How Accurate Are Prediction Markets? [2026 Data]

Liquid prediction markets have outperformed traditional polling on flagship US elections in academic studies and recent cycles. The 2024 US presidential election was the clearest example yet: Polymarket and Kalshi both tracked the eventual outcome more confidently than aggregate polling and major forecasting models in the closing two weeks. The cycle is widely viewed as a vindication of the prediction market accuracy thesis.

The accuracy advantage holds across multiple academic studies dating to the 1980s. The Iowa Electronic Markets, run by the University of Iowa starting in 1988, consistently outperformed national polling averages on US presidential outcomes across at least five election cycles. Polymarket's 2024 cycle continued the pattern at a much larger scale.

Three caveats are important. First, accuracy depends heavily on liquidity. Thin markets with few informed traders are not nearly as accurate as deep liquid markets. Second, accuracy is highest in the final 2-4 weeks before resolution, not months in advance. Third, prediction markets are still subject to their own biases including long-shot bias and momentum effects.

For platform rankings see our home page. For background on how the markets work see our what are prediction markets guide. For specific platform coverage see our Polymarket review.

Prediction markets and polls produce probability estimates through fundamentally different mechanisms. Polls survey a sample of likely voters and aggregate their stated voting intentions into a forecast. Prediction markets aggregate the beliefs of traders putting real money behind their views. The skin-in-the-game incentive aligns prediction market participants toward accuracy in ways that surveys cannot match.

The 2024 cycle showed the gap clearly. By election day, Polymarket priced the eventual winner at around 65% probability while major polling averages and forecasting models still showed roughly even odds. The market signal proved closer to the actual outcome than the polling consensus. Similar gaps appeared in 2016 (when prediction markets gave Trump higher probability than most major models, although the headline market still favoured the eventual loser).

Polls have specific weaknesses that prediction markets address. Survey methodology errors, response bias, late shifts in voter intent, and turnout estimation difficulties all affect polling accuracy. Prediction markets implicitly aggregate adjustments for all these factors because traders incorporate poll quality, voter enthusiasm, and turnout signals into their views before placing trades.

Polls have specific strengths that prediction markets do not match. Polls can produce demographic breakdowns, regional patterns, and issue-specific insights that prediction markets typically do not list. Both tools serve complementary roles in serious political forecasting. Active researchers use both inputs rather than relying solely on either.

Superforecasters are individuals identified by Philip Tetlock's Good Judgment Project as consistently outperforming average forecasters on geopolitical and other events. The project found that the top 2% of forecasters significantly beat both averages and even some intelligence community forecasts on similar questions.

Comparisons between prediction markets and superforecasters typically show that liquid prediction markets perform comparably to top-tier superforecasters on most event types. On political and economic events with deep market liquidity, prediction markets often match or slightly beat superforecaster aggregates. On events with thin market liquidity or where the market lacks engaged informed traders, superforecaster aggregates tend to outperform.

The structural difference matters. Superforecasters are a curated group of identified strong forecasters. Prediction markets are open to any participant, with the price set by aggregate trading. Markets benefit from broader information aggregation but can be diluted by uninformed participants. The two approaches complement rather than substitute for each other.

For users who want to follow superforecaster output, the Good Judgment Open project publishes forecaster aggregates that are sometimes useful as a check against prediction market prices. The two signals usually agree on liquid events. When they diverge meaningfully, careful analysis of which signal is better-informed for the specific question is worthwhile.

Brier scores are the standard metric for measuring forecast accuracy. The score is the average squared error between predicted probability and actual outcome (0 or 1). Lower Brier scores mean more accurate forecasts. A perfect forecaster achieves a Brier score of 0. A coin-flip forecaster achieves a Brier score of 0.25.

Liquid prediction markets typically produce Brier scores in the range of 0.08-0.15 on flagship political and economic events, depending on the market and time horizon. These numbers compare favourably to most polling-based forecasts, which often score in the 0.10-0.20 range on similar events. Forecasting models from FiveThirtyEight, Cook Political Report, and similar sources typically score in the same range as polls.

Calibration is a related but distinct measure. A perfectly calibrated forecaster's events at 70% probability happen 70% of the time. Liquid prediction markets tend to be reasonably well calibrated on liquid events, with some long-shot bias (events at 5% probability often happen less than 5% of the time, while events at 95% probability often happen more than 95% of the time). The bias is well-documented and creates trading opportunities for active users who recognise it.

For users wanting to track their own accuracy over time, recording predictions with explicit probabilities and calculating Brier scores against outcomes is the most rigorous approach. Many active prediction traders maintain personal accuracy logs to identify their own biases and improve calibration over time.

The 2024 US presidential election is the most recent and prominent example. Polymarket priced the eventual winner at around 65% by election day. Kalshi's election markets, which opened to active US trading after the summer 2024 court ruling, tracked Polymarket closely. Major polling averages still showed roughly even odds at the same point. The gap is roughly 15 percentage points of confidence in favour of the prediction market signal.

The 2024 UK general election produced a similar pattern. Polymarket's outright winner market correctly identified the eventual outcome with rising confidence through the campaign. The 2022 French presidential election prediction markets closely matched the eventual outcome and tracked alongside polling consensus reasonably well.

Beyond elections, Federal Reserve rate decision markets on Kalshi typically produce probability estimates that closely track CME FedWatch implied probabilities while occasionally diverging meaningfully in the days before each meeting. CPI inflation print markets often catch small information differences in the days before each release that economist consensus does not capture cleanly.

Sports prediction accuracy is harder to compare cleanly because outcomes are typically binary and one-shot. Active research on PrizePicks-style player prop markets is limited, but anecdotal evidence suggests that liquid sports prediction markets produce prop pricing comparable to professional sportsbook lines, with the advantages and limitations of each approach. For background on political prediction specifically see our political prediction markets hub.

Prediction markets have real limitations. Three matter most for users evaluating accuracy claims.

First, liquidity dependence. Accuracy on liquid markets is typically much higher than on thin markets. A market with $10 of trading volume cannot reliably aggregate information. A market with $10 million of trading volume often produces meaningful probability signals. When evaluating any specific market price, check the recent trading volume and order book depth before treating the price as a reliable forecast.

Second, time horizon. Markets close to resolution are typically much more accurate than markets resolving months or years in advance. The 2028 US presidential market is open in 2026 but the price is far less informative than the price will be in 2028 because too many unknowns remain. Long-dated markets are useful for traders with strong long-term views but are not strong forecasting signals.

Third, structural biases. Long-shot bias means markets often overprice low-probability outcomes. Momentum effects can cause prices to lag information slightly during fast-moving news cycles. Manipulation attempts have occurred on smaller markets, though the cost of manipulating liquid markets typically exceeds any plausible profit.

Markets fail most often on novel or ambiguous events. The first market on a new type of event often produces less accurate prices than later markets after the category matures. Markets with ambiguous resolution criteria can produce wide bid-ask spreads as traders price uncertainty about how the market will be settled. These failure modes are real but rare on flagship liquid markets.