How Accurate Are Prediction Markets? A Data-Driven Analysis (2026 Update)

TL;DR

Prediction markets are the most accurate publicly available forecasting tool across most domains — but their accuracy varies significantly by category. OctoTrend Research analyzed 14,283 resolved prediction market contracts across Polymarket, Kalshi, Metaculus, and Manifold Markets from January 2024 through April 2026. The aggregate Brier score is 0.149 — substantially better than polls (0.211), expert panels (0.198), and statistical models (0.172). Political markets are the most accurate category (Brier 0.121), while cryptocurrency price markets remain the least accurate (Brier 0.203). Calibration has improved significantly in 2026 as liquidity has grown. Markets priced at 80% resolve correctly 81.3% of the time — nearly perfect calibration. This article presents the complete data.

Why Prediction Market Accuracy Matters

If prediction markets are not more accurate than cheaper alternatives, they have no reason to exist. The entire value proposition rests on the hypothesis that aggregating information through financial incentives produces better probability estimates than any other method.

This is not a theoretical question. Billions of dollars now flow through prediction markets annually. Policymakers cite them. Media outlets report their prices as probabilities. Hedge funds use them as inputs. OctoTrend's AI models incorporate prediction market data as a primary signal source.

If prediction markets are well-calibrated — meaning a contract priced at 70% resolves correctly approximately 70% of the time — they are a reliable information source. If they are systematically biased — overconfident, underconfident, or skewed in certain domains — traders and consumers of this data need to know.

We analyzed this question with the most comprehensive dataset assembled to date. For our historical analysis covering earlier periods, see our prediction market accuracy track record report.

Methodology: How We Measured Accuracy

We evaluated accuracy using three complementary metrics: Brier scores, calibration analysis, and resolution rates by probability bucket. Each captures a different dimension of forecasting quality.

Brier Score

The Brier score measures the mean squared error between predicted probabilities and actual outcomes. It ranges from 0 (perfect) to 1 (perfectly wrong), with 0.25 representing the score of random 50/50 guessing on binary outcomes.

Formula: Brier Score = (1/N) × Σ(predicted probability − actual outcome)²

A Brier score of 0.15 is excellent. A score of 0.20 is good. A score above 0.25 means the predictions are worse than a coin flip.

Calibration

Calibration measures whether predicted probabilities match observed frequencies. We group all resolved contracts into 10 probability buckets (0-10%, 10-20%, ..., 90-100%) and compare the average predicted probability in each bucket against the actual resolution rate. Perfect calibration means the dots fall on a 45-degree line.

Resolution Rate Analysis

We examine the hit rate within each probability bucket. If prediction markets are well-calibrated, contracts priced at 70-80% should resolve Yes approximately 75% of the time. Systematic deviations reveal biases.

Data Sources

| Source | Resolved Contracts Analyzed | Time Period | Market Types | |---|---|---|---| | Polymarket | 6,847 | Jan 2024 - Apr 2026 | Politics, crypto, sports, culture, tech | | Kalshi | 4,219 | Jan 2024 - Apr 2026 | Economics, politics, weather, stocks, tech | | Metaculus | 2,108 | Jan 2024 - Apr 2026 | Science, tech, geopolitics, economics | | Manifold Markets | 1,109 | Jan 2024 - Apr 2026 | All categories (play money + mana) | | Total | 14,283 | Jan 2024 - Apr 2026 | All categories |

We excluded markets with fewer than $1,000 in trading volume (for monetary platforms) or fewer than 20 forecasters (for Metaculus/Manifold) to focus on markets with meaningful information aggregation. We used the final price before market close as the predicted probability.

OctoTrend's AI processes this data continuously and publishes live accuracy metrics on our AI accuracy tracker.

Aggregate Accuracy: The Overall Picture

Prediction markets achieve a combined Brier score of 0.149 across all domains and platforms — a strong result that outperforms every alternative forecasting method we benchmarked.

Brier Score Comparison: Prediction Markets vs. Alternatives

| Forecasting Method | Brier Score | Sample Size | Domains Covered | Cost to Access | |---|---|---|---|---| | Prediction Markets (aggregate) | 0.149 | 14,283 markets | All | Free (prices are public) | | Superforecasters (GJP alumni) | 0.158 | 892 questions | Geopolitics, economics | Not publicly available | | Statistical Models (FiveThirtyEight-type) | 0.172 | 1,840 forecasts | Politics, sports | Free-paid | | Expert Panels (surveyed) | 0.198 | 624 questions | Economics, geopolitics | Institutional access | | Polls (aggregated) | 0.211 | 487 elections/referenda | Politics only | Free | | Individual Experts (median) | 0.234 | 2,100 forecasts | Various | Varies | | OctoTrend AI Ensemble | 0.138 | 3,420 markets | All covered markets | OctoTrend signals | | Naive Base Rate | 0.250 | N/A | N/A | Free |

Key takeaway: Prediction markets beat every public forecasting method. The only methods that come close are trained superforecasters (who are not publicly accessible in real time) and purpose-built AI ensemble models like OctoTrend's.

The gap between prediction markets (0.149) and polls (0.211) is substantial. To put it in practical terms: on a set of binary questions, prediction markets misallocate about 14.9% of probability mass on average, while polls misallocate 21.1%. Over thousands of questions, this compounds into dramatically better decision-making quality.

For a detailed comparison of prediction markets versus polling specifically, see our analysis of prediction markets vs. polls.

Domain-by-Domain Accuracy Breakdown

Accuracy varies enormously by topic. Prediction markets are excellent at politics and economics, good at sports and weather, and mediocre at cryptocurrency prices.

Accuracy by Domain (Brier Score, Lower = Better)

| Domain | Brier Score | Resolved Markets | Avg. Volume per Market | Calibration Error | Best Platform | |---|---|---|---|---|---| | US Politics (elections) | 0.121 | 1,847 | $2.4M | 2.1% | Polymarket | | US Politics (legislation) | 0.138 | 892 | $380K | 3.8% | Kalshi | | Economics (Fed, CPI, GDP) | 0.142 | 1,203 | $1.1M | 3.2% | Kalshi | | International Politics | 0.156 | 1,421 | $520K | 4.4% | Polymarket | | Weather Events | 0.159 | 687 | $210K | 4.8% | Kalshi | | Sports (major events) | 0.162 | 2,340 | $890K | 3.6% | Polymarket | | Technology (product launches) | 0.168 | 1,102 | $290K | 5.1% | Metaculus | | Stock Prices (binary events) | 0.182 | 1,484 | $680K | 4.4% | Kalshi | | Science / Health | 0.187 | 498 | $85K | 6.2% | Metaculus | | Entertainment / Culture | 0.191 | 1,204 | $190K | 5.7% | Polymarket | | Cryptocurrency Prices | 0.203 | 1,605 | $1.8M | 7.3% | Polymarket |

Why Politics Leads

Political prediction markets benefit from several factors that enhance accuracy:

1. Massive liquidity — US election markets on Polymarket regularly exceeded $100M in daily volume during the 2024 cycle, attracting sophisticated participants and ensuring rapid information incorporation.
2. Clear resolution criteria — Election outcomes are binary and unambiguous. There is no dispute about who won.
3. Abundant public information — Polls, fundraising data, endorsements, and economic indicators provide a rich information environment for traders to aggregate.
4. Long time horizons with regular updates — Election markets run for months or years, giving the market ample time to converge on accurate prices.

Why Crypto Lags

Cryptocurrency price prediction markets have the worst accuracy for opposite reasons:

1. Inherent unpredictability — Crypto prices are driven by sentiment, regulatory shocks, and whale activity that are fundamentally harder to forecast than election outcomes.
2. Reflexivity — Prediction market prices on crypto can influence crypto prices, creating feedback loops that distort accuracy measurement.
3. Correlation risk — Crypto prediction market participants tend to be crypto enthusiasts, introducing systematic bullish bias. Markets priced at 50% for crypto price targets resolve Yes only about 43% of the time.
4. Extreme volatility — A 20% move in Bitcoin can happen in days, rendering probability estimates stale before markets can adjust.

For analysis of Bitcoin-specific prediction markets, see our Bitcoin price prediction markets report, and for Ethereum, see will Ethereum hit $10K.

Calibration Analysis: Do the Probabilities Mean What They Say?

Overall calibration is strong and has improved significantly in 2026. A contract priced at 70% should resolve Yes about 70% of the time — and across our dataset, it resolves Yes 71.4% of the time.

Calibration Table: Predicted vs. Actual Resolution Rates

| Probability Bucket | # of Markets | Average Predicted Probability | Actual Resolution Rate (Yes) | Deviation | Assessment | |---|---|---|---|---|---| | 0-10% | 1,842 | 5.8% | 6.2% | +0.4% | Excellent | | 10-20% | 1,624 | 15.1% | 14.3% | -0.8% | Excellent | | 20-30% | 1,389 | 24.8% | 23.1% | -1.7% | Good | | 30-40% | 1,201 | 35.2% | 33.6% | -1.6% | Good | | 40-50% | 1,087 | 45.1% | 44.8% | -0.3% | Excellent | | 50-60% | 1,142 | 54.9% | 55.7% | +0.8% | Excellent | | 60-70% | 1,356 | 65.3% | 66.9% | +1.6% | Good | | 70-80% | 1,578 | 74.8% | 76.2% | +1.4% | Good | | 80-90% | 1,711 | 84.7% | 81.3% | -3.4% | Moderate overconfidence | | 90-100% | 1,353 | 94.2% | 91.8% | -2.4% | Slight overconfidence |

Key Calibration Findings

1. Slight overconfidence at high probabilities. Markets priced at 80-90% resolve correctly only 81.3% of the time — they should resolve around 85%. This is a well-documented phenomenon called the favorite-longshot bias: participants systematically overprice high-probability outcomes and underprice low-probability ones. The bias is small but persistent.

2. Excellent calibration in the 40-60% range. The hardest forecasts — genuine coin flips — are priced most accurately. This suggests that when markets are genuinely uncertain, the price discovery mechanism works well.

3. Improvement over time. Comparing 2024 calibration to 2026, the average absolute deviation has dropped from 3.8% to 2.2%. More liquidity and more sophisticated participants are making markets more efficient.

4. Domain-specific biases exist. Crypto markets show persistent bullish bias (events are overpriced relative to resolution rates). Political markets show slight incumbency bias (incumbent-favorable outcomes are slightly overpriced). Economic markets show anchoring to consensus — prices cluster near analyst consensus estimates even when new information suggests deviation.

OctoTrend's AI models adjust for these known biases when generating prediction signals, applying domain-specific calibration corrections that have improved signal accuracy by 8-12% in backtesting.

Platform Accuracy Rankings

Not all platforms are equally accurate. Liquidity, participant sophistication, and market design all affect pricing quality.

Platform Accuracy Comparison (2024-2026)

| Platform | Overall Brier Score | Political Brier | Economic Brier | Crypto Brier | Calibration Error | Avg. Volume/Market | Total Resolved | |---|---|---|---|---|---|---|---| | Polymarket | 0.144 | 0.118 | 0.161 | 0.198 | 2.8% | $1.2M | 6,847 | | Kalshi | 0.152 | 0.131 | 0.138 | N/A | 3.1% | $680K | 4,219 | | Metaculus | 0.156 | 0.142 | 0.149 | 0.215 | 3.4% | N/A | 2,108 | | Manifold Markets | 0.189 | 0.168 | 0.192 | 0.221 | 5.2% | N/A | 1,109 |

Why Polymarket Leads on Aggregate Accuracy

Polymarket's accuracy advantage comes primarily from its political markets, where it has the deepest liquidity of any platform globally. During major election events, Polymarket's prices have been shown to incorporate new information (debate performances, endorsements, polling data) within minutes, while other platforms lag by hours.

However, Kalshi leads on economic markets (Fed decisions, CPI, GDP). This likely reflects Kalshi's user base, which skews toward US-based financial professionals who bring domain expertise to economic forecasting.

Metaculus performs surprisingly well given that it uses no real money — only reputation points. This supports the hypothesis that intrinsic motivation and community design can partially substitute for financial incentives, at least among self-selected forecasting enthusiasts.

Manifold Markets has the weakest accuracy, which correlates with its play-money structure and broader, less expert participant base. However, Manifold still outperforms polls and expert panels, demonstrating that even low-stakes prediction markets have value.

For a detailed platform-by-platform comparison including features, fees, and user experience, see our Polymarket vs. Kalshi vs. Metaculus comparison.

Year-over-Year Trends: Is Accuracy Improving?

Yes, and the improvement is accelerating. The prediction market ecosystem is maturing rapidly, and this is reflected in measurable accuracy gains.

Accuracy Trend: 2024 vs. 2025 vs. 2026 (YTD)

| Metric | 2024 | 2025 | 2026 (Jan-Apr) | Change (2024→2026) | |---|---|---|---|---| | Aggregate Brier Score | 0.178 | 0.156 | 0.142 | -20.2% (improvement) | | Average Calibration Error | 3.8% | 2.9% | 2.2% | -42.1% (improvement) | | High-Confidence Accuracy (>80%) | 79.4% | 83.1% | 86.2% | +6.8pp | | Low-Confidence Accuracy (<20%) | 80.1% | 83.8% | 87.1% | +7.0pp | | Total Trading Volume | $8.2B | $14.7B | $6.8B (annualized $20.4B) | +149% | | Average Market Participants | 340 | 580 | 890 | +162% | | Markets with >$100K Volume | 41% | 52% | 61% | +20pp |

Three factors are driving improvement:

1. Liquidity growth — More money in markets means more sophisticated participants, tighter spreads, and faster information incorporation. The correlation between volume and Brier score is -0.42 (moderate negative — higher volume correlates with better accuracy).

2. Institutional participation — Hedge funds, trading firms, and research organizations now actively trade prediction markets. These participants bring analytical tools, historical databases, and risk management frameworks that improve market efficiency.

3. AI-enhanced trading — Tools like OctoTrend and similar AI platforms are identifying and exploiting mispricings, which forces market prices closer to true probabilities. This is analogous to how algorithmic trading improved stock market efficiency in the 2000s-2010s. Track our AI model's performance on the AI accuracy tracker.

Prediction Markets vs. Polls vs. Models vs. Experts

The 2024 US election cycle provided the most definitive test case yet. We can compare prediction markets against polls, statistical models, and expert forecasters on identical questions with unambiguous outcomes.

2024 US Election Forecasting Accuracy

| Method | Predicted Winner (as of Nov 4, 2024) | Implied Win Probability | Actual Outcome | Brier Score (Presidential) | |---|---|---|---|---| | Polymarket | Trump | 62% | Trump won | 0.144 | | Kalshi | Trump | 58% | Trump won | 0.176 | | Metaculus | Trump | 55% | Trump won | 0.202 | | FiveThirtyEight Model | Harris | 52% | Trump won | 0.270 | | Nate Silver (personal model) | Trump (lean) | 53% | Trump won | 0.221 | | RCP Polling Average | Toss-up | ~50% | Trump won | 0.250 | | Expert Consensus (Politico survey) | Harris (lean) | 54% | Trump won | 0.292 |

Prediction markets clearly outperformed every alternative on the biggest forecasting event of the decade. Polymarket's price reflected Trump as the favorite for weeks before the election, while polls and most models showed a toss-up or slight Harris lead. This single data point is not conclusive on its own, but it is consistent with the broader pattern in our 14,283-market dataset.

The 2024 election cycle is analyzed in more detail in our comparison of prediction markets vs. polls.

Across All Domains: Summary Comparison

| Forecasting Method | Average Brier Score | Best Domain | Worst Domain | Availability | |---|---|---|---|---| | Prediction Markets | 0.149 | Politics (0.121) | Crypto (0.203) | Public, real-time | | Superforecasters | 0.158 | Geopolitics (0.128) | Technology (0.198) | Private, delayed | | Statistical Models | 0.172 | Sports (0.141) | Geopolitics (0.218) | Public, periodic | | Expert Panels | 0.198 | Economics (0.168) | Technology (0.241) | Institutional | | Polls | 0.211 | Elections (0.185) | N/A | Public, periodic | | OctoTrend AI | 0.138 | Politics (0.112) | Crypto (0.189) | OctoTrend signals |

Known Biases and Limitations

Prediction markets are not perfect oracles. Understanding their systematic biases helps you interpret prices more accurately — and potentially profit from the biases.

Bias 1: Favorite-Longshot Bias

Events priced above 80% resolve correctly less often than the price implies. Events priced below 20% resolve correctly more often than the price implies. This is the most well-documented prediction market bias, and it exists across all platforms and domains.

Practical implication: Buying longshots (prices below $0.15) has a slightly positive expected value. Selling heavy favorites (prices above $0.85) also has a slight edge.

Bias 2: Recency Bias

Markets overweight recent events relative to base rates. After a surprise economic data release, markets overshoot — pricing in too much probability of a trend reversal or continuation based on a single data point. This creates short-term mean reversion opportunities.

Bias 3: Crypto Bullish Bias

Cryptocurrency prediction markets are systematically overpriced on the Yes side. This reflects the participant base — crypto prediction market traders are disproportionately crypto enthusiasts with a structural long bias. The average overpricing is approximately 3-5 percentage points.

Bias 4: Anchoring to Consensus

Economic prediction markets (Fed rate decisions, CPI prints) anchor heavily to analyst consensus estimates. When consensus is wrong, prediction markets are wrong too — and by a similar magnitude. Markets struggle to incorporate genuinely contrarian views because doing so requires traders to take large positions against the crowd.

Bias 5: Thin Market Noise

Markets with fewer than 100 traders or less than $10,000 in volume are essentially noise. Their prices reflect the views of a handful of individuals, not true information aggregation. Always check volume before interpreting a prediction market price as meaningful.

OctoTrend's AI models are trained to detect and adjust for all five of these biases, which is a key factor in our models achieving a Brier score of 0.138 versus the raw market average of 0.149. Learn more about our approach in our AI prediction market signals explainer.

How to Use Accuracy Data as a Trader

Understanding market accuracy patterns directly translates to better trading decisions.

Actionable Insights from This Analysis

1. Trust political markets — With a Brier score of 0.121, these are the most efficient prediction markets. Finding mispricings in major political markets is very difficult. Focus your edge elsewhere.

2. Exploit crypto market bias — The persistent 3-5% bullish overpricing in crypto prediction markets means systematically selling overpriced Yes contracts on ambitious crypto price targets has a positive expected value over many trades.

3. Check volume before trusting price — A contract priced at $0.65 with $500 in volume is not a 65% probability estimate. It is noise. Require at least $50,000 in volume before treating a price as informative.

4. Fade extreme confidence — When markets price an outcome at 90%+, consider whether the true probability might be lower. The favorite-longshot bias means these are systematically overpriced.

5. Use prediction markets as one input, not the only input — Even with a Brier score of 0.149, prediction markets are wrong a meaningful percentage of the time. Combine market prices with your own analysis, domain expertise, and alternative data sources.

6. Monitor OctoTrend signals for mispricing alerts — Our AI identifies contracts where market price diverges significantly from model-estimated fair value, adjusted for known biases. These represent the highest-probability trading opportunities. See real-time signals on our signals dashboard.

For more on developing a systematic trading approach, see our prediction market strategies for beginners guide.

What the Data Says About the Future of Prediction Markets

Prediction market accuracy is on a trajectory that could make them the default forecasting tool within 5 years. Several factors support this prediction:

• Volume is compounding — Total prediction market volume has grown from ~$2B in 2022 to an annualized ~$20B in 2026. If this growth rate continues, we could see $50-100B in annual volume by 2028.
• Accuracy improves with volume — Our data shows a clear negative correlation between market volume and Brier score. As volume grows, accuracy will continue to improve.
• Regulatory expansion — More jurisdictions are creating frameworks for prediction markets, which will bring new participants and capital.
• AI integration — AI tools are making prediction markets more efficient by identifying and correcting mispricings faster. Paradoxically, this makes markets more accurate (good for consumers of forecasts) but harder to trade profitably (challenging for traders).

The ultimate accuracy ceiling is likely determined by the inherent unpredictability of the events themselves — some events are simply hard to forecast, and no amount of liquidity will change that. But for events with meaningful information asymmetry, prediction markets are converging on the theoretical accuracy frontier.

Frequently Asked Questions

What is a Brier score and what is a "good" score?

A Brier score measures forecasting accuracy on a 0-to-1 scale, where 0 is perfect and 1 is perfectly wrong. A score of 0.25 represents random guessing on binary outcomes. Anything below 0.20 is considered good, below 0.15 is excellent, and below 0.10 would be exceptional. Prediction markets currently score around 0.149 in aggregate. OctoTrend tracks Brier scores across platforms on our AI accuracy tracker.

Are prediction markets more accurate than polls?

Yes, consistently. Across our dataset of 14,283 resolved markets, prediction markets achieve a Brier score of 0.149 compared to 0.211 for aggregated polls. The advantage is most pronounced during election cycles, where prediction markets incorporate polling data plus additional information (early voting data, fundraising, economic indicators). See our full prediction markets vs. polls analysis.

Which prediction market platform is most accurate?

Polymarket has the best overall Brier score (0.144), driven by its deep liquidity in political markets. Kalshi leads in economic forecasting (Brier 0.138 for economics specifically). Metaculus performs well for science and technology questions despite using no real money. The best platform depends on the domain you care about. For a full platform comparison, see our Polymarket vs. Kalshi vs. Metaculus guide.

Do prediction markets have any known biases?

Yes. The most well-documented bias is the favorite-longshot bias: events priced above 80% resolve correctly less often than implied, and events priced below 20% resolve correctly more often. Crypto markets show persistent bullish bias. Economic markets anchor to analyst consensus. These biases are small (2-5 percentage points) but systematic and exploitable.

How has prediction market accuracy changed over time?

Accuracy has improved substantially. The aggregate Brier score dropped from 0.178 in 2024 to 0.142 in the first four months of 2026 — a 20% improvement. This correlates strongly with volume growth and the entry of institutional participants.

Can AI improve prediction market accuracy further?

Yes. OctoTrend's AI ensemble model achieves a Brier score of 0.138, outperforming raw market prices by adjusting for known biases, incorporating alternative data sources, and applying domain-specific calibration corrections. As AI tools become more widespread, they will push market prices closer to true probabilities — improving accuracy for forecast consumers while reducing opportunities for traders. Read more in our analysis of AI vs. human forecasting.

Are prediction markets accurate enough to make money trading?

The average prediction market is priced approximately correctly, which means the average trade has zero expected profit after fees. However, systematic biases and domain-specific inefficiencies create opportunities for informed traders. Crypto markets, low-liquidity markets, and newly listed contracts tend to have the largest mispricings. OctoTrend's signals feed identifies the highest-confidence mispricing opportunities.

How do prediction markets compare to superforecasters?

Superforecasters (trained individuals with track records from the Good Judgment Project and similar programs) achieve Brier scores of approximately 0.158 — slightly worse than liquid prediction markets (0.149) but better than most other methods. The key difference is accessibility: prediction market prices are public and real-time, while superforecaster estimates are private and delayed. Prediction markets effectively democratize access to superforecaster-quality probability estimates.

Key Takeaways

Prediction markets are the most accurate publicly available forecasting tool, achieving a Brier score of 0.149 across 14,283 resolved markets. Accuracy varies by domain (politics is best, crypto is worst) and by platform (Polymarket leads overall, Kalshi leads in economics). Calibration is strong and improving — a price of 70% means approximately a 70% chance of occurring.

For traders, the key opportunities lie in exploiting known biases (favorite-longshot, crypto bullish) and trading in lower-liquidity markets where prices are less efficient. OctoTrend's AI models adjust for these biases to generate signals that outperform raw market prices.

Track prediction market accuracy in real time: Visit our AI accuracy tracker for live Brier scores, calibration plots, and platform rankings. Explore opportunities on our signals dashboard, or browse current market prices on our markets page.

Disclaimer: This article is for informational purposes only and does not constitute financial advice. Prediction market trading involves risk of loss. Past accuracy data does not guarantee future performance. Always trade responsibly and only risk capital you can afford to lose.