N/A: When Tennis Data Reports Come Back Empty in the Middle of a Grand Slam Season

In this year's Grand Slam season, I received an empty disciplinary report file. Four hours after the semifinal, the official data system sent our Manchester newsroom a spreadsheet containing exactly one thing: every cell carried the letters N/A. No player names, no match minutes, no warning types, no foul counts, not even match duration. I sat in the newsroom, heard the printer running down the corridor, and remembered a line I wrote for myself seven years ago: my first mistake was not the red card I gave to the wrong player, it was believing I would never give one to the wrong player.

That incident forced me to rewrite the report by hand, line by line, while the clock ran down to deadline. While doing it, I understood something no training course ever taught me: in the era of datafied tennis, the greatest danger is not a wrong number, but an empty number presented as a complete fact. Both can ruin a story. Only one leaves a trace.

To understand why an N/A file is more dangerous than an incorrect one, we need to look at the architecture of professional tennis data. A Grand Slam match today runs through at least four parallel recording layers: Hawk-Eye logs the ball's trajectory with more than twenty cameras; the chair umpire's team keeps a paper record; the organizer's statistics system compiles serve and return metrics; and both players' media teams feed data to newsrooms. These four layers do not always match. When they diverge, a writer like me has to decide which layer to trust.

Hawk-Eye is the most reliable layer geometrically, but it only answers whether the ball was in or out. It cannot count a tactical drop shot, a hesitation during the changeover, a moment when a player stands still and looks at their coach. The umpire's paper record captures what the human eye catches, but the human eye catches less than the lens records. And the organizer's statistics system depends on a data entry operator. I used to be that operator.

In 2026, as a first-year sports science student at the University of Manchester, I volunteered as a data analysis assistant for the amateur club FC United of Manchester. In the match against Radcliffe Borough, I found that the referee had missed two fouls inside the penalty area that the official statistics system did not record. I spent three days reviewing all the footage, counting every collision, and building a comparison table against the match report. The result showed the official report was missing two lines of data. Those two lines might not have changed the match result. But if it had been a final, those two missing lines would have become part of history.

Since then, I have applied a non-negotiable principle to every piece I write: never accept a single number. Every figure I publish must pass three verification layers: the number's origin, its historical context, and its deviation from the statistical norm. My editor at the Daily Mail once described me as slow but solid. He was right. And that slowness has saved me many times.

In 2026, as a second-year student, I nearly lost my young career over a card. I wrote that the referee had shown a yellow card to defender Trent Alexander-Arnold in the twenty-third minute of the derby between the University of Manchester and University of Liverpool teams. In reality, the card went to his teammate. The error earned me a severe reprimand from my editor and forced me to write an apology letter. As a result, I spent the following six weeks memorizing FIFA's card rules and recording 189 card incidents from the 2026 World Cup as reference data.

A card placed in the wrong position can change the flow of an entire season. I was once the person who wrote that error.

But that story only taught me how to handle wrong data. It did not prepare me for empty data. And empty data is what I face when tennis enters the Grand Slam season.

Grand Slam season is when data volume grows exponentially. A Grand Slam runs two weeks with 128 players in each singles draw, plus doubles and mixed doubles. A single match day can generate tens of thousands of data points. Systems must run continuously, editors must publish continuously, and the gap between an event happening and a number going to press is compressed to a fragile sliver.

Under those conditions, data errors stop being exceptions. They become the rule.

I once spent four weeks analyzing Morocco's twelve matches at the 2026 World Cup after they reached the semifinals. I counted a total of 87 tactical fouls and discovered their defensive system relied on off-ball interception rather than direct tackling. Notably, Morocco had an average card rate 32 percent lower than European teams, despite clearing the ball more often. That number did not appear in any official report I could find in the early stages. I had to reconstruct it from footage.

The key point lies here: empty data is not neutral data. It carries an implicit assumption that the event did not exist.

When a cell in a disciplinary report reads N/A, the reader assumes it means no event needed recording. But in most cases, N/A means the event happened and simply was not logged. The gap between these two readings is the entire foundation of data journalism.

I call it the data silence, meaning the gap between a real event and the official record of it. In tennis, data silence appears in unexpected places. A tactical drop shot in the third game of the second set is not counted as an error, but it affects the rhythm of the whole set. A moment when a player stands still for three seconds before serving at break point is not recorded in any metric, but it is precisely the sign of lost focus. A moment when an umpire ignores a line judge's signal for the server's advantage will never make it into the record.

These silences are not Hawk-Eye's fault. The system does its job correctly. The problem lies with the operators and with how we interpret the report the system leaves behind.

Hawk-Eye is not wrong. The Hawk-Eye operator is wrong. And that is exactly where my work begins.

In 2026, I found an anomaly while tracking disciplinary data at the Euros. Portugal had a card rate 41 percent higher in matches officiated by French referees. I analyzed 23 matches from 2026 to 2026, combined with head-to-head historical data, and wrote a 3,500-word investigation. The piece was used by a UEFA referee researcher as reference material when assessing the consistency of officiating teams at Euro 2026.

What I learned from that case was not that Portugal was treated unfairly. That conclusion is too easy. What I learned is that numbers say nothing on their own until you place them in a long enough timeline and a large enough sample. Forty-one percent is an impressive figure. But if the sample is only four matches, that number may just reflect coincidence. I had to expand the sample to 23 matches before I dared to write.

Distance covered and sprint counts are the two most beloved broadcast metrics. They are packaged as measures of effort. But I have seen many beautiful numbers produced by useless running: a player chasing an out-of-reach ball just to create the appearance of fighting, a player moving laterally a lot but never getting into position to return a difficult serve. Running a lot does not mean running right. And in tennis, where every step has a cost, running out of position is worse than standing still.

There is another metric I have tracked across many seasons: how often a player wins a break point and then immediately loses the next service game. This metric rarely appears on television stat boards, but it says more than break point conversion rate. A player can post an impressive break point conversion rate but frequently collapse right after. Looking at conversion rate, people praise their cold-bloodedness. Looking at the post-break recovery metric, they see an entirely different psychological problem.

Top players today have their own analytics teams, and they feed data to newsrooms through private channels. That creates a new problem: data from a player's team is not always neutral. It is filtered through the lens of someone protecting the client's image. A player who loses due to injury will have that clearly noted. A player who loses due to lost focus will be described in vague language. I have to read those notes with a skeptic's eye, but I am not allowed to write in a skeptic's voice.

During Grand Slam season, pressure on the data system peaks. That is when empty reports appear most often, and also when fewest people notice, because everyone is chasing deadlines.

There is a reflex I see in most young colleagues, and I carried it for years. When the report is empty, they fill it with feeling. They recall the match scene, they trust their eyes, and they write. That reflex sounds reasonable. But it is the starting point of every serious error in this profession.

When data conflicts with the eye, trust the data, but do not forget to check where it came from.

I understand why people do not want to do that. Verifying data origins takes time, and in Grand Slam season, time is the rarest commodity. But there is one thing I learned from my own mistakes: an empty data cell is an opportunity, not an obstacle. It forces me to go back, review the footage, call the operator, and reconstruct the event from scratch.

That is why I record every card, every minute of added time. Because a wrong number repeated three times becomes truth in the end-of-season report.

In tennis, Hawk-Eye controversies are the perfect example. Fans see the ball on the big screen and see it out. The system shows it in. Both sides believe they are right. But both miss one detail: Hawk-Eye does not display the actual ball trajectory, it displays a predicted model of the ball. That model has an error margin. The margin is calibrated, but it is still a margin. System operators know this. Fans do not. The gap between those two understandings is where every controversy begins.

The same is true for disciplinary reports. When a report states that a player received no warnings during the match, the reader understands it as a clean match. But the report does not say the umpire warned verbally, ignored two slow serves, or failed to log a net collision. Those events do not appear in the card column, but they existed.

I used to ask myself whether I was too strict. The answer came from an older colleague at the Daily Mail who had covered tennis for more than thirty years. He told me something I hold to this day: people do not remember the correct report. They only remember the wrong one. And the person who wrote the wrong report never gets to forget it.

That was not a threat. It was a reminder.

Since receiving my first N/A file this season, I have changed my workflow. I built a three-layer checklist for every disciplinary event: player name, match minute, warning type. If any cell is blank, I do not write. I go back to the footage. I call the umpire. I cross-check against at least two independent sources. Only when all three layers match do I let the number go to press.

That process makes me slower than my colleagues. But it lets me sleep.

In a Grand Slam season, where everything happens at social media speed, slowness becomes a competitive advantage. Not because it gives me time to write better, but because it gives me time to check more carefully. And in this profession, checking carefully is everything.

I am not writing this piece to blame the data system. I am writing it for myself, and for anyone holding an N/A file in the middle of a Grand Slam season. An empty report is not the end. It is an invitation to return to the court, sit down, and count again from the start.

If another empty report arrives at my newsroom this season, I will not fill it with feeling. I will reopen the footage, reconstruct every phase, and rewrite the number with my own hands. Because in tennis, as in every sport, people do not remember who wrote the fastest story. They only remember who wrote the correct one.