---
title: "Decoding the Fit Score: The Key to Talent Matching | Bryq Help Center"
description: Learn how Bryq’s Fit Score works and how it helps match candidates to roles using data-driven insights on skills and potential.
---
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December 24, 2025
# Decoding the Fit Score: The Key to Talent Matching | Bryq Help Center
## Familiarize yourself with the Fit Score introduction in the Bryq Platform
Understanding how individual scores compare to others is essential when analyzing assessment results. To determine if a score is high, low, or average in relation to others, we rely on a norm group. Norms allow us to compare an individual's evaluation score to a relevant group of peers.
At Bryq, we have developed a standardized 1-10 scale called the **Fit Score** to interpret Bryq scores. This score provides valuable insights into an individual's position within a specific population. It is derived from a standard normal distribution, which is visually represented by the bell curve you see below. The marker's placement on the curve indicates where the candidate stands compared to the norm group.
💡 **What is the norm group in Bryq?** It is referred to as the "**Global Population"** and is a representative sample of candidates who have taken the Bryq assessment. Our sample size of 10,000 candidates is designed to accurately represent the demographic composition of our population across different regions and departments. We have ensured proportional representation from various regions such as Africa, Asia, Europe, Latin America, North America, and Oceania, reflecting the actual distribution of our population. Similarly, within each department category including Administration, Blue Collar, Call Center, Consulting, Customer Service or Support, Data Science, Finance and Accounting, Human Resources, Information Technology, Legal, Marketing, Operations, Product, Project Management, Retail, and Sales, we have maintained proportional distribution to capture the diversity of roles existing in our data pool. This approach enables meaningful comparisons and conclusions to be drawn regarding demographic factors under analysis.
The **Fit Score** ranges from 1 to 10, with scores between 4 and 7 considered typical, as they are obtained by approximately 68% of the norm group. Scores of 1 and 10 represent extreme values, each obtained by approximately 2% of the norm group.
In the Bryq assessment process, talent responses are compared to other people who have taken the Bryq assessment and share common characteristics with the talents of the position such as business interests. The results are then transformed into a **Fit Score**, providing candidates with a standardized measure of their fit within the norm group.
Bryq **Fit Score** classification is presented below:
[.png)](https://downloads.intercomcdn.com/i/o/976794149/9ce25b43662f39b9baf6a50d/graph+%281%29.png?expires=1765189800&signature=37acd51384f2cced63041db01a2a386d231ab3f95fe04f75a23afa666d69285e&req=fSchEcB6nIVWFb4f3HP0gL4jRLIQfTP9Isq80fU9%2BEWMyElMiRgnvFXbDV3G%0AnlpiLdCNsdlHHsOlDg%3D%3D%0A)
💡 Do note that you should use the **Fit score** comparatively, not absolutely. No score is good or bad in itself, thus, it is advised to use them to find the best candidate from a *specific* pool. In order to gain more detailed insights into one’s position as compared to a specific role, it is recommended to look at the comparison section in the Bryq platform.
**Example:**
Let’s say that we have a candidate who applies for a call center agent role and scores 55. This translates into a **Fit Score 7** which is a **good fit for the Call Center Agents role.**
However, if we were to examine the **same candidate for a Data Scientist role** they would be a **below average fit positioned in Fit Score 3** for the Data Scientists pool.
Likewise, someone scoring a 65 for a data scientist role would be **a good fit belonging to a Fit Score 7,** but an **exceptional fit if they would be considered for a call center agent role.**
💡 The Fit Scores are dynamic and not bound to specific ranges. To give you an indicative example, for a profile that has 25% weight for all cognitive attributes the Bryq score is typically distributed as follows:
| Fit Score | Label | Cognitive Match Score | Top % Percentile |
| --- | --- | --- | --- |
| 1 | Poor | **1-31** | 0 |
| 2 | Below Average | **32-38** | 98 |
| 3 | Below Average | **39-45** | 93 |
| 4 | Average | **46-52** | 84 |
| 5 | Average | **53-59** | 70 |
| 6 | Good | **60-67** | 50 |
| 7 | Good | **68-74** | 30 |
| 8 | Great | **75-81** | 16 |
| 9 | Great | **82-88** | 6 |
| 10 | Exceptional | **89-100** | 2 |
Please note, that the above ranges will be adjusted depending on the weight of the specific profile. Therefore, it's evident that the Fit Score plays a crucial role in accurately translating and interpreting the Bryq score based on the specific requirements of each role profile.
📌The introduction of the Fit Score aligns with the innovative Talent Matching feature, which serves as a crucial guide for recommending talents for various role profiles. The Fit Score plays a vital role in this process by providing a standardized measure of alignment between talents and specific job requirements. By utilizing the Fit Score, you can make more informed decisions when identifying and matching the right talents to the appropriate role profiles, ultimately enhancing the efficiency and effectiveness of your talent selection process.
🎥 To visually understand how the Fit Score integrates in Bryq - check it out [here](https://145953192.hs-sites-eu1.com/bryq-knowledge-base/video-fit-score-bryq-help-center?hsLang=en).
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