Introduction
Election forecasting is a vital tool in modern democracy, shaping public opinion and influencing how citizens and analysts understand politics. Forecasting methods have evolved from traditional polls to advanced models and prediction markets, such as Kalshi, Polymarket, and PredictIt, that react in real time to new information. Historically, the public relied on polling data, which is static, weeks old, and increasingly prone to error with lower response rates, to gauge the temperature of a political race or election. Today, however, we have transitioned into a world where information moves at the speed of light, so we must demand that the methods we use to track it do the same.
This article explores the structural reasons why market-based odds provide a more responsive "truth signal" than traditional surveys. We will break down the mechanics of how political odds translate into probabilities, and more importantly, how you can interpret these real-time movements to identify shifts in information flow before they hit the evening news. By shifting the focus from simply describing forecasts to analyzing the signals behind them, we can better navigate the uncertainty inherent in any high-stakes election.
Primary methods in modern election forecasting
In the high-stakes arena of contemporary election forecasting, two primary methodologies dominate the conversation: traditional opinion polls and prediction markets. While they both aim to quantify the likelihood of a victory, they function in vastly different ways.
Polls
Polling remains the baseline for most political discourse, but it is a tool built on retrospective data.
Sampling Methods: Pollsters attempt to contact a statistically significant group of “registered voters” or "likely voters," often filtering by demographics like age, race, and education to match census data, and then attempting to refine this pool by estimating who will actually vote in the election, a subjective and uncertain exercise. Polls are typically conducted through phone calls or online surveys, but are now increasingly having low response rates.
The Weighting Problem: Because certain groups are harder to reach (non-response bias), pollsters "weight" their results to ensure a representative sample. If a group, for instance, working-class voters in the Rust Belt, refuses to answer calls at a high rate, the model's accuracy degrades.
The Lag Factor: Most high-quality polls operate on a "survey cadence" of several days. By the time data is collected, cleaned, and published, it is often a snapshot of a sentiment that existed 72 to 96 hours ago. This inherent lag means polls are perpetually chasing the news cycle rather than leading it.
Prediction markets
Presidential prediction markets serve as real-time information aggregators. On platforms like Kalshi, Polymarket, and PredictIt, forecasting is not about what people say they will do; it is about what beliefs they are willing to put their own money on. Prediction markets are a form of futures trading for events, which is why Kalshi is regulated by the Commodity Futures Trading Commission (CFTC).
Price as Probability: A Kalshi contract for a candidate winning typically trades between $0.01 and $0.99. A price of $0.62 means the market assigns a 62% probability to that outcome.
Achieving Accuracy: Prediction markets achieve forecast accuracy through financial incentives. Participants are not just answering a survey; they are putting "skin in the game" on the election outcome. This attracts informed traders who spend their time analyzing polling data, fundraising metrics, campaign strategies, candidate quality, and historical patterns among countless other factors that drive a candidate’s probability to win. If the odds are "wrong" based on available data, an incentive exists for a trader to correct it, driving the price toward the true probability.
How presidential prediction markets work in practice
To a trader, a Kalshi, Polymarket, or PredictIt market is more than a place to trade or buy shares on one's favorite candidate; it is a market-based information machine.
Market mechanics
Prediction markets are structured as limit order books, similar to equity markets. Buyers and sellers post the prices at which they are willing to trade "Yes" or "No" contracts.
Binary Settlement: These contracts are "winner-takes-all." If the event occurs, the contract settles at $1.00; if not, it goes to $0.00.
Information Incorporation: Because the market is open nearly 24/7, it reacts instantly to exogenous shocks. Whether it is a surprise economic report or a candidate's health scare, the election market prices it in before the first pollster can even draft a new survey question.
Role of participants
The market is populated by a diverse array of actors, each providing a different kind of "alpha" (what prediction markets call information that informs their traders). These actors might have different motives. For some, it’s having skin in the game for a political movement in which they believe, for others it’s purely about making money; for another group, it might be about hedging risk.
All participants are unique, coming from different walks of life, basing their trades on slightly different pieces of information, and holding different opinions on how to process this information. Some might be looking at local precinct trends, some might be watching candidate speeches intensely, and some might be basing their trades on a conversation they overheard at their son’s baseball game. What is beautiful about these markets is that they aggregate all this scattered information into an efficient price, odds, or probability that each candidate is going to win.
Case studies
The June 2024 presidential debate: This is perhaps the most famous example of an election market speed vs. polling lag. Within 15 minutes of the debate starting, Biden's odds on prediction markets began a vertical descent (Trump's rose slightly). Traders recognized the performance's impact instantly. Conversely, it took major polling outfits nearly two weeks to confirm what the presidential prediction market had already priced in.
Election night prediction market speed: In 2024, markets called Trump winning hours before the networks. As early numbers from Florida and Georgia trickled in, traders recognized Trump’s "over-performance" relative to expectations and adjusted the presidential trading odds toward a swing-state sweep, while TV anchors were still calling the race "too close to call" between Trump and Harris.
The Trump “shy voter” effect: In 2020 and 2024, polls struggled to reach "low-propensity" voters. Presidential prediction markets remained stickier for Trump, as traders priced in the "hidden voter" theory based on 2016's precedent, leading to a much more accurate calibration of the final results.
Measuring accuracy: Prediction markets vs. traditional polls
Historically, both methods have their merits, but they excel in different areas. While polls provide a broad view of the electorate's sentiment, election markets provide a sharper view of the outcome. It is important to note that markets do incorporate polls into them where the opposite cannot be done. It is likely that prediction markets would be less accurate without any polling data.
Accuracy rates & update advantages
The primary advantage prediction markets have over traditional polls is responsiveness. A poll is a "point-in-time" measurement, whereas a market is a "continuous-time" measurement.
Volatility as a feature: In a poll, a sudden shift is often dismissed as an "outlier." In a market, a sudden shift is a signal that new information has entered the arena.
Bias correction: Markets are less susceptible to "social desirability bias,” the tendency of poll respondents to lie about supporting a controversial candidate. Traders are motivated by profit, not social approval, and will buy shares accordingly, leading to a more honest aggregation of probability.
When polls have been more accurate than prediction markets
2022 Midterms: This was a rare instance where high-quality polls (like NYT/Siena) outperformed the markets. The "Red Wave" narrative became so dominant in the trading community that it pushed Republican odds to 70%+ to win the Senate. The polls correctly signaled that the race was actually a lot closer. This illustrates that markets are not always efficient and can be susceptible to narratives. However, it is likely that going forward the prediction markets will be more accurate because they will be able to price in this example where they were not as accurate.
What shapes forecasts: Key factors behind market prices and poll results
Traders and pollsters look at the same world but see different things.
Political climate & sentiment: Polls measure how a voter feels today. Markets measure how that feeling will translate into a result in November. For example, a candidate might be unpopular (based on low approval ratings in polls), but if the opponent is seen as incompetent, the market might favor the "lesser of two evils" even if the polling data sees both as unpopular and incompetent
Media influence: Media coverage often creates "noise." Prediction markets help in reducing bias because they force people to differentiate between a "scandal of the week" and something that actually moves voters. If a scandal breaks, but the Kalshi price doesn't budge, it tells you that the "smart money" doesn't believe it will impact the ultimate result of the election
Public sentiment and behavior: Pollsters often struggle with "turnout models." Markets incorporate the turnout models by looking at countless different factors (both quantitative and qualitative) as leading indicators for Election Day participation.
Conclusion
Modern election forecasting has moved beyond the era of the "expert pundit" and into the era of the "efficient market" through political trading. By understanding the mechanics of how these odds are formed, analysts can navigate the noise of a campaign cycle with greater clarity.
Encouraging a deeper understanding of prediction markets (e.g. Kalshi, Polymarket, and PredictIt) and the dynamics that affect them fosters a more informed public discourse. In the race for political truth, the market doesn't just predict the future; it helps us understand and price the present.