Rank to Percentile Calculator
Estimate what percentile a given rank roughly corresponds to in a field of N candidates.
- percentile ≈ ((candidates − rank + 1) ÷ candidates) × 100
- = ((1000000 − 15000 + 1) ÷ 1000000) × 100 = 98.5001
- Generic estimate only — NTA and state CETs publish their own percentile algorithms.
How rank maps to percentile
Generic estimate only
Estimated percentile ≈ ((candidates − rank + 1) ÷ candidates) × 100. This ignores ties and official normalisation. For counselling decisions, rely on the exam authority’s published percentile, not this estimate.
Frequently asked questions
Why +1 in the formula?
It treats your rank as including yourself among those at or below your performance — a common textbook inversion of the percentile→rank estimate.
Tied ranks?
Not modelled — official bodies share ranks differently.
Session-wise percentiles?
NTA multi-session exams need their method; this tool is single-pool generic math.
Other direction?
Use Percentile to Rank Calculator.
For developers & AI agents
The Rank to Percentile Calculator is also available as a free, unauthenticated JSON API — no API key, no sign-up, CORS enabled for all origins. Send a GET request with the parameters below and receive the computed result as JSON, including the source of the format or formula used. Invalid input returns HTTP 400 with a JSON { "error": "..." } body.
GET https://knwdle.com/api/tools/rank-to-percentile-calculator| Parameter | Required | Description |
|---|---|---|
rank | Yes | Rank (integer ≥ 1) |
candidates | Yes | Total candidates (integer ≥ rank) |
Request:
GET https://knwdle.com/api/tools/rank-to-percentile-calculator?rank=15000&candidates=1000000
Response:
{
"tool": "rank-to-percentile-calculator",
"input": {
"rank": "15000",
"candidates": "1000000"
},
"result": {
"primaryLabel": "Estimated percentile",
"primaryValue": "98.5001",
"steps": [
"percentile ≈ ((candidates − rank + 1) ÷ candidates) × 100",
"= ((1000000 − 15000 + 1) ÷ 1000000) × 100 = 98.5001",
"Generic estimate only — NTA and state CETs publish their own percentile algorithms."
],
"extras": [],
"tables": [],
"listSections": []
},
"formula": "percentile ≈ ((N − rank + 1)/N)×100 (generic).",
"source": "percentile ≈ ((N − rank + 1)/N)×100 (generic)."
}