https://www.nytimes.com/interactive/2026/10/28/polls/times-siena-mn-poll-crosstabs.html

Trump net approval

Minnesota: -26
Florida: -10
South Carolina: -13

"The voters in S.C. are not buying the argument that everyone has to ‘settle’ for a candidate that has been propped up by special interest groups and a Republican president spending millions of dollars spreading false narratives about why Darlene [sic] Graham is qualified to be elected to the U.S. Senate for the next six years," Norman told Fox News Digital via text message in all capital letters.
He said the general election "is about attempts to ‘force’ a candidate on the voters of S.C. and regardless of the outcome, it didn’t have to be this way as the great people living in South Carolina deserve ‘better!!’"

Couldn’t have said it better myself, thank you Ralph Norman!

Ranking competitive Senate seats from left to right by election rating, polling average, and estimated margins from RTWH, SplitTicket, and VoteHub.

Link to toplines above ⬆️

Link to article with results

Link to toplines above ⬆️

Link to article with results here

Link to toplines above ⬆️

Link to article with results here

Prop 1 ($11.25B Housing Bond) - 55% Yes, 43% No

Prop 2 (Rainy Day Fund) - 60% Yes, 39% No

Prop 3 (Make Tax on High Incomes Permanent) - 58% Yes, 41% No

Prop 4 (Public Financing of Campaigns) - 45% Yes, 52% No

Prop 5 (Change Recall Process) - 45% Yes, 52% No

Prop 37 (Middle-Income Home Loans) - 65% Yes, 32% No

Prop 38 ($8.4B Immunology Bond) - 57% Yes, 41% No

Prop 39 (Voter ID) - 41% Yes, 58% No

Prop 40 (Wealth Tax) - 46% Yes, 52% No

Prop 41 (Prohibits Certain New State Taxes) - 48% Yes, 49% No

Prop 42 (Prohibit Retroactive State Taxes) - 51% Yes, 47% No

Prop 43 (Restore 2/3rd Threshold to Pass Special Taxes) - 42% Yes, 55% No

Prop 44 (Community Health Clinic Mandates) - 25% Yes, 71% No

Prop 45 (CEQA Reform) - 46% Yes, 50% No

(Credit to Rob Pyers on Twitter for this list:)

Preamble

On basically every poll post, you'll see comments along the lines of:

• D is winning, but undecideds in an R state will come home
• Candidate X reached the magic 50%, so undecideds don't matter
• will undecideds split 50/50? or not vote?
• impact of 3rd party voters?

It's a pretty opaque topic. The general rule of thumb from modelers is "undecideds split 50/50", which is why they have no directional impact on FLIPR or other model predictions (besides increasing uncertainty). On the other hand, many people intuitively think that a close race in MN is not a big deal for democrats, while a similarly close race in SC is a longshot. We have a tendency to assume the worst and assign all undecideds to the opponent (thus the magic 50% threshold).

But what does the data actually say?

Inputs

Specific hypothesis: we can use information about a state's partisan lean to allocate undecided's beyond "50/50" and better predict the election results.

So we need:

• a database of historical polls
• a consistent, unbiased measure of partisan-lean from pre-election
• actual results

for as many elections as possible. For simplicity (and immediate applicability), I'll only look at senate races.

I'm particularly interested in further out from the election (september/october especially, since that's when this question arose the most for me), so typical poll datasets (which only keep the last 3 weeks) won't work.

After looking at archival 538 pages, I found their model inputs which provide the senate fundamentals, all senate polls, and results for every 2020 and 2022 senate race. Jungle primaries and runoffs take more effort to handle, so I'm ignoring GA and AK in this analysis. I'm also removing any races without an R or D running, since the partisan lean would be useless. The result is 741 polls across 37 senate races from 2020 and 2022.

Method

Testing 4 things against the actual election margin:

• normal poll margin, with undecideds split 50/50
• normal poll margin, but with undecideds split by the partisan lean of the state
• normal poll margin, but with undecideds and 3rd party vote share split by the partisan lean of the state
• normal poll margin, but all undecideds allocated to the candidate that matches the state's partisan lean

E.g, let's say a poll is 45/43/2 in 2020 Delaware, which had a fundamental partisan lean of D+30.

• By the first method, the margin is the normal D+2.
• By the second, the 10% undecided are split 65/35, so it becomes 51.5/46.5/2 = D+5
• By the 3rd, the 12% of undecided+3rd party are split 65/35, so it's D+5.6
• By the last, all 12% go to the D, so D+14

If the result were D+15 the last method would look good, and if it were R+1 the first would look the best.

Anyway, do that for every poll, then calculate some basic accuracy metrics (MAE, RMSE, R^2, median average error, and % races called correctly), and see what does best.

Results

First, for everything

3 things to note: 1. Method 3 (splitting undecideds by state's fundamentals) is the best by standard accuracy metrics, plus a 2% bump in races called correctly

1. Even though it's the best, the impact is small. The MAE (mean average error) only drops by 0.3 points (on average, this method is 0.3 pts closer to the true margin than splitting undecideds 50/50). So it isn't a massive change

2. Allocating all undecideds to the partisan candidate is clearly the worst by accuracy (a full 2 pts worse by MAE), but best by call % (a 14% increase!). Weird, let's look from some other POVs

by month

September polls

October polls

November polls

The trend is obvious: the improvement is higher in september, lower in october, and almost gone in november (also, for fun, note that the average error on a senate poll in september was 6.85, vs 5.23 in november). But the improvement, especially the call %, persists in all 3 months. And our "assign all undecided to one candidate" is still bad on margin, but very good on call %.

competitive races

Next let's separate the competitive and non-competitive races (defined as a fundamental partisan lean of <10 in either direction), to make sure some D+40 vs D+15 miss isn't skewing our numbers

comp vs noncomp

same trends

partisan vs non partisan

maybe partisan polls are driving this behavior?

partisan vs non

I guess its notable that assigning all undecideds improved on margin for partisan polls, otherwise same trends

2020 vs 2022

Finally, maybe the large polling miss in 2020 in skewing this analysis somehow.

2020 vs 2022

kind of? We see a massive (25%!) increase in call % in 2020, but the a smaller magnitude increase (6%) still exists in 2022.

Conclusion

Based on this data, it's smart to have a mental model where you assign undecideds and 3rd party voters more towards Ds in D states and Rs in R states. This is a stronger effect further out from the election and especially for partisan polls. Strangely, the heuristic of "assign all undecideds to the R in R states and the D in D states" would've been much more successful at predicting winners in 2020 and 2022, even if it would've missed badly on the actual margins. So maybe the magic 50% threshold is real, after all.

self-critique

Number 1 is sample size. 37 unique races across 2 cycles is not a lot. A better analysis would find a way to include data from 2024 and way further back, add in presidential elections, and include jungle primaries/runoffs. It would also use the polling average, instead of individual polls. It could also look at vote share instead of margin.

This was a pretty basic MAE analysis. I'm sure there's room for more sophisticated regression/bayesian techniques to better understand the impact.

Also, this may just be double counting the "fundamentals" component of most models. They don't explicitly weight undecideds by fundamentals, but by assigning the fundamentals a higher weight it may kind of do that accidentally? So instead of showing why splitting by partisan lean has been better, I might have only justified the inclusion of fundamentals in models.

Finally, it isn't obvious to me that I didn't just find a complicated way to say "polls underestimated Rs in R states and Ds in D states the last 2 elections, duh".

Some people have even begun to wonder if a glimmer of blue could break into Louisiana's long red Senatorial history. Is this the year, they ask, when Louisiana elects its first Black U.S. Senator in more than a century?

Louisiana pollster John Couvillon still believes that Letlow will win, he told The Lens. But "this is not going to be a slam dunk race for [her]," he warned.

It seems nearly impossible to push that list of flipped seats to Louisiana, which Trump carried by 22 points in 2024. But the race has become close, Couvillon acknowledged. Part of that is due to Republicans who are crossing over, defecting from their party, to vote for Democrats. A typical rate of defection in elections is 5%. But in this election, across the nation, the rates are more than double, "I am seeing defections from Republicans towards Democratic candidates in about the 12% range," he said.

The model I developed using several real time data signals has the Dems taking Senate +7 and House +21. Check it out!

https://www.makeamericatrumpfreeagain.com

I know I posted this a few months ago but wanted to bump it as it's more timely now

The monkey's paw: finally a non partisan but it's the weirdest poll ever, ffs