For decades, polls have been the standard tool for forecasting elections and gauging public opinion. But prediction markets have emerged as a powerful alternative, and the 2024 US presidential election brought the debate into sharp focus. Markets saw what polls missed.
This guide compares both forecasting methods — how they work, where each excels, and how to use them together for the most accurate picture of the future.
How Polls Work
Traditional polling follows a well-established process. A polling firm contacts a random sample of people (typically 800 to 2,000 respondents), asks them questions, and uses statistical methods to extrapolate the results to the broader population.
Modern polling firms use several methods to reach respondents: live phone calls, automated calls (robopolls), online panels, and text message surveys. Each introduces different biases. Live phone polls tend to reach older, more engaged voters. Online panels skew younger and more tech-savvy. Getting a truly representative sample has become increasingly difficult as response rates have plummeted — from roughly 36% in the 1990s to under 6% today.
Polling firms attempt to correct for these biases by weighting responses based on demographics like age, race, education, and past voting behavior. The quality of these adjustments varies enormously between pollsters and is often the difference between an accurate forecast and a miss.
How Prediction Markets Work
Prediction markets take a fundamentally different approach. Instead of asking people what they think will happen, markets let people put money on what they believe will happen. Traders buy and sell contracts priced between $0.01 and $0.99, where the price represents the market's estimated probability of an outcome.
The mechanism is elegant in its simplicity. If you believe a candidate has a 70% chance of winning but the market prices it at $0.55, you have a financial incentive to buy — and your purchase pushes the price closer to what you believe is the true probability. Thousands of traders doing this simultaneously produce a consensus probability estimate that incorporates diverse information, analysis, and local knowledge.
Markets update in real time. When a major news event breaks — a debate performance, an economic report, a scandal — prices adjust within minutes as traders react to new information.
The 2024 Election: Markets Get It Right
The 2024 US presidential election was the most decisive test yet of prediction markets versus polls. In the weeks before the election, the RealClearPolitics polling average showed a razor-thin race, with most national polls within the margin of error and many swing state polls showing a near tie.
Prediction markets told a different story. Polymarket consistently showed Donald Trump at 60-65% probability in the final two weeks. Kalshi showed similar odds. Individual traders with large positions, most notably a French trader who placed over $30 million in bets on Trump, drew media attention and skepticism — but the markets proved correct.
Trump won decisively, carrying all seven swing states. The prediction market consensus was materially closer to the actual outcome than the polling consensus. This was not the first time: markets outperformed polls in 2016 as well, though the 2024 result was more dramatic.
Academic Research on Accuracy
The debate over prediction markets versus polls is not just anecdotal. Economists have studied it rigorously.
Wolfers and Zitzewitz (2004) published foundational research showing that prediction market prices outperformed polls in predicting election outcomes. Their analysis of the Iowa Electronic Markets found that market prices on the eve of elections were more accurate than final polls in the majority of elections studied.
Berg, Nelson, and Rietz (2008) found that the Iowa Electronic Markets produced forecasts that beat major polls 74% of the time when comparing predictions made on the same dates. This advantage was most pronounced when predictions were made further from election day, when polls are least reliable but markets can still incorporate broader information.
Arrow et al. (2008) — a letter signed by multiple Nobel laureates in economics — argued for reducing regulatory barriers on prediction markets specifically because of their demonstrated forecasting superiority over polls and other methods.
Rothschild (2009) showed that properly debiased prediction market prices outperformed raw polling averages by a significant margin, particularly in US presidential elections from 1988 to 2008.
Where Polls Excel
Despite the market's advantages, polls retain important strengths that prediction markets cannot replicate.
Demographic breakdowns. Polls reveal how different groups — by age, race, gender, education, income, and geography — plan to vote or feel about issues. Prediction markets produce a single probability number and cannot tell you anything about the composition of support.
Issue polling. Polls can measure public opinion on policy questions, approval ratings, and attitudes that are not directly tradeable. No prediction market can tell you what percentage of Americans support a particular immigration policy.
Local and down-ballot races. Prediction markets typically only have deep liquidity for major national events. For a state legislative race or a school board election, there may be no active market at all. Polls remain the primary tool for local forecasting.
Structural information. Polls provide underlying data — like enthusiasm gaps, late-deciding voters, and turnout models — that help explain why an outcome is likely. Markets only provide the bottom-line probability.
Where Prediction Markets Excel
Markets have distinct advantages that polls structurally cannot match.
Real-time updates. Markets adjust instantly to new information. A surprise jobs report, a viral debate moment, or a breaking scandal shifts prices within minutes. Polls take days to field and process, creating an inherent lag.
Skin in the game. Poll respondents face no consequences for inaccurate or dishonest answers. Traders lose real money if they are wrong. This financial incentive filters out noise and encourages genuine analysis rather than cheerleading for a preferred candidate.
Information aggregation. Markets attract participants with diverse information — political operatives with on-the-ground knowledge, data analysts running their own models, industry insiders with non-public information. Polls sample ordinary voters who may not have deeply researched the question.
Resistance to herding. Polls can create feedback loops where a leading candidate attracts more support simply because they appear to be leading. Markets resist this because buying an overpriced contract loses money, creating a natural correction mechanism.
Long time horizons. Markets can produce probability estimates months or years before an event when polling is unreliable or nonexistent. Kalshi had active markets on the 2028 presidential race years before any meaningful polls existed.
When Markets Fail
Prediction markets are not infallible. They can be inaccurate or misleading in several scenarios.
Thin markets. When few traders participate and little money is at stake, prices can be noisy and unreliable. A market with $5,000 in total volume is not a reliable signal.
Manipulation. Large traders can temporarily move prices, especially in low-liquidity markets. While arbitrageurs typically correct manipulated prices quickly, short-term distortions can be misleading.
Novel events. When an event has no historical precedent, both markets and polls struggle. Markets may anchor to irrelevant baselines or be dominated by a few traders with strong opinions but no real information.
Regulatory constraints. Betting limits on platforms like PredictIt ($850 per contract) artificially constrain the ability of informed traders to move prices, reducing accuracy.
Combining Both Methods
The most sophisticated forecasters use both tools. Polling aggregation models like those built by FiveThirtyEight and Silver Bulletin incorporate prediction market data alongside polling averages. The idea is that polls provide the underlying structural data while markets provide a real-time probability adjustment that accounts for factors polls might miss.
For individual consumers of forecasts, the practical advice is straightforward:
- Use polls to understand the landscape — who supports whom, which issues matter, how demographic groups differ.
- Use prediction markets for the bottom-line probability — how likely is each outcome.
- When they diverge, investigate why. A large gap between polling averages and market prices often signals that traders have identified something the polls are missing — or, less commonly, that markets are being driven by speculation rather than information.
- Trust markets more as election day approaches, when information is richest and participation is highest.
The 2024 election demonstrated that prediction markets have earned a permanent seat at the forecasting table. They are not a replacement for polls, but they are an indispensable complement — and for the bottom-line question of who will win, they have proven to be the sharper tool.