A statistic arrives with a name attached, and the name is written by a person. Readers take the name as a description of the thing counted. Often it is a description of what the writer hoped the thing meant. A tennis dataset of 116,382 matches is a clean place to watch the gap open.
The number and the word put on it
The dataset reports that a player who loses the opening set goes on to win 16.52% of the time. It calls this the comeback rate.
Nothing in the measurement is about comebacks. The count is mechanical: take every match where a player lost set one, count how many of those matches that player won, divide. No effort, resolve or momentum enters the arithmetic anywhere.
But “comeback” carries all three. The word implies a person deciding to turn something around. A reader who meets 16.52% under that heading walks away with a belief about character, from a figure that only ever described a frequency.
One word covering a threefold spread
The same label sits on top of results that have very little in common. Split by how the first set was lost, the rate runs from 21.92% after a 7-6 set down to 7.18% after a 6-0.
Those are both filed as comebacks. One is a player who was level until a tiebreak. The other was not competitive for twelve games. Three times the rate, one noun.
Where the same dataset gets it right
The same publication defines its terms properly elsewhere, which is what makes the contrast useful and not a complaint.
Its second study calls the player who lost the first set the chaser. It then spells that out. Chaser means the player who lost the first set, and each state is counted once per match. The word is unfamiliar enough that nobody arrives with assumptions, and the definition is attached anyway. Two sentences, and the reader cannot misread it.
The exclusion that changes what the word can mean
The method note says retirements and walkovers are excluded. That sounds like housekeeping. It is doing real work on the vocabulary.
A player who trails by a set and then retires injured has not failed to come back. Leaving those matches in would have put them in the denominator as failures, and the word would then be describing fitness as well as frequency. The exclusion is what keeps the label even roughly honest.
How to read a named statistic
Three questions, and they take a minute.
What was actually counted, in the plainest words available? Here it is the share of matches won by the player who lost set one. Does the name add anything the count does not contain? “Comeback” adds intent. Would a different name fit the same number? “Rate of winning after losing the first set” fits, and nobody would read character into it.
Writers are not being dishonest when they do this. A dry name is harder to publish and harder to remember, so the vivid one wins. The cost lands on the reader, who did not choose the word and cannot see what it replaced.
What to do with your own numbers
Name the measurement, not the story you want from it. If the honest name is dull, put the dull name in the heading. Put the story in the sentence underneath, where a reader can tell which is which.
And define any term you invent, even an obvious one. The chaser note in this dataset costs nine words and removes a whole class of misreading. The comeback label costs nothing and invites one.
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. Counted from the point-by-point record.
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