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The 5.3% Gap Between Polls and Price Discovery on Predictions Markets

Polls missed Democrats by 5.3% in the 2026 midterms while Kalshi and Polymarket called nearly every race. What new Senate oversight changes in January.

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Feature: The 5.3% Gap Between Polls and Predictions Markets

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The 5.3% Gap Between Polls and Price Discovery on Predictions Markets

Tech Buzz Editorial Feature

Polls missed Democrats by 5.3% in the 2026 midterms while Kalshi and Polymarket called nearly every race. Now Senate oversight changes in January.

Polls underestimated Democrats by an average of 5.3% in the 2026 midterms. Prediction markets did not. Kalshi reports that its favorites won or led in 479 of 487 races, priced at 5pm ET before a single vote was counted. The result arrives just as supervision of those markets changes hands. Democrats won the Senate, which puts Elizabeth Warren in the Senate Banking chair in January with prediction markets in her crosshairs.

The Misses All Ran One Way

Of 412 congressional, gubernatorial and generic ballot polls fielded in the final two weeks, 334 underestimated Democrats. 75 underestimated Republicans. 3 matched the result. That is a systematic lean rather than random scatter.

Pollsters are working with a weakening instrument. Survey response rates sat near 35% in the 1990s and now run below 5%, which leaves every poll resting on heavier modeling and weighting. Late in a cycle, firms also tend to converge toward the average rather than publish an outlier. Both effects push the same way when an electorate moves faster than the models assume.

The Markets Moved Months Before Election Day

Traders flipped to Democrats for Senate control in April, seven months out. By election day the market put Democratic Senate odds at 75%.

Individual races tell the sharper story. In Texas, traders favored Democrat James Talarico from mid-August, in a state that had not elected a Democrat statewide since 1994. That race drew nearly $198M, the most heavily traded contest of the cycle. In Maine, traders backed Troy Jackson over five-term Senator Susan Collins from August. In Kansas, where no Democrat had won a Senate seat in 94 years, the market moved to Adam Hamilton in the final five days.

Speed followed. Kalshi's House market hit 99% certainty at 8:18pm ET and its Senate market at 9:02pm ET. In the key Senate races, the winner was priced above 99% an average of 115 minutes before CNN made the call.

Kalshi Was Not the Only Book

Polymarket ran the same test on a smaller book and landed in much the same place. Its favorite won eight of the nine Senate races called by early Wednesday. Its odds on Democrats taking both chambers sat at 73.5% an hour before the first polls closed and reached 98.7% by 3am.

The one miss is the useful part. In South Carolina, Republican Darline Graham, appointed in July to the seat held by her late brother Lindsey Graham, was priced at 69.5%. Democrat Annie Andrews won, the first Democrat the state has sent to the Senate since 1998. A favorite at those odds should lose roughly three times in ten, so a single upset is calibration working rather than breaking. However, this was a unique case as Graham was objectively a weak candidate with no experience or qualification other than being the sister of the late former senator Lindsay Graham, and endorsed by Trump, which in this cycle was not a positive thing.

Scale is what separates the two venues. Polymarket drew about $67M across 14 Senate markets. Kalshi drew nearly $198M on Texas alone.

Prices Update, Surveys Do Not

A poll is a snapshot that takes days to field and weight. A price moves the moment someone with money at risk disagrees with it. Traders fold in polling, early voting data, fundraising, local reporting and anything else they think matters, and they pay for being wrong.

One detail in Kalshi's trader data complicates the assumption that local knowledge wins. In Maine, out-of-state traders put 61% of their money on Jackson while in-state traders put 36%. In Michigan the split was 65% against 49% for Abdul El-Sayed. The outsiders were closer.

The Edge Carries an Exposure

Open markets reward whoever knows something first, including people who should not be trading at all.

In May, Manhattan prosecutors and the CFTC charged Michele Spagnuolo, a Google software engineer based in Switzerland, with reading Year in Search data on an internal tool before publication and betting on which names would top the list. He risked roughly $2.7M and allegedly cleared $1.2M.

Government produced the same pattern twice. Gabriel Perez, a former White House teleprompter operator, was ordered in August to repay $107,539 and pay a $65,000 penalty for trading Kalshi mention markets, which settle on specific words a speaker uses, while he held the speeches in advance. Gannon Ken Van Dyke, a US Army soldier, was charged in April over Polymarket contracts on the ouster of Venezuelan President Nicolás Maduro, using information he held through his service.

Apple is not waiting for Congress. Its latest 10-K tells employees and contractors “Do not participate in ‘prediction markets’ or similar platforms,” a line drawn well inside the law because the law has not caught up.

Every one of those trades pushed a price closer to the truth and made the market harder to defend.

January Changes the Terms

Warren takes the Banking chair with prediction markets among her stated priorities. An aide told The Block on 9 November that she wants to end what they called a "Wild West-style lack of oversight in this area, with corrupt insiders profiting."

The commercial stakes run wider than elections suggest. Kalshi traded $98.2B across all markets in October, up 61% on September. Sports accounted for 78.3% of that volume. Election contracts are the public face of a business that runs mostly on games. Kalshi raised $1B in October at a $40B valuation. Polymarket, back in front of US traders through a CFTC-licensed exchange it bought, carries a $2B investment from ICE, the owner of the New York Stock Exchange.

What the Record Actually Settles

One cycle is not a law of nature, and both exchanges are grading their own homework. Kalshi's headline figure counts races where its favorite was leading alongside those already called. What makes the claim harder to wave away is that two separate books, with different traders and very different liquidity, missed in the same small places. The firmer check is the Brier score, a standard measure of how far a forecast lands from the result. Kalshi scored 0.0140 on governor races against 0.0183 for the polling model average.

The honest reading is narrower than the marketing. Markets held better information this cycle, partly because they accept information that polls cannot reach. Whether that holds after January depends on rules nobody has written yet.

“No matter what they call themselves, prediction markets like Kalshi are gambling platforms, plain and simple.”

— Letitia James, Attorney General, New York (PBS NewsHour, 24 Sep)

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