The N/A Trap: When a Tennis Analysis Sheet Comes Back Empty

In June 2026, at Orlando City Stadium, I sat behind two monitors in the data desk, headset on, eyes locked to the live feed. On air, commentator Gary Whitfield declared that Orlando Pride held 62 percent of possession and were dominating North Carolina Courage completely. My system returned 45.7 percent. Pride's passing accuracy was 72.3 percent; Courage's was 82.1 percent. I wrote a short analysis with charts and published it within twenty minutes. By the second half, Gary had to correct himself live on air.

Seven years later, a tennis analysis file was placed in front of me. It had all nine dimensions: technical and tactical, data and form, tournament system and schedule, tour landscape and player positioning, rules and governance, team and management, risk, media narrative and expectation, and industry transmission. Not one cell contained anything. Every row carried a single word: N/A, insufficient information. No player. No tournament. No date. Not a single score, set, or ranking.

To a data editor, a file like that is itself a data point. It says nothing about tennis. It says everything about the process that produced it, and about a habit more corrosive than fabricating numbers: the habit of filling blank space with belief.

Nine empty cells, and a sport that is not short on data

Tennis is strange in that it records almost everything. A match is made of discrete units: points, games, sets. There is no running clock, no stoppage time, no foggy possession battle of 62 percent versus 45.7 percent. Every point has a beginning and an end, and every point goes into the record.

From the early 2000s, Hawk-Eye began appearing at Grand Slams to adjudicate whether a ball was in or out. By the 2020s, several events had moved to Hawk-Eye Live, removing line judges entirely on some courts. IBM SlamTracker delivers point-by-point data in real time at the majors. The ATP and WTA publish detailed statistical pages for every player, every surface, every window of time. Independent projects such as the Match Charting Project log every shot of thousands of matches, allowing researchers to trace both ball direction and foot position.

Which means that in 2026, a tennis analysis written entirely in N/A does not reflect a poverty of data. It reflects a poverty of input.

I have watched this happen in newsrooms many times. When an empty cell sits in front of a content producer on deadline, the most natural reflex is to fill it with the cheapest and fastest material available: instinct. Instinct needs no verification. Instinct needs no source. Instinct sounds a great deal like expertise, especially when delivered in a confident voice.

The nine dimensions in that file, in the end, all demand the same thing: a named entity. Technical and tactical analysis needs a specific player. Data and form need a specific run of matches. Tournament structure needs a named event with a tier and a date. Tour landscape needs a ranking table. Rules need an incident. Team needs a coach. Risk needs a contract, an injury, a deadline. Media needs a story currently being told. Industry transmission needs money, broadcast rights, sponsorship. Without a name, no dimension stands.

And here is the part that bothers me most: that empty file was honest. It did not invent. It did not invoke expertise to guess. It simply said it did not know. In an industry that pays more for confidence than for accuracy, that is an almost anti-professional act.

Empty cell one: technique and tactics

To assess a player's technique, you have to answer very specific questions. What grip does she use on the forehand, where is the contact point relative to her hip, how much spin does the ball carry, and most importantly, whom does the shot pressure? Does her serve use the same motion on the first and second delivery, or has she altered the action to reduce risk? Where does she stand when returning, stepping inside the baseline or retreating behind it?

Without a player's name, none of those questions can be answered.

Take an example anyone following the WTA has seen: Aryna Sabalenka's serve. In 2026, her serve became a serious problem, with double faults rising to the point where opponents only needed to put the ball in play to win a game. It was a purely technical issue: the toss, the contact point, and the accompanying loss of stability. After rebuilding the motion with her coaching team, Sabalenka won the Australian Open in 2026 and defended the title in 2026. No analysis of her could have been written without her name, her dates, and the numbers on her first-serve percentage.

I learned to read technique from places other people do not look. The Russian locker room door closed in 2026, but I had left my glasses at the crack. In tennis, that crack is the practice court. The pre-match warm-up says more than the press conference. The height of a player's shoulder as she finishes her service motion tells you whether the back pain is gone. The rhythm of her lateral footwork tells you whether the knee can still be trusted. I do not write about how they win; I write about what they changed in order to win. And to know what they changed, you need a name to follow day after day.

Empty cell two: data and form

Here I lose patience entirely with the popular style. Writers often say a player serves well and leave it at that. Four numbers are enough to rebuild the whole story.

First-serve percentage. At tour level, the average sits roughly between 60 and 65 percent. First-serve points won is the decisive figure: the top tier typically holds above 70 percent, and in some events above 80. Second-serve points won usually lands between 50 and 55 percent, and that is where matches break. Return points won is the measure of the person standing on the other side of the net.

Out of those four numbers, a picture emerges that the eye cannot see. A player can land 68 percent of first serves and still lose, if her second-serve points won is only 38 percent. Another player lands 58 percent of first serves but wins 78 percent of first-serve points, and she wins the match. Broadcasters usually remember only the first number.

Then break-point conversion. The tour average tends to sit around 40 to 45 percent. When that figure drops below 25 percent in a match, it signals something specific: the player is unwilling to hit the line at the exact moment the line needs to be hit. It is not a general failure of nerve; it is the avoidance of one particular shot.

And the ratio of winners to unforced errors. This is the number I check first when someone praises a player for beautiful attacking tennis. Beautiful attacking tennis with twice as many unforced errors as winners is simply a faster way to lose.

People worship the commentary of legends; I see a wrong number. I learned that through a specific shock, in June 2026, when a famous commentator missed possession by sixteen percentage points. The legend's error landed in front of me that year, and I understood: no one is immune to statistics. Not someone with thirty years in the business. Not someone with a permanent seat on broadcast.

An elite tennis match lasts two to three hours and produces roughly 150 to 250 points. Every point is a row of data. After a match like that, declaring that there is insufficient information to analyse is not caution. It is a refusal.

Empty cell three: rankings and points structure

Tennis rankings operate on a rolling 52-week window. Points are not accumulated forever; they expire. A Grand Slam title is worth 2,000 points, and precisely twelve months later, if the player fails to defend it, those 2,000 points leave her account in a single morning.

This structure creates what I call the points cliff. A player who wins Roland Garros in June walks into the following June with 2,000 points hanging over her head, while her rivals have nothing to lose. This is why the world number one ranking is sometimes held not by the strongest player at that moment, but by the player with the fewest points about to expire.

To analyse anyone's position on tour, you have to separate three things: points earned at Grand Slams, points earned at the 1,000-level events, and points earned at smaller tournaments. Those three structures tell three different stories. A player who lives on Grand Slams is a player capable of peak performance across two consecutive weeks. A player who lives on smaller events earns her living through consistency. Both can be world number one, but they are number one in different senses.

Iga Świątek is the most instructive case in this dimension. She won Roland Garros four times between 2026 and 2026, plus the 2026 US Open. Her points are densely concentrated on clay, which means that every spring she arrives in Europe with an enormous block of points to defend. Anyone analysing her position while ignoring that structure is describing a different player altogether.

Without a name, without a points table, all of this is just a page describing a mechanism.

I made a judgement about points structure years ago and still stand by it. Ashleigh Barty announced her retirement in March 2026 while holding the world number one ranking, with three Grand Slam titles to her name, including an Australian Open won just two months earlier that ended a 44-year wait for a home women's singles champion. A player walking away at the peak shows that the number one ranking does not automatically answer the question of motivation. Points structure analysis must always travel alongside human analysis.

Empty cell four: surfaces and scheduling

Tennis is the only sport where the surface completely changes the nature of the contest, and the calendar forces adaptation within weeks.

Clay at Roland Garros rewards footwork, sliding, and patience. Grass at Wimbledon rewards the serve, the first strike, and the acceptance of risk. Hard courts in Melbourne and New York sit between those extremes, but with different bounce, and sometimes a small change in the surface is enough to change the outcome.

The gap between the Roland Garros final and the opening day of Wimbledon is roughly three weeks, including a transition week. The grass season lasts about four to five weeks in total for the entire year. That is the single most important fact in the professional tennis calendar, and it turns every discussion of form into a maths problem about conversion time.

That is why I never judge a player on her most recent form alone. A champion on one surface can lose in the first round on another, and that says nothing about her quality. It says something about her ability to convert her strokes, and that ability is measurable.

Markéta Vondroušová won Wimbledon in 2026 as an unseeded player, the first woman in the tournament's history to do so. The fairy tale is not the point. The point is a player whose skill set matched the surface, plus a draw that opened at the right moment, plus physical preparation sufficient to last two weeks. All three factors are measurable. None of them requires magic.

Empty cell five: the points that compress

There is a group of points in tennis I call compressed points: break points, tie-breaks, serving for the match. Their number in a single match is tiny, usually five to twenty, yet they decide nearly the entire outcome.

This is where tennis data becomes dangerous, because the sample is too small. A player who wins three of three break points in a match will be called steel-nerved by the media. If she wins one of three, she is called mentally weak. The difference between those two scenarios can be a single ball clipping the line.

The 2026 Wimbledon women's singles final is the cleanest example I have ever used to teach this. Simona Halep beat Serena Williams 6-2, 6-2, and according to the tournament's official statistics she made only three unforced errors in the entire match. Three errors across more than an hour of tennis at Grand Slam final level. That figure shows Halep's tactics were to minimise risk to the absolute limit and let her opponent defeat herself. She did not need a legend. She needed the ball in play.

With another player, Emma Raducanu in 2026, the story sat in different numbers. She came through qualifying and won the US Open without dropping a set, the first player in the Open Era to win a Grand Slam title after playing qualifying. That was a run of ten matches in which every match could have been the last. Data cannot explain where that energy came from. But data can point precisely to where she won, with which shot, against whom.

Empty cell six: rules, governance, and grey areas

This is the most neglected analytical dimension, and the easiest one to fabricate.

Professional tennis has a detailed body of rules about time. A 25-second serve clock. Regulations on medical timeouts. Rules on off-court coaching, banned for decades and gradually loosened from the early 2020s, which completely changed how coaches communicate with players. Anti-doping. Match integrity.

Each of these can become a piece of analysis, but only if there is a specific event to anchor it.

There is something I have never told in full on air. When I discovered that Maya Thompson had tested positive for a banned substance, I faced a choice between two things: preserving a professional relationship with a player I had covered for years, or publishing the truth. I published. Truth above reputation. Not because I enjoy being the villain, but because if I had stayed silent that time, every number I published over the following twenty years would have meant nothing.

The rules dimension in any sports analysis contains a trap: without an event there is no risk, and without risk the writer slides toward one of two extremes, either inflating it into a scandal or going entirely silent. Both are different ways of lying.

Empty cells seven through nine: teams, expectations, and money

The remaining three dimensions are team and management, risk, and media narrative with expectation. Here I want to state plainly something the industry usually avoids.

A women's tennis player does not travel alone. Behind her stand a head coach, a fitness coach, a physiotherapist, a manager, a commercial agent, and sometimes an entire communications company. When a player declines, the first thing I want to know is not what caused the loss of form, but who left her team in the past six months. A coaching change is a data event. It has a date. It can be verified. And it often explains changes that the eye reads as form.

The risk dimension is tightly bound to the calendar. Tournament density, consecutive travel weeks, flight hours, surface switches. These are dry numbers, but they predict injury far better than instinct.

The media and expectation dimension is where N/A cells die fastest. Nobody needs data to say a young player is under great expectations. What gets called expectation is usually a loop created by the media itself, which then analyses that loop as though it were an objective event.

Here I also have to talk about money, because without money there is no tour. Broadcast rights, sponsorship contracts, prize money, and all the figures nobody wants to publish about the revenue gap between men's and women's events. A tennis analysis that ignores the money flow is an analysis that explains only half the match. The other half lies with whoever pays for the court, the tournament, and the player herself.

The counterintuitive angle: the frightening figure is not the one who fabricates numbers

When people discuss error in sports analysis, they always point at the fabricator. The one who said 62 percent when the truth was 45.7. The one who called a player steel-nerved after three winning points. The one who built a legend out of two matches.

I think the more dangerous figure sits on the opposite side, and is far more polite.

An analysis composed entirely of N/A is honest and useless. It answers the question of what you know, and the answer is nothing. It commits no act of invention. It simply stops. In this profession, stopping at the point of not knowing is not a virtue; it is an abdication dressed in administrative language.

Data is both shield and sword for me. The shield keeps me from being swept along by ready-made commentary. The sword forces a claim to stand in front of a number. If I used it only as a shield, I would become the person who writes page after page of N/A and waits for something to arrive. I refuse to live that way.

On the other side, the market does not pay for caution. Readers say they want truth, but what they consume most is certainty. A piece that says I do not yet have enough data has a far lower completion rate than a piece that says here is why she will win. This is an uncomfortable thing to write, but it happens every day. If we do not admit it, we will keep criticising the fabricators while continuing to consume their product.

Transfer markets move on rumour, but I trust the spreadsheet over the price tag. The same logic applies to tennis: a player can be valued by sponsorship money, but her competitive value only shows up at break points. Those two numbers frequently do not match, and the gap between them is exactly where this profession needs someone to sit down and check.

And there is one more thing I owe women readers. There are two ways to belittle women's sport. The first is to say it does not deserve serious analysis. The second is to say it deserves serious analysis, then write it with numbers taken from instinct. The second is subtler and far more common in major newsrooms, where nobody dares say the first out loud anymore.

What is changing

Tennis fans now check. They have statistical pages on their phones, they screenshot after every set, they compare commentary against the numbers while the match is still going. A new generation of viewers no longer accepts sentences like this player is controlling the match with nothing standing behind them.

That is one reason I built places to hold data differently. The Data Queens podcast was born during the pandemic, because when the crowd disperses, the data has to gather. When tournaments froze and press rooms closed, what remained were the tables of numbers, and the women patient enough to sit with them.

They blocked me at the World Cup door, so I learned to get in through data. That door still exists, in many places, in many forms. But once the number is in the reader's hands, it no longer needs anyone to open a door for it.

Every women's player I write about has a number she does not want to look at; I pull her back to look at it. Sometimes it is second-serve points won. Sometimes it is consecutive travel weeks. Sometimes it is the points expiring in the next three months. Not to humiliate anyone. But to change the question from whether she has enough nerve to what she is actually up against.

As for that nine-dimension analysis file full of N/A, I kept it. Not as a failure, but as a reminder of two kinds of people in this profession. The first fabricates numbers. The second says there are no numbers. Both leave behind the same gap, and that gap is always filled with whatever is on hand: prejudice, expectation, and very compelling stories about a player nobody bothered to verify.

No one is immune to statistics, including me. But at least I know what I am missing.