
For Min/Max fields (identified by the “Minimum Score” option), values are normalized relative to the group.
This is done by identifying the minimum and maximum values across all participants and converting each result into a percentage within that range.
Example:Using "Number of Employees" metric found in Praxar Kayak:
The Minimum Score setting ensures that no participant falls below a defined threshold.

We then apply the weight assigned to each field. This is a straightforward multiplication:
The final grade is calculated by summing all weighted values across the selected metrics.
This grading approach is designed to reflect relative performance within a competitive environment, similar to real-world business contexts. It ensures that results are evaluated based on outcomes and decision quality in comparison to peers, rather than in isolation.
By normalizing results across the group, the model maintains consistency and fairness while reinforcing critical thinking, trade-off management, and performance analysis.
It is important to note that a higher ranking does not always translate directly to a high percentage score. For example, a team may rank near the top of the group, but if performance across teams is closely clustered or significantly below the top performer, the resulting percentage may still be moderate.