Scoring Systems

A Scoring System assigns numerical scores (measured in "points") to a selection of Data Points.

Talent Protocol intentionally separates Data Points (objective data) from Scoring Systems (subjective data). Both are autonomous and composable protocol primitives.

Multiple scoring systems can exist in parallel, with each designed to evaluate talent in a specific domain:

• Builder Score identifies people creating software

• Creator Score recognizes content and community builders (coming soon)

• Degen Score highlights skilled traders and market participants (coming soon)

Each scoring system operates independently with its own set of relevant Data Points and scoring logic, tailored to what matters for their specific persona.

This modular approach allows Talent Protocol to serve different communities with metrics that genuinely reflect their unique contributions, rather than forcing diverse talents into the same box.

Reach out if you want to create a custom scoring system.

Scoring Framework

A Scoring System is composed of:

1. a selection of Data Points to be considered for this particular score

2. a value conversion (optional) to normalize Data Point values (ex.timestamp_to_year or sqrt)

3. a signal strength (strong, medium, or weak) attributed to each Data Point

4. a maximum score (in "points") that each Data Point can contribute to the overall score

5. a scoring function or condition that converts the current value of a Data Point into a numerical score (measured in "points")

Scoring systems are absolute, meaning they don’t include any form of normalization or percentile-based approach. The final score should be a simple sum of all Data Point individual scores.

This framework intends to keep every scoring system simple, transparent and scalable.

Calculating a Score

To calculate a score, Talent Protocol will follow the steps below:

1. Fetch new data for each account and store them in the respective Data Point.

2. If the Data Point value changed, we create an Event (coming soon).

3. We then apply the scoring framework to all Data Points with a value change.

   1. If there are multiple sources for the same data point (example: a user with multiples wallet addresses), we pick the one with the highest value.