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Olympic Cycling Track
Team
17 team lists · 89 player entries · avg team size 5.2
Read this sport as a comparison between raw coincidence and what team size alone predicts. Here, the real rate is -3.0 points below the birthday-paradox baseline.
Real team lists
0.0%
teams with shared birthdays
Why it matters: This is the headline rate, but it should never be read without team size.
Expected from size
3.0%
from avg team size
Why it matters: This is the fair baseline: what same-size random teams would do.
Gap from expected
-3.0 points
real − expected
Why it matters: Negative means this sport is quieter than team size alone predicts.
Team lists
17
teams analysed
Why it matters: Small sample: treat the rate as a lead, not a verdict.
What to notice: The best question on a sport page is not "is the real rate high?" but "is it high after accounting for team size?" That is what the gap card is answering.
Country comparison
Countries with the most team lists for Olympic Cycling Track, charted by the share of teams with at least one shared birthday.
Gender split inside this sport
Real and expected rates for labelled team lists in this sport.
Why it matters: The gender gap is mostly a team-size story. Men have the larger average team size here (5.3 athletes), so their birthday-match rate naturally rises.
By country
Top 40 countries by team-list count.
| Country | Team lists | Avg players | Real |
|---|---|---|---|
| AUS | 2 | 5.0 | 0.0% |
| CAN | 2 | 5.0 | 0.0% |
| FRA | 2 | 5.0 | 0.0% |
| GBR | 2 | 6.0 | 0.0% |
| JPN | 2 | 5.0 | 0.0% |
| ITA | 2 | 5.5 | 0.0% |
| NZL | 2 | 5.0 | 0.0% |
| DEN | 1 | 6.0 | 0.0% |
| IRL | 1 | 5.0 | 0.0% |
| USA | 1 | 5.0 | 0.0% |
What to notice: Countries with larger average teams will naturally show more shared birthdays. The country list is most useful for finding which samples are driving this sport's overall rate.
By gender
Where the dataset records it.
| Gender | Team lists | Real | Expected | Gap |
|---|---|---|---|---|
| Women | 9 | 0.0% | 3.0% | -3.0 points |
| Men | 8 | 0.0% | 3.0% | -3.0 points |
Sample team lists
Largest teams in the dataset.
| Team | Season | Players | Repeats | Expected chance |
|---|---|---|---|---|
| Denmark - Men's Team Pursuit · DEN | 2024 Summer | 6 | 0 | 4.0% |
| Great Britain - Men's Team Pursuit · GBR | 2024 Summer | 6 | 0 | 4.0% |
| Great Britain - Women's Team Pursuit · GBR | 2024 Summer | 6 | 0 | 4.0% |
| Italy - Women's Team Pursuit · ITA | 2024 Summer | 6 | 0 | 4.0% |
| Australia - Men's Team Pursuit · AUS | 2024 Summer | 5 | 0 | 2.7% |
| Canada - Men's Team Pursuit · CAN | 2024 Summer | 5 | 0 | 2.7% |
| France - Men's Team Pursuit · FRA | 2024 Summer | 5 | 0 | 2.7% |
| Italy - Men's Team Pursuit · ITA | 2024 Summer | 5 | 0 | 2.7% |
| Japan - Men's Team Pursuit · JPN | 2024 Summer | 5 | 0 | 2.7% |
| New Zealand - Men's Team Pursuit · NZL | 2024 Summer | 5 | 0 | 2.7% |
What to notice: The sample team lists show the mechanics: once the player count gets large, the expected chance climbs quickly, and each repeat is another player landing on a date already present.