Methodology
Data sources
The statistics on this site are drawn from three Social Security Administration datasets: the national baby names file, which records name, sex, and count by year back to 1880; the state-level version of that file, available from 1910 onward; and the SSA's period life table, which provides survival probabilities by age and sex. The Social Security Administration excludes any name given to fewer than five babies in a given year, so a sufficiently rare name will display a “limited data” notice rather than a full profile.
Living-age distribution
This is the point most visitors misunderstand: a name's age distribution today is not the same as it was during the name's period of popularity. Each birth-year cohort is weighted by its survival probability to the present, using the number-of-lives column (lx) from the SSA life table, calculated separately by sex. A name that peaked in 1950 has had 75 years for its cohort to age and, actuarially, thin out; a name that peaked in 2015 has not. The median and 15th/85th percentile ages shown come from this survival-weighted distribution, rather than from raw birth counts.
Gender balance
Gender balance draws on the sex field already present in the SSA national baby names file: each name's overall share of male and female births, along with the median and 15th/85th percentile living ages calculated separately by sex. For names given overwhelmingly to one sex, the split is reported as a single dominant percentage rather than a full breakdown, since the minority share is usually too small to be meaningful.
Popularity arc & trend archetype
Popularity is measured as a name's share of all births in a given year rather than as a raw count, which allows for fair comparison across eras with very different total birth volumes. The archetype badge is generated through k-means clustering on several shape features of each name's popularity curve: the recency of its peak, the duration it remained active, the proportion of peak strength that persists today, and whether the curve shows a decline followed by a resurgence. Each resulting cluster is matched to the closest of six predefined archetypes.
Popularity forecast
The ten-year projection shown on the popularity arc is produced by Holt's damped-trend exponential smoothing, fit separately to each name's history. Rather than fixing the smoothing parameters (alpha, beta, phi) by hand, they are chosen by grid search to minimize one-step-ahead error. Fit quality and the resulting confidence interval are scored only against the trailing twenty-five years of data, not a name's full history: a name like Mildred swung sharply in the 1910s and has been flat and rare for decades, and scoring against a century of irrelevant volatility would produce an uselessly wide interval for its current, quieter era. The damping term keeps the projection from extrapolating an unrealistic runaway trend, and no forecast is shown for names that are no longer actively given.
Rarity
Rarity ranks each name by its all-time total births against the roughly 106,000 names that meet the SSA's five-births-per-year reporting threshold. The rank is the count of names with a strictly higher total, plus one; the reported percentile is the share of all names that a given name is more common than.
Name neighbors
Name neighbors are computed using cosine similarity between full-resolution yearly popularity curves, identifying names whose rise and fall tracked one another closely over the decades. Simple spelling variants are filtered out deliberately; without this step, most results would consist of alternate spellings of the same name rather than genuinely related names.
Geographic fingerprint
For each state and Census region, a name's share of local births is compared to its share of national births over the same period, from 1910 to the present, matching the availability of state-level data. An index of 2.0 indicates that a name appears twice as frequently in that state as would be expected based on the national baseline. On the map, red indicates a state where the name is under-indexed and blue indicates a state where it is over-indexed.
Head-to-head compare
The compare view reuses the same per-name statistics computed for individual profiles; nothing is recalculated for the comparison itself. Its state map colors each state by the ratio of the two names' own geographic indices there. Because both indices are already normalized to that name's national average, comparing them directly answers which name leans harder into a given state relative to how each name typically behaves, rather than comparing raw, differently-scaled popularity.
Peak popularity prediction
The forecast above projects a share of births, but milestones like Top 100 are ranks, and translating one into the other means knowing where every other name stood that year. Each projected year's share is compared against 2025's actual popularity distribution to find the equivalent rank, which implicitly assumes the overall shape of that distribution doesn't shift much across the ten-year window. Milestone probabilities come from the forecast's own uncertainty band: each year's projected share is treated as normally distributed around its point estimate, and the odds of clearing a threshold are combined across years as if independent, which overstates them slightly for a name sitting right at the line, since a strong year is usually followed by another strong year rather than a fresh coin flip. The reported peak rank isn't always taken from a name's highest-share year either. The SSA now counts around 28,600 distinct names in a single year, versus under 1,900 in 1880, so a smaller share today can outrank a bigger share from decades ago, and the model scans a name's full history for its actual best rank rather than assuming it lines up with its best share.
Name life-cycle stage
Life-cycle stage asks a different question than the archetype badge above: not what shape has this name's curve traced across its whole history, but where does it stand right now. A short decision tree assigns one of six stages (Emerging, Growing, Peaking, Declining, Historic, Extinct) based on a handful of engineered features: the compound growth rate of a name's share over the last five years, whether that growth is accelerating or cooling, how close its current share sits to its all-time peak, and how recently that peak happened. It's rule-based rather than trained on purpose, since the point of a life-cycle label is being able to say exactly why a name got it, and a short decision tree makes that explanation exact instead of approximated after the fact. Because it runs off recent momentum rather than a whole curve's shape, the two labels are allowed to disagree: a name with a Faded Classic archetype can still show a rising trend here, if births have ticked up over the last five years without coming near its historic peak.
Popularity survival model
The survival model answers something the forecast can't: once a name reaches a rank tier, Top 100, Top 500, or Top 1000, how long does it typically hold on before falling back out? It's Kaplan-Meier survival analysis, the standard technique for time-to-event data, applied to every historical run any name has had above each tier as its own observation. A name that spent nine years in the Top 100, dropped out, then came back for four more contributes two runs, not one; runs still going in 2025 are right-censored rather than counted as an exit, since the name hasn't actually left yet. That adds up to 111 runs through the Top 10 and 7,488 through the Top 1000 across the whole dataset, thin enough at the top end that a single unusual name can move the curve more than it would further down. For a name already inside a tier, the model reports conditional survival, the odds of lasting another five, ten, or twenty years given it has already lasted this long, rather than starting the clock over. For a name that hasn't reached a tier yet, the chance of entering it isn't a separate estimate; it's the same forward-looking probability computed for the milestones above, reused rather than recalculated by a second method.