Forecasting vs Polling: What Better Predicts Elections?

Every election cycle raises the same question: can we trust the polls? After several high-profile misses in recent years, public confidence in traditional polling has eroded, and alternative approaches have gained attention. Forecasting models, which combine polls with other data, and prediction markets, which let people bet real money on outcomes, both claim to offer better predictions. But which method actually performs best? This guide examines how polls, forecasting models, and prediction markets work, compares their historical accuracy, and draws practical lessons from recent elections including 2024. Understanding the strengths and limitations of each approach makes you a more informed consumer of political information.

How Polls Work and Why They Matter

Public opinion polls attempt to measure what a population thinks by surveying a representative sample. A well-designed poll contacts a random selection of people, asks structured questions, and weights the results to match the demographic profile of the broader population. The goal is to produce a snapshot of public opinion at a specific moment in time.

Polls serve a critical function in democratic societies. They provide a feedback mechanism between citizens and elected officials, they inform campaign strategy, and they help media organizations cover elections with empirical data rather than pure speculation. At their best, polls are remarkably accurate. The final polling averages in most U.S. elections come within two to three percentage points of the actual result.

The challenge is that two to three points of error can be the difference between predicting the right winner and the wrong one in a close election. And in an era of tight races and polarized electorates, that margin matters enormously.

The Limitations of Modern Polling

Polling faces structural challenges that have worsened over the past two decades. The most fundamental is declining response rates. In the 1990s, telephone polls reached roughly 35% of the people they contacted. By 2024, that figure had dropped below 5% for most polling organizations. When 95% of the people you call refuse to participate, the 5% who do respond may not be representative of the broader population.

Nonresponse bias is the technical term for this problem, and it has proven stubbornly difficult to solve. If people who answer polls differ systematically from people who do not, in their political views, demographics, or likelihood of voting, then no amount of statistical weighting can fully correct the distortion. There is growing evidence that this is exactly what has happened in recent election cycles, with certain voter groups consistently underrepresented in poll samples.

Likely voter screens introduce another layer of uncertainty. Not everyone who is registered to vote actually shows up on election day. Pollsters use models to estimate who will vote and filter their samples accordingly. These models rely on assumptions about turnout that may not hold, especially when candidates or events motivate unusual levels of participation from groups that historically vote at lower rates.

Social desirability bias and herding further complicate the picture. Some respondents may not truthfully report their preferences, particularly for candidates perceived as controversial. And some pollsters, consciously or not, adjust their methodologies when their results diverge too far from the consensus, a phenomenon known as herding that reduces the apparent disagreement among polls while potentially masking real uncertainty.

What Forecasting Models Do Differently

Election forecasting models go beyond raw polling data. They combine polls with structural factors, historical patterns, and economic indicators to produce probabilistic predictions. The most well-known models, like those published by FiveThirtyEight, The Economist, and academic researchers, treat polls as one input among many.

A typical forecasting model might incorporate the current polling average, the state of the economy (GDP growth, unemployment, inflation), presidential approval ratings, incumbency advantage, campaign fundraising data, and historical patterns about how polls shift between the current date and election day. The model then runs thousands of simulations to produce a probability distribution of outcomes.

The key advantage of forecasting models is that they account for uncertainty explicitly. A model might say that Candidate A has a 70% chance of winning, which means that Candidate B wins in 30% of simulated scenarios. This probabilistic framing is more honest than a poll that shows Candidate A leading by two points without clearly communicating how often two-point leads evaporate.

Forecasting models also account for correlated errors across states. If a polling error causes one swing state to be off by three points, similar states are likely off by a similar amount. Good models capture these correlations, which means their estimates of upset probability are more realistic than simply multiplying independent state-level probabilities.

Prediction Markets as a Third Approach

Prediction markets offer a fundamentally different methodology. Rather than surveying voters or building statistical models, they aggregate the judgments of people who put real money behind their beliefs. Platforms like Polymarket allow participants to buy shares in election outcomes, with prices reflecting implied probabilities.

The theoretical appeal is powerful. Markets create financial incentives for accuracy. If you have private information or superior analysis, you can profit by trading on it. And because markets are continuous, they incorporate new information in real time rather than waiting for the next poll to be conducted and published.

Prediction markets also aggregate information from diverse sources. Some traders rely on polling data. Others use forecasting models. Some have on-the-ground knowledge from canvassing or campaign work. Some trade on gut instinct informed by years of political observation. The market price reflects the weighted average of all these perspectives, with more weight naturally going to participants who put more money behind their convictions.

Historical Accuracy Comparison

Comparing the accuracy of these three approaches is complicated by the fact that they measure slightly different things. Polls estimate vote share. Forecasting models and prediction markets estimate the probability of winning. A model that gives a candidate a 70% chance of winning is not wrong if that candidate loses, any more than a weather forecast calling for a 70% chance of rain is wrong when it stays dry.

With that caveat, the evidence suggests that forecasting models modestly outperform raw polling averages, and prediction markets perform comparably to or slightly better than forecasting models. A comprehensive analysis of U.S. elections from 2000 through 2024 found that prediction markets were better calibrated than polls in most election cycles, meaning events they priced at 70% actually occurred close to 70% of the time.

However, the differences are not dramatic. All three methods tend to converge as election day approaches, and all three struggle with the same underlying challenge: predicting turnout and the behavior of voters who are difficult to survey.

Why Polls Miss: Lessons from 2024

The 2024 U.S. presidential election provided another data point in the ongoing debate about polling accuracy. Pre-election polls underestimated support for certain candidates in key states, continuing a pattern observed in 2016 and 2020.

Several factors contributed. Nonresponse bias remained a challenge, with certain demographic groups, particularly non-college-educated voters in rural areas, proving difficult to reach and survey. Late-deciding voters broke disproportionately in one direction, a dynamic that polls conducted days before the election could not capture. And differential turnout, where the actual electorate differed from the electorate pollsters modeled, amplified the errors.

The 2024 experience reinforced an important lesson: polling errors are not random. They tend to be correlated across elections and across states, which means they can systematically benefit one party over another. This autocorrelation in polling error is something forecasting models attempt to account for but cannot fully eliminate.

Prediction markets in 2024 adjusted more quickly than polling averages as election night results came in, demonstrating their real-time information processing advantage. However, pre-election market prices showed that markets were also surprised by some results, suggesting they rely heavily on the same polling data that proved imperfect.

Which Method Should You Trust?

The honest answer is that no single method deserves unconditional trust. Each has strengths that complement the others.

Use polls to understand the range of plausible outcomes and to track how public opinion shifts over time. Pay more attention to polling averages than individual polls, and look for polls that are transparent about their methodology and response rates.

Use forecasting models to understand the probabilistic landscape. A model that gives a candidate a 65% chance of winning is telling you something importantly different from a model that says 95%. Pay attention to the uncertainty intervals, not just the topline probability.

Use prediction markets as a real-time consensus indicator that incorporates information beyond polls. Markets are particularly useful for tracking how probabilities shift in response to breaking news, debates, and other events.

The most sophisticated approach is to triangulate across all three, treating disagreements between methods as informative signals. If polls show a tight race but prediction markets heavily favor one side, that divergence itself is a piece of data worth investigating.

For a comprehensive understanding of how prediction markets work and how to interpret them, explore the free textbook Learning Prediction Markets on DataField.dev. For a broader look at how data analytics is transforming political analysis, check out Political Analytics. These resources will give you the tools to evaluate elections and political forecasts with the rigor they deserve.