2026 Senate forecastThe Upper Chamber

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SC · Senate 2026 · Updated Aug 27, 2026, 2:03 a.m. ET

South Carolina

Rated Likely R

Darline Graham wins this seat in 88 in 100 simulations. Call it clearly favored.

  • Portrait of Darline GrahamDarline GrahamRIncumbent88 in 100
  • Annie AndrewsD12 in 100

Over time

How the forecast has moved

0255075100AprJulOctJan ’26AprJulD 12 in 100R 88 in 100Aug 27, 2026
Chance in 100 the seat goes to each side: blue for the Democratic caucus, red for the Republicans · one point per day · hover or tap to read the lines

The margin

How close it lands

Each simulation produces a final margin. The curve shows where those margins fall; the tinted areas on either side of even are the futures each party wins.

EvenR +10.3
Darline Graham aheadAnnie Andrews ahead →
Where the simulated margins land · marker at the average margin

Under the hood

What's driving the number

The forecast blends a weighted poll average with a fundamentals model: the state's partisan lean, incumbency, the national environment, and fundraising. Positive points favor the Democratic side of the margin; negative favor the Republican side.

  • Partisan leanR +16.3
  • IncumbencyR +2.5
  • National environmentD +5.1
  • FundraisingR +0.4
  • Fundamentals sayR +14.2
  • The polls sayD +0.1
  • Blended forecast marginR +10.2

The blend puts 28 percent of its weight on the polls and 72 percent on the fundamentals; polls take over as more arrive and election day nears.

The markets

What the betting markets say

Prediction markets put a price on this seat too: what people are paying for a contract that pays out if Darline Graham wins, read as an implied probability. Shown for comparison only. This is not betting advice, and market prices never feed the model.

  • Darline Graham winsThis forecast88 in 100
  • Darline Graham winsKalshi88 in 100
  • Darline Graham winsPolymarket86 in 100

Market prices as of Aug 27, 2026, 2:03 a.m. ET

The evidence

The polls, as the model sees them

Every public poll of this race, with the weight the model gave it in the latest run. Weights reward recency, sample size, and pollster track record; the house adjustment corrects each pollster's habitual lean before averaging.

PollDatesSampleD–RMarginHouse adj.Weight
Impact ResearchAug 18–24700 LV41–41Even−0.199%
Impact Research (D)Jun 17–22700 LV45–48R +3.0−0.1<1%
Impact Research (D)Feb 25–Mar 1700 LV42–47R +5.0−0.1<1%
Public Policy Polling (D)Nov 21–22704 V36–42R +6.0+0.40%

How the weights, the blend, and the simulation work is spelled out on the methodology page.