2026 U.S. Senate Forecast

Model

Hierarchical Bayesian election forecasting model for the 2026 U.S. Senate (35 races), estimated via Stan HMC (Hamiltonian Monte Carlo). Constructs a fundamentals-based prior per race from recency-weighted presidential lean, generic congressional ballot (GCB), presidential approval, the economy (unemployment), incumbency advantage, and candidate quality. National-environment terms (GCB, approval, economy) are scaled per state by an elasticity factor derived from Silver Bulletin 2026 CES-based estimates, so some states pass national mood through more than others. Key structural parameters—incumbency bonus, GCB-to-Senate coefficient, presidential lean weight, poll bias, and a personal-vote decay parameter—are jointly estimated in Stan with bounded informative priors centered on values from the political science literature, propagating structural uncertainty into the forecast rather than conditioning on fixed point estimates. Polls are incorporated via a hierarchical Stan model with Student-t poll likelihood (robust to outliers), national and regional random effects, and jointly estimated poll bias and degrees of freedom. Partial pooling across states within 7 demographic subregions borrows strength from related races. States with unresolved primaries are modeled as probability-weighted nominee scenarios with an additional uncertainty bonus reflecting the unknown matchup. Incumbents who last ran six years earlier receive a personal-vote adjustment from their prior over- or under-performance, applied only in competitive races.

Uncertainty is propagated through 50,000 Monte Carlo draws from the Stan posterior, plus three forward-looking drift layers: national GCB drift (fat-tailed t-distribution), correlated cross-state drift (via a continuous demographic-similarity matrix), and state-level idiosyncratic drift. Drift magnitudes are time-dependent, scaling by √(months to election / 8). The model is validated against 8 historical cycles (2010–2024) via leave-one-cycle-out cross-validation; the backtest is used for calibration diagnostics, not parameter optimization, since the loss surface is flat over wide parameter ranges with only 8 cycles.

Interactive Controls

The National Environment slider adjusts the generic congressional ballot (GCB)—a measure of the national partisan mood derived from polls asking voters which party they prefer for Congress—with presidential approval tied via a recalibrated midterm approval-GCB relationship. The Customize Assumptions panel lets you override three structural parameters: incumbency advantage (how much sitting senators benefit from holding office), national mood effect (how strongly the GCB translates to individual Senate race margins), and polling bias (systematic overestimate of Democratic margin in polls). Adjustments shift race margins additively, with win probabilities and seat totals recomputed in real time.

Data Sources

• Senate polls: New York Times / 538 polling database
• Generic ballot: Silver Bulletin GCB model (daily estimate)
• Presidential approval: Silver Bulletin approval tracker (daily estimate)
• Historical polls (backtest): NYT Upshot (2010–2012), RealClearPolitics (2014–2016), 538 historical Senate polls (2018–2024), CES state-level estimates (2010–2024, October only)
• Fundamentals: Presidential election results (MIT Election Lab), FRED unemployment data