🔮 Name Origin, Nationality & Age Predictor
Discover the most probable nationality, country of origin, and median age associated with any first name using global demographic data.
Each result is a statistical association between a name and records in a dataset, not a fact about any individual. Check the record count shown with every figure — it is what separates a signal from noise.
What Name Origin, Nationality & Age Predictor Does
This tool queries three public APIs with a first name and reports what they associate with it: the countries where the name appears most often, a predicted age, and which gender it is more often recorded as.
None of these is a fact about a person. Each is a statistical association between a string and a pile of records scraped from public sources, and the honest version of the output includes how many records that is. A name backed by 600,000 records and a name backed by eight produce output that looks identical unless the count is shown, which is why it is shown here beside every figure.
The second thing worth knowing before reading any result: even for a very common name the top country is usually a minority. Alexander's most-associated country is Germany at 12.7%, which means roughly seven in eight Alexanders in the dataset are not German.
How to Use Name Origin, Nationality & Age Predictor
- Type any first name into the search bar
- Click Analyze Name
- Review predicted gender confidence, median global age, and top nationality probabilities
Formula Used by Name Origin, Nationality & Age Predictor
Why the percentages do not add up
displayed total = sum of the top five probabilities only
- probability
- Share of records for this name that came from a given country
- top five
- All the API returns — the remaining countries are not listed
Worked example
The live nationalize.io response for "Alexander", 629,559 records.
- Germany 12.7462%
- Colombia 9.7844%
- United States 8.2869%
- Russia 7.4112%
- Austria 4.0229%
- Sum: 42.25%
Result: The five listed countries account for 42% of records. The other 58% sit in countries the API does not return. A "top match" of 12.7% is not a prediction that someone is German — it is the largest slice of a very fragmented distribution.
How much a result is worth, by record count
confidence depends on count, not on the number of decimal places shown
- count
- Records behind the estimate, returned by all three APIs
Worked example
Three names queried live on 11 September 2026.
- Alexander — 629,559 nationality records, 71,295 age records, 645,315 gender records
- Sakura — 6,316 / 1,475 / 6,399
- Zzyzx — 20 / 8 / 19
Result: Zzyzx returns "age 49" from eight records and "13.6% Sweden" from twenty. Both render to one decimal place and look exactly as authoritative as Alexander's. This tool now warns below 100 records, because at that scale the output is noise wearing a percentage sign.
The Same Query, Three Very Different Samples
Live responses from nationalize.io, agify.io and genderize.io on 11 September 2026. Read the count column before the result column.
| Name | Records | Top country | Predicted age | Gender |
|---|---|---|---|---|
| Alexander | 629,559 | Germany, 12.7% | 42 (from 71,295) | Male, 100% of 645,315 |
| Sakura | 6,316 | Japan, 18.7% | 44 (from 1,475) | Female, 94% of 6,399 |
| Zzyzx | 20 | Sweden, 13.6% | 49 (from 8) | Male, 76% of 19 |
What Each API Actually Returns
Three separate services, run by the same team, queried in parallel by this tool.
| API | Returns | Also returns |
|---|---|---|
| nationalize.io | Up to five country codes with probabilities | count — total records for the name |
| agify.io | A single predicted age | count — records the age is derived from |
| genderize.io | male or female, with a probability | count — records behind the split |
What This Cannot Tell You
The gap between what the output looks like and what it supports.
| Looks like | Actually is |
|---|---|
| This person is German | This name appears more in records tagged Germany than in any other single country |
| This person is 42 | The central tendency of birth years associated with this name |
| This person is male | This name was recorded as male in most records containing it |
| This name originates in Japan | Nothing about etymology — the data is where the name is used now, not where it came from |
How to Read Your Result
Read the count first, then the percentage
Every one of these APIs returns a record count alongside its prediction, and it is the only thing that separates a usable estimate from a coin flip dressed up with decimals. The failure mode is specific: a rare name returns a small number of records, the arithmetic still produces a clean-looking 76% or 13.6%, and the interface presents it in the same typeface as a result backed by half a million records. Below roughly a hundred records nothing here should influence anything. This tool now says so on the page rather than leaving you to notice.
Where the data comes from, and what that implies
The operators state that the data is collected from publicly available sources across the web, gathered over more than a decade. That has consequences worth carrying into any result. Populations with lower internet visibility are underrepresented, so a name common in a country with less public web presence will show fewer records and a distorted country split. The dataset reflects where a name currently appears online, not where it originated — which is why this page describes "countries associated with" a name rather than its origin. And a name popular among a diaspora will surface the destination country rather than the source.
The global figure is the weakest version of the question
The operators note that accuracy improves when a prediction is scoped to a specific country, and the reason is straightforward: a name can skew old in one country and young in another, and a global average of the two describes neither. "Alexander" at 42 blends German, Colombian, American, Russian and Austrian populations whose name-popularity cycles peaked in different decades. A single global number for a name used across dozens of countries is an average over incompatible distributions, and should be read as a very rough indication rather than an estimate.
Do not use this to make decisions about people
Inferring gender, age or nationality from a name is inexact by construction, and it is wrong in exactly the cases where being wrong matters most — people whose names cross the boundaries the dataset draws. Gender is reported as a binary here because that is what the API returns, which is itself a limitation rather than a description of reality. Using output like this to sort applicants, target advertising by inferred ethnicity, or personalize a greeting by presumed gender means acting on a guess about someone and being confidently wrong about a predictable minority. It is a reasonable instrument for studying name patterns in aggregate, and a bad basis for any decision about an individual.
Limitations & Accuracy Notes
- A wrapper over nationalize.io, agify.io and genderize.io. It adds no data and inherits every bias in theirs.
- The free unauthenticated endpoints are limited — the response carries an x-rate-limit-limit of 25 with a reset counter. When that is exhausted the tool reports it rather than showing an empty result.
- Global figures only. The APIs accept a country_id parameter that materially improves accuracy, and this tool does not send one.
- Nationality results are the top five countries at most, typically covering well under half the records, so they do not sum to 100%.
- Gender is returned as male or female only. There is no third option and no way to express that a name is not gendered.
- Names in non-Latin scripts, and names that are transliterated inconsistently, are represented poorly or not at all.
- Nothing here concerns etymology, meaning or historical origin — only where a name appears in scraped records.
- A result of no data is common and usually correct rather than a failure.
Frequently Asked Questions
How does the name nationality prediction work?
How is the estimated age calculated?
How reliable are these predictions?
Why is the top country only around 12%?
Does this tell me a name's etymology?
Why is gender reported as only male or female?
Why did my lookup fail?
Is my search stored?
References & Further Reading
- Nationalize.io — Returns up to five country probabilities and a record count for a given name
- Agify.io — Returns a predicted age and record count. Its documentation states the prediction is a statistical central tendency rather than an exact birth year
- Genderize.io — Returns a binary gender with probability and record count
- Agify.io — Frequently asked questions — Source for the statement that the data is collected from publicly available sources across the web, and for the recommendation to scope predictions with country_id