Republicans win US House majority in 2026 midterms?
Market 52.0% against model 59.0%. Resolves in 138d 10h, data updated 13d ago.
Decision layer
Watchlist candidate
The market is worth monitoring, but the current edge or evidence does not justify an actionable label.
Expected value after costs, not raw probability spread.
How much support the model sees across available inputs.
Thin markets can erase apparent edge through spread and slippage.
Resolution ambiguity, timing, and data quality pressure the decision.
usable feature coverage.
Why / why not trade
One decision layer for the market read.
This public box mirrors the internal diagnostic style without exposing execution controls: decision, probability gap, cost-adjusted edge, blocker, and next thing to monitor.
side YES
59.0% model / 52.0% market
fees, spread, slippage, risk
Interesting disagreement, but the full action threshold is not met.
Watch resolution risk, timing, and data quality before trusting the gap.
Read this market in three passes
Model 59.0% vs market 52.0%.
Raw disagreement is reduced by fees, spread, slippage, and risk controls.
Watch, do not force it
Why this read matters
The market is directionally interesting, but at least one evidence, edge, liquidity, or risk condition is not strong enough.
Republicans win US House majority in 2026 midterms?
Volume $115,600
Market-implied vs model probability
Factor attribution
The model estimates a 7-point higher probability than the market, primarily driven by historical base rate.
| FACTOR | SIGNAL | WEIGHT | LOG-ODDS ΔLog-odds contribution measures how much each factor shifted the model's probability estimate in log-odds space — the mathematically correct way to stack independent evidence. Formula: Δlog-odds = weight × signal. Positive values push the probability up; negative values push it down. Log-odds are converted back to probability via the logistic function at the end. | DIRECTION | DESCRIPTION |
|---|---|---|---|---|---|
| Historical base rate | 52% | — | +0.080 | Bullish | Historical frequency for this kind of event — the prior before any market-specific evidence. |
| Model probability | 59.0% | Prior: 52% · Market: 52.0% | |||
| Confidence (λ)Confidence λ (lambda) controls how much weight to give the model vs. the market. Formula: p_final = λ·p_model + (1−λ)·p_market. λ is derived from data quality, factor agreement, and liquidity. When inputs are weak, the model shrinks toward the market — not toward 50%. | 0.64 | Final: 59.0% = λ·model + (1−λ)·market | |||
Comparable eventshistorical base rate 72.7% - n=11
| Event | Date | Outcome | Prior mkt prob. |
|---|---|---|---|
| US Presidential Election 2024 — Trump vs Harris | 2024-11-05 | Trump won. Prediction markets had correctly tilted Trump. | 56% |
| UK General Election 2024 — Labour landslide | 2024-07-04 | Labour won 412 seats. Conservatives collapsed to 121. | 95% |
| Brazilian Presidential Election 2022 — Lula vs Bolsonaro runoff | 2022-10-30 | Lula won 50.9% vs 49.1%. Extremely close. | 65% |
| French Presidential Election 2022 — Macron re-election | 2022-04-24 | Macron won 58.5% vs Le Pen 41.5%. | 78% |
| German Federal Election 2021 — SPD narrow win | 2021-09-26 | SPD won narrowly (25.7% vs CDU 24.1%). Scholz became chancellor. | 52% |
| US Presidential Election 2020 — Biden vs Trump | 2020-11-03 | Biden won. Prediction markets slow to call it. | 65% |
| Australian Federal Election 2019 — Morrison upset | 2019-05-18 | Morrison (LNP) won. Labor was favored. Major polling miss. | 68% |
| US Midterm Elections 2018 — Democratic House pickup | 2018-11-06 | Democrats won House (+41 seats). Republicans kept Senate. | 78% |
| UK General Election 2017 — Conservative majority expected | 2017-06-08 | Hung parliament. Conservatives lost majority. Major upset. | 85% |
| French Presidential Election 2017 — Macron vs Le Pen runoff | 2017-05-07 | Macron won 66% vs 34%. | 85% |
| US Presidential Election 2016 — Trump vs Clinton | 2016-11-08 | Trump won. Upset. Clinton was heavy favorite. | 83% |
Real historical events from the comparable-events library (showing 11 of 11 matched). The model's base rate is the realized frequency over the full matched set.
Scenario treeEngine template
Node probabilities are conditional on the parent; hover for cumulative path probability. Leaf EV is per $1 YES contract at the current price, before fees (fee-adjusted EVs in the table on the left).
| Path | Path prob. | YES pays | EV (YES, after costs) |
|---|---|---|---|
| Election held as scheduled > Outcome favors YES | 59.0% | $1 | +44.7c |
| Election held as scheduled > Outcome favors NO | 40.0% | $0 | -55.3c |
| Postponed / invalidated | 1.0% | $0 | -55.3c |
Root-implied probability 59.0% reconciles with the model's 59.0% (±1pt invariant).
Description
2026 midterm House control. Model has marginal edge over market pricing.
Resolution criteria (verbatim, with analyzer flags)
analyzed by heuristicResolves YES if the Republican Party wins a majority of seats in the U.S. House of Representatives in the November 2026 midterm elections.
Resolves Thu, 12 Nov 2026 03:48:52 GMT. The contract pays on these exact criteria, not on the thesis.
Suggested paper position
Paper position only. No real-money execution
Live open-market tracking
Since the first stored model read on 2026-06-09, the market has moved from 52.0% to 52.0%.
This is a directional diagnostic for unresolved markets, not final performance. Resolved outcomes still determine the official live record.
Data quality62/100 - usable
When features are unavailable, the model increases uncertainty and weights the final estimate closer to the market price. Lower data quality does not mean the market is wrong. It means the model is being appropriately humble.
Risk factor breakdownsim
| Inverse liquidity | 32 | |
| Price volatility | 28 | |
| Resolution proximity | 0 | |
| Data quality | 35 | |
| Category base risk | 55 | |
| Resolution ambiguity | 8 | |
| Regulatory exposure | 0 | |
| Portfolio concentration | 0 |
Composite score 43/100, higher = riskier.