---
id: candidate-analysis
name: Candidate Analysis
description: Analyse the applicant pool's quality, and how much of it has actually been screened.
category: hiring
pages:
  - candidates
  - candidates-analysis
status: active
version: 1
triggers:
  - candidate quality
  - quality of candidates
  - score band
  - score bands
  - screening coverage
  - how strong*candidates
  - how good*candidates
owliver:
  enabled: true
  suggestions:
    - label: How strong is the candidate pool?
      capability: summary
    - label: Show candidates by score
      capability: table
    - label: Score bands
      capability: progress
  capabilities:
    - summary
    - stats
    - table
    - list
    - progress
    - insight
  responses:
    summary:
      title: Candidate quality
      source: candidates.quality
    stats:
      title: Candidate quality
      source: candidates.quality
    table:
      title: Candidates by score
      source: candidates.quality
      limit: 10
    list:
      title: Strongest candidates
      source: candidates.quality
      limit: 5
    progress:
      title: Candidate quality
      source: candidates.quality
    insight:
      title: Candidate quality
      source: candidates.quality
---

# Candidate Analysis

## Purpose

- Report how strong the applicant pool is.
- Report how much of it anyone has actually looked at, beside the quality figure.
- Rank candidates by score so a shortlist has a starting point.

## Capabilities

- Summarize pool size, screening coverage, average score and interview count.
- List or tabulate candidates by score.
- Break the scored pool into quality bands.

## Data

Reads `candidates.quality`, which counts `JobApplication` records and their
`ai_score`, joined to `AIInterview` records for interview coverage.

## Analysis

Coverage is reported next to quality, always. An average score computed from a
fifth of the pool is not the pool's average, and reporting the first without the
second is how a hiring dashboard talks itself into confidence.

Scored candidates are grouped into four bands — 80 and above, 70 to 79, 50 to 69,
and below 50 — because a mean hides whether a pool is uniformly mediocre or
split between strong and weak.

## Output

Pool size, share screened, average score across scored candidates only, and
interview count. Then candidates ranked by score with their role and stage.

## Limitations

- Unscored candidates are excluded from the average rather than counted as zero.
  They are reported separately as the unscreened share.
- A score is an AI screening score, not an interview outcome or a hiring decision.
- Filtering by period counts applications by when they were received, not by when
  they were screened.