Chamber control · 2026 cycle
Who controls Washington?
20,000 Monte Carlo simulations with a national-environment swing — each iteration draws a national shift and applies it to every race in logit space before drawing outcomes. Within a sim races are independent; across sims they correlate via the shared swing.
Senate
35 racesP(D CONTROL)
1.1%
P(R CONTROL)
98.9%
1.0999999999999999 D0 TOSSUP98.9 R
EXP D-SEATS 4580% CI 41 – 46
House
443 racesP(D CONTROL)
0.0%
P(R CONTROL)
100.0%
0 D0 TOSSUP100 R
EXP D-SEATS 15880% CI 135 – 179
Governors
38 racesP(D CONTROL)
94.0%
P(R CONTROL)
6.0%
94 D0 TOSSUP6 R
EXP D-SEATS 2880% CI 25 – 29
Original vs Bayesian-shrunk thresholds
From the calibration page| Chamber | P(D) original | P(D) recalibrated | Δ pt | Exp D-seats orig. | Exp D-seats recal. |
|---|---|---|---|---|---|
| Senate | 1.1% | 0.3% | −0.8 | 44.6 | 44.0 |
| House | 0.0% | 0.0% | +0.0 | 157.5 | 147.8 |
| Governors | 94.0% | 91.1% | −2.9 | 28.1 | 27.6 |