## Price = Probability
In a prediction market, the price of a contract directly represents the market's consensus probability. A Yes share trading at $0.72 means the crowd estimates a 72% chance the event will occur.
This isn't arbitrary — it's mathematically grounded. If traders collectively believed the true probability was 80% but the price was at 72%, rational actors would buy Yes shares (expecting to profit), driving the price up toward 80%. This continuous arbitrage process ensures prices stay calibrated to actual probabilities.
## The Mathematical Foundation
### Expected Value
A trader buys a Yes share at price P. If the event has true probability p:
- **Expected profit** = p × ($1 - P) - (1-p) × P = p - P
If p > P, buying is positive expected value. If p < P, selling (or buying No) is profitable. This means any mispricing gets corrected.
### Market Scoring Rules
OraclBet uses the **Logarithmic Market Scoring Rule (LMSR)**, developed by economist Robin Hanson. The LMSR:
- Always provides liquidity (you can always trade) - Prices move based on the ratio of Yes to No shares outstanding - The liquidity parameter (b) controls how much each trade moves the price
Price formula: `P(Yes) = e^(q_yes/b) / (e^(q_yes/b) + e^(q_no/b))`
Where q_yes and q_no are the outstanding shares and b is the liquidity parameter.
## Why Market Prices Are Accurate
### Information Aggregation
Each trader brings unique information. A crypto analyst might know about upcoming protocol upgrades. A policy wonk understands regulatory trends. A data scientist has built predictive models. When all of them trade, the price synthesizes their collective knowledge.
### The Marginal Trader Hypothesis
You don't need every participant to be rational. You only need a small number of informed traders willing to correct mispricings. Research shows that even if 90% of traders are noise traders, the 10% who are informed are sufficient to keep prices accurate.
### Empirical Evidence
Studies across thousands of prediction markets have found:
- Events priced at 70% happen approximately 70% of the time - Calibration is remarkably tight — prediction markets are among the best-calibrated forecasting tools known - Markets with more liquidity and more traders tend to be more accurate
## How to Read Prediction Market Prices
### Price Ranges and What They Mean
| Price Range | Interpretation | |-------------|---------------| | $0.00 - $0.10 | Very unlikely (0-10% chance) | | $0.10 - $0.30 | Unlikely but possible | | $0.30 - $0.50 | Could go either way, leaning No | | $0.50 - $0.70 | Could go either way, leaning Yes | | $0.70 - $0.90 | Likely | | $0.90 - $1.00 | Very likely (90-100% chance) |
### Price Movements
Large, sudden price moves typically indicate new information entering the market. If a political market jumps from $0.45 to $0.70 overnight, it's worth checking what news event caused the shift.
## Trading Strategies Based on Probability
### Contrarian Trading When you believe the market probability is wrong, buy the underpriced side. This requires having information or analysis the market hasn't yet incorporated.
### Event-Driven Trading Trade before known catalysts (earnings reports, elections, protocol upgrades). Markets often don't fully price in the impact of scheduled events.
### Arbitrage If the same event is priced differently across platforms, buy the cheap side on one and sell on the other for risk-free profit.
## Conclusion
Understanding the price-probability relationship is fundamental to prediction market trading. Prices aren't just numbers — they're real-time probability estimates backed by financial incentives. Learning to read and interpret these signals gives traders a powerful edge in forecasting future events.