Curiosities · weird, quirky, interesting

The Birthday Paradox is the headline. But once you have 298,568 athlete birthdays in one place, you can ask much weirder questions. Which month produces the most athletes? Are sports really biased toward kids born in January? Which team lists somehow dodge the math entirely?

Unique athletes analysed

298,568

deduplicated across all datasets

Why it matters: This is large enough that small calendar skews become visible instead of anecdotal.

Most populous birthday

Jan 2

1,056 athletes share this calendar date

Why it matters: The top dates cluster near the start of the year, which lines up with the relative-age pattern.

Leap-day athletes

209

vs about 204 expected by calendar math

Why it matters: Leap day is almost exactly where calendar math predicts, so the dataset is not simply broken.

Q1 advantage

27.6%

vs 24.7% expected (+2.9 points)

Why it matters: This is the clearest non-random signal: early-year births are overrepresented across athletes.

When are athletes actually born?

Share of athletes born in each calendar month, vs the share you'd expect if birthdays were evenly spread across calendar days. The orange line adjusts for month length, so February gets a lower fair-share baseline.

Based on 298,568 unique athletes across all loaded datasets. Records with missing or year-only DOBs are dropped at ETL time, so what you see here is real signal.

Surprise: Jan has the most athletes by raw count, but Feb is furthest above its fair share after month length is accounted for. Dec is the lightest month relative to expectation.

The relative-age effect

Share of athletes born in each calendar quarter, by sport. Under a flat distribution every quarter would be roughly 25%. A spike in Q1 means athletes born early in the year are over-represented — a well-known signature of youth-team age cutoffs.

Dashed lines mark the 25/50/75% break-points of a perfectly uniform distribution. Overall across all sports: Q1 27.6%, Q2 25.0%, Q3 24.5%, Q4 22.9%.

Surprise: The most revealing signal is not that Q1 is slightly high overall; it is that Football (Soccer) is +6.5 points above its calendar expectation. That looks like selection pressure, not random birthday noise.

Calendar skew

January has the most athlete birthdays by raw count. February is slightly more surprising only after we account for its shorter month.

Jan-Feb share

18.3%

+2.2 points vs calendar expectation

Most athletes

Jan

28,530 birthdays

Furthest above fair share

Feb

1.14×

Lightest month

Dec

0.88×

Q1 comes in at 27.6%, while Q4 lands at 22.9%.

What to notice: The clean version is: January has the most athletes, February is furthest above its fair calendar share, and the whole first quarter is heavier than expected.

Dates that overperform and underperform

Compared with an even spread across non-leap calendar dates. Jan 1 is excluded because year-only DOB placeholders were removed during ETL.

Hot date	Athletes	Difference
Jan 2	1,056	+236
Jan 5	1,036	+216
Feb 14	1,034	+214
Jan 4	1,023	+203
Feb 2	1,023	+203
Jan 18	1,010	+190

Cold date	Athletes	Difference
Dec 26	587	-233
Dec 24	647	-173
Dec 6	651	-169
Oct 29	656	-164
Dec 4	657	-163
Oct 21	668	-152

What to notice: Hot and cold dates are less important individually than their shape: the hottest dates are mostly early-year, while the coldest lean late-year and holiday-adjacent.

Sports with the strongest Q1 tilt

Minimum 500 athletes per sport. The gap column compares Q1 births with Q4 births.

Sport	Players	Q1	Early-late gap
Football (Soccer)	88,356	31.1%	+11.5 points
Ice Hockey	873	30.8%	+10.9 points
Olympic · Ice Hockey (Ice Hockey)	4,665	29.7%	+8.7 points
Olympic · Volleyball (Volleyball)	2,641	29.4%	+7.6 points
Olympic · Artistic Swimming (Aquatics)	617	28.5%	+7.0 points
Olympic · Alpine Skiing (Skiing)	3,092	28.2%	+3.4 points
Olympic · Football	550	28.2%	+8.5 points
Olympic · Cross Country Skiing (Skiing)	2,787	28.2%	+5.1 points

What to notice: Soccer and hockey are the loudest examples of relative age effect: many development systems group children by age cutoffs, giving early-year athletes a maturity edge.

Sports that tilt late instead

The rare cases where Q4 is heavier than the calendar would predict.

Sport	Players	Q4	Above fair share
Cricket	6,645	31.0%	+5.8 points
Olympic · Short Track Speed Skating (Skating)	613	27.9%	+2.7 points
Olympic · Artistic Gymnastics (Gymnastics)	3,914	26.5%	+1.3 points
Baseball	22,437	26.0%	+0.8 points
Olympic · Rhythmic Gymnastics (Gymnastics)	642	25.9%	+0.7 points
Olympic · Rugby Sevens (Rugby)	521	25.5%	+0.3 points
American Football	30,300	25.3%	+0.1 points
Olympic · Equestrian Jumping (Equestrian)	943	25.2%	+0.0 points

What to notice: Cricket going the other way is useful because it proves the early-year tilt is not universal. Different sports and countries can encode different age-cutoff calendars.

Most birthday repeats in one team list

The single team list with the most extra players landing on an already-used calendar birthday.

USA Athletics (2016 Summer)

Olympic · Athletics · USA · 2016 Summer

22 same-birthday repeats across 128 players. Source: Olympics.

What to notice: This is not one birthday shared by 22 pairs; it means 22 players landed on dates already occupied by someone else.

Most populous calendar dates

Top 10 days of the year by athlete count, after dropping unknown / year-only DOB placeholders.

#	Date	Athletes
1	Jan 2	1,056
2	Jan 5	1,036
3	Feb 14	1,034
4	Jan 4	1,023
5	Feb 2	1,023
6	Jan 18	1,010
7	Jan 3	1,009
8	Feb 10	997
9	Jan 15	997
10	Feb 1	992

What to notice: Jan 1 is absent by design because year-only placeholder dates were removed. That makes Jan 2 and the surrounding dates more credible than they would be in a raw scrape.

Team lists that dodged the paradox

These large team lists had no shared birthdays at all, even though the theoretical chance of at least one match was already very high.

Team	Players	No-match odds	Source
DEN American Football · USA · 2002	58	0.83%	NFL
URS Rowing (1976 Summer) Olympic · Rowing · URS · 1976 Summer	55	1.37%	Olympics
Southampton - English Premier League 2022/2023 Football (Soccer) · ENG · 2022-2023	54	1.61%	footballcsv
KC American Football · USA · 1986	53	1.89%	NFL
Dynamo Kiev - Ukrainian Premier League 2009/10 Football (Soccer) · UKRAINE · 2009-2010	53	1.89%	footballcsv
USA Rowing (1988 Summer) Olympic · Rowing · USA · 1988 Summer	52	2.20%	Olympics
GBR Wrestling (1908 Summer) Olympic · Wrestling · GBR · 1908 Summer	51	2.56%	Olympics
Toronto Blue Jays Baseball · USA · 2022	51	2.56%	MLB

What to notice: These are the anti-birthday-paradox team lists. Denver's 2002 NFL team list had only a 0.83% chance of avoiding every shared birthday under the simple model, yet it did.

Four teammates, same calendar day

Largest same-date pileups from the cleaner team-list sources.

4 on May 17

NFL

American Football · ATL · 1987

Scott Case, Rick Donnelly, Art Price, Wayne Radloff

4 on Apr 26

NFL

American Football · NE · 1987

Tim Jordan, Julius Adams, Steve Nelson, Mosi Tatupu

4 on Jan 22

NFL

American Football · RAM · 1987

Vince Newsome, Chris Matau, Chris Pacheco, Charles White

4 on Jan 30

NFL

American Football · IND · 2023

Tyquan Lewis, Malik Turner, Arlington Hambright, Trey Sermon

4 on Jan 8

NFL

American Football · NYJ · 2025

Ja'Sir Taylor, Jermaine Johnson II, Ochaun Mathis, Qwan'tez Stiggers

What to notice: Four people on one calendar date is rare, but big football team lists make enough attempts that these pileups eventually appear.

Same birthday, same year

Triples where teammates shared the exact date of birth, not just month and day.

3 born Apr 4, 1965

NFL

American Football · SEA · 1987

Dave Hollis, Tony Burse, Chad Stark

3 born May 17, 1962

NFL

American Football · ATL · 1987

Scott Case, Rick Donnelly, Art Price

3 born Jan 8, 1959

NFL

American Football · SD · 1987

Mark Herrmann, Anthony Steels, Mike Humiston

3 born Jan 8, 1999

NFL

American Football · NYJ · 2025

Ja'Sir Taylor, Jermaine Johnson II, Ochaun Mathis

3 born Nov 2, 1994

NFL

American Football · PHI · 2020

Hassan Ridgeway, Jordan Howard, Corey Clement

What to notice: Exact-date triples are stricter than the birthday paradox because the year must match too. These are tiny coincidences inside already large team-list histories.

Youngest sports (by avg current age)

Average age of athletes still in our team-list snapshots, as of today. Min 50 athletes per sport.

Sport	Players	Avg age
Olympic · 3-on-3 Ice Hockey (Ice Hockey)	195	21.8
Olympic · Rhythmic Gymnastics	70	22.0
Olympic · Skateboarding	88	23.8
Olympic · Artistic Swimming	106	25.2
Olympic · Artistic Gymnastics	119	25.5
Olympic · Swimming	394	25.7
Olympic · Football	550	26.4
Olympic · Diving	64	26.5

What to notice: This table is really about source freshness. Current Olympic-style snapshots make youth-skewed sports look young because they include active or recent athletes.

Oldest sports

Same calculation, the other tail. Equestrian wins for obvious reasons (athletes compete into their 60s+).

Sport	Players	Avg age
Olympic · Equestrian Eventing (Equestrian)	663	66.0
Olympic · Equestrian Jumping (Equestrian)	637	65.6
Olympic · Equestrian Dressage (Equestrian)	378	63.8
Olympic · Cycling Road (Cycling)	2,753	63.8
Olympic · Shooting	3,464	63.7
Olympic · Basketball (Basketball)	3,039	61.9
Olympic · Fencing	2,761	61.7
Olympic · Cycling Track (Cycling)	1,840	61.5

What to notice: The oldest list is dominated by historical Olympic disciplines and lifetime datasets, so read it as dataset age plus sport longevity.

Where real team lists disagree with the math the most

Sports with the largest gap between real shared-birthday rates and same-size random teams. Some gaps may be real selection effects; some may be data-quality artifacts.

Sport	Team lists	Real	Expected	Gap
Olympic · Artistic Swimming (Aquatics)	58	24.1%	8.6%	+15.5 points
Olympic · Badminton	110	20.0%	10.5%	+9.5 points
Olympic · Hockey	309	48.9%	39.9%	+8.9 points
Olympic · Handball	242	42.1%	34.5%	+7.6 points
Olympic · Football (Football)	429	36.8%	30.5%	+6.3 points
Olympic · Volleyball (Volleyball)	226	36.3%	30.2%	+6.1 points

What to notice: Positive gaps mean shared birthdays happen more often than the simple random-team model expects. That can reflect real selection effects, repeated age groups, or data quirks.