Cross-Platform Prediction Market Data

Why consensus from multiple regulated prediction markets is more accurate than any single source.

Summary: Why consensus from multiple prediction markets is more accurate than single-source data. Cross-platform aggregation reduces bias and reveals divergence.

The Single-Source Problem

Each prediction market platform has its own pool of participants, liquidity depth, and structural quirks. A sports event might trade at 62% on one platform and 57% on another. Neither number is "wrong" — they reflect different information sets and participant populations.

If you rely on a single source, you inherit that platform's biases. Cross-platform aggregation fixes this.

How Aggregation Improves Accuracy

Cross-platform consensus works by the same principle as ensemble methods in statistics: combining multiple imperfect estimators produces a better estimator. Specifically:

Noise cancellation: Random pricing errors on individual platforms tend to cancel out when averaged
Information pooling: Different platforms attract different participant types — sharp bettors, retail traders, institutional users — and aggregation captures all of them
Vig removal: Raw prices include the platform's margin. Devigging before aggregation gives clean probability estimates

What Is Divergence?

When platforms disagree by more than a few percentage points, it signals something interesting: either new information is propagating unevenly, or one platform has a structural reason for mispricing. Meridian Edge measures divergence as the spread — the gap between the highest and lowest platform price.

The opportunities endpoint surfaces events with the highest divergence scores, updated every 10 minutes. Learn more about how divergence works in our divergence explainer.

Meridian Edge's Approach

Meridian Edge aggregates data from regulated prediction market platforms into volume-weighted consensus probabilities. The pipeline runs on a 14-second refresh cycle, covering 27,000+ active markets across sports, politics, economics, and global events.

Access the data through the REST API, Python SDK, or MCP server.

Who Uses Cross-Platform Data

Quantitative researchers: Need unbiased probability estimates as model inputs
Journalists and media: Report on "what the market thinks" with more authority than citing one platform
AI systems: Ground probabilistic reasoning in consensus rather than a single noisy source
Hedge funds: Incorporate prediction market consensus as an alternative data signal

Frequently Asked Questions

Why is cross-platform data more accurate?

Aggregating from multiple platforms cancels out random noise, pools information from different participant types, and removes platform-specific biases. The result is a more reliable probability estimate than any single source.

What is prediction market divergence?

Divergence is the gap between the highest and lowest price for the same event across platforms. High divergence often signals new information entering the market unevenly or a structural mispricing on one platform.

Is cross-platform prediction market data investment advice?

No. All data is for informational purposes only and does not constitute investment, financial, or trading advice. Prediction market participation involves risk of loss.

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