2026 election odds

What the prediction markets imply for each 2026 race, next to the Scopes Poll Composite: the median of independent forecast models. Each card leads with the widest gap between the two; every flagged divergence is tracked to resolution on the public scorecard.

As of Sep 30, 2026Delayed · publicLive prices, full history & gap alerts → Dashboard

Board List

Alaska Senate 2026

Resolves Nov 3, 2026

30.2-pt divergence

Outcome	Kalshi	Polymarket	Fair value	Divergence
Republican	29.5%	28.2%	59.7%	30.2 pts

2 outcomesDetails →

US House control after 2026

Resolves Nov 4, 2026

14.2-pt divergence

Outcome	Kalshi	Polymarket	Fair value	Divergence
Republicans keep the House	8.6%	8.4%	22.8%	14.2 pts

2 outcomesDetails →

US Senate control after 2026

Resolves Nov 4, 2026

12.0-pt divergence

Outcome	Kalshi	Polymarket	Fair value	Divergence
Republicans keep the Senate	37.5%	37.5%	48.8%	11.3 pts

2 outcomesDetails →

Maine Senate 2026

Resolves Nov 3, 2026

11.1-pt divergence

Outcome	Kalshi	Polymarket	Fair value	Divergence
Democratic	59.5%	59.5%	70.5%	11.1 pts

2 outcomesDetails →

Kansas Senate 2026

Resolves Nov 3, 2026

9.7-pt divergence

Outcome	Kalshi	Fair value	Divergence
Democratic	29.5%	39.2%	9.7 pts

2 outcomesDetails →

Iowa Senate 2026

Resolves Nov 3, 2026

7.0-pt divergence

Outcome	Kalshi	Polymarket	Fair value	Divergence
Republican	58.9%	58.5%	65.9%	7.0 pts

2 outcomesDetails →

Texas Senate 2026

Resolves Nov 3, 2026

4.5-pt divergence

Outcome	Kalshi	Polymarket	Fair value	Divergence
Democrats win	61.8%	62.5%	66.3%	4.5 pts

2 outcomesDetails →

Ohio Senate (special) 2026

Resolves Nov 3, 2026

4.4-pt divergence

Outcome	Kalshi	Polymarket	Fair value	Divergence
Democratic	60.5%	60.1%	64.9%	4.4 pts

2 outcomesDetails →

Michigan Senate 2026

Resolves Nov 3, 2026

2.9-pt divergence

Outcome	Kalshi	Polymarket	Fair value	Divergence
Democrat	73.5%	72.5%	76.4%	2.9 pts

2 outcomesDetails →

North Carolina Senate 2026

Resolves Nov 3, 2026

2.5-pt divergence

Outcome	Kalshi	Polymarket	Fair value	Divergence
Republican	5.7%	4.5%	8.2%	2.5 pts

2 outcomesDetails →

Georgia Senate 2026

Resolves Nov 3, 2026

1.0-pt divergence

Outcome	Kalshi	Polymarket	Fair value	Divergence
Republican	5.0%	3.6%	6.0%	1.0 pts

2 outcomesDetails →

California Governor 2026

Resolves Nov 3, 2026

Outcome	Kalshi
Republican	—

Insufficient models: no fair value published.

2 outcomesDetails →

Wisconsin Governor 2026

Resolves Nov 3, 2026

Outcome	Kalshi
Republican	—

Insufficient models: no fair value published.

2 outcomesDetails →

How the fair value is built

Rules, not picks.

The fair value here is the Scopes Poll Composite: the median of a named, pre-declared slate of independent forecast models. The models are fixed in advance, not chosen race by race to land on a number. When only one qualifying model is present the composite is that model; the median engages as more are added.

One of those models is the Scopes Poll Average, which is itself rules-based: published rules decide which polls qualify (discloses methodology, named matchup, unsponsored, a minimum sample), one poll per pollster keeps a single shop from dominating, and the median of their margins is the aggregate. No poll is hand-added or dropped by result.

We don't claim these are the “most accurate” models or polls; no one can know that in advance. Every model is named and every input poll links to its original publisher, so you can reproduce the number, or disagree with the rules and build your own; the scorecard keeps the record of who proved right. See the full inclusion rules.

Background reading: what a poll actually measures: why a survey of opinion is not a probability of an outcome, and why the benchmark here is a forecast model rather than a raw poll. Market structure, not a strategy.

A deadline on the Polls desk.

A reporter is filing on the Michigan Senate race. The prediction markets have the Democrat at 54%. The Scopes Poll Composite, the median of named public forecast models, each one listed and linked, sits at 61%. A seven-point divergence, flagged and timestamped.

That's no longer a vibe or a hot take; it's a fact with a source: the betting crowd and the modelers disagree about Michigan, and here is by how much, as of this morning.

One click away, the scorecard shows how disagreements like this one have resolved before, how often the markets proved right, how often the models did, across every tracked race, wins for either side displayed alike.

What the reporter writes (lead with it, question it, ignore it) is entirely their call. Scopes measures; it never spins. But "the markets and the models split on Michigan, per Scopes" is a sentence with a citation, a timestamp, and a public record behind it, which beats a sentence with none.

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