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Prediction markets can offer useful forecasts of election outcomes, but that does not establish that Kalshi is more accurate than polls across elections—or that markets can replace polling. The headline refers to a Fast Company abridged interview with Kalshi co-founder Luana Lopes Lara; the available listing says she argues markets can beat polls, but the interview itself is not available to verify her supporting arguments or exact words.
What “beat the polls” means
A poll and a prediction-market price measure different things. Polls ask people about preferences or intended behavior; a market price reflects the beliefs of people trading contracts tied to an outcome. Kalshi’s own explainer puts the distinction this way: “polls are about what people want to have happen,” while markets concern what participants think will happen. It also cautions that odds are not polling percentages. Kalshi’s 2026 midterm-market explainer says, “Kalshi is not a poll, nor is it an oddsmaker.”
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A market price interpreted as a probability is therefore a forecast, not a vote count, a direct measure of voter support, or a guarantee. A market aimed at predicting who wins does not necessarily predict vote share, identify voter preferences, or explain why people support a candidate.
What the evidence says about election forecasting
Markets may help forecast outcomes, but a single comparison proves little
The Washington Post examined 268 candidates who had a 70 to 80 percent chance of winning on Kalshi or Polymarket on at least one day during the two months before their primary. That analysis concerns this selected group and time window; it is not a universal accuracy rate for prediction markets. The article describes markets as potentially useful for predicting results, while noting that political professionals are not replacing polls with them. PredictIt co-founder John Aristotle Phillips drew the distinction: “Prediction markets [are] pretty damn good at telling you what the outcome’s gonna be,” but “Prediction markets are no good at telling you why people feel the way they do.” The Washington Post’s June 2026 analysis
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Historical research is not a current Kalshi-versus-polls test
A University College Dublin working paper by Constantin Bürgi, Wanying Deng, and Karl Whelan, dated July 2025, notes earlier research on the Iowa Electronic Markets reporting that its vote-share forecasts for the 1988 U.S. presidential election outperformed opinion polls. That is a historical finding about another market and election, not proof that Kalshi’s current election markets outperform polls.
The same UCD paper, Makers and Takers: The Economics of the Kalshi Prediction Market, reports average contract returns of minus 20 percent before fees and minus 22 percent after fees in the Kalshi data it analyzed, alongside favorite-longshot bias. Those are participant-return and pricing findings—not measures of election-forecast accuracy and not a head-to-head comparison with polls. Read the UCD working paper.
A Federal Reserve staff paper studies how Kalshi prices can be used to construct forecasts for macroeconomic measures and describes event contracts as financial positions whose payoffs depend on real-world outcomes. It offers context about the product and its possible use as a forecasting data source, but does not assess whether election markets beat election polls. Read the Federal Reserve staff paper.
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A fair comparison must align the forecast and poll on the same race, date, target, and horizon. Comparing a market’s favorite with a poll’s topline can mislead if one is forecasting the winner while the other reports candidate support.
- Match the target: Compare winner probabilities with winner outcomes, or vote-share forecasts with vote share—not one with the other.
- Match the timing: Use the market price and poll information available at the same forecast date, and specify how far the election is from that date.
- Check what the poll measured: Note field dates and the surveyed population, as well as whether the poll asks about preference, intention, or another measure.
- Check market conditions: Record contract rules, trading volume, and liquidity. A probability from a thin market may warrant more caution than one from an active market.
- Score many forecasts: A probability forecast should be assessed across a sufficiently large set of outcomes, using calibration or a proper scoring rule such as the Brier score. One surprising win or loss cannot establish which method is generally better.
Kalshi says its 2026 midterm markets display Brier scores, low-volume labels, and activity feeds. The company describes lower Brier scores as better and says its displayed scores use historical data weighted by volume and time until resolution. It also says forecasting success tends to increase with trading volume. These are Kalshi’s explanations of its product and methodology, not independent validation of the scores or proof of superior performance. Kalshi’s explainer
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why market odds can be informative—and easy to misread
Prices can change as traders react to new information, so a market can provide a concise, changing estimate of an outcome. But readers should not treat odds as polling percentages or assume that a high probability guarantees a result. Liquidity, the question’s wording, the contract’s resolution rules, and the forecast horizon all shape what a displayed price can tell you.
Associated Press reporting on 2026 election markets described election administrators’ concern that the public could confuse market odds with polling or vote counts. It also reported a concern that a wealthy partisan might move odds to shape perceptions. Kalshi general counsel Rick Heaslip countered that in a highly liquid market, traders would push a manipulated price back and the manipulator would lose money. That is the company’s argument as reported by AP—not evidence that manipulation is impossible or that attempted moves have a known success rate. Associated Press reporting on election markets
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What the claim does—and doesn’t—establish
Lara’s claim, as described in Fast Company’s listing, raises a reasonable question: can market prices sometimes forecast an election outcome better than polling? The available evidence supports treating markets as a potentially useful, distinct source of information. It does not establish that Kalshi consistently beats polls, and it gives no basis for treating markets as a substitute for polls’ information about voter preferences or motivations. A stronger conclusion would require a transparent, comparable evaluation across many races and dates, with the target, market liquidity, poll populations, and scoring method made explicit.
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