-up on this follow-up, see this updated analysis: KILL THE VAMPIRES.

The average error of a Senate or governor primary election poll is about 13 points. This is because pollsters have fewer benchmarks for variables that correlate with vote choice in a primary, and because the race can change very quickly late.

As of now, no pollster who was in the field in Wisconsin has published a full retrospective of their numbers based on a reanalysis of their data. In this article, I’m going to do just that.

Last year, a non-profit called State Navigate contracted me to develop a flexible survey weighting program that they could use on any poll of a statewide or state legislative district primary or general election. This program let them adjust the data they got back in their polls to be representative of the population they were trying to sample.

I did not ask for money from State Navigate, but instead to have access to the raw data and to be able to analyze it publicly for posts like these. State Navigate has agreed to let me publish the analysis below. I did not share it with them beforehand. (For what it’s worth, State Navigate was the most accurate pollster in both the Virginia and New Jersey governor elections in 2025.)

Based on the analysis below, I conclude:

• 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.

I would usually paywall an analysis like this, but I am releasing it for the public so readers can have better-informed expectations for the accuracy of primary polls in the future.

If you learned something from this, please consider upgrading your subscription to a paid membership to make sure I can continue publishing analysis like this in the future. Paying readers make original data journalism like this possible.

Act now and get 25% off an annual subscription — before prices go up for the election season.

Upgrade to support Strength In Numbers

Three types of errors in Wisconsin polling:

State Navigate fielded two polls of the Wisconsin Democratic Primary in late July and the first week of August. The August survey was partially a recontact survey, where respondents from the July poll were asked their vote likelihood and candidate preference again.

State Navigate’s July poll had Francesca Hong at 44% and David Crowley at 15% — a 29-point lead. Their August poll, which finished interviewing on August 6, still had Hong up 44-22. (Five days later, Crowley won, 39.8% to 39.3%.)

A 22-point error on the final published margin is not a normal polling miss, even by the standards of primaries.

One thing I can rule out immediately: this was not a data-processing problem. When I take State Navigate’s raw files and re-weight them to the actual weighting targets they shared with me, I reproduce their published toplines almost exactly (Hong 44.0 / Crowley 23.3 in August, against a published 44 / 24). Instead, the problem is mostly with what the poll weighted to.

I have identified three big problems in this pollster’s data.

1. The actual primary electorate was older and more moderate than pollsters assumed

Chaz Nuttycombe, Executive Director of State Navigate, said the demographic benchmarks for his organization’s surveys were generated to be representative of state voters by predicted primary participation. First, he used the state’s voter file to compute demographic breakdowns for the race, education, region, race by region, age, gender, and registration status of participants in past Wisconsin primaries. Then he adjusted those targets based on recent primaries in 2026 and 2025. (This adjustment looks to have increased the percent of young voters in the sample — with 9% 18-29-year-olds in the poll vs 2% in the voter file.)

Other pollsters appear to have produced their primary polls in a two-stage process. First, samples were adjusted by known demographic factors to be representative of the total pool of Wisconsin registered voters. Then, respondents were given the chance to self-select into either voting in the Democratic or Republican primary (or not at all). This means the accuracy of the polls was dependent on getting a representative sample by which party’s primary voters were opting into. It looks like the voters pollsters got, however, were too young and too liberal.

It looks like both of these processes failed to anticipate an older, more moderate primary electorate. Or, at the least, weighting to a slightly older benchmark would have improved performance.

The chart below shows the August State Navigate poll under four different sets of weights, as well as the unweighted breakdown of support for each candidate and the unofficial results of the election:

Two things jump out. First, the poll’s published weights are nearly indistinguishable from applying no weights at all. That’s not because State Navigate failed to hit their targets, but because their sample already looked like their targets on age, gender, race, education, and region — so the weights had almost nothing to do.

The problem is that the targets from State Navigate themselves described a much younger electorate than the one that shows up to a low-turnout August primary — which looks like the small, old, habitual-voting slice of the state’s registered voters.

To check the impact of weighting decisions, I pulled two different pictures of the Wisconsin electorate from the L2 voter file. One universe consisted of of Democrats and nonpartisans who have voted in at least 11% of the even-year primaries they were eligible for. The other was built by taking the L2 list of active voters who cast a ballot in at least one Democratic primary since 2016. The two universes agree with each other almost exactly, which should give us some confidence in the targets being used. Among voters with a known age, both say the primary electorate is about 52-53% seniors and 2-3% under 30.

The poll samples were nowhere near that. The July sample was 36% seniors and 17% under 30 — roughly half as old and eight times as young as the electorate it was trying to measure.

When I re-weight the polls to the L2 universes (weighting by party, age, race, sex and education, all from the voter file), the August margin error drops from 24 points to about 14. These results are shown in the table below as L2A and L2B for the two universes.

Pollster performance may have been improved further by weighting on ideology. State Navigate’s published toplines showed 17% of respondents identified as Democratic Socialists, for example, and 43% additionally called themselves Progressive, Very Progressive, or Extremely Progressive:

Even after pinning age, race, sex, education, and party to the voter file via our L2A or L2B weights, the weighted sample still self-identifies as about 38% “very liberal.” I don’t have a gold-standard benchmark for what that number should be (nobody does, which is the whole problem) but when I pin it to the ideological profile of 2016 primary voters (25% very liberal — per an exit poll, so your mileage may vary), the error on vote margin collapses.

Of course, official population benchmarks for ideology do not exist, so weighting by ideology adds some researcher degrees of freedom into the pipeline. But if one does not want to weight final estimates by ideology, they could create a set of several different poll toplines assuming different ideological makeups from past exits, primaries, etc. This at least gives readers something to bite into in understanding how assumptions in the poll impact accuracy. But it is understandable that a pollster would not want to be perceived as putting their thumb on the scale and weighting a survey on an attitudinal variable without a population benchmark.

Adding targets for income brings the error to about 3 points. The recontact portion of the August sample under that final weighting scheme actually had Crowley ahead.

Share

2. There was a lot of late movement toward Crowley

Because part of the August State Navigate poll was a recontact survey, I calculated how voters interviewed in July who said they were undecided, or supported a candidate that then dropped out, said they were voting in August. I estimate Crowley was gaining about two-thirds of a point a day in the final weeks of the race — a number that could’ve been higher on the final election weekend, and likely tipped the scale.

The recontact design is frankly the coolest thing about what State Navigate did in Wisconsin this year. The August poll re-interviewed 633 of the July respondents, which I can match across polls person-by-person using a standardized ID from the voter file. That lets us watch individual voters change their minds — which they did.

Among 633 recontacts, the Hong-Crowley margin fell about 7 points in ten days, from +33.6 in State Navigate’s July interviews to +27.1 in their August ones. And that was still five days before voting day.

Where was Crowley’s growth coming from? Basically everywhere. Mandela Barnes withdrew between the two waves, and his voters in the panel went to Crowley 25% to 19% for Hong (another 14% went to Kelda Roys). Undecideds who made up their minds broke 40% Crowley, 40% other candidates, and just 20% Hong. And nearly half of them were still undecided on August 6 — providing for a mass of anti-Hong votes to flow Crowley’s way by Tuesday, August 11.

If you remove undecided voters from the survey, you can see this consolidation at work. Hong’s weighted share was within about 3 points of her actual result. Crowley’s was 6 short — and Kelda Roys was 6.6 too high and Joel Brennan 4.1 too high. The moderate lane hadn’t finished consolidating when the poll stopped calling. It finished on Election Day.

Because we have the polling microdata, we can go one step further: What if we take all the undecideds in the August poll, and then allocate them to remaining candidates based on the vote flows of respondents who were undecided in July but had decided by August?

Warning: small sample size! Among the 633 recontacted panelists, 40 were undecided in July but had picked a candidate by August. Those respondents broke 40% for Crowley, 20% for Hong, 20% for Roys, 17.5% for Brennan, and one respondent for Hughes. Apply that same break to the 10% of the weighted August poll that was still undecided, and the race becomes a dead heat: Hong 34.4, Crowley 34.3 — a 0.1-point margin, against an actual margin of 0.5. In other words, the L2C weights plus a realistic undecided allocation reproduce the closeness of the race almost exactly.

Yet even after allocating undecideds, Both frontrunners remain about 5 points below their actual vote shares, because Roys (+7) and Brennan (+5) are still holding a lot of votes that flowed away in the final days.

3. There is mixed evidence of higher survey non-response among more moderate Democrats.

In 2016 and 2024, polls overestimated support for Democrats even if a pollster accounted for party, past vote, demographics, and geography in their samples. That is, the types of Democrats and Republicans answering polls were unrepresentative of those who turned out on election day. Or, put the other way, the Americans who refused to answer polls were more pro-Trump, conditional on every demographic and political factor, than respondents to polls.

The evidence below suggests non-responders to primary polls were pro-Hong by about 2 points on vote margin vs Crowley. (That’s not so bad for a primary.)

There are a few different ways to try to measure non-response. To start with the easier one: First, I took the August poll with State Navigate’s weights and added one more weighting variable: the election result itself. In effect, I forced the poll to “predict” the outcome perfectly, and then looked at what that did to its demographics.

Not much changed. Seniors rose from 43% to 46% of the weighted sample; self-described “very liberal” voters fell from 41% to 36%.

This suggests the non-response (and late movement) favoring Hong was within demographic cells. If the poll’s error had mostly come from interviewing the wrong demographic mix, forcing the poll to match the results of the race would have reshaped the sample dramatically. Instead, matching the result required upweighting Crowley voters within every demographic group. The older and more moderate voters who did take the poll were still more Hong-friendly than the older and more moderate voters who actually showed up.

Second, we can look at the recontacts and see if Hong’s supporters were more likely to answer the poll.

Among July’s 1,284 unique respondents, 51.2% of Hong’s supporters completed an interview in the August recontact — as did 51.2% of Crowley’s. No evidence here of Hong supporters being more likely to take surveys, at least IF you already condition on taking the first survey.

But that’s a really big if! Even if Crowley supporters were just as likely as Hong supporters to respond to a poll a second time, that doesn’t mean they were just as likely to get on the horn the first time.

For that, we need a much more sophisticated analytical strategy. (Alert, it’s about to get really nerdy.)

3b. Detecting potential non-response bias ahead of time

And thus enters the “measure of unadjusted bias for proportions,” or MUBP. In a 2019 paper published in the Journal of the Royal Statistical Society, Rebecca Andridge, Brady T. West, Rod Little and co-authors propose a method for (1) combining everything observable about a respondent — demographic, geographic, and political traits — into a single score and measuring how well it predicts the survey outcome (in this case, a vote for Hong, Crowley, etc.), and (2) projecting what the true electorate-wide number would be if people’s decision to take the poll depended not just on those observable traits, but partly on their vote itself (eg, Hong voters being more eager to answer a poll about the race). Dialing that assumption up from “not at all” to “entirely” traces out the range of results different amounts of non-response bias could produce.

The MUBP, in other words, is a stress test for non-response bias. It works in four steps:

First, I take everything we record in the poll about each respondent — their age, race, education, income, party, ideology, region, how likely they say they are to vote etc — and boil that down to one number: a score that predicts how likely they are to vote for Francesca Hong v David Crowley. This score is produced by a statistical model called a regularized elastic-net regression, which is trained in such a way as to not overfit the data. In our poll, this score is strongly (but not perfectly!) predictive — it correlates with actual vote choice at about 0.6 — meaning most but not all of what makes someone a Hong voter is visible to a pollster.

Second, we take the model that generates those scores for respondents and score the electorate we believe will show up on Election Day. Then, we can measure the difference between the score for the electorate and the survey. That gap tells us how much the poll over-recruited Hong-looking people — similar to the same exact calculations you could make by, e.g., raking weights to population marginals:

• Ignoring undecideds, respondents were 49.2% Hong (unweighted) in the August poll. Projecting the predictive model for Hong Score onto the L2C electorate, we get an implied 38.8% — so the over-recruitment of Hong-looking people is worth ~10.4 points of her share even in the most weighting-friendly reading.

• This is roughly the same correction the weights themselves deliver: the L2C weights put Hong at 36% of decided voters (32.3% of all respondents — see the first chart of this post). The small difference is model-versus-crosstab bookkeeping.

So far, we have just rediscovered survey weighting. But now we do something really cool: we can calculate what the gap between the electorate and poll says about non-responders. That depends on an assumption no one can verify, which makes this a simulation analysis or sensitivity test, rather than a prediction. These assumptions are:

• Data is missing at random: Your weighting model has adjusted for all the things that might influence response, so you’re in the clear! Don’t worry about non-response bias!

• Data is missing not at random: There is some variable that is exerting a Jupiter-scale gravitational force on selection into taking your poll. If there is unexplained variance between survey response and the Vote Score you give to your respondents, be scared.

• Everything in between 0% MNAR and 100% MNAR.

Imagine, to editorialize a bit, a big dial from “Your survey is good” to “You might be totally screwed.” At one end of the dial, people answered the poll because of visible traits that flow into the Hong/Crowley/Etc Score — young, very liberal people just answer more surveys. If that’s the story, the missing voters differ from respondents only as much as the score predicts, and good weighting can fix everything.

At the other end, people answered because of how they planned to vote — the score is only a proxy for the real differences between voters, and the missing voters are far more different than they look, and no weighting can fix it. Each point on the dial thus implies a different “true” result of a survey if you had a better proxy for selection into the sample. Sweeping the dial from left to right generates a curve of possible poll results.

That’s basically what’s happening in the chart below. Against the voter-file electorate with ideology in the weighting scheme, Hong’s curve crosses the result right at the left, non-problematic end of the non-response dial: her overstatement was ordinary, observable selection, no spooky mechanism required. That’s the dark blue line. If you use different weights, however (the other dashed and dotted blue lines), you have to assume some amount of spooky uncaptured non-response force increasing Hong voters’ likelihood of taking a survey in order to get her estimated vote share back down to what it actually was (39.3%). And under State Navigate’s own targets (the dotted line), even the maximum amount of spookiness doesn’t get her there: because their weights barely moved the sample, the curve sits at about 49% no matter what you assume about non-response.

But in terms of relative probability, it does look like Hong voters were more likely to answer the poll. Crowley’s lines (orange) only reach his actual vote share about halfway up the “you’re screwed” dial — and that’s under the friendliest electorate, the L2C targets. Under the L2B and State Navigate targets, his curve never gets there at all.

This table (for the nerds) tells us what level of non-ignorable selection bias we have to assume (column 3) to explain the misfire in the polls, after controlling for demographics. A phi (the dial) of 1 is the worst-case scenario. The authors say a phi of 0.5 is a good guess for unobserved bias in your data. You have to use a phi of 0.7 to recreate Hong’s actual vote share using the data — which is not great but not terrible.

But before you get carried away, there are two caveats to the MUBP approach. First is that (because it’s just using poll results) it doesn’t take into account late movement in the election. The method is a snapshot, so it can’t distinguish “people wouldn’t answer” from “people changed their minds after the poll ended.” Second, the amount of non-response needed to explain the election result also depends entirely on what you think the electorate will look like (which is exactly why I ran it three different ways).

Using only the weights with only demographic and political variables (L2A — eg, no ideology or income in the targets), my best guess from the MUBP is that non-response was increasing support for Hong by something around 0 to 5 points, depending on whether a pollster weighted by ideology. Graded against the actual results, that explains about a tenth of the gap between the polls and the election result (the unweighted August sample had Hong up 23.8 points among decided voters; she lost by half a point). The rest was a combination of late movement and (fixable) weighting problems.

That last finding is why I think this method belongs in pollsters’ regular toolkits. You can run it before publication to assess the potential error in your poll that could be attributed to non-response bias for a candidate, as proxied by the correlation between the variables in your weighting model and the survey response.

Share

The bottom line

It would be easy to read all this as an indictment of one pollster. I don’t think that’s the right reading. State Navigate hit their weighting targets, published their demographics, ran a genuinely valuable recontact design, and — to their great credit — handed over their raw data and let me publish whatever I found. Very few pollsters would do that.

The right reading of this is that primary polling is hard in ways that general-election polling is not. It is reassuring that most of the 20-point miss by the polls came from two problems that are either (1) fixable (weighting model) or (2) unavoidable in primaries (late movement), rather than, e.g., pervasive non-response by moderates.

The first problem is fixable with accurate voter-file targets (and bias is further reduced with some handy guesswork about ideology and income). The second is fixable by fielding later — and by everyone, poll-readers included, treating a primary poll as a snapshot with a very short shelf life.

But don’t conclude the data is useless: re-weighted with corrected targets, these polls missed by about as much as the average primary poll. And the smart recontact sample also helped indicate late movement that should have prompted readers to treat the poll as less precise in the final days of the campaign.

Recommendations

Based on all of this, I would give pollsters four recommendations:

1. Weight primary polls to voter-file data on primary voters — not to an all-registered-voters frame with a screen on top, or to targets derived from other states or sources. On State Navigate’s end, adjusting weights is common practice, but appears to have driven additional error in this election (maybe by coincidence). That is an unfortunate error.

   But many pollsters also used demographic benchmarks that made the poll sample representative of Wisconsin’s registered voters generally, and then trusted respondents to self-select into the Democratic primary at random. The sample likely skewed young, ideological, and hyper-engaged — over-representing the Hong coalition. But weighting data to targets from the L2 voter file largely fixes the observed error. Weighting choices alone were worth roughly 10 points of margin here.

2. Field into the final weekend. When the race is moving quickly, being in the field as late as possible can generate more accurate data.

3. Assess sensitivity to ideology targets — if they impact results, increase uncertainty. The results of this analysis also hint at an oversampling of progressive Wisconsinites. But because there is no official population benchmark for political ideology to weight to, pollsters don’t use it (in one specification above I use the ideological makeup of 2016 primary voters, which helps). But that doesn’t mean pollsters can’t use the variable at all; a sensitivity analysis can be done without a hard target. I find that adding ideology to the weighting scheme moved the August margin about 11 points even after weighting by demographics and political party. This can be a clue to pollsters that their results are susceptible to error from the ideological makeup of their survey. One possibility is to produce multiple estimates with various ideological benchmarks.

4. Run a non-response analysis as a routine diagnostic. Above, I also apply a newer method from academic survey research — called the “MUBP,” a diagnostic for nonresponse bias — that lets a pollster stress-test a poll for non-response before publishing it. The idea is simple (the execution is not). First, combine everything you can observe about each respondent — age, education, party, ideology, and so on — into a single score that predicts how they’ll vote. In this data, that score is strongly but not perfectly predictive (it correlates with vote choice at about 0.6): most of “who you’ll vote for” is visible in traits a pollster can see. Second, use the same score to compare the poll’s respondents against the electorate you believe will show up. If the weighted poll still looks different from that electorate on the score, there is bias left over that better weighting could fix. You can also adjust how a candidate’s response score might be correlated with taking the survey at all, which allows the pollster to simulate different poll results given different levels of non-response. If the result of the poll changes under high non-response (because your support score does not fully capture support for a candidate), that’s another opportunity for exploration — and explanation to the public.

Thanks to Chaz at State Navigate for sharing his data.

In the end, the polls in Wisconsin don’t look so bad — or at least, no worse than the average primary poll in a race with a lot of movement in the final days.

Strength In Numbers is a reader-supported publication. If you read this far, you will definitely get a lot out of the additional exclusive posts each week, and our private community caht on Discord.

Upgrade to support Strength In Numbers

Footnotes

The Wisconsin voter file is notorious for having a lot of missing and incorrect data. One of the variables that is particularly errant is the age variable.

In their methodology report State Navigate wrote:

“many voters in Wisconsin have a birth year of 1800, and we don’t believe in vampires. These generally are newer voters who skew very young; about half of voters who we polled with a birth year of 1800 say they are in the 18-29 age bracket. Thus, if you were to simply take the voter file’s birth year data, you’ll get a Democratic primary electorate that is majority 65+ and has less than 2% of voters under the age of 30. Absentee requests for the Wisconsin primary (which includes requests for Republican ballots) are 5-6% 18-34. Given that this age group has been notorious for voting late/on Election Day, we find it hard to imagine a Wisconsin Democratic primary electorate that is <8% 18-29. Maybe it is; we don’t know for sure due to the ridiculousness that is the Wisconsin voter file”

Well, I am more than a little wary of using polling data that had too many responses from young people to make any sort of predictions about what age numbers would be on the voter file, so is another opportunity for sensitivity analysis.

In the plot below, I re-raked the August survey with the under-30 target set by increasingly aggressive assumptions about how old the “vampires” are — from my baseline a priori estimate (unknown ages allocated in proportion to the known ages) up through State Navigate’s estimate (half of unknowns under 30) and past it (70%) — under both the L2C weights and the ideology-free L2B weights.

Notably, stepping up the share of voters who were <30 increases error linearly. Under the L2C weights (which account for ideology and income), the margin goes from Hong +2.4 at my baseline, to Hong +4.9 using State Navigate’s stated floor of an 8%-under-30 electorate, to Hong +6.6 if you assume half of unknowns are under-30, and Hong +8.4 at 70%.

Under the L2B weights (which don’t have ideology in the targets) the same exercise runs from Hong +13.8 up to Hong +19.7, nearly all the way back to the unweighted poll’s +23.8. The reason is simple: the young people who actually answered this survey were overwhelmingly Hong voters (likely too pro-Hong, as the non-response analysis suggests), so every additional young voter you add to the electorate model pushes the poll further from the result.

Notably, the adjusted weighted estimates I present — even at a 7-10% under-30 percentage — are still much closer to the result than the original weighted or unweighted numbers.

Whatever the true age of Wisconsin’s vampires, the topline takeaways from the analysis stands: the polls had too many progressive in them, and a large amount of late movement toward Crowley.

I also don’t see why pollsters couldn’t present sensitivity analyses like this in their reports. It would add a lot of context for consumers of their estimates.

But graded against the polls, Hong disappointed. Our average of polls at FiftyPlusOne had Hong 20 points ahead of the competition Tuesday morning, with about 20% of the electorate left undecided:

The difference between expectations and reality prompted the New York Times to call Hong’s defeat versus challenger David Crowley a “stunning loss” for the left:

So the polls are causing not just high expectations for the left, but also journalists to react to world events in weird ways — grading candidates by their performance relative to the polls, not their performance in absolute terms.

This is the second time this year that polls have overestimated support for a left-leaning candidate in a marquee race. Since the rules of punditry dictate that two is a pattern, Tom Nichols, a conservative writer for The Atlantic, raises the possibility of uniform polling bias in progressives’ favor:

For what it’s worth, this is silly on its face; the same night polls overestimated Hong, they underestimated support for progressive candidate Peggy Flanagan in Minnesota by the same amount:

But Nichols isn’t totally off base. The polls in both Wisconsin and Minnesota last night performed worse than the average primary poll.

Are primary polls just useless? Are all polls so bad that you should just ignore them? Let’s take a quick look at the data.

I would usually paywall an analysis like this, but I am releasing it for the public so readers can have better-informed expectations for the accuracy of primary polls in the future.

If you learned something from this, please consider upgrading your subscription to a paid membership to make sure I can continue publishing analysis like this in the future. Paying readers make original data journalism like this possible.

Upgrade to support Strength In Numbers

Primary election polls are off by 13 points on average — but that number hides some huge misfires

Earlier this year I wrote up a historical study of the accuracy of primary polls that I conducted with my colleague Mary Radcliffe at FiftyPlusOne. We concluded then that primary election polls for Senate and Governor elections in 2014, 2016, and 2024 (years where we had the most complete data) had an expected error of about 13 points on average in the difference between the margin for the top two candidates:

On the margin between the top two candidates — which is what we really care about in plurality races, like the Democratic primary in Texas — the numbers are even worse. The expected error on the margin between two primary candidates in a race for governor or U.S. Senate is a whopping 13.0 points. And in our historical data, polls picked the wrong winner for competitive primary races (those where the margin between candidates in polls was less than 10 points) in 2 out of every 10 elections. So both Crockett’s 3-point edge and Paxton’s 3-point advantage are well within the historical predictive margin of error for Senate primary polls.

But the average also obscures some truly enormous misses. In the crowded 2024 Nevada Republican Senate primary, polls had Sam Brown up 18 points on average over the last 3 weeks of the race; he won by 45. In the 2014 Iowa Republican Senate primary, Joni Ernst’s polling lead over her closest competitor was 16 points; she won by 38. And the 2024 Ohio Republican Senate primary polls showed a near-tie between Bernie Moreno and Matt Dolan; Moreno won by 18.

The 2024 cycle was also the least accurate of the three we studied, with an expected error of 16.1 percentage points on the margin of victory between the candidates polling in the top two. The 2014 cycle was the most accurate, at 10.8 points. Neither of these numbers should make you confident in a 3-point lead.

We made the following graph of polling averages for key primaries. Note that on average, polls are right in primaries; they just have a huge spread:

But this chart really doesn’t give you a good look at how off primary polls can be. The following histogram does, showing absolute errors in some cases of more than 30 points!

Running the math with this data, a primary candidate that is up by 10 points has a corresponding win probability in their race of only 70%. (In contrast, in general elections, that number would be well above 95%.)

A related question is how often polls miss by as much as they’ve been missing recently. Here’s a table showing the probability of primary election polls being X points off:

About 10% of the time, primary polls miss the winner’s margin of victory by about 20 points.

(All the data here is from FiftyPlusOne.)

Based on this data, I would recommend taking primary polls seriously but not literally. To put a finer point on that statement: The expectation is that primary polls will be off by about 13 points on average in the difference between the margin for the top two candidates. You should expect them to err, not to provide perfect predictions.

Share

Polling primaries is just really hard

Primary election polls are not general election polls.

As Mary and I wrote:

Part of covering public opinion polls is knowing when they are useful and when they are not. While polls give us good predictions of horse-race contests (especially presidential elections) on average, they are less predictive in primary elections.

This is in part because electoral dynamics can change quickly in primaries (candidates can drop out and endorse others), but also because primary polls are simply harder to conduct. [...] In addition, primary polls tend to have significantly more undecided respondents than general election polls. This all adds up to a lot of uncertainty about the polls ahead [in primary raced]

(Emphasis is mine.)

The primary (hah) problem with primary polls is that pollsters have fewer variables they can use to make sure their polls are representative of the electorate. The response rate to the average poll today is well below 1%, forcing pollsters to make statistical adjustments to their data to try to make it representative of the electorate. This usually means comparing the demographic makeup of a poll to the Census data in a region and giving more weight to the groups that are underrepresented in a sample, and less weight to those that are overrepresented.

These adjustments work well in a general election where the variables pollsters have are highly predictive of election outcomes. Things like race, education, income, past vote, and party identification are very good proxies for how someone will vote in an election between a Democrat and a Republican.

But, as Mary and I wrote, there are fewer highly predictive variables available to weight on in primary elections, where candidate support is more correlated with non-demographic factors like ideology, issue prioritization, beliefs, and electability, and so on.

As the quote shows, other hurdles exist, too. First, it is harder to predict who will turn out to vote in a primary. The population pollsters are trying to represent — people who turn out to vote on Election Day — is an ephemeral group, coming into existence in an instant, not able to be polled precisely ahead of or after the contest. Second, the electorate’s preferences are also formed later and based more on things like media coverage and advertisements — both of which can cause a lot of late change in the race (see: 2026 MI-Sen Democratic primary).

Measurement of preference in a primary election, in summary, is just really, really hard. Expect error, not precision.

Share

So, back to Wisconsin. Was Tuesday night a “stunning loss” for the left?

Only if your baseline was that the polls would be perfect. As this analysis shows, that is not a well-calibrated expectation.

With a proper model of primary polling data, losing while being somewhere at +10 to +20 in polls is somewhere between a 1-in-10 and 1-in-30. That’s rare, but not unthinkable. And it looks like polls actually got Hong’s support pretty close, they just underestimated how well her opponent would do — in an environment with lots of undecided voters and dropouts for other candidates that came very late in the race. This is an argument for even more caution.

The practical takeaway for the rest of the 2026 primary calendar is:

1. Expect primary polls to be off by 13 points on average. This will help you mentally prepare for misfires. A 10-point lead is roughly a 3-in-4 favorite, not a lock.

2. Expect a blowout sometimes. With misses of 20+ points striking about once in every ten races, and a dozen contested primaries left this cycle, at least one more “shocking” result is the expected outcome, not a crisis for polling.

3. Don’t build theories of ideological bias on two data points. Historically, left-leaning candidates do no better versus the polls than centrists or right-leaning candidates. As we saw on Tuesday night, the tails are fat in both directions: Polls in Wisconsin overestimated a progressive by 20 points while polls in Minnesota underestimated one by the same amount. People writing about polling error with a sample size of n=2 are doing punditry, not statistics.

Moreover, I would argue that the failure of the conventional wisdom to see a Hong loss on Tuesday as possible is both a problem of the polls and of the media’s expectations for them. This is the central argument I made in my book, Strength in Numbers: How Polls Work and Why We Need Them: polls are indispensable tools for hearing what the public actually wants, especially between elections, and they can only fulfill that duty if we treat them with the amount of uncertainty they deserve. Attaching false precision to electoral projections sets polls up for failure, which prevents the people from using them to hold leaders accountable when they run afoul of public opinion.

Primary polls are really hard to get right. As such, they come with large margins of error — even when combined. Don’t expect perfection, and you won’t be disappointed.

Share

These are the so-called “anti-system” voters I have written about before. The general gist is that there’s a group of voters out there who have very weak ideological attachments but very strong anti-system orientation. Donald Trump used this group to win the presidential elections of 2016 and 2024.

After my column last Friday, my friend Alexander, a political science PhD, texted me to say he thinks this anti-system thing “could be big and a distinct overlooked factor.” But he wondered how much of it could have just been fervent opposition to the Trump administration and to current Republican policies. That wouldn’t be a standing disposition about how American democracy works (or doesn’t) for a person, so much as their reaction to the particular person in office today.

Alexander asked me to produce two extra figures on this. First, he wanted me to look at how anti-system attitudes today predicted attitudes toward Republican politicians and political figures — do they predict support for, or opposition to, Trump? And second, to figure out if anti-system Democrats are gravitating toward lefty Democrats just because they dislike Trump — or if, as the broader theory suggests, anti-system voters are drawn to anti-system candidates regardless of their ideology or orientation to Trump.

In this week’s Deep Dive, I answer both of these questions. Then, below the paywall, I introduce a new way of looking at the electorate that combines both ideology and anti-system orientation, using the following grid chart. (The chart axis and title are intentionally clipped to keep you guessing!)

But let’s take it one question at a time. Is anti-system sentiment, in fact, distinct from anti-Trump sentiment? Let’s dive in.

Strength In Numbers is a reader-funded small business supported by paying members. Subscribers get perks including premium articles like this Deep Dive, can access our private community Discord server, and support the data collection and analysis work that powers my independent, data-driven journalism.

If you want political analysis that’s honest and data-driven, not beholden to publishers’ ideological agendas or donations from corporate Super PACs, upgrade to a paid subscription.

Upgrade to support Strength In Numbers

“Anti-system” does not just mean “anti-Trump”

First, I will acknowledge that anti-system sentiment is strongly correlated with a person’s feeling toward Donald Trump (in this case, a feeling-thermometer rating from 0 to 100). Among all adults, the correlation between the 3-way “direction of country” question and Trump’s feeling thermometer is −0.68, and it holds at −0.45 among Democrats and leaners, −0.48 among Republicans and −0.52 among pure independents.

The more anti-Trump someone is, the more anti-system they tend to be — and vice versa. That holds for Democrats and Republicans.

But note, a correlation of -0.48 and -0.68 is really not that strong. There are a lot of people out there who are both anti-system and pro-Trump, or anti-Trump and pro-system (a much smaller group, to be sure). This presents an opportunity for persuasion for anti-system candidates.

Another hint that the anti-system variable is picking up a real concept, not just anti-Trump sentiment, is that anti-system voters tend to view Trump and other “in-system” figures such as JD Vance and Elon Musk much more negatively than anti-system Republicans, like Tucker Carlson.

Among Republicans and Republican-leaning independents, the ones who say the country needs “major, disruptive changes” rate Trump 37 points colder than other Republicans do, Vance 34 colder and Musk 29 colder:

In contrast, Tucker Carlson comes in at a 6.3-point gap between pro-system and anti-system Republicans. The former Fox News host has spent the better half of 2026 criticizing the president for his war against Iran, and has distanced himself from much of the MAGA movement.

Of course, this does not give us 100% statistical confidence about which way the causal arrow between anti-Trump and anti-system is pointing, but we can at least be reasonably confident in the separateness of the anti-system axis. This is consistent with the academic literature on the subject.

Subscribe now

“Anti-system” does not just mean “progressive”

We can tease the two constructs here — anti-Trump and anti-system — apart a bit more using a regression analysis. For each Democratic political figure in the original survey, I modeled a respondent’s 0–100 “feeling thermometer” rating as a function of their age, educational attainment, race/ethnicity, income, sex, ideology, anti-system sentiment, and Trump approval. Crucially, the last three variables are entered into the same regression, so that we can distinguish the effects of being anti-system relative to a person’s evaluation of Trump.

This graph shows the results. Points to the left indicate the variable (if blue, anti-system — red, Trump approval) is predictive of viewing the given figure/party more favorably. So, for example, controlling for all key demographic and political factors, anti-system Democrats view Zohran Mamdani about 15 points more favorably than those who say the current system is doing well or merely poor (but not bad enough to need “major, disruptive changes.”)

There are things worth pointing out here. First, both anti-system sentiment and anti-Trump sentiment are independently predictive of viewing lefty Democrats favorably — and, of viewing them more favorably than centrist/party figures. Senate Minority Leader Chuck Schumer is, as ever, viewed particularly negatively among Democrats, and particularly negatively by anti-system Democrats.

Second, look at Obama’s two points. Wanting the system torn up moves his rating up by just 3.7 points, which the model cannot distinguish from zero — but disapproving of Trump moves it up by 24.2 points. Obama’s is the largest Trump coefficient of anyone in the chart. This is because both pro-system and anti-system Democrats love Obama.

Compare Obama’s dots to Mamdani’s, where anti-system sentiment moves the rating 15.6 points with Trump approval and ideology both held fixed; or to Alexandria Ocasio-Cortez’s, at 14.8, or Bernie Sanders’ +13.4

Per this data, being anti-system appears to be a distinct psychological construct from ideology or partisan loyalty. And it is predictive of certain downstream traits — like voting for Democratic Socialist candidates — that are being hotly debated today.

Of course, we can’t know for now whether being anti-system would still predict voting for Zohran Mamdani in a year with a Democratic president. If you asked this during a Democratic presidency, would the same pattern appear, or is this anti-system picking up something additionally related to views about the current party in charge? What if it’s just measuring intensity of opposition to Trump, for example?

We can’t know until Democrats control the presidency again. But if the item only tracked opposition to people in charge today, it should predict support for any anti-Trump Democrat — Barack Obama very much included. But it doesn’t. It also shouldn’t separate the lefty “insurgent” Democrats from more “establishment” Democrats, since both of them oppose Trump — and yet it separates them by better than eighteen points, after controlling for potential confounders.

To understand this even more, I devote the remaining half of this post to dividing the electorate by (1) anti-system sentiment and (2) ideological self-identification.

Subscribe now

Left-wing Democrats are winning over moderates and conservatives who oppose the system

Back in 2017, the political scientist Lee Drutman made the following graph of U.S. voters using survey data from the Voter Study Group. This chart places every person that surveyed onto a 2-dimensional grid based on the x-axis, their “economic” ideology, and, on the y-axis, their “social” ideology. Then, each person is given a color depending on how they voted:

This is one of those charts you see everywhere now. In many ways, it made the idea of “multi-dimensional” political ideology go mainstream.

I wanted a similar chart, but one that sorted Americans not into Trump vs Clinton voters, but based on whether a person feels more favorably toward lefty candidates or the Democratic Party — and one where the vertical axis isn’t “social ideology,” but anti-system orientation. With such a chart, we would be able to distinguish whether/how much the appeal of figures such as Mamdani and Ocasio-Cortez is ideological vs rooted in anti-system orientation.

The chart below puts every adult on a 2-D grid with self-reported ideology across the bottom, and the anti-system “direction of country” question up the left side. Each person then gets a color from pink to blue representing how warmly they feel toward Mamdani, Ocasio-Cortez and Sanders vs Chuck Schumer, Hakeem Jeffries and the Democratic Party itself — the party’s congressional leadership and its own brand.

Here’s that chart:

The rest of this Deep Dive is for paying subscribers. I write at least one post a week reserved for the members of this community who keep Strength In Numbers running. Upgrade to continue reading and support the time and data collection it takes to do analyses like this one.

Read more

This is a wonky episode, but one with profound implications for anyone who cares about what the public wants and how — or if — the government responds to that.

Share

Here are the big takeaways:

• One of the most famous results in modern political science looks like a statistical fluke. From 2004-2014, Martín Gilens and Ben Page took thousands of opinion polls and broke down preferences for public policy among rich, middle-income and poor Americans. Then they identified policies where the rich and poor disagreed, and calculated how how often either group got what it wanted. The problem is that middle and the affluent agreed about 94 percent of the time — so Gilens and Page set aside every policy where the two groups weren’t at least 10 points apart, which meant throwing out 80 to 90 percent of 2,000 policies. The way the scholars analyzed the remaining subset produced a statistical paradox called “Simpson’s Paradox,” which obscured an underlying relationship of responsiveness to the poor even when the rich wanted something different.

• The paper looks at income differences among the wrong groups. One misfire of Gilens’ approach is it pits the top 20% of earners ($150k) against the bottom. But, e.g., two public school teachers in Bakersfield make enough to land in the top 10 percent of American households — a comfortable income, but not the world of private fundraisers and super PACs that influence politics. Page and Bartels once ran a special survey of the genuinely wealthy precisely because a normal 1,000-person poll turns up no multimillionaires at all, and using it Enns shows that the top 10 percent’s opinions track the middle class far more closely than they track the very rich. But they didn’t look at policy responsiveness.

• The system responds to the public as a whole. When Enns measures how policy tracks overall public opinion, while accounting for the influence of organized interest groups, he finds Congress does respond to what Americans want, and at least as strongly as it responds to those groups. A separate 2017 reanalysis of the Gilens data by Branham, Soroka and Wlezien reached a similar place, finding something close to a coin flip when the middle class and the rich disagree. The unexciting answer may be the correct one: policy does loosely follow public opinion, and most groups (excluding the super-rich) want the same things.

• This matters because telling people their government ignores them, when it doesn’t, discourages participation. Enns concludes by saying Americans agree with each other far more than they realize, that policy does follow them when they do agree, and that the right reaction to seeing unrepresentative slices of the electorate or society get their way is to get more involved, not less.

For more of the methods details, see this blog post from Peter about his new paper, and the study link here.

If you value this work and want to help keep it going, please consider becoming a premium subscriber to Strength In Numbers. Paid subscriptions support this podcast, the newsletter, and the time it takes to do this kind of data-driven analysis of politics and elections. Paying subscribers also gain the ability to send in questions during our live streams, so you can directly shape the conversations we’re having on the podcast.

Upgrade to support Strength In Numbers

You can also subscribe to this podcast on your favorite podcast app to listen on your own time. And if you do listen via one of those apps, please drop us a five-star rating and review if you feel we’ve earned it — it really helps people discover the show!

A reminder that paid subscribers to Strength In Numbers get to participate in our live Q&A!

You can also read the transcript of our conversation by clicking the headline of this article to take you to the web version of the podcast, then clicking the button just below the byline that looks either like a piece of paper or is labeled “Transcript,” like so:

Subscribe to Strength In Numbers

Upgrade to support Strength In Numbers

Dear readers,

Before the roundup, I wanted to share two long interviews from this week that you may be interested in. First, a conversation with Paul Krugman about the midterms, in which I argue that people are updating their priors about November far too slowly. It looks like a big blue wave. A House majority bigger than the one Democrats won in 2018 is in reach, and full control of Congress is just past even odds.

Paul Krugman

Talking With G. Elliott Morris About the Midterms

For all my interviews and more, subscribe on YouTube…

Listen now

a month ago · 1111 likes · 168 comments · Paul Krugman

Second, I was back on WNYC’s On the Media for a wide-ranging interview this Friday, talking about the latest polling on Trump and immigration, the Michigan primary, the new forecast at FiftyPlusOne, and what role public opinion ought to play in a democracy. The link to that is here.

And with that…

Leading off: I revisit the polling on the war in Iran. A lot of smart people still think Donald Trump is escaping public accountability for it, but the data says otherwise. He’s doing even worse than Joe Biden after botched Afghanistan withdrawal.

On deck this week: Tuesday’s Deep Dive is about why Trump’s numbers on the economy are so sour. It’s not just that conditions are poor, but also that voters see him as prioritizing the wrong issues.

Plus: Data about price anxiety in 2024, and the true costs of mass deportations.

Thank you for reading Strength In Numbers for another week!

If you’re not subscribed yet, enter your email here to get these in your inbox.

Subscribe now

1. Iran is worse for Trump than Afghanistan was for Biden

Pollster Kevin Collins argued Friday afternoon against a sentiment he keeps running into, and that we’ve dealt with here at SIN a number of times — that the public has given President Donald Trump a comparative pass on a wildly right-wing presidency. Specifically, Collins calls out the below post:

“perhaps [that] is an accurate reflection of media coverage,” Collins wrote. “But I don’t think it captures public opinion accurately, which has indeed been savaging Trump.”

He’s right, and the data is quite clear here. Collins notes that Echelon Insights, a Republican firm (I think their newest data just came out, thats why Collins used it) has found Trump 24 points underwater on foreign policy, for instance — actually below Joe Biden’s -10 at this point in his term:

Now it’s true that Biden had a worse rating for handling the Afghanistan situation that he did overall; Quinnipiac had Biden at 31% approve vs 65% disapproval on his handling of the withdrawal itself. So the eighteen-month comparison above is kinda the best case.

But if you look at polls of Trump’s handling of Iran, the public is even more bleak. According to the average I run for FiftyPlusOne (right now on the SIN data portal, moving to the polls site soon), Trump’s approval for handling Iran is -30 today. The graph below shows Trump’s approval overall, on foreign policy in general, and Iran in particular over the last 18 months:

But what’s also different about Iran is how how little support there is for the president’s goals. By and large Biden’s withdrawal from Afghanistan in 2021 was popular, it’s the execution of the withdrawal that dragged Biden down. Pew found 54% of Americans said leaving Afghanistan was the right callagainst 42% who said it was wrong, even as roughly seven in ten rated the administration’s handling of the exit only fair or poor.

Iran is not an analogous case. In a poll back in March, Pew found 59% of Americans said striking Iran was the wrong decision, vs 38% who said it was right. Just 37% approved of Trump’s handling of the conflict; and 61% disapproved. Unlike for Biden, where the public was aligned with his goals, Trump’s in the position of having both his policy and execution disapproved of simultaneously.

And it has gotten worse for Trump since March. In July, Quinnipiac pegged Trump’s approval on Iran at 28% against 66% disapproval, and foreign policy at 34% against 62%. His overall job approval in that poll was 32% — the lowest Quinnipiac has ever measured for him.

Update your priors about Trump

Back in September 2025, after ABC/Disney pulled Jimmy Kimmel’s show off the air when FCC Chairman Brendan Carr threatened the licenses of the stations that carried it, I made the argument that “powerful people” (network executives, CEOs, donors, celebrities) systematically overestimate how popular the president is and how much mass support there is for his agenda. From my experience I see even an overestimation of the incidence of cultural MAG thinking in the population.

Trump won about half of the votes cast in 2024. That is only about a third of American adults. Accounting for defections and disaffection, maybe 15-20% of the population is fully on board with super MAGA stuff like shutting down ABC-owned news affiliates.

After an outpouring of support for Kimmel, he was back on the air in six days. ABC/Disney, to its credit, appears to have revisited Trump’s numbers. In early May, it told the FCC in a 52-page filing that the agency was violating Disney’s First Amendment rights by ordering an ABC station to relitigate a 24-year-old exemption for “The View” under the equal-time rules. Then, it filed its station license renewals under protest, calling the order behind them “unlawful, arbitrary, and unconstitutional.”

On July 29, ABC asked the FCC to throw out the petitions against its stations, pointing to more than 150,000 public comments made in favor of the new organization.

So chalk one up in the win column. A company updated its estimate of public opinion, and acted differently when it found out the public was on its side.

Still, not all commentary has caught up to the data. There is still an implicit impression I get when talking to many folks in the media and Washington that Trump is more popular than he seems, or not getting punished like Biden was for inflation and foreign policy.

That is not true. Trump’s numbers are historically bad — not for any one big thing he did wrong, but many small ones that each marginalize additional Americans. He is deeply, deeply unpopular. Update your priors accordingly!

Share

2. What Strength In Numbers published last week

Last Sunday’s roundup was about how Trump’s deportation agenda is underwater in every swing state, and why it looks like some Republicans in Congress have started to notice:

On Monday, our sister publication FiftyPlusOne launched its 2026 congressional election forecast, which puts Democrats above even odds in both chambers. Crossposted here:

Tuesday’s Deep Dive dug into the two groups — Trump’s 2024 voters and pure independents — who are dragging down the president to his current 36% approval and 61% disapproval in our average, a net -25 and a record low.

On Thursday, I argued that the confident takes about how new Democratic Senate candidate Abdul El-Sayed is a weaker general-election candidate (as usual with his brand of story) very thin on data:

Also Thursday, David Nir and I opened the hood on the election model and worked through what went wrong with the Michigan primary polls:

And on Friday, new numbers on the divide inside the Democratic coalition — anti-system voters rate Zohran Mamdani and Alexandria Ocasio-Cortez higher than mainstream Democrats even after controlling for age and ideology:

If you’re a frequent reader of Strength In Numbers, I’m confident you will get a lot of value out of a paid subscription. You’ll get access to all of my Tuesday Deep Dives, monthly polling data, our private community of election nerds on Discord, and more.

Become a paying member today

3. Even more numbers!

• he Economic Policy Institute built a calculator for what mass deportation costs you personally. The answer is $2,358 per taxpayer, or $268.9 billion across fiscal 2025 through 2029 — enough, for example, to cover SNAP for another 118 million recipients over the rest of Trump’s term. Trump deportations cost taxpayers $2,358 each

• Ruth Igielnik explains why Michigan’s primary polls were so far off El-Sayed’s actual one-point win. Most surveys assumed independents would be 30-40% of a Democratic primary electorate, and only one poll in the final month came from a select pollster. Why Polling Overestimated Abdul El-Sayed’s Advantage in Michigan

• Pew has a piece out charting Trump’s approval alongside every modern president in their first term. He’s last, 3 points below Biden in 2022 and 6 below his own first-term number. Republican approval is cracking too, down to 69% from 84% in February 2025. Trump’s job approval rating is low by historical standards

• Mary Radcliffe and Cooper Burton write about how Americans are more anxious about prices than they were in 2024

Share

And that’s it for this week! Thanks for reading. Strength In Numbers will be back in your inbox on Tuesday.

Got more for next week? Email your links or add to the comments below! Comments are now UNLOCKED on the Sunday roundup.

Leave a comment