When the Data File Is Empty: Lessons from a Night of Table Tennis Analysis

One night in Chengdu, I opened two windows on my screen: on one side, a recording of a match from the WTT circuit; on the other, a statistics file generated automatically by a platform. I needed to cross-check the points won in extended rallies to write a piece about decision-making at the decisive moment. When I opened the file, every column was empty. No player names, no scores, no rally durations, not even the name of the tournament. Only the title frame remained, lines reading “insufficient information,” and a multi-layered analysis structure waiting for data to pour in. A colleague called it a pipeline error. I call it an echo from an empty excavation pit. Nine years in the trade, I have grown used to scraping through layer after layer of numbers to find one true detail. But never before had I faced a completely empty file. I sat for a long time in front of the screen, rechecked the link, reopened the source file, wrote back to the sender. Nothing was wrong on my end. The fault lay elsewhere, and it was not mine to fix. That seemingly small incident says a great deal about how the sports industry handles data. Since 2026, when WTT restructured the international tournament system, the number of events, matches and recorded indicators per season has surged. A player competing across continents can play dozens of matches in a few months, each generating thousands of data points. Analysis platforms, media outlets and independent content teams all rush to mine that source. But the more data there is, the longer and more fragile the road from raw data to a correct analysis becomes. One faulty extraction step, one misaligned data field, one record skipped because a player’s name was spelled differently, and the entire chain behind it collapses. What is frightening is that such a collapse is not loud. It raises no error. It simply leaves a gap, and that gap is very easily filled with something else. In table tennis, data is not an accessory. Service win rates, the number of transitions from defence to counterattack, distance covered per rally, win rate in deciding rallies — these are what separate a professional assessment from a line of emotional commentary. When I follow a match, I often chart each rally by hand, marking winners in blue pencil and losers in red. That manual method is slow, but it forces me to look at every ball instead of trusting a ready-made summary table. Once I found that a summary indicator credited a player with winning 68% of long rallies, while my handwritten notes said the opposite: that player won through short rallies, and faded late in long ones. A wrong indicator ruins the article, and, more than that, ruins how we understand a human being. The problem grows worse when analysis is generated automatically. A model can produce thousands of fluent words from an empty file, and if no one checks, those words enter the system as fact. It could assign Ma Long a playing style he never used, credit Sun Yingsha with a deciding-rally win rate no tournament ever recorded, or conjure a head-to-head between Fan Zhendong and an opponent he has never met. Such distortions do not expose themselves. They wear the face of professionalism. They have structure, numbers, charts, even conclusions that sound very certain. That is exactly why they are more dangerous than an anonymous rumour, because people still doubt a rumour, whereas they take a handsome table of statistics at face value. For an observer of youth development systems, the data gap hurts somewhere else. World table tennis is witnessing a long-distance race between table tennis nations. China still holds a dominant position, especially in women’s singles, where the next generation is constantly refreshed. Japan has built a methodical youth system, with players who mature early and compete internationally from a very young age. Europe, most notably Sweden, has been returning step by step with new faces. To follow that race, I need youth data that is stable over many years, not summary tables rebuilt every time a tournament comes around. I have spent years recording data from Asian and European youth events, trying to reconstruct that curve from scattered fragments. Each time a youth event withholds detailed data, I lose another mesh in my observation net. When data breaks, I lose the ability to see the maturation curve of an entire generation. I see only scattered dots. And from scattered dots, one can very easily draw a straight line that does not exist. There is a paradox here that few are willing to state plainly. We believe that the more data there is, the more accurate analysis becomes. The reality is often the opposite. More data brings more processing steps, and every step is an opportunity for error. What the sports industry needs is not more data, but more people willing to say “I do not know.” An honest analysis has the right to leave some cells blank. It has the right to state that in this category, the source is not sufficient to conclude. That does not weaken the piece; it makes the piece more credible. Defeat is not a full stop, but the deepest geological layer of truth. Conversely, an analysis that looks flawless but is built on sand will collapse the moment a reader bothers to dig down one layer. Old matches are fragments of bone; I piece them together to see the shape of who I was that year. Every defeat is a layer of sediment; others see dirt, I see stratigraphy. And when a data file reaches me with every cell empty, I choose to preserve the gap rather than fill it with something I imagine. If the next generation of the world’s table tennis players is judged by analyses built on empty data, then what we are building is not a scouting system, but a mirror reflecting our own laziness. The gap is still there, waiting for someone willing to admit that the analysis could not yet begin.