# Understanding Fit Score & Candidate Ranking

SmoothHiring uses a multi-dimensional scoring system to evaluate and rank candidates objectively. Understanding how scores are calculated helps you make faster, data-driven hiring decisions. This guide explains every scoring component, how rankings work, and how to use scores effectively in your workflow.

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## The Scoring ComponentsEach candidate receives multiple scores that together paint a complete picture:

![Fit score and ranking](/files/flow-applicants-table690164690164.png)

| Score | Range | Source | What It Measures |
|-------|-------|--------|-----------------|
| **Resume Match Score** | 0–100% | AI resume analysis | How closely the resume matches job requirements |
| **Job Fit (Match Type)** | 5 levels | Behavioral assessment | Behavioral alignment with the role profile |
| **Smart Fingerprint Recommendation** | 4 levels | AI multi-factor analysis | Overall candidate suitability |
| **Star Rating** | 1–5 | Manual recruiter input | Recruiter's subjective evaluation |
| **Assessment Scores** | Varies | Skill assessments | Performance on specific tests |

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## Resume Match Score (0–100%)

### How It's Calculated

The Resume Match Score uses AI to compare the content of a candidate's resume against the job description and requirements. The algorithm evaluates:

1. **Skills Match** – Do the candidate's listed skills align with required and preferred skills?
2. **Experience Relevance** – Are their past roles and responsibilities relevant to this position?
3. **Education Alignment** – Does their educational background meet the job's requirements?
4. **Keyword Density** – How frequently do job-relevant terms appear in the resume?
5. **Seniority Alignment** – Does their career level match what the role demands?

### Score Ranges

| Range | Interpretation | Typical Action |
|-------|---------------|---------------|
| **80–100%** | Excellent match | Priority review, fast-track |
| **60–79%** | Good match | Standard review |
| **40–59%** | Moderate match | Review if pipeline is thin |
| **20–39%** | Weak match | Consider for related roles |
| **0–19%** | Poor match | Likely auto-reject candidate |

### Visual Display

The Resume Score appears as a **circular gauge** (donut chart) in the applicant list. The gauge fills proportionally and uses color coding:
- **Green** (high scores) – 80%+
- **Amber** (moderate scores) – 50–79%
- **Red** (low scores) – Below 50%

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## Job Fit (Match Type)

### The Five Fit Levels

| Level | Internal Value | Meaning |
|-------|---------------|---------|
| **Strong Fit** | `strong` | Behavioral profile very closely matches the ideal for this role |
| **Good Fit** | `match`, `good` | Solid behavioral alignment with minor variations from ideal |
| **Fair Fit** | `fair` | Mixed alignment; some traits match well, others diverge |
| **Weak Fit** | `none`, `weak` | Significant behavioral mismatch with the role's requirements |
| **Distortion** | `distortion` | Response patterns suggest inconsistency; interpret with caution |

### How Job Fit Is Determined

1. A **Job Profile** defines the ideal behavioral characteristics for the role across multiple dimensions
2. The candidate completes a behavioral assessment
3. Each dimension is scored and compared to the target
4. The overall fit level is calculated based on:
   - Number of dimensions that meet or exceed the target
   - Severity of any gaps
   - Overall pattern consistency (distortion detection)

### Visual Indicators

In the applicant list, Job Fit appears as a **colored chip**:
- Each fit level has a distinct icon for accessibility
- Chips show the fit label text (e.g., "Strong Fit", "Good Fit")
- The chip uses outlined style for a clean appearance

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## Smart Fingerprint Recommendation

### How It Works

The Smart Fingerprint system is an AI layer that analyzes **all available data** for a candidate and produces a holistic recommendation. It considers:

- Resume match score
- Behavioral assessment results (Job Fit)
- Assessment performance
- Skills alignment
- Experience relevance
- Any red flags or exceptional strengths

### Recommendation Levels

| Recommendation | Color Swatch | Meaning |
|---------------|-------------|---------|
| **Highly Recommended** | Green | Strong signals across all dimensions; prioritize |
| **Recommended** | Blue/Teal | Good overall candidate; proceed with confidence |
| **Consider** | Amber | Mixed signals; investigate specific areas in interview |
| **Not Recommended** | Red/Grey | Significant concerns; likely not suitable |

### Reasoning

Each recommendation includes **reasoning bullets** explaining why the system arrived at its conclusion. Hover over the recommendation in the list view to see reasoning such as:
- "Strong alignment between skills and job requirements"
- "Below-target scores on key behavioral dimensions"
- "Limited relevant experience for seniority level"

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## Star Rating (1–5)

### Purpose

The star rating is a **manual** score assigned by recruiters or hiring managers to capture subjective evaluation that automated scoring can't measure—like interview performance, cultural impression, or gut feeling.

### How to Set

- In the **list view**: Click the star widget on any applicant row
- In the **profile view**: Click the star widget in the professional summary section
- Ratings save automatically

### Using Ratings for Filtering

Filter by rating in Advanced Filters using numeric filters:
- Show only 4+ star candidates
- Exclude unrated candidates
- Sort by rating to surface top-rated candidates first

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## Assessment Scores

### Per-Assessment Scoring

Each assessment produces its own score. Depending on the assessment type:

| Assessment Category | Score Type |
|--------------------|-----------|
| Cognitive/Aptitude | Percentage correct + percentile rank |
| Skills tests | Score + pass/fail threshold |
| Personality | Dimensional scores (no single number) |
| Cultural fit | Alignment percentage |
| Simulations | Task-completion metrics + accuracy |

### Cohort Standing

Some assessments include **cohort standing** which shows how a candidate performed relative to others who took the same assessment. This provides valuable context—a 75% score means different things if the average is 50% vs. 90%.

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## AI-Powered Analysis (Smart Recruiter)

### How AI Analysis Scores Work

When you run a Smart Recruiter analysis, the AI scores each candidate on a **0–100 scale** against your custom prompt. Results show:

- **Match Score** – How well the candidate matches your described criteria
- **Match Strength** – Categorized as Strong, Moderate, or Weak
- **Reasoning** – Bullet points explaining the match
- **Key Skills** – Highlighted skills relevant to your query
- **Concerns** – Any potential issues or gaps identified

### Score Colors in AI Results

| Score | Color | Category |
|-------|-------|----------|
| 80+ | Green (#27ae60) | Strong match |
| 50–79 | Amber (#f39c12) | Moderate match |
| Below 50 | Red (#e74c3c) | Weak match |

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## Ranking Candidates

### Default Sort Order

By default, applicants are sorted by **application date** (newest first). However, you can leverage scores for ranking:

### Sorting Options

| Sort Field | How to Access | Best For |
|-----------|--------------|----------|
| **Date** | Click "Date" column header | Reviewing in chronological order |
| **Name** | Click "Name" column header | Finding a specific person |
| **Resume Score** | Available in table view | Identifying highest-scoring resumes |
| **Rating** | Available in table view | Surfacing recruiter favorites |

### Using Filters to Rank

Combine filters for a powerful ranking workflow:

1. **Filter by Fit**: Show only "Strong Fit" and "Good Fit" candidates
2. **Sort by Resume Score**: Highest scores first
3. **Review top results**: These are your best candidates based on both behavioral fit and resume match

### Stage-Based Ranking

Use stage folders to create ranking contexts:
- **New stage** sorted by Resume Score → Prioritize which new applicants to review first
- **Interview stage** sorted by Rating → See how candidates ranked after interviews
- **All stages** filtered by Strong Fit → Find your best candidates regardless of where they are in the process

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## Using Scores in Batch Auto Reject

Scores directly power the [Batch Auto Reject](/help/applicants-pipeline/auto-reject) feature:

- Set a **Resume Score Threshold** – Candidates below this score are flagged
- Select **Fit Levels to reject** – Target Weak Fit and Distortion candidates
- Combine with **YOE ranges** – Further narrow by experience level

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## Interpreting Score Combinations

| Resume Score | Job Fit | Recommendation | Interpretation |
|-------------|---------|---------------|---------------|
| High (80%+) | Strong | Highly Recommended | Top candidate – prioritize |
| High (80%+) | Weak | Consider | Good on paper but behavioral mismatch – investigate |
| Low (<40%) | Strong | Consider | Behavioral fit is good but resume doesn't match – may be a career changer |
| Low (<40%) | Weak | Not Recommended | Poor match on all dimensions |
| Moderate | Fair | Consider | Average candidate – depends on pipeline depth |

> **Tip:** Never rely on a single score. The power of SmoothHiring is the **combination** of multiple scoring dimensions giving you a complete picture.

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## Related Pages

- [Managing Candidates](/help/applicants-pipeline/managing-candidates) – Viewing and sorting candidates by score
- [Candidate Screening](/help/applicants-pipeline/candidate-screening) – How screening feeds into scoring
- [Batch Auto Reject](/help/applicants-pipeline/auto-reject) – Using scores for automated decisions
- [Smart Resume Search](/help/applicants-pipeline/smart-resume-search) – Finding specific qualifications beyond scores