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Olympic Ice Hockey (Ice Hockey)
Team
303 team lists · 6,637 player entries · avg team size 21.9
Read this sport as a comparison between raw coincidence and what team size alone predicts. Here, the real rate is +2.9 points above the birthday-paradox baseline.
Real team lists
46.9%
teams with shared birthdays
Why it matters: This is the headline rate, but it should never be read without team size.
Expected from size
43.9%
from avg team size
Why it matters: This is the fair baseline: what same-size random teams would do.
Gap from expected
+2.9 points
real − expected
Why it matters: Positive means the sport has more birthday matches than size alone predicts.
Team lists
303
teams analysed
Why it matters: Enough team lists for a useful sport-level read.
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 Ice Hockey (Ice Hockey), charted by the share of teams with at least one shared birthday.
What to compare: This view is best for spotting sample-shape differences inside a sport. Among the highest-sample countries, FIN has the highest observed rate, with an average team size of 27.6.
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. Women have the larger average team size here (31.3 athletes), so their birthday-match rate naturally rises.
By country
Top 40 countries by team-list count.
| Country | Team lists | Avg players | Real |
|---|---|---|---|
| USA | 25 | 23.1 | 52.0% |
| CAN | 24 | 24.0 | 66.7% |
| SWE | 24 | 23.7 | 58.3% |
| FIN | 19 | 27.6 | 68.4% |
| SUI | 19 | 24.6 | 36.8% |
| GER | 17 | 21.4 | 58.8% |
| TCH | 16 | 16.8 | 25.0% |
| AUT | 13 | 18.5 | 15.4% |
| POL | 13 | 16.5 | 30.8% |
| JPN | 12 | 20.1 | 50.0% |
| NOR | 12 | 20.8 | 50.0% |
| FRA | 10 | 17.1 | 30.0% |
| SVK | 9 | 24.7 | 33.3% |
| URS | 9 | 18.3 | 55.6% |
| CZE | 9 | 24.7 | 66.7% |
| ITA | 9 | 21.3 | 33.3% |
| RUS | 7 | 33.6 | 14.3% |
| FRG | 6 | 19.8 | 50.0% |
| LAT | 6 | 20.5 | 50.0% |
| YUG | 5 | 18.6 | 20.0% |
| CHN | 4 | 26.0 | 75.0% |
| BEL | 4 | 8.8 | 0.0% |
| GBR | 4 | 12.0 | 25.0% |
| ROU | 4 | 18.0 | 50.0% |
| BLR | 3 | 23.0 | 33.3% |
| KAZ | 3 | 21.0 | 33.3% |
| HUN | 3 | 13.7 | 66.7% |
| SLO | 2 | 23.0 | 100.0% |
| ROC | 2 | 46.5 | 100.0% |
| DEN | 2 | 32.5 | 50.0% |
| BUL | 1 | 18.0 | 0.0% |
| AUS | 1 | 17.0 | 0.0% |
| COR | 1 | 26.0 | 0.0% |
| EUN | 1 | 23.0 | 100.0% |
| KOR | 1 | 23.0 | 100.0% |
| GDR | 1 | 18.0 | 0.0% |
| NED | 1 | 20.0 | 100.0% |
| UKR | 1 | 22.0 | 100.0% |
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 | 32 | 62.5% | 64.7% | -2.2 points |
| Men | 271 | 45.0% | 41.5% | +3.5 points |
Sample team lists
Largest teams in the dataset.
| Team | Season | Players | Repeats | Expected chance |
|---|---|---|---|---|
| DEN Ice Hockey (Ice Hockey) (2022 Winter) · DEN | 2022 Winter | 48 | 3 | 96.1% |
| ROC Ice Hockey (Ice Hockey) (2018 Winter) · ROC | 2018 Winter | 47 | 3 | 95.5% |
| SWE Ice Hockey (Ice Hockey) (2018 Winter) · SWE | 2018 Winter | 47 | 3 | 95.5% |
| CHN Ice Hockey (Ice Hockey) (2022 Winter) · CHN | 2022 Winter | 47 | 3 | 95.5% |
| SWE Ice Hockey (Ice Hockey) (2014 Winter) · SWE | 2014 Winter | 46 | 4 | 94.8% |
| CAN Ice Hockey (Ice Hockey) (2018 Winter) · CAN | 2018 Winter | 46 | 5 | 94.8% |
| SUI Ice Hockey (Ice Hockey) (2018 Winter) · SUI | 2018 Winter | 46 | 4 | 94.8% |
| ROC Ice Hockey (Ice Hockey) (2022 Winter) · ROC | 2022 Winter | 46 | 3 | 94.8% |
| CZE Ice Hockey (Ice Hockey) (2022 Winter) · CZE | 2022 Winter | 46 | 5 | 94.8% |
| CAN Ice Hockey (Ice Hockey) (2022 Winter) · CAN | 2022 Winter | 46 | 2 | 94.8% |
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.