How Prediction Markets Actually Work
A practical guide to how prediction markets like Kalshi and Polymarket work, how prices translate into probabilities, how contracts settle, and where traders can misread the market.
Prediction markets turn future events into tradable contracts.
Instead of buying a share of a company, traders buy contracts tied to outcomes such as an election result, an economic release, a sports event, a company announcement, or whether a particular event happens before a deadline.
Platforms such as Kalshi and Polymarket use slightly different market structures, but the basic idea is similar: market prices move as participants buy and sell contracts based on what they think is likely to happen.
That makes prediction markets useful as forecasting tools.
It also makes them easy to misunderstand.
A displayed probability is only meaningful when you understand the exact contract underneath it: what counts as a winning outcome, when the measurement period ends, which source determines settlement, and what happens in unusual edge cases.
The video below is my longer breakdown of how these markets work and why they have started attracting significantly more attention from traders, institutions, and the broader financial industry.
A prediction market is a market for an outcome
The simplest prediction-market contract is binary.
Something either happens or it does not.
For example:
Will Candidate A win the election?
A YES contract may trade at 63 cents while NO trades at 37 cents.
If the contract settles YES, a winning YES contract pays $1.
If the contract settles NO, it pays $0.
Because the payout is fixed, traders often interpret the market price as an implied probability.
A YES price of 63 cents is therefore commonly described as the market assigning roughly a 63% probability to the event.
That interpretation is useful, but it comes with an important qualification.
The market is not necessarily predicting the broad event described in a headline. It is predicting whether the specific contract will resolve YES under its written rules.
That distinction becomes important surprisingly often.
Implied probability ~63%
The rules matter as much as the price
Every prediction market needs an objective way to determine the winner.
That means each contract has some combination of:
- a defined event
- a deadline or observation period
- a settlement source
- qualifying conditions
- exclusions
- rules for unusual outcomes
For simple markets, these rules may barely matter.
For complicated markets, they can determine the entire trade.
A market asking whether a company will "achieve AGI," for example, may actually resolve based on whether an eligible company makes a specific qualifying announcement before a particular date.
A shipping market may resolve using a specific data provider even if other datasets estimate a different real-world number.
This is why reading only the market title can be dangerous.
The contract is the thing being traded.
Kalshi and Polymarket use different structures
Kalshi operates regulated event contracts in the United States.
Its markets can cover elections, economics, weather, technology, culture, financial events, and other measurable outcomes.
Many Kalshi markets use a familiar YES/NO contract structure, although some events contain several contracts around the same underlying variable.
For example, instead of asking only whether a temperature will exceed one number, an event may contain several ranges or thresholds.
Polymarket also lets participants trade event outcomes, but its infrastructure and market structure differ from Kalshi.
The useful comparison is not which interface looks better.
It is that both systems turn disagreements about future outcomes into prices.
Prices aggregate information
Prediction markets become interesting because every participant has an incentive to act on information they believe the market has mispriced.
One trader may know more about polling.
Another may understand regulatory procedure.
Another may have better weather models.
Another may specialize in shipping data.
Another may simply believe the current consensus is wrong.
When those views are expressed through buying and selling, the resulting price becomes a compressed representation of the market's collective expectations.
That does not make the market automatically correct.
It makes the market continuously updateable.
New information can change prices almost immediately.
Market probabilities are not facts
A 70% market does not mean an event will happen.
It means traders are currently willing to transact at prices consistent with roughly that probability.
Markets can be wrong.
They can also be:
- thinly traded
- temporarily distorted
- driven by incomplete information
- affected by unclear settlement rules
- exposed to measurement problems
- influenced by large individual positions
This is why liquidity and market structure matter.
A heavily traded market with many independent participants generally tells you more than a contract with very little volume and a wide bid-ask spread.
Settlement is where prediction markets become concrete
At some point, forecasting stops and the contract has to pay.
That requires a source of truth.
Depending on the market, settlement might reference:
- election certification
- government economic data
- an official company announcement
- a weather station
- a financial index
- a sports result
- a court ruling
- another specified dataset
The key question is not simply:
What happened?
It is:
What does the contract say happened?
Those are usually the same thing.
Occasionally they are not.
That gap is one of the most interesting parts of prediction markets because it creates what I think of as measurement risk.
A contract can settle correctly according to its rules while still measuring an imperfect version of the broader real-world event.
What actually happens in reality (inflation, progress, weather)
The exact agency, index, or announcement named in the contract rules
Payout executed strictly based on that source, even if measurement is imperfect
Why prediction markets are attracting more attention
Prediction markets have existed for a long time.
What has changed is their accessibility, liquidity, range of markets, and cultural visibility.
Major political events brought enormous attention to the category, but the broader opportunity extends well beyond elections.
Markets now exist around:
- interest rates
- inflation
- artificial intelligence
- company events
- geopolitics
- weather
- sports
- legislation
- entertainment
- economic releases
That means prediction markets increasingly function as a kind of real-time forecasting layer for the internet.
You can look at what traders are pricing before an event occurs and then watch those expectations move as new information appears.
Where prediction markets are most useful
I think prediction markets are most valuable when three things are true.
The question is clearly defined.
The settlement source is credible.
And enough informed participants are trading for the price to contain meaningful information.
When those conditions exist, the market can become a useful forecasting signal even for someone who never places a trade.
You can use the price as another piece of evidence alongside polling, economic models, expert forecasts, company information, or other research.
The mistake is treating the number as an oracle.
Read the contract before the percentage
The most important habit when looking at any prediction market is simple:
Read what actually settles.
The headline tells you what the market is about.
The price tells you what traders are currently willing to pay.
The contract tells you what you're actually betting on.
Those three things are related, but they are not always identical.
That distinction becomes especially important in complicated markets, which is why I have started looking more closely at settlement rules, data sources, and the information underneath the displayed probability.
Related Library Content
Sources & References
- [1]
- [2]Polymarket Information Markets(Polymarket)
- [3]CFTC Event Contract Framework & Rulemaking(Commodity Futures Trading Commission)