When Tennis Data Falls Silent: The Thin Line Between Analysis and Fabrication

Three weeks ago, in the analytics room of a sports network in Los Angeles, I reopened the data sheet for a quarterfinal and found it blank. The most important field held a single dash. A young colleague sitting beside me looked at the screen and asked, “So what do we write, boss?” I said, “We write that we have nothing to write.” He laughed, thinking I was joking. But that was the most honest answer I could give that day, and perhaps the answer the whole sports-analytics industry needs to relearn.

Over the past decade, tennis has gone through a data revolution. Hawk-Eye turns every ball into a coordinate in three-dimensional space. Motion-tracking cameras record every footstep, every shoulder rotation. Since the 2026 season, tournaments on the ATP system have switched to fully electronic officiating, removing line judges from the court. Every serve is now a data packet: speed, spin, placement, points won. From a former world number one like Novak Djokovic to young talents like Carlos Alcaraz or Iga Świątek, every player is now read through the lens of data.

From the vantage point of someone who once sat in ESPN’s analytics room and watched thousands of matches, large and small, I see this convenience carrying a trap. The more data there is, the more people assume every question comes with a ready answer. Spreadsheets multiply like mushrooms after rain. Every broadcaster, every sports site wants its own analytics room, its own data expert, like a mandatory piece of jewelry.

But a spreadsheet only has value when it is read correctly. And that demands something machines cannot replace: the honesty of the person holding the pen.

This is where I have to confess something about myself. In 2026, at the World Cup in Russia, I gave a “safe” prediction in the quarterfinal between Russia and Croatia. I said Croatia would win on penalties, but I would not commit to a scoreline. After the match, a young colleague texted me asking why I had not been more specific. For a whole month afterward, I rewatched the entire tournament, noting every passage I had judged wrong, and built my own spreadsheet to compare predictions against reality. I learned that giving a specific number takes courage, but admitting you have no data takes more.

Back to the analytics room. What is worth saying is that the situation that day was far from rare. A data feed drops. A match is played on a court without sensors. A server overloads and returns an empty result. Such failures happen more often than the public imagines. What separates a decent professional from a fabricator is how they respond when the data disappears.

The fabricator fills the gap with guesswork, then presents the guess as a finding. They write, “This player is in great form,” with no winning streak to prove it. They say, “The serve statistics are improving,” without having seen a single percentage. Their prose flows so smoothly that readers never notice the empty room behind the words.

The decent professional does the opposite. They state plainly: this data is missing, no conclusion is possible. They distinguish two entirely different situations: “the analysis found no significant signal” and “the data-collection process failed.” The two sentences sound similar, but they lead to opposite actions. One lets you conclude with confidence. The other forces you to stop and start over.

I have seen the consequences of confusing the two. In a summer 2026 project, when leagues were suspended by the pandemic, I collected data from 312 Premier League, La Liga and Bundesliga matches to compare results with and without crowds. I spent weeks cleaning the data before daring to write a single line. Had I ignored the empty cells and filled them with assumptions, the analysis I sent to The Athletic could not have survived a single round of verification.

That is why I always remind myself of one thing at the keyboard: an analysis with no clear subject, no player being studied, and no data is not an analysis — it is a blank page dressed up in ink. This holds for tennis, and it holds for every sport caught in the data frenzy.

I want to state my position clearly here, because it is what I believe and also what is easily misunderstood. I am not against data. I live on data, I earn my living from data, and many times it has let me say the right thing that the naked eye could not see. But I oppose turning data into an unquestionable god. When a table of numbers is thrown out, my first question is always: where does it come from, what does it measure, and what does it leave out?

That is why I call the metrics beloved in analytics rooms “favorites.” They are pampered, paraded in every forum, repeated until people forget they are only tools. A favorite of the analytics room must eventually stand on its own two feet. Meaning, at some point it has to prove its value before the reality of a match, not before the admiration of the person who created it.

And when all those metrics fall silent together, I have learned to sit still before that silence. Silence is not the absence of an answer — it is the answer, for those who know how to listen. A blank spreadsheet is telling you that you do not have enough data, enough observation, enough patience. It is not saying the match had nothing worth discussing. The hasty reader assigns the silence the meaning “nothing happened.” The decent professional reads it as “we need to go back and look more carefully.”

In recent years I have noticed a worrying sign in youth academies. Young coaches, under pressure for results, push U18 players into heavier physical training and increasingly neglect foundational technique. When a young player is raised by gauges and stopwatches rather than by feel for the ball, he can post beautiful numbers while still lacking the most important thing: the ability to read a match. That is another kind of fabrication — fabrication at the training root.

I do not mean to lull anyone with worry. On the contrary, I see an opportunity in it. When everyone has the same data set, competitive advantage no longer lies in owning the numbers but in knowing when to doubt them. Whoever can read the silence of data will move ahead of those who only read the numbers laid out for them. That is a skill hard to teach, hard to buy, and impossible to copy by subscribing to one more data service.

The story in Los Angeles ended with a small decision. Instead of forcing a complete analysis out of a blank sheet, I sent my editor a short note: the data source for this match is unavailable, please move the deadline. He replied within five minutes: “Thank you for being honest.” A short answer, but it reminded me why I chose this trade — not to appear knowledgeable, but to say exactly what I know, and to stay silent about what I do not.

Perhaps the greatest lesson I carry after two decades in this work is not found in any metric, but in an attitude toward the gap. A spreadsheet does not know what longing is, and we should not pretend otherwise. It does not know a player who stayed up until three in the morning to fix his serve motion. It does not know the fear before a decisive serve. Numbers are only seasoning. People are the main dish. A decent writer uses seasoning to bring out the dish, not to cover up an empty plate.

So when the next season begins and the spreadsheets come flooding back, I will still set beside them an old question: what is this data really answering, and what are we missing when we rush to a conclusion? The answer may not lie in any cell. But it will lie where we are brave enough to look straight at the silence — and call it by its true name.