Prediction markets are often described as “surprisingly accurate.”
Sometimes they are.
Sometimes they aren’t.
The key question is not “did the market get it right?”
The real question is:
How wrong was it — and when?
Because prediction markets don’t produce answers.
They produce probability paths.
And those paths can be evaluated.
This is where forecast error, prediction quality, and accuracy tracking actually matter.
Why “Right vs Wrong” Is the Wrong Lens
Prediction markets usually resolve in binary outcomes.
Yes or No.
1 or 0.
That makes it tempting to judge them like this:
- Market said 70% → outcome happened → “correct”
- Market said 70% → outcome failed → “wrong”
But this completely misunderstands probability.
A 70% forecast is not a claim of certainty.
It is an explicit statement of uncertainty.
If a 70% outcome fails once, that does not mean the market was inaccurate.
Accuracy in prediction markets only makes sense across many events — not individual outcomes.
Forecast Error: The Metric That Actually Matters
Forecast error measures distance, not outcome.
At resolution:
- If the event happens → error = 1 − probability
- If it doesn’t → error = probability
So:
- 90% → fails → huge error
- 55% → fails → small error
- 55% → succeeds → still moderate error
This is why overconfidence is the real enemy.
Not being wrong. Markets that stay closer to true uncertainty outperform markets that swing to extremes too early.
Why “Close” Often Matters More Than “Correct”
Consider two markets forecasting the same event:
- Market A: 95%
- Market B: 60%
The event happens.
Both were “right.”
But now consider the failure case.
Market A produces a massive forecast error.
Market B produces a smaller one.
Across hundreds of markets, systems that avoid extreme confidence too early tend to be better calibrated.
Prediction quality is not about boldness. It’s about honesty.
How Prediction Market Accuracy Is Actually Measured
Professionals don’t evaluate prediction markets with one number. They look at behavior over time.
Here are the core metrics used to track accuracy:
| Metric | What it measures | Why it matters |
| Forecast Error | Distance between probability and outcome | Penalizes overconfidence |
| Calibration | Do X% forecasts resolve ~X% of the time? | Tests probability honesty |
| Convergence Timing | How early belief stabilizes | Early signals are more useful |
| Late Volatility | Uncertainty close to resolution | Flags fragile consensus |
| Correction Speed | How fast mistakes are reversed | Measures crowd learning |
| Liquidity-Weighted Error | Error adjusted by participation | Filters thin-market distortion |
Accuracy is not a score.
It’s a profile.
Polymarket Data: Fast Signals, Early Errors
Polymarket markets are extremely reactive.
They tend to:
- price breaking news quickly
- overshoot on first reaction
- correct as more traders enter
A common pattern in Polymarket data is:
sharp move → partial reversal → stabilization
From a forecast error perspective:
- early probabilities can be too extreme
- mid-path calibration often improves
- final probabilities are usually reasonable
Polymarket is strongest as an early signal engine, not a final oracle.
Kalshi Data: Conservative, Better Calibrated Near Resolution
Kalshi markets typically move more slowly.
You often see:
- smaller probability steps
- less emotional volatility
- tighter clustering near resolution
Kalshi data tends to:
- underreact early
- avoid extreme overconfidence
- produce lower forecast error close to settlement
The trade-off is clear:
Kalshi is less informative early, but often more accurate late.
Myriad Data: Mechanism-Driven Stability
Myriad markets are useful for studying market design effects.
Typical characteristics include:
- smoother probability curves
- fewer emotional spikes
- clearer belief transitions
Forecast errors in Myriad data are usually structural, not emotional.
This makes Myriad valuable for:
- calibration studies
- aggregation research
- mechanism comparison
Manifold Data: Intuition Over Incentives
Manifold uses play money, which changes behavior.
Surprisingly, this often leads to:
- more frequent belief updates
- less anchoring to early prices
- good long-horizon calibration on niche topics
Manifold markets may miss fast-breaking news, but often perform well on:
- timelines
- research outcomes
- long-range questions
It’s a reminder that accuracy doesn’t only come from money. It comes from willingness to update.
When Prediction Markets Are Most Likely to Be Wrong
Across platforms, large forecast errors cluster in similar conditions:
- low liquidity
- asymmetric information
- unclear resolution criteria
- narrative dominance
- unprecedented events
These weaknesses are visible before resolution.
Which is why tracking accuracy over time matters more than judging outcomes.
The Real Takeaway
Prediction markets are not accurate because they are always right.
They are valuable because:
- they expose uncertainty honestly
- they update continuously
- they allow correction
- they record their own mistakes
Forecast error is not a flaw.
It is the measurement.
Measuring Prediction Accuracy With FinFeedAPI
To evaluate prediction accuracy across Polymarket data, Kalshi data, Myriad data, and Manifold data, you need historical probability paths — not screenshots.
FinFeedAPI’s Prediction Markets API provides:
- historical OHLCV probability series
- market lifecycle status
- activity and liquidity context
So you can measure forecast error, prediction quality, and accuracy tracking the way professionals do.
👉 Explore the Prediction Markets API at FinFeedAPI.com and analyze prediction markets as forecasting systems, not bets.
Related Topics
- Prediction Markets: Complete Guide to Betting on Future Events
- Markets in Prediction Markets
- Confidence Scores: Measuring How Certain a Market Is
- From Market Data to Predictive Models
- Historical Prediction Market Data: What to Analyze
- Why Crowds Sometimes Get It Wrong
- From Yes Price to Probability: How Odds Are Formed
