Prediction markets faced their defining test during the 2026 midterm elections, correctly forecasting Democratic majorities in both chambers of Congress after roughly $1.3 billion in trading activity.
Platforms such as Kalshi and Polymarket indicated a Democratic “blue wave” well before major television networks projected the outcomes.
At the same time, the markets underestimated the scale of several Democratic upsets relative to conventional polling, according to an analysis by New York magazine.
A week prior to Election Day, participants heavily favored Republican candidates in multiple contests that Democrats ultimately flipped.
With Democrats now controlling Congress, the platforms confront an uncertain regulatory path ahead.
The overall chamber calls proved accurate. Both Kalshi and Polymarket assigned Democrats high probabilities of capturing the House—often above 93 percent before polls closed—and roughly 75 percent odds of taking the Senate. Those assessments held.
Early trading and real-time price movements on the platforms reflected the emerging Democratic advantage hours ahead of traditional media projections, underscoring the markets’ ability to aggregate information quickly as results arrived.
Combined trading volume across election-related contracts reached about $1.3 billion, a substantial figure for a midterm cycle and a marker of the sector’s growth since the 2024 presidential contest.
Performance on individual races was more mixed.
New York magazine examined Kalshi markets for several competitive, well-polled Senate races one week out.
Traders correctly anticipated Democratic wins in contests such as Texas (James Talarico), Ohio (Sherrod Brown), Michigan (Abdul El-Sayed), and Maine (Troy Jackson).
They also correctly projected Democratic control of both chambers. However, the same analysis found larger misses elsewhere: traders favored Republican Darline Graham in South Carolina by a wide margin, and Ashley Hinson in Iowa and Roger Marshall in Kansas by substantial spreads.
Democrats flipped those seats.
A contemporaneous New York Times/Siena poll pointed in similar directions overall but was closer on several of the races the markets mispriced.
In short, while the platforms captured the national direction and many individual outcomes, their implied probabilities on certain upsets diverged more sharply from the eventual results than traditional polling did.
Supporters note that market favorites ultimately prevailed in the large majority of resolved races, with one Kalshi figure cited at roughly 98 percent accuracy on called contests before votes were fully counted.
Critics emphasize that many races were not competitive, that markets draw heavily on the same polling inputs as traditional forecasts, and that thin liquidity in narrower contests can amplify errors.
Platforms adjusted rapidly once early returns confirmed the Democratic wave, which some observers view as a strength of continuous trading versus static polls.
The midterms have elevated prediction markets’ visibility in political coverage, with logos appearing alongside traditional decision desks and analysts appearing on election-night broadcasts.
Yet the same success places them under closer scrutiny.
A Democratic-controlled Congress is positioned to examine or advance regulation of the sector.
Existing state-level lawsuits already challenge the platforms’ sports and political contracts, while prior proposals in Congress have addressed insider trading, election-related wagers, and jurisdictional questions between the Commodity Futures Trading Commission (CFTC) and state gambling authorities.
Industry advocates argue for clearer federal rules that would preempt fragmented state actions; opponents raise concerns about integrity, influence, and the blending of financial speculation with electoral processes. The coming legislative session will test whether the markets’ predictive record translates into political insulation or invites tighter oversight.
