Prediction Markets as a Data Source
Markets where contracts settle at a fixed value if an event occurs, so the trading price reads directly as an implied probability. They are useful as a reference series, provided you account for spread, thin liquidity, and differences in how contracts are defined.
Why the price is convenient
In a market where a contract pays a fixed amount if an event occurs and nothing otherwise, the price is the market's probability estimate, scaled. A contract trading at sixty three cents on a one dollar settlement is a sixty three percent implied probability. No conversion from a quoted price format, no removing a built-in margin from a paired set of prices.
This makes prediction market data easy to line up against a model's own probability output, which is why it is worth ingesting even when you have other price sources.
It also means the quantity is directly comparable across events, sports, and time in a way that sport-specific price formats are not. A record of your own probabilities against market probabilities is one table with one unit.
The catch is that the convenience is only clean at the midpoint of a liquid market. Everything below is about the gap between that ideal and what an API actually returns.
Reading the price as a forecast
One caveat on interpretation. The price is the market's estimate, which is a claim about what participants collectively believe rather than a measurement of anything. On a liquid contract with many informed participants that belief is usually a strong forecast and worth treating as a benchmark. On a contract with a handful of traders it is a weak one, and the price looks identical either way. Nothing in the number itself tells you which case you are in, which is why the liquidity and spread fields have to travel with it.
What to handle before using the number
Use the midpoint of the current bid and ask. Last traded price is a historical fact about one transaction and can be arbitrarily stale on a contract nobody has touched in hours. Many APIs return last price as the headline field, which makes it the easy mistake.
Record the spread alongside the price. A wide spread means the midpoint is a weak estimate, and treating a two cent spread and a fifteen cent spread as equally informative will distort any aggregate you build.
Filter on liquidity. Thin contracts move on tiny volume. A price series from a market with almost no participation is not a consensus estimate, it is a record of a few individual opinions, and including it in an average degrades the average.
Read the resolution criteria. Two venues can list contracts that sound identical and settle differently on edge cases: what counts as the event occurring, what happens on postponement, how ties resolve. Comparing prices across venues without checking this compares two different questions.
Check whether prices sum correctly. Across a complete set of mutually exclusive outcomes, prices should sum near the settlement value. Deviation tells you about the margin and the liquidity, and a large deviation is a signal to be careful with that contract.
Storage shape
Store bid, ask, last, volume, and open interest with a timestamp, rather than collapsing to one price at ingest. The collapse is easy to do later and impossible to undo, and the fields you discarded are exactly what you need when a series looks wrong six months on.
Where they fit alongside other sources
Prediction markets are a useful reference series and not a replacement for other price data.
Coverage is narrower. These venues list a fraction of the markets a conventional book prices, concentrated on higher-profile events. For most of what a sports model covers, there is simply no contract.
Depth differs by event. Even where a contract exists, participation on a mid-tier fixture can be too thin to be informative, so coverage in the catalogue is not coverage in practice.
Timing differs. Contracts often list well ahead of conventional pricing and stay open through periods when other prices are unavailable, which makes them the only reference series for some windows.
Availability is jurisdictional and changes. Whether a given venue is accessible depends on where you are, and that has not been static. Build the ingestion so a source can be absent without breaking the pipeline, and record which sources were available for each observation, because a series that silently changed composition is a series you cannot analyse.
The pattern that works is to treat them as one input into a comparison rather than as the reference price, and to store enough context that you can tell later which they were.
This page describes data sources and is not betting advice.
Frequently asked questions
- What is a prediction market in data terms?
- A market where a contract settles at a fixed value if an event occurs and at zero otherwise. Because of that structure, the trading price reads directly as an implied probability, with no conversion from a price format and no margin to remove first.
- Should you use the last traded price?
- No, use the midpoint of the current bid and ask. Last traded price describes one historical transaction and can be arbitrarily stale on a contract nobody has traded in hours. Many APIs return it as the headline field, which makes it an easy mistake.
- Why does liquidity matter so much here?
- Thin contracts move on tiny volume, so their price is a record of a few individual opinions rather than a consensus estimate. Including thin markets in an aggregate degrades the aggregate, which is why a liquidity filter belongs in the ingestion step.
- Can prices be compared across venues?
- Only after checking resolution criteria. Two venues can list contracts that sound identical and settle differently on edge cases such as postponement, cancellation, or ties. Without that check you are comparing prices on two different questions.