Soulless Data: When Tennis Is Analyzed With Empty Cells

I still keep a strange file on my hard drive. It has a title, nine analytical sections, neatly squared tables, even a risk-assessment framework and an "information value" section rated by stars. But when you open it, every cell is empty. No player name. No tournament. No number. Just three letters repeating like a refrain: N/A — undetermined, no information, cannot be assessed.

One could dismiss it as a technical glitch, a data pipeline that needs restarting. I read it as a metaphor. Because in modern tennis, we are producing thousands of reports shaped like analysis but missing the very soul of truth. Nine sections perfectly formal. Nothing to analyze.

That is why I sat down to write this piece. Not to tell the story of a broken file. But to tell the story of what that broken file accidentally exposed: when data becomes a religion, one can build a magnificent cathedral on empty ground and still bow one's head as though praying.

When Hawk-Eye Became Scripture

I remember the afternoon in 2026 when Hawk-Eye first appeared at a hard-court Grand Slam. Before that, arguing over whether a ball was in or out was the business of umpires, of memory, of intuition — and of unprovable anger. Hawk-Eye arrived, and suddenly everything became transparent to the point of cruelty. A ball lands two millimeters out, and the system projects a three-dimensional image onto a giant screen so the whole stadium sees it. No one can argue anymore. No one can argue anymore — that was the phrase most spoken over the following two seasons.

I stood in the press room that day and heard a veteran colleague tell me: "From today, tennis has no room for feeling." I remember staying silent for a long time. Because I understood, deep down, that he had spoken half a truth. Hawk-Eye did not kill feeling. Hawk-Eye killed argument — which is something else entirely.

But the following decade witnessed another flood. If Hawk-Eye gave us data about the ball, new analytical systems gave us data about the player himself. Serve-plus-one. Return-plus-one. First-serve points won. Baseline points won. Break-point conversion. These data samples that used to be viewed only by a handful of insiders became daily fare for anyone with a phone and a social account.

I once sat in a sports-data conference in Manhattan. On stage, an analyst presented a prediction model based on forty-two variables. When the Q&A opened, an older man raised his hand. He asked exactly one question: "Does your model know that the world number one could not sleep last night because of a family matter?" The room fell silent. The analyst laughed awkwardly and said that variable was not in the model. The older man nodded, sat down, and said nothing more.

That is the question I have carried for eighteen years in sports documentary work. It is not a technical question. It is a question about the limits of data.

Context: The Economics of the Number

To understand why a report full of N/A could exist so smoothly, one must understand the ecosystem that nurtured it.

Modern professional tennis runs on three pillars of information. First is official tour data, published after every match — serves, winners, unforced errors, decisive rallies. Second is shot-by-shot data, collected by systems such as Hawk-Eye or by community projects like Jeff Sackmann's Match Charting Project, where hundreds of volunteers record every stroke of every match over many years. Third is market data — odds, money-flow shifts, public expectation — constantly updated by the major bookmakers.

These three pillars serve more than fans. They serve a new economy: the economy of attention. In that economy, an analysis with beautiful tables spreads further than a storytelling piece. A graph with a colored line creates a feeling of certainty better than a sentence describing doubt. And a headline with a number gets more clicks than a headline about emotion.

I do not deny the progress. Data has made tennis more transparent, fairer, and more knowledgeable. But when data becomes a commodity, a paradox appears: the more data there is, the more people tend to believe they understand. What is sold is not understanding — it is the feeling of understanding. And that feeling can be mass-produced, even when there is not a single real line of data.

That is exactly what the file exposed. It showed me a process capable of generating the form of analysis without the content of analysis. An empty table can still be treated as a table. A section titled "cannot be assessed" can still be bolded as a conclusion. The interface is indistinguishable from the truth.

I once spoke with a sports editor at a major U.S. outlet. She admitted that during a peak week of a Grand Slam, her newsroom published more than forty "analysis" pieces — an average of nearly six a day. No one in the newsroom had time to watch a match from start to finish. They analyzed based on clips, based on stat tables, based on live commentary, and based on what others had already written. "We don't analyze tennis," she told me, without a trace of irony. "We analyze other analyses."

That sentence kept me thinking for a long time. When analysis becomes the object of analysis, data no longer matters — only its shell does.

The Core: What a Table Never Says

I want to tell a story. It is not a tennis story, but it is the story that shaped how I write about tennis.

In June 2026, I was in Nizhny Novgorod for the Croatia–Argentina group-stage match at the World Cup. I did not sit in the high-end commentary box. I chose a spot in a corner of the stands where I could see every movement of Luka Modrić. I brought a small notebook and wrote like a zealot — not recording the score, not recording time, only recording position and intention.

When Modrić scored in the eightieth minute, I did not cheer like the colleagues around me. I quietly wrote one line: "He was not running to win; he was running to tell a story."

After the match, I was interviewed on local television. I spoke of the patience of the midfield as a mirror of my own life. The host asked me a question I did not answer directly: "Do you have data proving Modrić played better than other midfielders tonight?" I smiled. I said I had twelve pages of notes on his positions before receiving the ball, and none of them could be digitized into an Excel table. But they answered a question Excel never could: what did he want?

That is my first lesson about the limits of data: data records what happened, while a notebook records what was intended to happen.

I once wrote a book about Emma Raducanu — about the summer of 2026 in New York, when an eighteen-year-old emerged from qualifying and won the US Open without dropping a set. The book sold reasonably well, but the thing I remember most is not the sales. What I remember most is a small detail no one wanted in a stat table: the evening before the final, she called her mother. Not to ask for tactical advice. Just to hear her mother's voice.

What did the analytical tables say about Raducanu that season? They said she held an unusually high first-serve points-won rate. They said she converted break points at a superior rate. They said she capitalized on important points. Every number was true. Every number was insufficient.

No number said she had chosen to play like someone who knew she was living in a dream she did not want to wake from. No number said her opponent, across the net, was defeated before the match began — by an atmosphere Arthur Ashe Stadium created on a September afternoon. No number measured what I, sitting in the stands, called "the weight of relief."

That is the second lesson: a player does not only fight the opponent, but fights the entire story unfolding around them — and that story has no column in the spreadsheet.

I have spent years understanding why three players called the Big Three dominated tennis for two decades. Federer, Nadal, Djokovic. Three men. Sixty-six Grand Slam titles combined. Analysts tried to explain with data: first-serve points won, return effectiveness, stability in long rallies, break-point conversion, career peak longevity. Every number was real. But the three of them remain a question that numbers cannot answer.

That is the third lesson: dominance is not explained by data, but by a hard-to-measure trait — stubborn endurance across the shifting of eras.

I once remarked to a colleague that Modrić does not run fastest, but every step he takes has intention. I said that about football, but I think it applies to tennis. Nadal does not have the prettiest backhand on tour. But every backhand of his has intention — the intention to pull the opponent wide, to push him into a corner, to create a gap and then fill it with a forehand down the line. Djokovic does not have the fastest serve. But every serve of his is chosen — chosen by surface, by opponent, by score, by the specific point within a game.

When data cannot measure intention, one must sit in the stands. There is no other way.

I once attended a workshop on the Match Charting Project. The volunteers here spend thousands of hours recording every stroke of every match. They have a golden rule: if you are unsure whether a shot was a cross-court forehand or a down-the-line forehand, rewind and watch. Do not guess. Do not estimate. Do not infer. Watch. Rewind. Confirm. Record.

That is the fourth lesson: the golden rule of good data is not volume but reproducible confirmation. Nothing replaces rewinding and watching.

And this is what I want to say about that file with nine sections full of N/A. It did not violate the golden rule of good data. It violated the golden rule at a deeper level: it did not admit it had no data. It wrote as though it did. It presented a table for a subject never identified. It analyzed a match that never took place. An entire cathedral was built, and when people asked where the god was, the answer was: "The divine is in the form."

The Contrarian Angle: More Data, Less Truth

This is what I believe, and it runs counter to common intuition: modern tennis does not lack data. It lacks verification.

People often say that analysis used to be poor because data was scarce. I do not think so. Analysis used to be poor because it lacked tools. Today analysis is poor because it lacks responsibility.

Look at how stat tables are presented to the public. An article about Djokovic's quarterfinal can cite twelve metrics. The reader nods and thinks they understand the match. But when you ask the source of those twelve metrics, the answer is often: "Compiled from multiple sources." No one checks whether those twelve metrics came from the same match. No one checks by what standard they were calculated. No one checks whether they excluded tiebreak points. A table of twelve numbers can contain three different definitions of the same concept and still be read as a single truth.

This is the central paradox: when the barrier to producing data falls, the barrier to verification falls too — but the barrier to verification is never mentioned in any sports broadcast.

I once sat with a data analyst at one of the top tennis academies in the United States. He told me something I cannot forget: "The best data we have is not the metrics. It is the coaches' journals. But those journals are never digitized, because they contain subjective judgments no one wants publicized."

I asked him why not make them public. He laughed and said: "Because if we did, people would see that a top-20 player's coach wrote in last week's journal: 'Today he played like someone who was bored.' There is no column for boredom in a spreadsheet. But if you watched that match, you would see it clear as day."

That is what data fails to capture: boredom, excitement, loneliness in the locker room at the third round of a Grand Slam when you won the previous match too easily and are starting to feel everything is meaningless. These are psychological states that appear in no prediction model, yet they decide many matches.

I also want to address another dimension: the storm of fake data in the tennis-betting world. In recent years, the number of "analysis" channels on social media has exploded. Many of them offer "prediction models" with advertised success rates up to seventy percent. When I asked the operator of one such channel about methodology, he answered bluntly: "We don't need a real model. We just need an interface that looks real."

This is the most frightening answer I have heard in eighteen years in this profession: the public does not buy truth — it buys the form of truth.

When I reread the file with nine sections of N/A, I realized it was only the most extreme version of a spreading trend. A report with real tables but wrong data can cause more harm than an empty report. Because an empty report only deceives the cursory reader. A report with wrong tables deceives even careful readers — readers who believe they are led by the light of science, when in fact they are led by a skillful hand that has curated the numbers.

I once witnessed a debate on American television about a player. The host offered a number: "Over the last three months, he has a conversion rate above eighty percent on decisive points." A former Grand Slam champion seated across from him smiled wryly and said: "Do you know what a 'decisive point' is? For that player, it is the point he feels he controls. For another player, it is the point when he thinks of his mother. Same word, two different worlds."

Words like 'decisive point' carry the illusion of unity. But for each player, they carry a private meaning — and those meanings are not equivalent.

I remember 2026, when the tournaments in New York were suspended by the pandemic. I had a documentary contract about a stadium, but shooting was halted indefinitely. I fell into an emptiness that lasted three weeks, unable to write a single line of script. Every night I rewatched the 2026 Champions League final to cry alone. I turned off my phone, keeping in touch with only Sarah — the editor I trusted most. It was she who told me: "You don't need to find the meaning of football. You need to find meaning when football does not exist."

By the end of April, I returned, and chose to write about something else. I wrote about the stadium cleaner — someone who kept coming to work every day even though no match was being played. I arrived at the stadium in the morning, stood in the empty stands, and heard the sound of her broom sweeping across the seats. When the stands are empty, we hear the breath of the match more clearly. That broom was the breath. No table has a column for it. But if you stood there, you would know.

When I told that story to an editor, she asked: "What data do you have to prove this story matters?" I replied that I had no data, only an image. But I believe an honest image can convey more truth than a table designed to impress.

Football Means, and So Does Tennis

I want to open a parenthesis to speak about football. Because in football, the debate about data ran about five years ahead of tennis and can offer tennis some valuable lessons.

When Expected Goals (xG) became the standard in football analysis, a great debate erupted. One side said xG allows a fairer evaluation of attacking efficiency. The other side said xG kills football's magic — when every shot is assigned a probability, Zidane's volley in the 2026 Champions League final is no longer an eternal moment but an expected value of 0.03.

I side with the latter, but for a different reason than the nostalgic. I do not oppose xG because it strips away magic. I oppose it as the sole standard, because it has become an excuse for people to stop watching football. You can sit at home, open the xG table, and talk about a match you never watched. Technically, you are right. Spiritually, you have lost the most important thing about sport.

Football does not mean through numbers. It means through unpredictable moments. That is why I wrote about Modrić running in the Argentina match in Nizhny Novgorod, not about the 60-40 possession statistic. That is why I wrote about Mancini giving the Italian national team a way to look at each other with belief in Rome 2026, not about their chance-conversion rate. Mancini did not give Rome a tactic — he gave them a way to look at each other with belief. A number can never give that.

I think tennis is now at the point football was at around 2026. We have enough data to no longer need to watch. But we have not yet realized that when we no longer watch, we no longer analyze. We are only chewing what others have already chewed.

I once told a student studying sports journalism that if she wanted to become a tennis writer, she should spend her first summer watching tennis. No need to write, no need to interview, no need to file. Just watch. Sit in the stands, watch a match from start to finish. Do not open your phone. Do not check stat tables. Do not watch highlights. Just watch.

She asked: "But what will I learn?"

I answered: "You will learn what no table can teach — that every match is a story, and the story is not in the numbers. When you come back and read the numbers, you will understand what they are trying to say. You will know when a number speaks truth, and when it is only hiding."

On Sweepers and Table-Makers

I want to return to that file. It is not the product of bad people. It is the product of a system that believes form matters more than content. A process designed to produce tables. A user who believed those tables had meaning. And a result of nine perfect analytical sections with nothing to analyze.

But this is what I want to say: I do not despise that file. I pity it. Because it is a mirror reflecting ourselves.

We — the writers, the analysts, the sellers of tennis to the public — are in a similar state. We have built beautiful tables. We have learned to present data as art. We have created complex models, colored line graphs in blue and red, headlines with numbers that force the click. And in doing so, we have gradually forgotten the most basic question: what is the real substance of what we are saying?

I remember an evening in a Brooklyn café, when I met a young data analyst who had just been fired by a sports outlet. She told me she had spent three months building a match-prediction model based on more than sixty variables. The model performed beautifully on historical datasets. But when applied to real matches, it predicted correctly in only about sixty percent of cases — essentially the weighted flip of a coin. She said the reason she was fired was not the weak model. It was that she had honestly told the editorial board the model was not reliable. The board did not want to hear that. They wanted to hear it was eighty-five percent accurate. She refused to lie. And she was let go.

That is the greatest lesson I have drawn about the relationship between data and truth: sometimes honesty is the enemy of the product, and the product usually wins.

When I told this story to a colleague, she asked: "So what is your solution?" I thought for a long time. I do not have a macro solution. But I have a small principle: whenever I am about to cite a number, I ask myself whether I could explain it to someone who has never watched tennis. If not, I do not cite it. If I can, I cite it with source context. If the number comes from a single source I cannot verify, I do not cite it.

This is a small principle, and it does not solve the systemic problem. But it is what I can do.

Conclusion: A Question With No Column in the Table

I want to close this piece with a question I once asked myself on a sleepless summer night in 2026, after the Croatia–Argentina match in Nizhny Novgorod.

That night, I could not sleep. I scrolled through every move Croatia made, trying to find the "charm" that allowed them to control the entire rhythm of the match. I had detailed notes with more than seventy annotations. I could put all those lines into a spreadsheet and compute metrics on Modrić's position. I could generate an analytical report that looked highly professional and could be published in any outlet.

But I did not. I closed the notebook and went to sleep. The next morning, I wrote a single paragraph: "Last night, a Croatian midfielder taught me that tennis — and football — does not live by goals. It lives by the heartbeat of the crowd."

That is the paragraph I still keep.

The question I want to leave readers with is not a technical question about data. It is a question about what we are really seeking when we follow sport.

When you watch a Grand Slam final, are you seeking data to evaluate, or a moment to remember? When you read an analysis about a player, do you want to understand the match, or to be reassured that you understood correctly? When a table appears before your eyes, are you seeing the truth, or the form of truth?

I have no answer to all of those questions. But I know that whenever I have too easy an answer, I recall the file with nine sections of N/A. It reminds me that certainty can be produced at low cost, and the only thing that cannot be mass-produced is genuine attention.

The regular season is underway. The rankings are updated weekly. Prediction models are running on servers all over the world. And somewhere in an empty stadium early in the morning, a cleaner is still sweeping the rows of seats, preparing for a day when no one knows whether a match will be played. The sound of her broom still rings, steady, needing no table to prove it.

An empty stadium does not only lack noise — it lacks the story being told. And that story, no matter how digitized, still needs someone to sit down and listen.