Scoring Systems
A Scoring System is a set of rules that convert Data Points into numerical reputation scores.
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.
Different Scores
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.
Scoring Framework
A Scoring System is composed of:
a selection of Data Points to be considered for this particular score
a value conversion (optional) to normalize Data Point values (ex.
timestamp_to_year
orsqrt)
a signal strength (strong, medium, or weak) attributed to each Data Point
a maximum score (in "points") that each Data Point can contribute to the overall score
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:
Fetch new data for each account and store them in the respective Data Point.
If the Data Point value changed, we create an Event (coming soon).
We then apply the scoring framework to all Data Points with a value change.
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.
Score Credentials
A Score Credential is the connection between a Data Point and a Scoring System. It defines:
How much a specific Data Point contributes to a given score
The criteria for evaluating the Data Point's value
The maximum points this credential can add to the total score
Example
For the Data Point "GitHub Stars":
In the Builder Score system, this might be worth up to 15 points (strong signal)
In the Creator Score system, this might be worth only 2 points (weak signal)
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