Prediction markets largely got the outcome of the 2026 US midterm elections right, correctly anticipating major Democratic victories in Congress. However, several unexpected results exposed the limitations of election betting platforms, particularly in closely contested Senate races.
Democrats secured control of both the Senate and the House of Representatives, delivering a significant political setback to President Donald Trump. The results also provided an important test for prediction markets, which attracted billions of dollars in bets during the election cycle.
According to Reuters, more than $1.48 billion was wagered on Tuesday’s elections, highlighting the growing influence of platforms that allow users to trade contracts tied to political outcomes.
Prediction markets anticipated a Democratic victory
Leading prediction market platforms, including Kalshi and Polymarket, signaled growing confidence in a Democratic victory throughout the final months of the campaign.
Kalshi traders assigned a 55% probability to a so-called “blue tsunami” as early as April. That forecast reflected expectations that Democrats could make significant gains across Congress.
The markets largely maintained that outlook as Election Day approached. Their odds also adjusted quickly as new voting information became available.
Kelly Ye, head of research at digital asset trading platform Talos, said prediction markets priced the overall midterm outcome within hours.
Harry Crane, a statistics professor at Rutgers University, described the election as the first major test of prediction markets on this scale.
He noted that the platforms consistently projected Democratic victories during the final weeks of the campaign and responded quickly to incoming election information.
Annie Andrews delivers a major South Carolina upset
Despite their broad accuracy, prediction markets missed some important results.
One of the biggest surprises came from South Carolina, where Democrat Annie Andrews defeated Republican Darline Graham to capture a US Senate seat.
The result challenged expectations in a traditionally conservative state. It also exposed a significant weakness in the forecasts published by major prediction platforms.
As late as Tuesday afternoon, Kalshi and Polymarket assigned Graham approximately a 70% chance of winning the race.
Andrews’ victory demonstrated that even strong market probabilities cannot guarantee electoral outcomes. A race that appears relatively predictable can still produce an unexpected result.
The South Carolina contest became one of the clearest examples of how political developments can challenge betting markets, even when their broader forecasts prove accurate.
Kalshi and Polymarket attract billions in election bets
The 2026 midterms marked another milestone for the prediction market industry.
Kalshi recorded more than $2.5 billion in total trading volume across its election markets during this cycle. That figure compares with approximately $1.3 billion during the 2024 election cycle, which included the presidential election.
The growing figures reflect increased interest in using financial markets to assess political developments.
Polymarket also reported strong forecasting performance. According to a person familiar with the matter cited by Reuters, the platform’s election forecasts were approximately 98% accurate.
However, these figures require context. Forecast accuracy depends on how platforms define successful predictions, measure probabilities and evaluate outcomes.
The industry’s expansion has also attracted greater attention from investors, political analysts and regulators.
Why prediction markets can get elections wrong
Prediction markets work by allowing users to buy and sell contracts connected to future events. Contract prices reflect the collective expectations of traders and can change as new information becomes available.
For example, a contract trading at 70 cents may represent a 70% implied probability of a particular outcome, depending on its structure and market conditions.
Supporters argue that these platforms can provide valuable insights because participants have financial incentives to make accurate predictions.
Brian Jacobsen, chief economic strategist at Annex Wealth Management, said prediction markets aggregate opinions from people who have an incentive to be right rather than simply express their views.
However, critics point to several weaknesses that can affect their accuracy:
- Limited liquidity: Markets with fewer participants may produce unstable or misleading prices.
- Speculative trading: Traders can make decisions based on sentiment rather than reliable information.
- Concentrated bets: Large positions from a small number of participants can influence market prices.
- Unexpected developments: Late campaign events and voter behavior can quickly change electoral outcomes.
These factors explain why prediction markets should complement traditional polling and political analysis rather than replace them.
The 2026 midterms put prediction markets under scrutiny
Prediction markets gained significant attention during the 2024 US presidential election, when their forecasts diverged from several traditional opinion polls and pointed toward a Donald Trump victory.
Their performance during the 2026 midterms offered another opportunity to evaluate whether these platforms could consistently translate market prices into accurate political forecasts.
Although their overall predictions aligned with the Democratic victory, previous high-profile forecasting mistakes have raised questions about their reliability.
Reuters also highlighted forecasting misses during US primary elections and Brazil’s presidential election as examples of the industry’s limitations.
The latest results suggest that prediction markets can provide useful information about political expectations. However, their accuracy depends on market participation, available information and the ability of traders to assess changing circumstances.
What the Democratic victory means for prediction markets
The 2026 midterm elections strengthened the case for prediction markets as an emerging source of political intelligence.
Platforms such as Kalshi and Polymarket demonstrated their ability to anticipate the broader direction of the election, even as individual races produced unexpected outcomes.
Their growing trading volumes also suggest that investors and political observers increasingly view these markets as tools for understanding public expectations.
Still, the South Carolina upset offers an important reminder: probability is not certainty.
As Jacobsen explained, prediction markets can indicate where available information is pointing, but only the actual election determines the winner.
For the prediction market industry, the 2026 midterms represent both a validation of its forecasting potential and a reminder that even billion-dollar markets cannot eliminate political uncertainty.
