A Market Scoring Rule is a method that replaces the traditional order book with a single automated market maker. Instead of matching buyers and sellers, it updates prices through a formula that reacts to each trade. This creates a smooth experience for traders because prices move in small, predictable steps.
The model was designed to make prediction markets easier to operate, even when liquidity is low. Traders don’t need someone on the other side of the trade—the scoring rule takes care of pricing. The result is a market that stays active and responsive, even with a small number of participants.
MSRs are often used by platforms focused on forecasting and probabilistic modeling. The rule ensures that prices always reflect the most recent belief about an event’s likelihood. It also helps prevent large price jumps and keeps the market stable.
Market Scoring Rules help prediction markets run smoothly without requiring constant human activity or deep liquidity. They support more accurate probability estimates and steady pricing, even when participation is limited.
Prediction markets use MSRs because they provide reliable pricing without the need for a traditional order book. The automated market maker can react to every trade and adjust the price to reflect new information. This reduces friction in markets where trading volume might be low. It also creates consistent and transparent pricing that users can trust. Many modern forecasting platforms rely on MSRs to scale efficiently.
An MSR improves accuracy by updating odds immediately after each trade. This rapid adjustment keeps the market aligned with the latest beliefs of participants. It also limits extreme volatility because the pricing formula controls how fast prices can move. The system encourages traders to share information through their trades, which strengthens the overall forecast. Over time, this leads to more stable and informative market signals.
Traditional automated market makers often focus on liquidity pools and balancing reserves. An MSR, by contrast, focuses on adjusting a probability estimate. The pricing shifts are designed around capturing information rather than balancing assets. This makes MSRs ideal for markets centered on forecasting instead of trading tokens. They are built to track beliefs, not inventory.
A platform running an election prediction market uses an MSR to update the probability of a candidate winning. When a trader buys shares suggesting the candidate has a higher chance, the MSR raises the market probability by a small amount. Every trade nudges the forecast, creating a running estimate of the outcome.
Market Scoring Rules rely on clear price updates and consistent event data, which makes FinFeed's Prediction Market API a natural fit. The API provides structured event outcomes and time-stamped market data that support analysis of how MSRs react to new trades. It can also help developers track probability changes over time and study how markets process information.
