An Empty Tennis Data Sheet in Qualifying: The Discipline of Saying 'Insufficient Information'

A June night in Hai Phong. Sea wind slipped through the gap in my window as I opened a spreadsheet freshly downloaded from a men's tennis qualifying match at a Challenger event. Eighteen columns. The first four held numbers: first-serve percentage, points won on first serve, points won on second serve, service games won. The other fourteen columns were blank. No per-game scores. No rally lengths. Nobody had sat courtside to record anything.

I left my hands on the keyboard for a long time. The temptation stood behind me like a person: one plausible-sounding sentence of speculation and the piece would feel fuller, rounder, easier to share. Then I remembered the first night I was ever assigned to write about sport, at sixteen, when I misspelled an athlete's name three times in one eight-hundred-word article and the national team coach texted me the correction the same night. A battered laptop taught me this: slow does not mean late, it only means telling the story another way.

That night I closed the file. The problem was not a closed file. The problem was this: if I had filled those fourteen empty columns, nobody would have had the patience to prove me wrong.

Tennis is the most heavily documented sport on earth. A main-draw singles match at a Grand Slam generates hundreds of data points: serve speed, spin rate, foot position, distance covered, net-point conversion, points won after a wide second serve. Machines in Melbourne, Paris, London and New York record nearly every beat. The analytics team of a top-20 player can rebuild a set as a graph and show that an opponent lost seven of ten points when dragged to the left.

Most of the careers of Vietnamese players happen somewhere else. Grand Slam qualifying. ITF events. Challengers across Asia. Court clusters with three outer courts, one camera, and a scoreboard that sometimes dies midway through the second set. There, a writer holds three things: the final score, a few short clips filmed by spectators near the court, and the account of someone inside the match. No Hawk-Eye. Often no statistician. Rarely anyone recording point by point.

The data base is thin. When the base is thin, the craft slides easily into the most dangerous state in any analytical profession: a feeling of certainty.

I have followed matches involving Ly Hoang Nam and Nguyen Thuy Linh for years, mostly on small screens and blurry recordings. What I learned was not which number matters most. What I learned was which number is most often misread.

Take first-serve percentage. It measures consistency, not power. A player landing 72 percent of first serves may be serving safely to hold rhythm, while a player landing 58 percent may be gambling on serves near the line to win points outright. One column, two completely different stories.

Take points won on first serve. This figure leans heavily on the opponent across the net. Against a strong returner, 68 percent is outstanding. Against a weak returner, 68 percent signals an afternoon of wasted opportunities.

Take break-point conversion. People love this metric because it sounds like "nerve". Imagine a player with seven break chances in a match who converts three. Three of seven. A sample of seven points spread across two hours, each point played in a different mental state, against an opponent with a different rhythm. Calling that a measure of character is an exaggeration. At that sample size, we are measuring luck more than nerve.

And take the winner-to-unforced-error ratio. A pretty figure here may simply reflect a player choosing a high-risk style against a slow-footed opponent. Change the opponent and the ratio collapses.

A metric only means something when we know how many points it was measured across, against whom, on which surface, and at what moment of the match. Remove those four things and the number still looks good, still prints nicely as a graphic, still makes readers nod. It just stops being true.

Surface is the most underrated variable. On clay, a good serve can still be returned into a long rally, so points won on first serve drop, and that says nothing about form. On grass, a big server can win a match while touching the ball a few dozen times. Same player, same month, two tournaments, two data sets that look like two different athletes.

Match timing works the same way. A player who wins the first set 6-1 and loses the second 3-6 has usually not "lost form". They adjusted rhythm, or paid the price for conserving energy. Read only the final summary sheet and you will tell a wrong story about a right match.

Rankings are another example. ATP and WTA points are not a pure measure of ability. They are ability multiplied by mobility. A Vietnamese player may be good enough for Grand Slam qualifying but lack the budget to fly sixteen weeks a year. That player's points are low because the number of weeks played is low, not because the backhand is weak. This is the kind of information a ranking column never states, and the kind most easily skipped in articles about rankings.

Based on my experience following matches across many seasons, most of a data writer's work is not collecting numbers. It is discarding the numbers that do not qualify as evidence.

I understand why people need metrics. After every match, a newsroom needs a piece. After every week, fans need a verdict. Without metrics, a writer has to say hard-to-hear things like "I do not yet have enough data to conclude". And those sentences do not make headlines.

But here is a paradox I have met often enough to believe: in tennis, over-analysis is more dangerous than under-analysis. A piece short on data lets readers know what they are reading. A piece full of data placed in the wrong spots leads readers somewhere without them noticing.

During the transfer window, the noise is thicker. Unverifiable numbers. Fees that sound very specific but no one confirms. "Sources close to the deal" appearing at both ends of a negotiation. In tennis, even if the transfer market is quieter than football's, the pressure is identical: there must be a verdict. Someone must be the winner, someone must be finished, someone must be the new talent.

In tennis, every defeat gets assigned a cause. Found out. Out of gas. Mentally weak at the decisive point. These labels are convenient, and they are often wrong. A player who loses a five-set match may simply have met someone better in the final two hours. No tactical secret. No mental collapse. Just a better player that day. Sports media has almost no room for that conclusion, because it is not dramatic enough.

I used to think my job was to deliver verdicts. Now I think my job is to determine whether there is enough ground to deliver one. Behind every tactical diagram is a person trembling, hoping, and forgetting how to breathe.

One evening in my second year of university, when tournaments worldwide shut down and I was temporarily out of work, I called my old athletics coach and asked something naive: if we cannot compete, how do we live? She told me she was coaching fifteen young athletes through a screen, each running in place before a camera, sending heart-rate videos. I asked to join that class, not as an athlete but as a note-taker. I learned that sport exists even when people are only trying to keep breathing.

That is why I began to distrust analyses that are too smooth. An article explaining everything in eight hundred words is usually ignoring most of what happened.

So what should a writer do when the data sheet is empty?

First, state clearly what exists and what does not. If all I have is the final score, I write that all I have is the final score. Readers are smart enough to see the difference between a piece built on complete data and one built on direct observation.

Second, call the people inside the match. I have learned to message strangers, sometimes at midnight, to ask one specific question. Not to harvest a good quote, but to fix a wrong assumption. A coach who answers my message is always worth more than three hours of my own speculation.

Third, accept that some questions will not be answered this week. An eighteen-column sheet with fourteen blank columns is a fact of this sport, not a personal failure of the writer.

I do not think data is useless. I believe data beats intuition when used correctly. But data is only strong when we are honest about where it came from, how long it was measured, and what it left out. The power of a metric lies in knowing its limits.

Some nights I sit a long time before an empty column and wonder whether I am being too careful. Then I think of the young Vietnamese players competing at events no one broadcasts, on courts with no recording equipment. No number protects them. If I write something wrong about them, wrong in a way that sounds perfectly reasonable, that error will follow them longer than a defeat.

An arena is not big because of the seats; it is big because of the stories willing to stay.

So that night I closed the file and wrote something shorter, drier, holding four columns of numbers and one sentence stating clearly that I did not yet know the rest. It was not shared widely. But the next morning, no one had to text me a correction.

One thing I remind myself every time I open a new data sheet: my job is not to make a story look complete. My job is to make a story true, even when true means short, thin, and a few columns left blank.

Honesty in a tennis data sheet does not live in the cells that hold numbers. It lives in the cells we dare to leave empty.