Astrology Data Analysis, Part 1

Key Takeaways

• In 45,727 AA-rated celebrity charts from AstraDataBank, sun signs cluster mildly by season: Pisces is the most common (4,120 charts, 9.0%) and Sagittarius the least (3,404, 7.5%) — a 21% gap consistent with a conception-season rhythm.
• Moon signs look almost perfectly even across the full sample (8.1%–8.5% per sign, a 5.2% spread), but the evenness is window-dependent: across the five years 1968–1973 the spread was 60%, and across 1973–1978 it was 63%, with the top and bottom signs nearly swapping places.
• Rising signs split sharply by geometry: Virgo rising (5,060 charts, 11.1%) outnumbers Pisces rising (2,110, 4.6%) by 2.4x. Inside the 48-49°N band the gap widens to 3.0x, close to the 2.9x ratio implied by the computed rise times of the signs at that latitude.
• Sun-by-house placement favors the left half of the wheel: the five most common houses — 11th, 10th, 2nd, 12th, and 1st — are exactly those the Sun occupies between midnight and midday.
• The analysis extends the statistical tradition begun by Michel Gauquelin — Mars with athletes, Saturn with scientists, the Moon with writers, Jupiter with actors and politicians — with birth-record-grade data and boundary conditions attached to every claim.

Introduction

Astrology's hardest problem has never been symbolism. It has been verification. Most astrological knowledge rests on accumulated case experience: a pattern noticed in charts, repeated in practice, codified in textbooks. That tradition carries real signal, but it is difficult to test. Two astrologers can read the same chart and disagree, and neither has a clean way to settle the question. Statistics offers one way out. In the mid-twentieth century, French psychologist and statistician Michel Gauquelin ran some of the first large-sample tests of astrological claims and reported reproducible associations between profession and planetary placement: Mars with athletes, Saturn with scientists, the Moon with writers, Jupiter with actors and politicians. The "Mars effect" remains disputed, and this series does not treat any of these findings as settled. What Gauquelin established was a method: define the population, fix the data quality, count, and publish the boundary conditions.

This is the first article in FateScript's astrology data analysis series — Part 2 applies the same dataset to the career patterns of Venus in the water signs. The dataset is 45,727 AA-rated celebrity charts from AstraDataBank. "AA" is the highest precision tier in that database: the birth time comes from a birth certificate or another official record, not from memory. The precision matters because the Ascendant and the houses move quickly — a birth time off by a few minutes changes the structure of the chart, so anything below record-grade data cannot support distribution statistics. Three questions drive the analysis:

1. Are the Sun, Moon, and Ascendant evenly distributed across a large population?
2. Where a distribution is uneven, what produces the imbalance — sampling artifact, observational geometry, or a real behavioral and biological rhythm?
3. What should these patterns change about the way an individual chart is read?

Every table below reports counts and shares exactly as extracted from the sample's distribution tables — 45,702 charts per dimension, drawn from the full 45,727-chart sample — with the filters stated in the method section.

Sun Signs: A Mild Spring-High, Winter-Low Pattern

Core finding. Sun signs are not evenly distributed, but the imbalance is modest. The distribution runs slightly higher for spring signs and slightly lower for winter signs. Pisces — the sign that straddles the winter–spring boundary — sits at the very top, and Sagittarius at the bottom.

Pisces outnumbers Sagittarius by a factor of 1.21 — about 21% more people. The three spring signs (Taurus, Gemini, Aries) all sit above the 8.33% mean, and the three lowest signs (Capricorn, Scorpio, Sagittarius) all sit below it.

Why it happens. Human gestation averages about 280 days. Shifting a birth date back by roughly nine months gives the approximate conception date. The shape of the table is consistent with a conception peak in late summer and early autumn and a trough in late autumn and early winter: the six highest signs run from Pisces through Cancer — births whose conceptions fall roughly between late spring and mid-autumn — while the lowest signs correspond to conceptions in the late-winter trough. This tracks the seasonal availability of food: in the environments where this birth rhythm was established, conception rates were higher when food was abundant. The sun-sign distribution is, at least in part, a faint demographic fingerprint of an ancient subsistence calendar.

What to do with it. A 21% gap at population level is a baseline, not a verdict. It is far too weak to support any claim that one sun sign is "better" than another, and it says nothing about an individual. Its practical use is corrective: when comparing groups by sun sign — say, two professions, or two birth decades — this seasonal bias sits inside the numbers and should be discounted before any other interpretation is layered on top. It is also a fading effect. Modern contraception, assisted reproduction, and scheduled births all weaken the conception-season rhythm, so the sun-sign distribution should keep flattening in more recent cohorts. That is a testable prediction for later installments of this series.

Moon Signs: Even Overall, Unstable Inside Short Windows

Core finding. Across the full sample, moon-sign frequencies are strikingly even: every sign falls between 8.1% and 8.5%, a spread of only 5.2% from highest to lowest. The evenness, however, is a property of the long time window, not of the Moon itself. When the same data is sliced into five-year periods, the distribution shifts visibly.

Why it happens. The Moon's orbit around Earth is an ellipse, and the Sun's gravity is the reason. A circular orbit would mean constant speed; an elliptical one does not. Astrology traditionally works with an average lunar speed of 13°11′ per day, with a peak of about 15° per day near perigee and a minimum of about 11° per day near apogee. Since the Moon crosses the whole zodiac in about 27.3 days, the number of degrees it covers each day changes through the month — and so does the share of the population born under each moon sign.

This is not a modern observation. Traditional technique has always treated lunar speed as a working variable. William Lilly and John Frawley both discuss the Moon's variable speed, and the difference between a fast and a slow Moon, in their horary works; medical astrology likewise uses changes in the Moon's motion when tracking the course of an illness.

Evidence from the data. To test the window effect, the sample was filtered into two five-year periods — January 1, 1968 to January 1, 1973 (1,622 charts), and January 1, 1973 to January 1, 1978 (1,289 charts) — with the smaller signs kept near or above 100 charts. The two windows produce dramatically different distributions from each other and from the full-sample average:

Three observations follow. First, the spread widens roughly twelvefold: highest-to-lowest runs 1.05x across the full sample but 1.60x in 1968–1973 and 1.63x in 1973–1978. Second, the ordering reshuffles: Cancer is the most common moon sign of the first window and nearly the rarest of the second (10.0% → 7.3%), while Scorpio falls from 9.3% to 6.1% and Aries climbs from 6.2% to 9.0%. Third, the "rarest moon sign" is a window-dependent statement: Virgo in the full sample, Aries in 1968–1973, Scorpio in 1973–1978. The overall balance is real, but it is the average of many unbalanced periods, not a constant.

What to do with it. Two practical consequences follow. In time-sensitive techniques such as horary astrology, the Moon's speed is already a standard factor and should stay one. In population statistics, any moon-sign distribution claim is meaningless without its time window attached: "moon signs are evenly distributed" is true of a 45,702-chart, multi-decade sample and false of most five-year slices.

Rising Signs: A 2.4x Gap Explained by Rise-Time Geometry

Core finding. The twelve Ascendants are distributed very unevenly. Virgo rising appears in about 5,060 charts; Pisces rising in about 2,110 — a ratio of 2.4 to 1, and the least intuitive finding in the dataset, coming right after the Moon's near-flat distribution.

The split is exact: the six long-ascension signs — Cancer, Leo, Virgo, Libra, Scorpio, Sagittarius — take the top six places, and the six short-ascension signs — Capricorn, Aquarius, Pisces, Aries, Taurus, Gemini — take the bottom six, with no overlap. The long group accounts for 29,186 charts (63.9%) against 16,516 (36.1%) for the short group.

Why it happens. Each sign does not spend the same amount of time rising. In the northern hemisphere, the long-ascension signs take longer to rise completely across the horizon than the short-ascension signs; the division runs along the Cancer–Capricorn axis. Demetra George, in Ancient Astrology in Theory and Practice, traces the concept to the obliquity of the ecliptic: because Earth's axis is tilted, a long-ascension sign needs more time to clear the eastern horizon than a short-ascension one. The rise time of each sign is fixed for a given latitude and does not change with the seasons, and the higher the latitude, the more extreme the difference becomes. South of the equator, the pattern inverts.

Testing it against the data. The sample was filtered to the 48-49°N latitude band, which runs through Paris-centered Europe and the northern United States — a reasonable match for a dataset drawn mainly from European and North American public figures — leaving 7,244 charts. The table below sets the computed rise time of each sign at 48°30′N beside the filtered sample:

The match is close. The six long-ascension signs again occupy exactly the top six places, now holding 66.1% of the band's charts against 66.6% of the day's rise time — and the Libra-to-Pisces ratio, 3.0x in the sample, sits right beside the 2.9x ratio of their rise times. Filtering to the latitude where the geometry was computed tightens the correspondence; the full-sample ratio of 2.4x is lower because it averages in lower latitudes, where the rise-time differences are milder. The fit is not perfect in every row — Sagittarius ties Libra for the longest rise time but ranks sixth in the sample — because births are not spread uniformly around the clock, and that clustering reshapes the Ascendant on top of the geometric baseline. That clustering is the subject of the next section.

What to do with it. Any rising-sign frequency claim must carry its hemisphere and latitude. "Virgo rising is more common than Pisces rising" is a statement about northern mid-latitudes; in the southern hemisphere the same geometry produces the reverse. Frequency is a clock measurement, not a ranking of worth — an Ascendant's time above the horizon says nothing about the quality of the people born under it. For placement-by-placement meaning, the reference is what the Ascendant is and how it reads in each sign; for the two extremes in this dataset, see Virgo rising and Pisces rising.

Sun by House: The Birth-Clock Effect

Core finding. Sun placement by house is strongly asymmetric: the left half of the chart wheel is overrepresented. The five most common houses are the 11th, 10th, 2nd, 12th, and 1st — every one of them on the left side of the wheel.

The left half of the wheel (houses 10 through 3) holds 54.2% of the sample; the right half (houses 4 through 9) holds 45.8%. The six lowest houses are precisely the 4th through the 9th — the afternoon and night houses — while the 3rd sits exactly at the 8.33% mean.

Why it happens. Houses are a function of birth time, so this distribution is really a distribution of birth times. Natural births cluster in the small hours and the morning. Before dawn the Sun is below the horizon, which puts it in the 12th house; around sunrise it crosses the eastern horizon into the 1st; through the morning it climbs through the 2nd; and as it approaches its highest point it works through the 11th into the 10th. The five houses the Sun occupies between midnight and midday are exactly the five that top the table. Nothing about the houses themselves is being measured here — the pattern is the daily birth curve reflected in the chart.

What to do with it. This is the clearest reminder in the dataset that chart statistics measure human schedules as much as symbolic meanings. The house distribution will move as medicine moves: cesarean rates, induction practices, and shift-work cultures all redistribute birth times, and the Sun-by-house curve will follow. It also explains why birth-time precision is non-negotiable in this kind of work — a birth time rounded to the nearest hour can move the Sun across a house cusp. For the interpretive side of house placements, see the Sun through the twelve houses.

Comparison, Method, and Limitations

Method. The source is AstraDataBank, restricted to AA-rated charts (birth time from an official record); the full sample is n = 45,727, and each distribution table reports the 45,702 charts counted in that dimension, with counts and shares as extracted from the sample's distribution tables. Filtering differed by target: the Ascendant analysis was re-run inside the 48-49°N band (n = 7,244), and the Moon analysis inside two five-year windows (1968–1973, n = 1,622; 1973–1978, n = 1,289). Sun-by-house placements follow the whole-sign house system. Every distribution claim was checked against an astronomical parameter — rise time, obliquity, lunar orbital speed — rather than left as a bare count.

Limitations. The sample is drawn mainly from European and North American public figures, so latitude and region are skewed, and the "celebrity" filter introduces professional self-selection that a general-population sample would not have. None of the patterns above should be quoted as population-wide facts for humanity at large; they are properties of this dataset, reported with the conditions attached.

FAQ

Q1. Does the uneven rising-sign distribution mean some Ascendants are stronger or luckier?

No. The imbalance is a measurement of how long each sign spends crossing the eastern horizon at a given latitude — a clock reading, not a value judgment. At 48-49°N, Libra spends 168 minutes rising while Pisces spends 58, and the sample shows 3.0x as many Libra risings; south of the equator the same geometry produces the reverse. What an Ascendant means in a chart is a separate question from how often it occurs in a dataset.

Q2. If moon signs are nearly even overall, does moon-sign placement matter less?

The opposite. The evenness exists only across long time windows: inside five-year slices the spread widens about twelvefold (from 1.05x to roughly 1.6x), because the Moon's elliptical orbit changes its speed between roughly 11° and 15° per day. Traditional horary technique has always treated lunar speed as a factor — William Lilly and John Frawley both wrote about it — and the data is consistent with that concern rather than against it.

Q3. Why use celebrity charts instead of general-population data?

AA-rated data requires an official birth record, and public figures' birth records are far more likely to be documented and published than private individuals'. That makes celebrity databases the highest-precision public source available — and for readings of individual celebrity charts, the site keeps a separate celebrity birth charts series with step-by-step classical interpretations. The trade-off is real — regional skew and professional self-selection — which is why every result here is published with its boundary conditions rather than as a universal claim.

Q4. Can the 21% sun-sign gap be used to predict personality or compatibility?

No. The gap reflects a conception-season rhythm at population level: it is a demographic baseline of about one-fifth at most, and it will keep shrinking as birth control and medical scheduling weaken the underlying seasonality. It is useful for correcting other comparisons, and useless as a predictor for an individual person.

Conclusion

Four distributions, four causes:

• Sun signs carry a faint seasonal fingerprint — Pisces at 9.0% against Sagittarius at 7.5%, a 21% swing tied to food availability and the ~280-day gestation cycle.
• Moon signs are even across decades but not across years: a 5.2% spread overall widens to 60–63% inside five-year windows, because the Moon's elliptical orbit varies its speed between about 11° and 15° per day.
• Rising signs split by rise-time geometry — Virgo rising roughly 2.4x Pisces rising overall, 3.0x at 48-49°N against a 2.9x geometric expectation — an effect of the obliquity of the ecliptic, not of sign quality.
• Sun by house leans left because natural births cluster between midnight and midday, making the house distribution a map of the daily birth curve.

The methodological point matters more than any single number. Statistical astrology earns trust the way Gauquelin's work did — not by proving that the stars dictate outcomes, but by making claims countable: a defined population, a stated data-quality standard, an astronomical mechanism behind each pattern, and boundary conditions published next to the findings. This is the standard every article in FateScript's astrology data analysis series will hold itself to. Part 2 turns the same 45,727-chart dataset toward planet–profession associations, comparing each Venus-in-water-sign subgroup against the other eleven Venus signs; later installments take on house distributions across eras and how these baselines move as medicine changes when and where people are born.

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Patterns described here are properties of the AA-rated AstraDataBank sample, reported with their filters and boundary conditions. They are not universal population claims, and nothing here predicts an individual outcome.