Prediction markets and polls: how do they differ?

A poll measures opinion, a prediction market measures probability

Who will win the election?

At first glance, this question seems easy to answer: just ask people who they intend to vote for. But two different things are actually colliding here — current public opinion and people's forecast of the final outcome.

A poll can show what people support today.

A prediction market tries to answer a different question:

What is the probability that a specific outcome will actually happen?

This distinction is one of the most important when trying to understand why polls and prediction markets should not be treated as two different ways of asking the same question.

They measure different things.

What does a poll actually measure?

Public opinion polls are used in politics, economics, social attitudes, consumer behaviour and many other fields.

For example, a poll might ask:

“If the election were held this Sunday, which candidate would you vote for?”

Suppose the results are:

• Candidate A – 37 %
• Candidate B – 32 %
• Candidate C – 16 %
• Others / undecided – 15 %

These numbers provide valuable information about respondents' choices at the time the poll is conducted.

But you cannot directly conclude from them:

“Candidate A's probability of winning is 37 %.”

37 % support and 37 % probability of winning are completely different quantities.

A poll primarily measures current attitudes, preferences or intentions.

A prediction market asks a different question

In a prediction market the question might be:

“Will candidate A win the election?”

Market participants do not need to say which candidate they themselves support.

They need to estimate what they believe will actually happen.

Suppose the market estimate is:

• Candidate A – 58 %
• Candidate B – 34 %
• Candidate C – 8 %

This is not a popularity poll of candidates.

It is a collective market estimate of the probability of the final outcome.

Therefore, at the same time, the following can perfectly logically coexist:

Poll: 37 % support candidate A.

Prediction market: 58 % probability that candidate A wins.

These numbers do not contradict each other.

They answer different questions.

Supporting and predicting are not the same thing

This is perhaps the simplest way to understand the difference between a poll and a prediction market.

Imagine a voter who supports candidate B.

In a poll asking:

“Who will you vote for?”

they answer:

B.

But asked:

“Who do you think will win the election?”

the same person might answer:

A.

There is no contradiction here.

A person's desired outcome does not necessarily match their predicted outcome.

They may weigh other people's choices, past election results, candidates' campaigns, polls, economic conditions, media information and many other signals.

Therefore:

Opinion is not the same as prediction.

Polls take a snapshot of the present

A poll can be imagined as a photograph of a particular moment.

It shows:

• what people think now;
• what they support now;
• what they intend to do now.

But the final outcome may depend on events that have not yet happened.

There may be six months left until the election.

During that time, the economic situation may change, new candidates may emerge, political scandals may occur, voter turnout may shift or campaign dynamics may change.

So a poll's result is not automatically a forecast of the final result.

This is especially important when there is still a long time until the predicted event.

A prediction market tries to look ahead

A prediction market participant can evaluate not only what the situation is today, but also how it might change before the final event.

For example:

Candidate A is supported by 35 % in polls.

Candidate B is supported by 32 %.

However, market participants might believe that candidate A has a better campaign organisation, a stronger position in a second round, or a greater potential to attract undecided voters.

Therefore the market might estimate:

Candidate A's probability of winning is 62 %.

This does not mean that the market “disagrees” with the poll.

It may use the poll result as one source of information, while also evaluating other information.

Polls can be an information source for prediction markets

This is an important reason not to frame the discussion as:

“Polls versus prediction markets.”

Prediction markets can use polls.

When a new significant poll is published, market participants can evaluate its results and adjust their positions.

For example:

Before the poll

Candidate A's probability of winning is 48 %

A surprisingly strong poll is published.

After the poll

Candidate A's probability of winning is 56 %

In this case, the poll became new information that the prediction market incorporated into its overall estimate.

But the market does not necessarily react to every poll in the same way.

Participants can evaluate the poll's sample size, methodology, the pollster's reputation, previous accuracy, how its results differ from other polls, and how much time is left until the election.

A single poll and an average of polls are also not the same thing

When assessing political forecasts, it is important to distinguish between a single poll and the aggregation of several polls.

A single poll has statistical uncertainty and may differ from other surveys conducted at the same time.

Therefore, election analysis often uses poll averages or models that combine results from several polls.

This is already a step from simple opinion measurement towards forecasting.

But even a sophisticated polling model is not necessarily the same as a prediction market.

A model's result is determined by its methodology and input data.

A prediction market's result is formed by participants' decisions and the aggregation of their information through a market mechanism.

Who participates also differs

In a traditional representative poll, who is surveyed is very important.

Researchers aim to build a sample that best reflects the population being studied.

If we want to know the opinion of Lithuanian residents, the poll sample must be designed so that the results reasonably reflect Lithuanian residents.

In a prediction market, the goal is different.

Its participants do not need to be demographically representative.

What matters more is whether participants:

• have useful information;
• can interpret it;
• are motivated to forecast accurately;
• can react to other participants' estimates and new information.

This means that a prediction market result cannot be presented as a representative public opinion survey.

Different methods, different questions

The difference can be summarised very simply.

Therefore these two percentages cannot be directly compared as the same indicator.

Which method is better?

There is no single answer.

It depends on what we want to know.

If the question is:

“How many people currently support candidate A?”

a well-conducted representative poll is the natural method.

If the question is:

“What is the probability that candidate A will win the election?”

a forecasting method is needed.

This can be a statistical model, an aggregation of expert forecasts, a prediction market, or a combination of methods.

So the more useful question is not:

“Which is better — polls or prediction markets?”

The more interesting question is:

“What information does each method provide, and how can they be used together?”

When opinion and prediction diverge

From a research perspective, situations where poll signals and prediction market signals diverge are especially interesting.

For example:

The polls' leader is candidate A.

But:

In the prediction market, candidate B is the favourite.

Why?

There can be several possible reasons.

• The market may expect that the current poll lead is not sustainable.
• A second round of voting may be anticipated.
• Predicted voter turnout may differ.
• Market participants may expect political coalitions or candidate withdrawals.
• There may be information not yet fully reflected in the polls.
• But it is also possible that the market itself is wrong.

Therefore, the difference between these signals is not the final answer but an interesting object of analysis.

It is important to watch not just the result, but also how it changes

One of the advantages of forecasting systems is the ability to track the history of probabilities.

Suppose:

The final 67 % figure is informative.

But an even more interesting question is:

Why did the forecast change from 32 % to 67 % over four months?

Then we can compare the probability changes with:

• new polls;
• political events;
• economic indicators;
• candidate decisions;
• public debates;
• other information signals.

Thus forecasting becomes not just a guess about the final result, but a study of how expectations change.

Are prediction markets always more accurate?

No.

Prediction markets should not be presented as a method that automatically solves the problems of traditional forecasting methods.

Their quality can depend on:

• the number of participants and how informed they are;
• market liquidity;
• participation incentives;
• question wording;
• market construction;
• potential for manipulation;
• participant bias;
• clear rules for determining the outcome.

Poll quality likewise depends on sample size, question wording, data collection methods, respondent answers, weighting and other methodological choices.

Therefore both methods should be judged by their methodology and actual long-term accuracy.

Forecasting quality can be tested

Here prediction markets and forecasting methods have an important property.

If forecasts are expressed as probabilities, their quality can be systematically evaluated.

For example, one can ask:

How often do events to which a 70 % probability was assigned actually occur?

If, over time, about 70 % of such events occur, the forecasts can be considered well calibrated.

Brier Score and other methods can also be used to evaluate forecast accuracy.

This allows us to move from the question:

“Who looked right?”

to a much more objective question:

“Which forecasting method consistently produces better calibrated and more accurate forecasts over time?”

Polls + prediction markets can be more valuable together

From LPMI's perspective, what is especially interesting is not the conflict between polls and prediction markets, but their combination.

We can observe at the same time:

• what people support;
• what people think will win;
• what the prediction market shows;
• what experts forecast;
• what statistical models forecast;
• what artificial intelligence forecasts.

Then, once the event is over, we can check which methods worked best.

Over time, this accumulation of data allows us to analyse not isolated cases but the accuracy of forecasting methods systematically.

From “what do we think?” to “what is the probability?”

Polls remain an important tool for measuring public opinion.

Prediction markets do not replace them.

They add a different layer of information.

A poll helps answer:

“What do people think today?”

A prediction market:

“What is the probability that a specific thing will happen in the future?”

Separating these two questions is important for building a better forecasting culture.

Because to understand the future, it is not enough to know what people want today.

We must also try to systematically assess what may actually happen tomorrow.

LPMI research direction

The Lithuanian Prediction Markets Institute studies the differences between polls, prediction markets, expert assessments, statistical models and artificial intelligence forecasts.

One of the important questions in the Lithuanian and Baltic context is:

Can combining different forecasting methods provide more information than any single method on its own?

This requires more than isolated experiments.

We need to consistently record forecasts, their changes and final outcomes, and then objectively evaluate forecast accuracy.

This would allow us to move from opinion about forecasting methods to a data-driven comparison of them.