The Prediction Reputation Score

The Problem

Financial analysts, crypto influencers, economic forecasters, and AI models all make predictions constantly. None of them are held accountable for accuracy in any standardized, persistent, public way. A talking head on CNBC can be wrong 70% of the time and still get booked next week. An AI model can claim superhuman forecasting ability with no verifiable track record. The market for trusted prediction has no trust infrastructure.

The Core Idea

A Prediction Reputation Score works like a credit score but for forecast accuracy. Every prediction must be explicit, falsifiable, and time-bound before it enters the system. No vague calls, no retroactive claims. When the outcome resolves, it's recorded immutably. The score reflects a rolling history of how accurate that analyst, influencer, or model actually is.

Verification Standards

Explicit only: "BTC will hit $100K by December 31, 2025" qualifies. "BTC is going up" does not. Time-bound: every prediction has a resolution date. Immutable outcomes: once a prediction resolves, the record cannot be changed. Human review: predictions are reviewed before entry to confirm they meet the falsifiability standard.

The Data Product

Individual scores are just the starting point. The real value is the data layer built on top: analyst leaderboards by asset class and timeframe, AI model vs. human prediction leagues, institutional forecasting dashboards, a verified analyst certification that high-scorers can display publicly, and eventually a prediction data API that newsrooms, hedge funds, and research firms can query.

The long-term vision is prediction ETFs, investable products constructed from the verified track records of top-ranked analysts. If you can prove someone has been right 73% of the time over five years on a specific asset class, that track record has financial value.

*Published as an open concept. Free to build on.*