Every group in a table gets the same number of decimal places. That is a typesetting decision, and readers take it as a statement about how well each group is known. A tennis dataset of 116,382 matches shows how far apart that can drift, because it publishes the uncertainty next to every figure.
The six groups and what each one costs
The question is how often a player who loses the opening set still wins. Split by level of competition, the dataset gives the rate and the margin either side of it.
Men’s Challenger: 17.32%, give or take 0.43, from 30,406 matches.
Women’s ITF: 15.97%, give or take 0.42, from 29,331.
Men’s ITF: 15.18%, give or take 0.42, from 27,415.
WTA: 17.04%, give or take 0.63, from 13,543.
ATP: 18.21%, give or take 0.72, from 10,945.
Women’s Challenger: 17.25%, give or take 1.08, from 4,731.
All six rates are printed to two decimals. The margins differ by a factor of two and a half.
What the widest margin does to a comparison
Women’s Challenger sits on 4,731 matches, a sixth of the men’s Challenger figure, and carries a margin of 1.08 points.
Now try to compare the two Challenger tiers. Men’s reads 17.32% and women’s 17.25%, a gap of 0.07. The women’s figure alone could sit anywhere from 16.17% to 18.33%. The question “do the two Challenger tiers differ?” has no answer in this data, and no amount of care in the reading will produce one.
Ask the same question of men’s ITF against women’s ITF and it works. 15.18% against 15.97%, margins of 0.42 each, ranges that do not touch. That comparison is settled.
Same table, same two decimals, and one pair of rows supports a conclusion while another pair cannot.
Why the thin group is usually the interesting one
This is the part that catches people. The group with the fewest records is often the one a reader most wants to know about, because it is new, or small, or unusual.
So the precision is worst exactly where curiosity is highest. A new product line, a minority segment, a recently added region. Each gets the same tidy percentage as the mature groups and none of the caution.
What to do instead of rounding harder
Publish the count beside every rate. It is one extra column and it tells a reader which rows can carry weight.
Publish the margin too if you can compute it. This dataset does, which is the only reason any of the above is checkable from outside. Without those margins the six rates would look equally solid and the Challenger comparison would read as a real finding.
And when a group is too thin to answer the question, say so. Printing a number and hoping is the worse option. “Not enough data yet” is a result. A percentage with no margin beside it is a guess wearing a result’s clothes.
The one minute version
Before you quote a difference between two groups, find the smaller group’s count. If it is a fraction of the other, assume the difference is unprovable until you have checked. Here the ratio was six to one and the comparison died on it.
All of the above comes from the Live Tennis API, a JSON service for developers. It covers live scores, fixtures, players and match events, with point-by-point history behind them. The figures are published openly under CC BY 4.0, free to reuse with attribution.
Method: completed best-of-three professional singles matches, 1 January 2023 to 14 September 2026. Retirements and walkovers excluded. Margins are 95% confidence intervals in percentage points.
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