An Empty Table Tennis Dataset, and the Choice to Say 'I Don't Know'

On the night of March 14, 2026, I sat in front of my screen and opened the data file for the national table tennis championship semifinal. The file opened: twelve columns of metrics, from serve-point win rate to the number of topspin loops in the fifth game. Every cell was empty. I refreshed the file three times, called two colleagues in the technical department, and waited another forty minutes. Nobody had the numbers. I had watched that match; I remembered every rally. But on paper it did not exist. Numbers know how to hold their breath, and I wait for them to exhale — but this time they did not.

This happens more often than the crowd realises. In the trade of table tennis data consulting, the data does not come from a single system. It comes from three or four different sources: the organiser's video recordings, the referee's scoring software, the assistant's handwritten notes, and sometimes a photo of a scoreboard someone posted on social media. Each source has its own format, its own units, and its own latency. When one link breaks — say the scoring software fails, or an assistant forgets to sync — the entire dataset behind it becomes a blank page.

For a young analyst, that blank page is a nightmare. For someone who has worked in the trade since 2026, like me, it is a gift wrapped the wrong way. Since 2026, when I anchored broadcasts of major events — the Table Tennis World Cup, the Sudirman Cup in badminton — I learned one thing: data does not arrive as steadily as we assume. It arrives on its own breathing rhythm, and sometimes it chooses silence.

The first thing I teach interns is how to handle empty data. There are three reflexes, and only one of them is right.

The first reflex is to fabricate. This is the most common reflex, and the worst. The writer looks at a blank page, recalls a few similar matches, and drops the old match's numbers into place. Player A's topspin metric becomes Player B's metric. Nobody checks, because everyone assumes 'data is data'. In a scholarly paper, that is plagiarism. In a table tennis analysis, it is systematic deception.

The second reflex is to speculate. The writer says 'perhaps', 'probably', 'by subjective observation'. This sounds modest, but it is still fabrication — just fabrication with a verbal shield. In table tennis, speculation is more dangerous than in football, because table tennis is a sport of spin. Spin cannot be seen with the naked eye; it only surfaces through ball trajectory and trajectory data. Without trajectory data, any speculation about spin is just the guesswork of a man sitting outside the court.

The third reflex — the one I choose — is to close the item and state clearly: 'Insufficient information to conclude.' Those words are harder to write than any attractive conclusion. But they are the truth. An empty dataset is a result, not a failure. It tells you the data source has a problem, and that information itself is worth more than any assembled number.

In the nine-dimension framework we use on the team, every dimension carries a mandatory label: high, medium, or low confidence. When the dataset is empty, all nine dimensions receive the same label — 'insufficient information'. Technique, tactics, equipment, head-to-head, event system, rules, coaching staff, risk, media expectation — none may proceed. It may sound rigid. But that rigidity protects the entire downstream analysis chain from one root error.

I have seen the consequences of skipping this step. In 2026, a viral article claimed my club won through 'fighting spirit'. I sat down, pulled the opponent's PPDA, and found it was only 8.2 — meaning the opponent deliberately dropped their press to counter-attack, not that we were superior in spirit. An entire long analysis had been built on a foundation with no data.

There is a paradox here that the crowd in the industry finds hard to accept. Sport lives on conclusions. Fans want to know who won, why, and what comes next. Broadcasters want a commentator to say something, whether or not it has a basis. So when an analyst says 'insufficient information', people hear 'this guy can't do the job'.

The reality is the opposite. The person willing to say 'insufficient information' is the one who checked carefully enough to know where the gaps are. The person who immediately offers a conclusion is usually the one who checked nothing. In table tennis, the difference shows clearly at the decisive moment: a player loses the fifth game because their left-side spin was locked down. If my dataset is empty for exactly that game, I have no right to claim the cause was technique, psychology, or stamina. I have the right to say only one thing: 'We need more data.'

The crowd looks at the scoreline; I look at the forgotten pass. And sometimes, that forgotten pass is an empty cell in a spreadsheet.

After the night of March 14, I sent the technical department a message of one line: 'The men's semifinal data item needs to be re-extracted.' Three days later, the numbers came in. The match turned out to be better than the scoreboard suggested. But I still keep the old empty file in a separate folder, named 'evidence of silence'.

Old footage is a mirror; only those who dare to look will see themselves. And sometimes, the mirror reflects a blank page — where I should have seen myself, rather than a beautiful conclusion I wanted to see.

To those in the trade, I leave one question: next time your dataset returns zero, will you fabricate a number, or choose an honest silence?