How FLIPR forecasts the midterm elections

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Cite

Silver, N. (2026, September 4). How FLIPR forecasts the midterm elections. Silver Bulletin. https://www.natesilver.net/p/flipr-midterms-model-methodology

Dated when The Aporia collected the article, usually within hours of publication. Check the original for its exact date and byline.

Silver Bulletin · collected 2026-09-04 · by Nate Silver commentary

Quick Summary

The author of FiveThirtyEight introduces a new name for their midterm election forecasting model: FLIPR, which stands for Forecast with Leading Indicators, Polls and (Expert) Ratings. The model forecasts Congressional and gubernatorial races, and is trained on both midterm years like 2022 and presidential election years. FLIPR consists of three layers: polls, leading indicators (fundamentals), and expert ratings from groups like the Cook Political Report, with multiple versions available. The author notes that while FLIPR has a new name, it is likely not a fundamentally new model, but rather an evolution of previous models dating back to 2010.
Written locally by qwen2.5:14b on 2026-09-05, using this article's own text rather than the other coverage of the same event (that is the story summary below).

AI analysis runs on qwen2.5:14b, locally

Story summary

Nate Silver's election forecasting model, FLIPR 🐬, has been launched by Silver Bulletin to predict the outcome of the midterm elections. The model uses a combination of factors, including the incumbent's margin in their last competitive race, fundraising based on individual contributions, and party loyalty scores. It also considers the impact of redistricting and whether the incumbent is in their first elected term. FLIPR forecasts both Congressional and gubernatorial races, not just midterms, making the name "midterms model" a misnomer. The model runs 40,000 simulations every time it's updated to account for uncertainty. Despite this, Silver acknowledges that even the most carefully designed models can struggle with unusual events like pandemics or candidate changes.

Written for “Midterm Election Analysis” on 2026-10-21, grounded in this article and the 1 other(s) covering the same event.

Story

📰 Midterm Election Analysis
Politics · 2 article(s) covering the same event. See how they differ ↓

Compared with similar articles

This article reads unscored and hedges 3% of its claims. Each row says how that neighbour differs.
Silver Bulletin
⚖️ leaning not scored 🔴 7% hedged 4 of 54
“Both articles describe the launch of a new election forecasting model called FLIPR on September 4, 2026.”
Silver Bulletin
⚖️ leaning not scored 🔴 7% hedged 38 of 569
“Article A mentions the introduction of a new model name (FLIPR) and an informal discussion about their 'midterms model', while Article B is a more technical preview of the same model's launch with updated forecasts for the upcoming midterms, indicating two related but distinct events”
How this article was measured signals, leaning charts, publisher record, byline, topics and claims

Signals How these are calculated →

Claims extracted
281
sentences that state something as happening or true
Uncertain
3%
8 of 281 hedged ("reportedly", "may", "according to")
Leaning
not political
takes no side on a contested political question
Publisher signals
not measured
these signals count newsroom corrections; Commentary has none to count
Outlets on this story
2
Politics
Same framing elsewhere
1
articles that frame the event the way this one does
Analyzed 2026-09-05 · how these are computed

How this is being covered How these are calculated →

Article leaning vs. publisher reliability
Source leaning vs. consistency

Publisher

Silver Bulletin · 45 article(s) · 0 correction(s) detected
No corrections detected for this publisher. That may mean careful reporting, or simply that nothing has been checked.

Who wrote this

Nate Silver
31 article(s) here · 1 carrying a prediction
🔮 But leave that philosophical question aside for now — because after a long week of travel,1 I could use a simpler Model Talk.
2026-10-17 · assertive framing · The good, bad and ugly outcomes in the Senate
🔮 Below, you’ll find our forecast for each 2026 Senate race, along with how our numbers have changed over time and which states are likeliest to decide control of the chamber.
2026-10-15 · assertive framing · FLIPR 2026 Senate forecast
🔮 Although our critics might disagree, we seek to avoid false equivocation at Silver Bulletin.
2026-10-09 · assertive framing · Could Minnesota spoil the blue wave?
🔮 It’s not unreasonable to approach election polls from a position of mild — maybe even more-than-mild — skepticism.
2026-10-03 · assertive framing · What if the polls are wrong again?
🔮 That definitely will not happen before the midterms.
2026-10-01 · assertive framing · SBSQ reboot thread
🔮 We think it’s an important story, providing historical context on midterm elections that helps to explain FLIPR’s increasingly optimistic forecast for Democrats.
2026-10-01 · assertive framing · Why did Ohio stop being a swing state?
🔮 The 2026-27 NBA season begins on Oct. 20, when the New York Knicks will lift the championship banner to celebrate their first NBA title in 53 seasons.
2026-09-28 · assertive framing · NBA Future of the Franchise Rankings 4.0
🔮 Our Silver Bulletin polling average forecasts a much closer race — with Turek ahead by 1.9 points — though that would still be an impressive result in a state that Donald Trump won by 13 points in 2024.
2026-09-22 · assertive framing · Diesel prices could crush Republicans in the heartland
🔮 It’s sometimes nice for forecasters when races are toss-ups; it keeps you from putting your neck out too far.
2026-09-20 · assertive framing · Expert ratings are ignoring signs of a blue wave
🔮 A snail will spend the rest of eternity trying to track you down.
2026-09-15 · assertive framing · We’re not ready for superpersistent AI
Also by Nate Silver
The good, bad and ugly outcomes in the Senate
2026-10-17 · Silver Bulletin
FLIPR 2026 Senate forecast
2026-10-15 · Silver Bulletin
Could Minnesota spoil the blue wave?
2026-10-09 · Silver Bulletin
What if the polls are wrong again?
2026-10-03 · Silver Bulletin
Nothing else under this byline is closely related to this article, so these are simply their most recent.
All 31 articles by Nate Silver →

Topics

FLIPR FiveThirtyEight House Lite Senate

Subjects

FiveThirtyEight ORG · 3× House ORG · 2× Senate ORG · 2× Congressional NORP · 1× Deluxe ORG · 1× ELWAY ORG · 1× Lite ORG · 1× PELE ORG · 1× Silver Bulletin ORG · 1× the Cook Political Report ORG · 1×

Narrative

It uses the incumbent’s margin in her last competitive race, adjusting for the political environment at the time of the last election and changes in district boundaries; It looks at whether the incumbent is in their first elected term, and whether they defeated an incumbent in their previous race;23 And for states that have redistricted, the incumbency advantage is less when there’s little overlap between a member’s old and new districts. Fundraising, based on individual contributions only (so not party or PAC money) with a bonus for contributions from within a candidate’s home state;24 Elected experience on a 4-point scale from 0 (never elected to anything) to 3 (current or former elected senator or governor); Party loyalty scores based on congressional voting records.
framing: assertive · carried by 1 article(s) · first seen 2026-09-05
🔮 In the Silver Bulletin/FiveThirtyEight tradition, we’ve always given names to our sports models (PELE, ELWAY) — but never to our election forecasts.
2026-09-05 · Silver Bulletin
How FLIPR forecasts the midterm elections · assertive framing

Claims (281 extracted, 8 hedged)

In the Silver Bulletin/FiveThirtyEight tradition, we’ve always given names to our sports models (PELE, ELWAY) — but never to our election forecasts. asserted
we → give → forecasts
I suppose we’d like to think that our presidential model is renowned enough at this point that it doesn't necessarily need any further identifier. asserted
it → suppose → identifier
But we’ve decided we do need a name for what we informally call our “midterms model.” asserted
we → decide → what
That’s because “midterms model” is actually a misnomer. asserted
model → ’ → ?
The model forecasts both Congressional and gubernatorial races, but it’s trained on both midterm years like 2022 and years where these races coincide with presidential elections. asserted
races → forecast → elections
So now we’re calling our model FLIPR 🐬 instead.1 asserted
we → call → model
In line with our tradition of silly backronyms, FLIPR stands for Forecast with Leading Indicators, Polls and (Expert) Ratings. asserted
FLIPR → stand → Indicators
We like this name because it’s sort of a triple entendre: asserted
it → like → entendre
The principal goal of these elections is flipping individual seats, and ultimately the House and Senate. asserted
goal → flip → seats
FLIPR is a hat tip to the inherently probabilistic nature of the forecast — we run 40,000 simulations every time the model is updated. asserted
model → run → simulations
It’s not as simple as “flipping coins”, but the simulations are necessary because of the complex relationships between different types of races. asserted
simulations → ’ → races
FLIPR essentially consists of three different layers stacked on top of one another: polls, non-polling leading indicators (e.g., fundraising, incumbency) or what we sometimes call “fundamentals”, and then a final layer of race ratings from longstanding outside expert groups like the Cook Political Report. asserted
we → consist → Report
In fact, there are three versions of FLIPR that correspond to each layer: asserted
that → be → layer
Lite derives as much information as possible from polls alone; Classic is polls + fundamentals; And Deluxe is polls + fundamentals + expert ratings. While Deluxe is our default version, almost all of the charts and tables on the FLIPR landing page allow you to, uh, flip back and forth between these versions. asserted
you → derive → versions
The way FLIPR is designed, the expert ratings layer is probably the least important — the proverbial “icing on top.” asserted
layer → design → top
But if you want a forecast without any expert ratings, we have you covered with Lite and Classic. asserted
you → want → Lite
I’d say probably not. asserted
I → say → ?
If you’re a former FiveThirtyEight reader — I retained the rights to all of the models I designed for them per my deal with Disney — you’ll recognize that some of what I’ve just described sounds familiar. asserted
I → ’re → what
In fact, some of the code in FLIPR just celebrated its 16th birthday, having survived since the first midterms forecast I built back in 2010 when FiveThirtyEight was stationed at the New York Times. asserted
FiveThirtyEight → celebrate → Times
We think this track record is one of the things that makes FLIPR unique — competing models come and go, succeed and fail, but few have been around long enough to truly have any sort of long-term track record. asserted
few → think → record
And our midterms forecast performed quite well in both 2018 and 2022. asserted
forecast → perform → 2018
However, FLIPR and its predecessors have a lot of moving parts. asserted
FLIPR → have → parts
And we’ve made refinements every year. asserted
we → make → refinements
While the basic foundation is similar to 2010, nearly every component of the model has been swapped out for a new version at some point. asserted
component → swap → point
It’s a little bit like the paradox of the Ship of Theseus: if every component of a model has been upgraded at some point, is it still the same model? asserted
it → ’ → point
The model underwent a near-complete refit in 2018 when the Lite/Classic/Deluxe concept was introduced. asserted
concept → undergo → 2018
It also underwent another suite of changes in 2022 to reflect increasing political polarization. asserted
It → undergo → polarization
And even though we really liked the 2022 version of the model, we wound up making more changes and improvements this year (2026) than I was originally expecting — rather than just “turning it back on.” asserted
I → like → it
These changes have a common theme: they don’t change the underlying structure or philosophy of the model so much as seek to execute on our original design with more precision, robustness, and internal consistency. asserted
they → change → precision
I have complicated feelings about AI coding tools — and I very much disagree with the idea that they’re a substitute for human judgment. asserted
they → have → judgment
But as a complement to human judgment, they’re pretty nice. asserted
they → ’re → judgment
Probably, in fact, they’re better for reviewing and refining an existing model than for building one from scratch. asserted
they → ’re → scratch
Basically, every single line of code from our 2022 model2 has been audited and, in many cases, improved. asserted
line → audit → cases
Many of these improvements are not highly visible: they concern things like faster program runtime, better error detection, and better “code hygiene” that makes FLIPR easier to audit and update. asserted
FLIPR → concern → runtime
But having software that speeds up some of the most tedious aspects of coding also freed up time to test out different hypotheses and clear a long list of items off of our punch list. asserted
that → have → list
Some of these upgrades fell under a longstanding category of “would be nice to have, if only we had the time.” asserted
we → fall → time
For example, FLIPR now explicitly simulates ranked-choice voting, as used in Alaska and Maine. asserted
FLIPR → simulate → Alaska
Building that code took a couple of hours and relied heavily on our expertise; it’s not nearly as simple as telling an AI coder “ok do ranked-choice voting now.” asserted
it → build → voting
It probably would have taken a couple of days instead of a couple of hours in the world before Claude and ChatGPT, however. asserted
It → take → Claude
Still, ranked-choice voting is a fairly low-stakes problem since the other 48 states don’t use this process. asserted
states → rank → process
…and 241 more, not listed.
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