How Accurate Are Prediction Markets? What the Research Says

In a study covering five US presidential elections from 1988 to 2004, the Iowa Electronic Markets beat 964 individual polls 74% of the time on eve-of-election accuracy. That number comes from Berg, Nelson, and Rietz's paper "Prediction Market Accuracy in the Long Run," and it's still the most-cited hard data point on whether prediction markets actually work. So the honest answer to "how accurate are prediction markets" isn't a vibe — it's a body of research with a real track record, real limitations, and a few places where the hype outruns the data.

The IEM Study: 964 Polls, One Consistent Winner

The IEM is a real-money academic market run out of the University of Iowa since 1988, trading under a CFTC no-action letter. Researchers compared IEM's closing prices on election eve against 964 separate poll results published across those same five cycles (1988, 1992, 1996, 2000, 2004). The market's implied vote share was closer to the actual result than the poll's headline number in 74% of the head-to-head comparisons. That's not a single lucky cycle — it's a pattern across sixteen years and four different presidencies' worth of elections.

The mechanism behind that edge is straightforward: a poll respondent pays nothing to be wrong. A trader on IEM, Polymarket, or Kalshi puts capital behind their view, and capital gets reallocated toward whoever is right more often. That's price discovery doing what it's designed to do — not magic, just an incentive structure polls don't have.

Calibration: Does a 70% Price Mean 70% Odds?

Accuracy and calibration are different questions. Accuracy asks "did the favored side win." Calibration asks "when the market says 70%, does the event actually happen about 70% of the time, over many markets." You test this by bucketing resolved markets by their traded price and checking the realized outcome frequency in each bucket.

The research on this is reassuring but not perfect: events priced around 70% have historically resolved YES roughly 68-72% of the time — close enough to call the market well-calibrated at that level. The buckets near the extremes (90-100% priced) tend to be the most accurate of all, which makes sense — near-certain outcomes are, by definition, easier to get right. The messiest zone is the 40-60% band, where genuine uncertainty concentrates and small changes in the news cycle can swing price without changing the underlying probability much at all.

This is also where prediction market liquidity starts to matter for accuracy. A thinly traded market can drift away from its "true" calibrated price on a handful of large orders, then snap back once more traders show up. Depth is part of what keeps a price honest.

Brier Scores: Markets vs. Polls, Head to Head

A Brier score measures the squared error between a probabilistic forecast and what actually happened, on a scale from 0 (perfect forecast) to 1 (maximally wrong), with 0.25 as the baseline for a forecast with zero predictive signal — the equivalent of a coin flip. Lower is better.

Secondary analysis of the 2024-2025 election cycle reported Polymarket beating traditional polling aggregates in about 73% of the races tracked, with average Brier scores in the 0.15-0.18 range versus 0.22-0.25 for the comparable polls. Flag that number for what it is: a reported figure from third-party analysis, not an audited statistic Polymarket itself publishes. Treat it as directionally useful, not gospel.

Prediction market accuracy — verified research vs. reported figures

Study / metric	Period	Sample	Result
IEM vs. polls (Berg, Nelson, Rietz)	1988-2004	964 polls, 5 elections	Market closer to actual result 74% of the time
Calibration, 70% bucket	ongoing	markets priced ~70%	Resolved YES ~68-72% of the time
Brier score comparison (reported)	2024-2025	Polymarket election markets	~0.15-0.18 avg vs. ~0.22-0.25 for polls

Markets Measure Expected Outcomes. Polls Measure Opinion.

The reason these two instruments produce different numbers isn't that one is "smarter" — they're measuring different things. A poll asks a sample of people what they currently believe or intend. A market prices what traders, weighted by the money they're willing to risk, think will actually happen. Wolfers and Zitzewitz laid this out formally in their 2004 Journal of Economic Perspectives paper "Prediction Markets": markets aggregate dispersed private information through price, and because that information gets bid on rather than volunteered, the aggregate tends to correct for individual bias faster than a static poll snapshot can.

Practically, this means a market can move on information a poll hasn't caught up to yet — a debate performance, a court ruling, a data leak — while a poll taken a week earlier is already stale. It also means a market can occasionally overreact to a single loud news cycle before liquidity pulls it back toward calibration.

Where Prediction Markets Get It Wrong

Four failure modes show up repeatedly in the data and in practice:

1. Thin liquidity. Long-tail, low-volume markets can be moved meaningfully by a single mid-size order, which temporarily disconnects price from the crowd's actual view.
2. Novel events. First-of-their-kind questions have no historical base rate to anchor traders, so early pricing is noisier until enough volume builds.
3. Whale positioning. A large position taken to hedge risk elsewhere, rather than to express a genuine probability view, can distort price near resolution.
4. Mid-range uncertainty. The 40-60% band is inherently the hardest zone to calibrate — that's where real disagreement lives, not measurement error.

None of this means the instrument is broken. It means accuracy is a function of participation and depth, not a fixed property of the platform.

If you're trading on the assumption that a market's current price reflects a well-calibrated probability, the way to check that assumption — instead of taking it on faith — is to look at how a strategy would have performed historically against resolved markets. That's exactly what backtesting is for: replaying past order-flow and price action to see whether your read on calibration actually held up before you risk live capital. PolyMarketMaker's terminal includes a backtesting and paper-sim mode built for this, alongside the candle, POC, and CVD tooling you'd use to study how a market's price actually moved into resolution.

FAQ

Are prediction markets more accurate than polls?

In the IEM study, market prices beat 964 individual polls 74% of the time across five presidential elections from 1988-2004. Reported analysis of the 2024-2025 cycle put Polymarket ahead of polling aggregates in roughly 73% of tracked races, though that's a secondary-source figure, not an official audited number.

What is a Brier score and why does it matter for prediction markets?

It's the squared error between a probability forecast and the actual outcome, 0 to 1, lower is better, with 0.25 as the no-signal baseline. Reported Brier scores for Polymarket's 2024-2025 election markets averaged 0.15-0.18 versus 0.22-0.25 for comparable polls.

Can prediction markets be wrong?

Yes — thin liquidity, novel events with no base rate, whale-driven distortion, and the inherently noisy 40-60% probability band are all documented failure points.

How is calibration measured in prediction markets?

By bucketing resolved markets by their traded price and checking the realized outcome frequency per bucket. The 70% bucket has resolved YES roughly 68-72% of the time historically.

Do Polymarket and Kalshi have the same accuracy?

Both run an order book where price equals implied probability, so the same calibration principles apply to each. Accuracy on any given market tracks liquidity and participation more than which venue hosts it — see how that pricing mechanism differs from a sportsbook's for the contrast with a house-set line.

For the broader mechanics of how these markets actually work, start with what prediction markets are and how they price probability. If you're building a trading approach around calibration data, see prediction market strategies and how to backtest a prediction-market strategy. For a live application of these accuracy questions, the 2026 midterms markets are the highest-volume test case running right now.

This article is for educational purposes only and is not financial advice. Trading involves risk of loss.