How Accurate Are Prediction Markets?
The honest answer: it depends on the type of market. Here is how calibration actually works — and where markets get it wrong.
What is calibration?
If a market says 70%, and you ran that kind of event 100 times, how often did the 70¢ side win? A perfectly calibrated market would see that event happen exactly 70 times out of 100. If it happens 90 times, the market was underpricing it. If it happens 50 times, it was overpricing it.
Key concept: calibration is not the same as prediction. A prediction market does not predict the future — it aggregates the beliefs of participants who have skin in the game. The price is a probability estimate, not a certainty.
Good calibration means the market's probability estimates are systematically accurate across many outcomes. Bad calibration means the numbers are noise — they look like probabilities but don't behave like them over time.
Where Prediction Markets Tend to Perform Well
These market categories consistently show better calibration: objective resolution sources, deep liquidity, and well-understood base rates.
Economic Events
Federal Reserve decisions, CPI releases, and jobs reports resolve against government-published data. Clear resolution source, no insider edge from within the market itself, and deep liquidity from institutional participants.
Sports Outcomes
Historical base rates are well-established and markets are large with fast resolution. Sportsbook lines provide a calibration anchor — PM prices that diverge significantly from Vegas lines tend to get arbitraged back quickly.
Weather Markets
NOAA and weather station data is objective, tamper-resistant, and fast to resolve. Nobody can influence whether it rains in a city on a given day — the resolution source is as clean as it gets.
Elections (Major)
Large national elections combine months of polling data with market-based conviction. Markets tend to show late-breaking responsiveness that polls miss, and the thick liquidity in major election markets reduces noise.
Where Prediction Markets Tend to Underperform
These conditions degrade market accuracy. Check for these before placing significant weight on a price.
Thin Liquidity
Small markets with low volume are easy to move with a single large order. Price is noise, not signal — one participant with strong conviction (or bad intent) can set the displayed probability regardless of actual information.
Information Asymmetry
Celebrity behavior markets, company announcement markets, and sports injury markets all have participants with non-public access. The MrBeast and OpenAI insider trading cases confirmed this attack vector is real.
Complex Resolution Rules
Mention markets (did X happen before Y date?), vague resolution terms, and markets where operator discretion governs the outcome create genuine uncertainty about what the market is even pricing.
Short Horizon + Low Information
When an event is days away and almost no public information exists, markets cannot aggregate meaningful beliefs. Early prices on fast-breaking events can be nearly random until information surfaces.
What a Prediction Market Price Actually Tells You
Most confusion about PM accuracy starts with misreading what the displayed price actually represents.
When you see 65¢ on a contract, here is what it means — and what it does not mean
✅ Does mean
- Market participants collectively believe ~65% probability based on available information
- Arbitrageurs have priced out obvious errors — if the price were badly wrong, someone would profit by trading against it
- There is real money behind this belief — participants have skin in the game
❌ Does not mean
- It will happen exactly 65% of the time in this specific instance — each event is one outcome
- Someone knows something you do not — it reflects aggregated public information, not insider knowledge
- The market is right — consensus can be wrong, especially with thin liquidity
- The spread and the mid-price are the same thing — the displayed price is often the ask, not the mid
The spread matters too. The displayed price is often the ask, not the true mid-price consensus. Learn how bid/ask spread affects the price you see →
Historical Base Rates
For markets with long track records, we can check how often 70¢ bets actually won. The cards below show calibration buckets by market category. All values are pending verification from primary academic calibration studies.
Federal Reserve rate decisions
Economic data markets with clear government sources tend to show strong calibration — prices reflect genuine probability rather than noise.
Values illustrative — Verify from FOMC market outcome records or academic calibration study
Major US sports game outcomes (NFL, NBA, MLB)
Large sports markets with high volume tend to track Vegas lines closely. The calibration question is whether PM prices add signal beyond sportsbook lines.
Values illustrative — Verify from peer-reviewed sports PM calibration research or Kalshi outcome data
US election outcomes (state + national)
Election markets have been the most studied PM category. Results are mixed — markets can show late-breaking responsiveness that polls miss, but thin early-market liquidity can skew prices.
Values illustrative — Verify from Polymarket 2024 election outcome data or academic election PM calibration study
Prediction Markets vs. Polls
The accuracy debate between markets and polling is more nuanced than either side admits.
📊 What Polling Does Well
- •Representative sampling — designed to capture the views of the full population, not just engaged traders
- •Demographic breakdown — can disaggregate results by age, income, geography, and other factors
- •Track record in stable elections — decades of methodology refinement in predictable electoral environments
- •Transparent methodology — sampling frames, weighting, and margin of error are publicly disclosed
📈 What Prediction Markets Do Well
- •Real-time repricing — prices update continuously as new information arrives, not on a publication cycle
- •Skin in the game — participants risk real money on their beliefs, which creates accountability for accuracy
- •Rapid response to breaking news — a major development reprices in minutes, not days
- •No survey bias — participants cannot give socially desirable answers when money is on the line
The honest take: These are complementary, not competing. Polls measure stated opinion; markets measure financial conviction. Both fail in different ways. Polls fail when respondents lie or the turnout model is wrong. Markets fail when liquidity is thin or information is asymmetric. The most informed view of any event uses both.