# Born in December: how much does birth month weigh in selecting young female athletes?

> An under-14 squad can contain two girls eleven months apart and three years apart in biological development, and the rulebook treats them as the same age. Across 57 studies and 308 samples, girls born in January-March are more represented than those born in October-December — small, but systematic — and in the federations where it has been tracked, those born late in the year quit sooner. What the numbers say, and what a club can change without waiting for a reform.

**In short:** In female sport the relative age effect exists but is small: in a meta-analysis of 57 studies and 308 independent samples across 25 sports, athletes born in the first quarter of the selection year are 1.25 times more represented than those born in the last (odds ratio 1.25; 95% CI 1.21-1.30), with the effect most pronounced up to age 14 and at higher competition levels (Smith et al., 2018).

- Page: https://www.babsport.com/en/blog/eta-relativa-selezione-giovani-atlete
- Markdown source: https://www.babsport.com/en/blog/eta-relativa-selezione-giovani-atlete.md
- Published: 2026-09-11
- Updated: 2026-09-11
- Author: Sajid Hossain
- Publisher: BAB — Breaking All Barriers (https://www.babsport.com/)
- Tag: relative-age-effect, maturation, peak-height-velocity, bio-banding, drop-out, clubs
- How to cite: BAB — Breaking All Barriers, "Born in December: how much does birth month weigh in selecting young female athletes?", https://www.babsport.com/en/blog/eta-relativa-selezione-giovani-atlete
- Note: educational content, not medical advice. Every health claim is anchored to a source listed under "Sources"; where a study was run on adults, the text says so.

---

An under-14 squad organised by calendar year can contain two girls born in January and in December: **eleven months apart**, roughly 8% of the life of a twelve-year-old. If one of them entered puberty two years before the other — entirely normal — the distance in centimetres, kilos and strength can be enormous. The rulebook, however, calls them the same age and has them compete for the same place.

That asymmetry produces the **relative age effect**: the tendency, within every category, to find more athletes born in the early months of the selection year than in the late ones. In male sport it is one of the best-documented phenomena in the literature. In female sport it was measured much later, and the results are less dramatic than the current narrative suggests — but they are not zero, and the part that really matters is not about the calendar.

> **Key points**
> - Meta-analysis of **57 studies and 308 independent samples across 25 female sports**: girls born in the **first quarter** are **1.25 times** more represented than those born in the last (95% CI 1.21-1.30). **Significant but small**.
> - The effect is **strongest up to age 14** and at **higher competition levels**.
> - In France, across **57,892 registered female footballers**, the **15,285** who quit the following year were over-represented in the **third and fourth** birth quarters.
> - In Ontario, across **9,908 female footballers** aged 10-16 followed for seven years, median survival was **four years** for first-quarter births and **three** for all others.
> - In the United States, across **3,364 athletes**, the effect was present at club and talent-centre level but **not in youth national teams**, which had more late-year births and more **late-maturing athletes**.
> - Among **113 Irish call-ups** aged under 15-17 there was **no** relative age effect, but a small advantage for the **biologically more mature** (d = 0.39).
> - **Bio-banding** has been studied almost exclusively in boys: **99.8% male** (859 of 861) in the most recent systematic review.

## What is the relative age effect?

It is an artefact of categories. Every federation sets a cut-off date — 1 January in Italy — and places every girl born in the following twelve months into the same group. At eleven, twelve, thirteen, those twelve months are not an administrative detail: they are months of growth, of accumulated training, of school, of coordination. A girl born in January has had more time to become the girl being watched today.

The mechanism is almost always the same, and requires bad faith from no one: whoever looks more ready gets selected, whoever gets selected receives more coaching, more minutes and more trust, and the initial advantage confirms itself. The literature calls it the Matthew effect, or a self-fulfilling prophecy. In practice it is simply a coach who, watching two girls on a trial day, picks the one who runs faster today.

## Does the effect really exist in female sport?

Yes, but it is small — and this is the first number to hold on to, because in the popular account the relative age effect is often presented as destiny.

The reference synthesis pooled data published between 1984 and 2016 on female sport: **57 studies**, **308 independent samples**, **25 sports**. Comparing girls born in the first quarter of the selection year with those born in the last, the pooled odds ratio is **1.25** (95% CI 1.21-1.30; 1.21 adjusted) — **statistically significant but small in magnitude** ([Smith et al., 2018](https://doi.org/10.1007/s40279-018-0890-8)).

An odds ratio of 1.25 means that, across large numbers, for every four athletes born in the last quarter there are about five born in the first. That is not the difference between playing and not playing: it is a light, constant pressure that becomes visible only when thousands of registrations are viewed together. On a squad of fifteen girls, no coach could possibly notice it.

## At what age does it weigh most?

In the subgroup analyses, the magnitude is not uniform: it is **larger in categories up to age 11 and between 12 and 14**, and **larger at higher competition levels**; it is also more pronounced in team sports and in individual sports with high physiological demands ([Smith et al., 2018](https://doi.org/10.1007/s40279-018-0890-8)).

The 12-14 band is exactly when the first regional selections are made, and it coincides with [peak height velocity](/en/blog/picco-di-crescita-giovani-atlete): two girls born in the same year can be one before and one after their fastest phase of growth, with performance differences that say nothing about talent and everything about the biology of the moment.

## Do girls born late in the year quit sooner?

This is the part that concerns families more than selectors, and it also has the largest samples.

In France, **every** female footballer registered with the federation in the 2006-2007 season was analysed — **57,892 athletes** — and then the **15,285** who did not re-register the following season. Among those who quit, first- and second-quarter births were **under-represented** and third- and fourth-quarter births **over-represented**, with significant differences in the under-10, under-14 and under-17 categories ([Delorme et al., 2010](https://doi.org/10.1111/j.1600-0838.2009.00979.x)).

The second study looked at the same question over time rather than in a single year: a cohort of **9,908 Canadian female footballers aged 10-16** followed for seven years. Median survival in the sport was **four years** for first-quarter births and **three years** for every other quarter. In the same work, though, the strongest predictor of dropout was not the birth date but the level: **55.9%** of players in the competitive stream remained engaged, against **20.7%** in the recreational stream; community size predicted nothing ([Smith and Weir, 2022](https://doi.org/10.3390/sports10050079)).

These are federation registries, so observational studies: they say that girls born late in the year stay slightly less long, not why. The plausible "why" is mundane — you quit where you enjoy yourself less and play less — but it remains a hypothesis. On what makes girls quit in adolescence the blog has a [dedicated article](/en/blog/abbandono-puberta), and the birth date is only one factor among several, probably not the main one.

## Why does the effect sometimes disappear at the top?

Because the filter changes character as you climb, and this is the most interesting finding of recent years.

In the US federation's talent pathway, **3,364 female players** active in the 2021-2022 season were analysed across three levels: club, talent identification centres, youth national teams. The over-representation of first-quarter births was present at **club** and at **identification centres** — in almost every band from under-13 to under-18 — but **not in the youth national teams**, where birth dates were evenly spread, with a prevalence of **last**-quarter births and a higher share of **late-maturing** athletes than elsewhere in the pathway ([Finnegan et al., 2024](https://doi.org/10.5114/biolsport.2024.136085)).

This is cross-sectional data, from one federation and one season: it does not prove that girls born in December become better players. But it is consistent with a hypothesis that has a name in the literature, the *underdog* effect: a girl who reaches the top while being the smallest in the group has had to build something other than size — technique, game reading, tolerance for frustration — and that something holds once everyone else finishes growing.

## Birth month or pubertal development — which matters more?

In elite female sport, the available data point to the second.

The Irish federation measured **113 call-ups** to its youth national teams (52 under-15, 32 under-16, 29 under-17). Relative to general population reference values there was **no** birth-date imbalance in any age group; there was instead a **small but significant** advantage for biologically more mature athletes (**d = 0.39**; p < 0.001), growing from under-15 (**d = 0.36**) to under-16 (**d = 0.44**). The authors stress that the picture differs **in both magnitude and shape** from what has been observed in international men's football ([Sweeney et al., 2025](https://doi.org/10.5114/biolsport.2025.144411)).

Why maturity shows up on the pitch is documented physically as well: among **157 female players from three elite English academies**, classified by years from peak height velocity, speed, change of direction, vertical jump and aerobic capacity were better in the more mature athletes ([Emmonds et al., 2020](https://doi.org/10.1519/JSC.0000000000002795)). It should be said plainly that this study used *magnitude-based inferences*, a contested statistical approach: the direction of the result is plausible, the precision of the estimates less so.

Two girls born in the same month can be **years** apart in biological development. It is that difference — not the calendar — that the eye mistakes for talent.

## Can you measure how mature an athlete is?

You can estimate it, provided you state the error. Research uses two main routes.

The first is **percentage of predicted adult height**: adult height is predicted from height, weight and parental heights, and you look at where the athlete currently sits along that path. This is the method used with the 113 Irish players, and its advantage is that it requires no medical test — only a tape measure and two numbers the family already knows.

The second estimates the **years remaining to peak height velocity** from anthropometric measures. Here validation is severe, and specifically so for girls: comparing estimates with actually observed growth in **198 girls and 193 boys** followed longitudinally, the equations place the peak **too late** in early-maturing individuals and **too early** in late-maturing ones; for average-maturing boys there is a window in which the estimate is acceptable, **for girls no such window was apparent**, and intra-individual variability is considerable ([Kozieł and Malina, 2018](https://doi.org/10.1007/s40279-017-0750-y)).

The practical consequence is worth spelling out: these tools serve to **group** girls in training, not to **label** an individual athlete, and they produce no prediction about who will become good.

## Does bio-banding solve the problem?

**Bio-banding** is the field's most discussed idea: group by biological maturity rather than by birth year, so that those ahead in development meet a sterner test and those behind can express themselves without simply being pushed aside by physique.

It is a sensible idea. The problem is the evidence. In the most recent systematic review — **13 studies, 861 young footballers** — **99.8% of participants were male** (859), against **0.2% female** (two athletes in total — that is not a typo). The authors themselves note that the available evidence concerns mostly immediate in-match responses and proxy measures, not selection accuracy or long-term development, and that maturity assessment methods and banding thresholds vary too much between studies to be compared ([Han et al., 2026](https://doi.org/10.52082/jssm.2026.446)).

So: worth trying in training, yes. Presentable to families as a proven practice, no. And on a girls' squad, proven even less.

## What a club can do from next season

None of these four practices has been validated by a controlled trial in female athletes. They are reasonable corrections to a documented bias, and should be presented as such — which is still better than doing nothing because the federal reform has not arrived.

- **Write the birth month next to the name** on assessment sheets. This is the zero-cost correction: whoever is watching knows whether they are comparing two athletes eleven months apart, and the mental adjustment happens on the spot.
- **Do not decide on a single day.** Assessments repeated at different points in the year reduce the weight of the instant snapshot, which is exactly what relative age distorts.
- **Vary groupings in training**: by height, by developmental stage, by technical task. You do not need a bio-banding protocol to occasionally let girls with the same body — rather than the same birth year — play together.
- **Treat maturity as temporary information.** The girl who is behind today is, in all likelihood, the one with the most room in three years — and in the meantime she needs to stay, which means she needs to play. It is the same reasoning that argues against [early specialisation](/en/blog/specializzazione-precoce-giovani-atlete) and in favour of careful [load management](/en/blog/allenare-ragazze-adolescenti) around peak growth.

## BAB's role

On this topic BAB does not select and does not produce talent rankings: that would be the exact opposite of what these data suggest.

What it offers is a **private athlete space** in which a girl records her own signals over time — load, energy, mood, pain, growth — and sees the patterns emerge. In a phase when the body changes faster than any category can represent, having her own timeline is useful to her first: it makes visible that a rough stretch may coincide with a growth phase rather than with a loss of value. Clubs receive **only aggregated, anonymous data**, never an individual diary.

The educational half is giving precise words to something that gets said by feel on the sideline — "she's behind", "she's small" — and that in most cases means only this: *today she has fewer months, or less puberty, than the others*.

## When to consult a professional

This article is about selection and categories, not health. There are two situations, though, in which growth stops being an organisational question and becomes a clinical one: when height or pubertal development **stall** or remain far from those of peers, and when [menstruation has not appeared by age 15, or disappears](/en/blog/red-s-bassa-disponibilita-energetica) after having appeared. In both cases the reference point is the paediatrician or family doctor, not the coach. The same holds for pain lasting weeks during a phase of rapid growth: [peak height velocity](/en/blog/picco-di-crescita-giovani-atlete) explains many things, but it is not a diagnosis.

## Frequently asked questions

### What is the relative age effect in sport?

It is the over-representation, within a single age category, of those born in the early months of the selection year, and the matching under-representation of those born in the late months. In female sport it has been quantified by a meta-analysis of 57 studies and 308 independent samples across 25 sports, using data from 1984 to 2016: comparing the first birth quarter with the last, the pooled odds ratio is 1.25 (95% CI 1.21-1.30) — statistically significant but small (Smith et al., 2018). In Italian youth categories the cut-off is 1 January, so the first quarter is January to March.

### My daughter was born in December — is she at a disadvantage?

Slightly, on average; not as an individual. What has been measured is a difference in distribution across large numbers — 1.25 times more girls born in the first quarter than in the last, across 308 samples (Smith et al., 2018) — not a prediction about a single athlete: in that same meta-analysis, girls born late in the year are present at every level. The practical point is not a label on your daughter, it is a question for the club: when groups are formed, does anyone account for how many months apart the girls being compared actually are?

### Is the relative age effect the same in boys and girls?

No, and this is one of the few things the data agree on: in female sport the effect is present but smaller. The reference meta-analysis on female sport reports a pooled odds ratio of 1.25 between first and last quarter (Smith et al., 2018), and in international women's football there are studies that do not find it at all: among 113 Irish under-15, under-16 and under-17 call-ups there was no birth-date imbalance in any age group (Sweeney et al., 2025). The leading explanation is that a few extra months turn into a performance advantage mainly where size matters, and in female development the link between maturation and performance is less linear.

### Do girls born late in the year quit sport more often?

In the two studies that tracked registrations over time, yes. In France, across the entire population of registered female footballers in one season (57,892 athletes), the 15,285 who did not re-register the following year were under-represented in the first and second quarters and over-represented in the third and fourth, with significant differences in the under-10, under-14 and under-17 categories (Delorme et al., 2010). In Ontario, following a cohort of 9,908 female footballers aged 10-16 for seven years, median survival in the sport was four years for first-quarter births and three years for every other quarter (Smith and Weir, 2022). These are observational studies of federation registries: they show an association, not a cause.

### Why does the effect sometimes disappear at the highest levels?

Because the filter changes character as you climb. In the US talent identification pathway, analysed across 3,364 female players in the 2021-2022 season, the over-representation of first-quarter births was present at club and at regional talent identification centres but not in the youth national teams, where birth dates were evenly spread, with a prevalence of last-quarter births and a higher share of late-maturing athletes than elsewhere in the pathway (Finnegan et al., 2024). It is cross-sectional data from a single federation, but consistent with the 'underdog' hypothesis: a girl who reaches the top from a less mature body has had to build something else — technique, game reading, tolerance for frustration.

### Which matters more, birth month or pubertal development?

In elite women's football, development. Among 113 players called up by the Irish federation, birth dates showed no imbalance at all, while relative to population reference values there was a small but significant advantage for biologically more mature athletes (d = 0.39; p < 0.001), growing from under-15 (d = 0.36) to under-16 (d = 0.44) (Sweeney et al., 2025). Two girls born in the same month can be years apart in biological development: it is that difference, not the calendar, that shows up on the pitch.

### Can an athlete's maturity be measured without medical tests?

It can be estimated, with an error margin that has to be stated. Research uses two routes: percentage of predicted adult height, which needs height, weight and the parents' heights (the method used with the 113 Irish players), and equations estimating the years remaining to peak height velocity. Validation of the second is unforgiving precisely for girls: compared with actually observed growth in 198 girls and 193 boys followed longitudinally, the equations place the peak too late in early-maturing athletes and too early in late-maturing ones, and for girls no window of acceptable accuracy was apparent (Kozieł and Malina, 2018). They serve to group athletes, not to label one.

### Does bio-banding work for girls too?

We do not know, and that needs saying. Bio-banding — grouping by biological maturity rather than by birth year — has been studied almost exclusively in boys: in the most recent systematic review, of 861 young footballers across 13 studies, 99.8% were male (n = 859) and 0.2% female (n = 2) (Han et al., 2026). The same review notes that the available evidence concerns mostly immediate match responses, not the quality of selection decisions over time. In other words: a reasonable thing to try in training, not a proven solution — and least of all proven in girls.

### What can a club actually do?

Four things that need neither budget nor federal reform: write the birth month next to the name on assessment sheets, so whoever is watching knows whether they are comparing two athletes eleven months apart; repeat assessments at different points in the year instead of deciding on a single trial day; use groupings by height or developmental stage in training, not only by birth year; and treat maturity as temporary information, because the girl who is behind today may be the one with the most room in three years. None of these practices has been validated by a controlled trial in female athletes: they are reasonable corrections to a documented bias, and should be presented as exactly that.

## Sources

- Smith K.L., Weir P.L., Till K., Romann M., Cobley S. **Relative Age Effects Across and Within Female Sport Contexts: A Systematic Review and Meta-Analysis.** *Sports Medicine*, 2018;48(6):1451-1478. (57 studies, 308 independent samples, 25 sports, data 1984-2016; pooled odds ratio first vs last quarter 1.25, 95% CI 1.21-1.30, adjusted 1.21; larger effects in the ≤11 and 12-14 age bands and at higher competition levels. **Female-only** populations; heterogeneous sports and countries) [doi:10.1007/s40279-018-0890-8](https://doi.org/10.1007/s40279-018-0890-8)
- Delorme N., Boiché J., Raspaud M. **Relative age effect in female sport: a diachronic examination of soccer players.** *Scandinavian Journal of Medicine & Science in Sports*, 2010;20(3):509-515. (the entire population of female players registered with the French federation in 2006-2007, n=57,892, plus n=15,285 who did not re-register the following year; those who quit were under-represented in Q1-Q2 and over-represented in Q3-Q4, significantly so in the under-10, under-14 and under-17 categories. **Registry-based observational** study: association, not cause) [doi:10.1111/j.1600-0838.2009.00979.x](https://doi.org/10.1111/j.1600-0838.2009.00979.x)
- Smith K.L., Weir P.L. **An Examination of Relative Age and Athlete Dropout in Female Developmental Soccer.** *Sports*, 2022;10(5):79. (cohort of 9,908 female footballers aged 10-16 in Ontario, Canada, followed across seven years of registrations; median survival 4 years for the first quarter against 3 years for the others; 55.9% retention in the competitive stream against 20.7% in the recreational stream; community size did not predict dropout) [doi:10.3390/sports10050079](https://doi.org/10.3390/sports10050079)
- Finnegan L., van Rijbroek M., Oliva-Lozano J.M., Cost R., Andrew M. **Relative age effect across the talent identification process of youth female soccer players in the United States: Influence of birth year, position, biological maturation, and skill level.** *Biology of Sport*, 2024;41(4):241-251. (3,364 US female players, 2021-2022 season, three levels of the talent pathway; effect present at club and talent identification centres and absent in youth national teams, where more last-quarter births and more late-maturing athletes appear. **Cross-sectional**, single federation; maturity **estimated**, not measured radiologically) [doi:10.5114/biolsport.2024.136085](https://doi.org/10.5114/biolsport.2024.136085)
- Sweeney L., Lundberg T.R., Sweeney C., Hickey J., MacNamara Á. **Biological maturity but not relative age biases exist in female international youth soccer players relative to the general population.** *Biology of Sport*, 2025;42(2):249-256. (113 players called up by the Irish federation: 52 under-15, 32 under-16, 29 under-17; maturity assessed with the Khamis-Roche method in under-15 and under-16 only; small but significant advantage for more mature players, d=0.39 overall, d=0.36 at under-15 and d=0.44 at under-16; **no** relative age effect in any age group. Small elite sample) [doi:10.5114/biolsport.2025.144411](https://doi.org/10.5114/biolsport.2025.144411)
- Emmonds S., Scantlebury S., Murray E., Turner L., Robsinon C., Jones B. **Physical Characteristics of Elite Youth Female Soccer Players Characterized by Maturity Status.** *Journal of Strength and Conditioning Research*, 2020;34(8):2321-2328. (157 female players from three elite English academies, grouped by years from peak height velocity; speed, change of direction, jump and aerobic capacity better in more mature players. Analysed with **magnitude-based inferences**, a contested statistical approach: plausible direction, uncertain precision) [doi:10.1519/JSC.0000000000002795](https://doi.org/10.1519/JSC.0000000000002795)
- Kozieł S.M., Malina R.M. **Modified Maturity Offset Prediction Equations: Validation in Independent Longitudinal Samples of Boys and Girls.** *Sports Medicine*, 2018;48(1):221-236. (validation on longitudinal data from the Wrocław Growth Study, 193 boys aged 8-18 and 198 girls aged 8-16; equations place peak height velocity later in early maturers and earlier in late maturers; for girls **no accuracy window was apparent**; considerable intra-individual variability) [doi:10.1007/s40279-017-0750-y](https://doi.org/10.1007/s40279-017-0750-y)
- Han C., Luo N., Zhao Z., Mou D. **The Effect of Bio-Banding On Talent Identification in Youth Soccer: A Systematic Review.** *Journal of Sports Science and Medicine*, 2026;25(2):446-458. (13 studies, 861 young footballers; **99.8% male** — 859 of 861 — and 0.2% female; good average methodological quality but no randomisation and no control of confounders; evidence concerns immediate match responses and proxy outcomes, not selection accuracy or long-term development) [doi:10.52082/jssm.2026.446](https://doi.org/10.52082/jssm.2026.446)
- Cumming S.P., Lloyd R.S., Oliver J.L., Eisenmann J.C., Malina R.M. **Bio-banding in Sport: Applications to Competition, Talent Identification, and Strength and Conditioning of Youth Athletes.** *Strength and Conditioning Journal*, 2017;39(2):34-47. (a **position and narrative review** article, not an experimental study: it defines bio-banding and its applications; it provides no efficacy evidence in female athletes) [doi:10.1519/SSC.0000000000000281](https://doi.org/10.1519/SSC.0000000000000281)

*This article is for information and education and does not constitute medical advice. It describes distributions measured across large populations of athletes, which do not predict any individual girl's path. If an athlete's growth or pubertal development differs markedly from that of her peers, or if menstruation has not appeared by age 15 or disappears after having appeared, the reference point is the paediatrician or family doctor.*