Are Prediction Markets More Accurate Than Polls?

What 2024 Data Proves (And How Traders Profit From It)

Last updated: February 2026

Prediction markets called the 2024 presidential election correctly. Polls did not. On November 4, 2024 — the day before the election — Polymarket priced Donald Trump at roughly 57% to win. The polling averages showed a toss-up, with some models giving Kamala Harris a slight edge. Trump won decisively.

That single result turned prediction markets into a mainstream topic. But one correct call doesn’t settle the question. Are prediction markets consistently more accurate than polls? When do they fail? And for traders, the real question is: how do you profit from the gap between what markets say and what polls say?

This guide covers the data — academic research, historical track records, and the 2024 results — and explains how traders use the polls-vs-markets divergence as a trading signal.

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The 2024 election: what actually happened

Prediction markets gained credibility in 2024 because they outperformed traditional polls on the highest-profile event of the year.

The market view

Polymarket‘s presidential market showed Trump leading consistently from late September 2024 onward. His price ranged from $0.55 to $0.65 in the final weeks, settling around $0.57 on election eve. Kalshi showed similar pricing. The markets weren’t just saying Trump had a slight edge — they were pricing a meaningful favorite.

The polling view

Major polling averages (FiveThirtyEight, RealClearPolitics, The Economist) showed a near-dead-heat through October and into early November. Some models gave Harris a 1–2 point lead in the national popular vote. State-level polls in battleground states showed margins within the error range. The consensus framing: this race is too close to call.

The outcome

Trump won the Electoral College with margins in swing states that exceeded most polling estimates. The prediction market price of $0.57 was closer to the actual outcome than the polling consensus of roughly $0.50.

Why the markets got it right

Several factors explain the divergence. Prediction market participants had financial incentives to be accurate — they were risking real money, not answering a survey question. Northwestern University researchers have found that the act of putting money on a prediction forces more careful reasoning than simply stating an opinion.

Markets also incorporated information that polls couldn’t capture easily. On-the-ground early voting data, voter registration trends, and late-breaking news all flowed into prediction market prices in real time. Polls, by contrast, are snapshots taken days earlier and reflect stated intentions, not revealed preferences.

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What the academic research shows

The 2024 election was dramatic, but researchers have been studying prediction market accuracy for decades.

Vanderbilt University: platform-by-platform accuracy

A Vanderbilt study by Clinton and Huang analyzed prediction market accuracy across three platforms during the 2024 cycle. Their findings:

• PredictIt achieved 93% accuracy on resolved markets
• Kalshi achieved 78% accuracy
• Polymarket achieved 67% accuracy

These differences matter for traders. PredictIt’s higher accuracy may reflect its smaller position limits ($850 max), which attract a more information-driven crowd with less whale distortion. Polymarket‘s lower accuracy may reflect the influence of large directional bets — including a well-documented French trader who placed tens of millions in Trump contracts, potentially moving prices beyond what the underlying probability justified.

The study also found that prices diverged across exchanges, meaning the three platforms weren’t always giving the same signal. This creates arbitrage opportunities for traders watching all three simultaneously. (For a step-by-step guide to executing these trades, see our prediction market arbitrage guide.)

The Iowa Electronic Markets: the original dataset

The Iowa Electronic Markets (IEM), run by the University of Iowa since 1988, provide the longest track record of prediction market accuracy. Research published over multiple election cycles found that IEM prices outperformed polls 74% of the time when compared at the same point in time before an election. The markets were particularly better at longer time horizons — months before an election, polls are notoriously noisy, while market prices already incorporate structural factors.

UCLA Anderson: the combination approach

Researchers at UCLA’s Anderson School of Business found that the most accurate forecasts come from combining polls, prediction markets, and economic fundamentals. None of the three is best in isolation. Their model showed that:

• Polls alone capture voter sentiment but miss turnout dynamics and late shifts
• Prediction markets alone capture aggregate information but can be moved by large individual bets
• Economic indicators alone capture structural conditions but miss candidate-specific factors
• Combining all three produced the smallest forecast errors across multiple election cycles

For traders, the takeaway is: prediction markets are not infallible truth machines. They’re one signal among several. When markets and polls diverge, the profitable question isn’t “which one is right?” — it’s “what is each one seeing that the other isn’t?”

Bayesian analysis of Polymarket data

A 2025 research paper using Bayesian Structural Time Series analysis compared Polymarket prices to polling aggregates across the 2024 election cycle. The analysis found that prediction market prices incorporated new information faster than polling averages, typically adjusting within hours of major events while polls took days (because polls take time to conduct and publish). However, the markets also showed periods of overreaction — sharp price swings on viral news that subsequently reversed.

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When prediction markets fail

Prediction markets are not always right. Understanding their failure modes is essential for trading.

Large trader distortion

The most well-documented failure mode is large individual bets that move prices away from the true probability. During the 2024 election, a single French national reportedly moved Polymarket’s Trump contract by several cents through massive directional bets. Whether this trader had superior information or was simply wealthy and opinionated remains debated.

The practical implication: on any given market, check whether the price movement is driven by broad-based trading or a few large orders. Order book depth data (available on both Polymarket and Kalshi) helps distinguish the two.

Thin markets

Prediction markets work well when there are enough participants to aggregate diverse information. On thinly traded markets — a niche political race, a specific economic data point, or a novel event type — the wisdom-of-crowds effect breaks down. A handful of traders with strong opinions (correct or not) can dominate pricing.

Rule of thumb: if a market has fewer than $50,000 in open interest, treat the price as a rough estimate, not a reliable probability.

Event types with poor track records

Prediction markets perform best on events where participants have access to relevant information and clear resolution criteria. They perform worst on:

• Long-duration forecasts (more than 6 months out): too much uncertainty for prices to be meaningful
• Novel events with no historical base rate: markets can’t aggregate information that doesn’t exist
• Events influenced by small groups (Supreme Court decisions, corporate executive decisions): the information asymmetry is too large

The favorite-longshot bias

Research across multiple prediction market platforms consistently finds a “favorite-longshot bias” — contracts priced below $0.10 tend to overestimate the probability of unlikely events, while contracts above $0.90 tend to slightly underestimate near-certainties. This is similar to biases found in horse racing and sports betting markets.

For traders, this creates a systematic edge: selling longshots (contracts priced at $0.05–$0.10 that resolve to $0.00 more often than the price implies) and buying near-certainties (contracts at $0.90–$0.95 that resolve to $1.00 more often than the price implies).

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How traders profit from the polls-markets gap

The divergence between prediction market prices and polling data isn’t just academically interesting — it’s a trading signal. It fits into a broader toolkit of prediction market strategies that systematic traders use.

Strategy: poll-market divergence trading

When polls shift before prediction market prices adjust (or vice versa), a temporary mispricing exists.

Setup: Monitor both polling averages and prediction market prices for the same event. When a new poll drops that meaningfully shifts the polling consensus, check if the prediction market has already adjusted.

Example from 2024: In mid-October 2024, a series of state-level polls in Pennsylvania showed a tightening race. The Polymarket price for the Pennsylvania outcome didn’t fully adjust for approximately 6–8 hours. Traders monitoring the polling data in real time could buy at prices that hadn’t yet reflected the new information.

When this works: Around scheduled polling releases, debate nights, and major campaign events. The window is typically hours, not days — markets adjust faster than polls update, but there’s a lag between poll publication and full price adjustment.

Strategy: polling error model

Polls have systematic errors that prediction markets sometimes don’t price correctly. The most well-documented error is the consistent undercount of Republican voters in US elections from 2016 through 2024. A trader who modeled historical polling error and applied it to current polling data would have seen a higher Trump probability than either the raw polls or the prediction markets showed.

How to implement: Research the historical polling bias for the specific event type. State-level polls in US elections have averaged a 3–4 point error in recent cycles. Build a simple model that adjusts raw polling numbers by this historical bias, then compare your adjusted probability to the prediction market price.

Risk: Polling bias isn’t constant. It changes across cycles, and the direction of the bias can reverse. Using historical bias as a fixed adjustment is better than ignoring it, but it’s not a certainty.

Strategy: event-day information trading

On days when events resolve (election day, economic data releases, Fed meetings), prediction market prices and last-minute polling or survey data can diverge sharply. As real results flow in, markets adjust faster than any polling or survey source.

How to trade it: Pre-fund accounts on Polymarket and Kalshi. On event day, monitor real-time data (precinct-level election returns, BLS data release feeds, Fed statement releases). When real data confirms or contradicts the market price, trade immediately.

The 2024 example: On election night 2024, as early state results came in showing Trump outperforming, Polymarket and Kalshi prices diverged by 3–8 cents for several hours. Different platforms adjusted at different speeds based on their user base activity. Traders with capital on both platforms could arbitrage the gap while simultaneously trading directionally based on incoming data.

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Prediction markets and the 2026 midterms

The next major test for prediction markets is the 2026 US midterm elections. Markets are already listing contracts for Senate and House control, and individual race markets are beginning to gain liquidity.

What to watch for

Early market formation: Pay attention to how prices develop 6+ months before the election. At this stage, prediction markets incorporate structural factors (generic ballot, presidential approval, historical midterm patterns) more than individual candidate information. As primaries resolve and campaigns intensify, individual race dynamics will matter more.

Polling bias adjustments: Will the systematic polling errors of 2016–2024 persist? If prediction markets price in a polling bias correction (making Republican candidates look stronger than raw polls suggest) and the bias doesn’t repeat, markets will have overcorrected. If markets don’t price in the bias correction and it persists, there’s a buying opportunity.

State-level discrepancies: Senate races across multiple states create opportunities for conditional probability analysis. If Party A winning in Pennsylvania has implications for Party A’s chances nationally, but the individual state markets and the national market aren’t priced consistently, a mispricing exists.

How Token Metrics’ AI applies here

Token Metrics processes data across 6,000+ crypto tokens daily, and the analytical framework extends to prediction market analysis. For crypto-related prediction markets (Bitcoin price targets, regulatory events, DeFi milestones), the AI compares:

• On-chain data signals against prediction market prices
• Technical indicator consensus against implied probabilities
• Social sentiment trends against current market pricing

When the AI’s probability estimate diverges from a prediction market price by a significant margin, it flags a potential mispricing. This is the same principle as the polls-vs-markets divergence — using an independent analytical signal to identify when market prices don’t reflect available information.

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The accuracy question, reframed

Asking “are prediction markets more accurate than polls?” is the wrong question. The better framing: “what information does each source capture, and how can I use both?”

Polls measure stated voter intentions at a specific point in time. They’re snapshots, subject to sampling error, nonresponse bias, and the gap between what people say they’ll do and what they actually do.

Prediction markets measure the aggregate information of participants who have financial skin in the game. They’re continuous, updating in real time, and they incorporate diverse data sources. But they’re subject to manipulation by large traders, thin liquidity on niche events, and the favorite-longshot bias.

The most informed prediction market traders don’t pick one source over the other. They use polls as an input, check prediction market prices for divergence, apply historical error models, and size positions based on the gap between their probability estimate and the market price.

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Frequently asked questions

Did prediction markets predict the 2024 election correctly?
Yes. Polymarket priced Trump at approximately 57% on election eve, while most polling averages showed a near toss-up. Trump won decisively, closer to the market’s prediction than the polls’. However, a single data point doesn’t prove universal superiority — it proves markets captured information that polls missed in that specific cycle.

Are prediction markets always better than polls?
No. Academic research shows that the best forecasts combine prediction markets, polls, and economic fundamentals. Markets outperform polls in certain conditions (close to election day, high-profile events with lots of participants) and underperform in others (thin markets, events subject to manipulation, very long time horizons).

Can large traders manipulate prediction markets?
They can temporarily move prices, yes. During the 2024 election, large individual bets on Polymarket visibly shifted prices. Whether this counts as “manipulation” or “informed trading” depends on whether the large trader had superior information. For other traders, the implication is the same: check whether price movements reflect broad-based trading or concentrated bets.

How do I use polls and prediction markets together for trading?
Monitor both. When a new poll shifts the consensus but the prediction market price hasn’t moved, that’s a potential opportunity. When the prediction market moves sharply but no new polling data explains it, investigate what information the market is incorporating that polls can’t capture (early voting data, fundraising numbers, campaign events).

Will prediction markets be accurate for the 2026 midterms?
We won’t know until November 2026. The conditions for prediction market accuracy — high participation, deep liquidity, clear resolution criteria — are likely to be met for major races (Senate control, House control). Individual district races with thin markets may be less reliable.

What’s the favorite-longshot bias and how do I trade it?
Prediction markets tend to overvalue unlikely outcomes (contracts below $0.10) and slightly undervalue near-certainties (contracts above $0.90). You can profit by selling overpriced longshots and buying underpriced near-certainties, though individual trades can and do go against you.

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Token Metrics’ AI analyzes data across 6,000+ crypto tokens daily, identifying when prediction market prices diverge from data-driven probability estimates. For crypto-related prediction markets, this means spotting mispricings before the crowd catches up. Learn more at tokenmetrics.com