Mean Reciprocal Rank (MRR) Calculator
Average of 1/rank of the first correct answer — the metric for QA, autocomplete and single-answer retrieval.
MRR only cares about the FIRST correct answer, making it ideal for known-item search, QA and autocomplete where one right answer suffices. An MRR of 0.5 means, on average, the first correct hit sits at rank 2.
Formula
Disclaimer: This tool is for general informational and estimation purposes only and is not professional financial, tax, accounting or legal advice. All figures are estimates — verify with a qualified professional before making decisions. Read the full disclaimer.
About Mean Reciprocal Rank (MRR) Calculator
When there's one right answer and the only question is how high you ranked it, Mean Reciprocal Rank is the metric. It averages 1/rank of the first correct result across queries — so an answer at position 1 scores 1.0, position 2 scores 0.5, position 4 scores 0.25, and a miss scores 0. That reciprocal weighting captures real user behavior: the difference between rank 1 and 2 matters far more than between 9 and 10. This calculator computes MRR from a list of first-hit ranks, ideal for evaluating QA systems, autocomplete, and known-item retrieval.
How to use Mean Reciprocal Rank (MRR) Calculator
- 1Enter your values into Mean Reciprocal Rank (MRR) Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Mean Reciprocal Rank (MRR) Calculator?
- ✓Computes Mean Reciprocal Rank (MRR) instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: MRR = (1/Q) Σ 1/rankᵢ — rank of the FIRST correct result for each query; 0 (not found) contributes 0.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
When should I use MRR instead of MAP or NDCG?+
MRR when each query has essentially ONE correct answer and you only care where it lands (QA, entity lookup, autocomplete, 'find the doc'). MAP and NDCG when queries have MULTIPLE relevant results and you care about the whole ranked list. Using MRR on multi-answer tasks throws away information about all but the first hit.
How do I interpret an MRR value?+
Its reciprocal is roughly the average position of the first correct answer: MRR 1.0 = always rank 1, MRR 0.5 = typically rank 2, MRR 0.25 = typically rank 4. But because it's an average of reciprocals, a few rank-1 hits can offset many misses — check the hit rate alongside it.
How are queries with no correct answer handled?+
They contribute 0 to the sum (rank → ∞, reciprocal → 0) but still count in the denominator. So MRR jointly reflects both whether you find the answer and how high you rank it. If you want to separate those, report hit-rate (recall) and MRR-over-hits separately, as this tool's secondary outputs allow.
Is MRR sensitive to the cutoff?+
Less than precision@K — if the first correct answer is at rank 50, it still contributes 1/50 rather than 0 (unless you impose a cutoff). In practice systems cap the candidate list, so very deep hits get truncated to 0. Report the cutoff you used (e.g. MRR@10) for reproducibility.
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