The Empty Cell in Table Tennis Data: When Silence Is Misread as Safety

The story unfolded on a July afternoon, in the café I always go to on Nguyen Thi Minh Khai Street. I opened my laptop and downloaded the statistics table from an international table tennis tournament that had just finished its group stage. The table had fourteen columns. Seven of them were completely blank. No serve data, no points-won-in-long-rallies metric, no distribution of points by set. The young coach sitting next to me looked at it and said: “So this match is clean.” I stayed quiet. A question surfaced in my head: clean because the data is good, or clean because nobody has entered it yet? Those two look identical on screen, but their consequences differ by an ocean. That was the moment I realised something the Vietnamese sports-data industry rarely talks about: the most dangerous error is not wrong data, but empty data read as fine data. Numbers hold their breath, and I wait for them to exhale — but this time, what I was waiting for was a silence nobody noticed. In engineering, that silence has a name: null value. In the mind of the person reading the table, it carries a different name: safety. I have worked as a data consultant for football clubs for years, but my roots lie in table tennis — the sport I still play every morning to sharpen my reflexes. In table tennis, a spin serve says nothing until you see how the opponent returns it. Same with data: an empty cell says nothing until you know why it is empty. That is the whole story of this article. The mainstream tendency in the industry is to read statistics tables linearly: analyse the columns that have numbers, ignore the columns that are empty. I believe that is one of the most harmful habits in sports data analysis, and it is quietly corroding the quality of tactical decisions at many levels — from a youth-team coach to the analytics rooms of national teams. Let us start by reconstructing the context. Professional table tennis data, at the international level, is collected in layers. The first layer is raw score data: point totals per set, who served, who won the point. This layer is almost always complete, because it is tied to the match result — something the organiser is obliged to record. The second layer is event data: serve type, spin direction, placement, stroke type, foot position. This layer depends on the recorder, the camera, the software. The third layer is sequence data: rally length, tempo, movement distance, rest time between points. This layer usually exists only at major events with tracking systems. The problem lies here: when a data table is handed to a coach or analyst, it rarely indicates which layer is full and which is missing. The reader looks at a fourteen-column table, sees seven columns with numbers and seven empty ones, and by inertia treats the seven populated columns as “the whole truth”. The seven empty ones become invisible. That is the first blind spot. I once fell into exactly this trap. In 2026, while analysing a V.League club’s winning streak, I used xG to show that their results outstripped their true level. The piece went viral, but I missed one detail: the PPDA figures I cited for some matches had no data, and I filled them with the league average. That mistake did not ruin the conclusion, but it taught me a lesson: every time you fill an empty cell with an estimate, you create a false truth that looks very real. Old footage is a mirror, and only those who dare to look will see themselves. Back to table tennis. One of the metrics I trust most when evaluating a player is the serve-win rate, split by spin type. At events with full tracking, you can see a player winning 68% of points on topspin serves but only 41% on backspin serves. That number captures their entire service strategy. But at events without tracking, the metric is blank. And when it is blank, many analysts quietly set it aside and console themselves that “serving was not the decisive factor in this match”. Nothing could be further from the truth. Serving is almost always decisive in elite table tennis — we simply lack the data to prove it. That is the central paradox of the trade: the absence of data does not mean the absence of a phenomenon. In logic, this is called the appeal to ignorance — absence of evidence is not evidence of absence. In table tennis, it plays out every day. I remember a match at an Asian tournament I watched live last year. A young Vietnamese player faced a higher-ranked opponent. The post-match statistics contained only the score. No serve data, no rally data. Looking at the table, the match looked like a simple 1-3 defeat. But when I rewatched the footage — and I watched it three times — I saw a different story. The young player won 9 of 11 points when serving short to the middle of the table, but lost almost every point when serving long. His coach, reading only the statistics, would never know that. He would train in the wrong direction for months. That is the price of empty cells. The crowd looks at the score; I look at the forgotten pass — in table tennis, those are the points nobody recorded. Every number is a puzzle piece, but I do not assemble them out of habit. The habit of the crowd is to assemble the pieces that are there. Mine is to go looking for the missing pieces first. So how do you identify a dangerous empty cell? There are at least four kinds of empty cells in table tennis data, and they demand four different responses. The first kind is an empty cell caused by a collection error. For example, the tracking camera loses the player during a rally because the angle was blocked. This kind can be fixed by re-recording from another source, or removed from the sample. The common mistake is to fill in the average. That makes the sample look more complete but actually dilutes the signal. The second kind is an empty cell caused by design. For example, the recording system does not support capturing serve spin, so that column is always blank for every match. This is not an error — it is a system limitation. But if you do not notice it, you will believe spin does not matter, when in fact it is simply not measured. The third kind is an empty cell caused by deliberate withholding. Sometimes the organiser or the team chooses not to publish certain metrics, such as detailed injury data or player GPS data. This kind is political rather than technical. When a team hides data, the right question is not “what is the value”, but “why won’t they tell us”. The fourth kind is an empty cell because the phenomenon never occurred. For example, a player never used a particular serve in a match, so the data on that serve is honestly blank. This is a “clean” empty cell — but it still carries information: it tells you about the player’s tactical limits. These four kinds of empty cells demand four different responses. If you read them all the same way — or ignore them all — you are analysing in the dark. In practice, I have built a working rule for myself: before analysing any table tennis data table, I spend the first ten minutes checking only the empty cells. I note which are empty, and for each one I ask which of the four kinds it is. It sounds slow. But it saves me weeks of analysis in the wrong direction. There is one example I still tell younger colleagues. At a continental team event, one team had a very high “points won after falling behind” metric across their first three matches. Analysts concluded the team had exceptional psychological resilience. But when I checked the empty cells, I found the metric was only recorded for matches where the team fell behind by at least two points in a set. In their first three matches, they fell behind only once, and only within a single set. So the “high” metric rested on a single event. The number held its breath, and when it exhaled, it was a very short breath. The conclusion about resilience collapsed. The truth is the team simply never faced enough situations of falling behind. This leads me to a broader observation about the sports-data industry. We live in an era that worships data as something objective. But data does not generate itself. It is collected by people, with devices designed by people, following processes set by people, and presented by people. Every one of those layers can create an empty cell. And every empty cell is an opportunity for subjectivity to slip in unnoticed. My data café is busiest when the pitch is empty. When there is no match, when the statistics table is silent, that is when I can sit back and read the silences. Conversely, when the stadium is full, when everyone is excited about a fiery match, the silences get drowned out by noise. I believe this is one of the structural problems of Vietnamese sport. We lack the habit of recording and publishing detailed data at the grassroots level. A youth coach in a provincial town has no tool to record his students’ serve types. This creates a long-term consequence: when players grow up, their data records are still empty. And when they reach the national team, the analyst has to start from zero — or worse, rely on memory and feel. I used to fear the microphone; now I let the data speak for me. But data can only speak when it is recorded. Here, the problem is not a lack of technology. The problem is a lack of discipline in record-keeping, and the absence of a common standard so that everyone records the same way. When every place records differently, the tables become riddled with empty cells when merged, and we return to the original problem. There is a relatively simple solution I always propose: minimum standardisation. You do not need to collect everything. You only need to agree on a minimum set of metrics that every level can record — for example: points won on serve, points won on receive, average rally length, unforced-error count. These four can be recorded by hand, on a phone, without camera tracking. But if every place records them the same way, we have a national data foundation. This is the point I want to stress, and it is also the counter-intuitive angle I believe in most: the industry’s biggest problem is not that we need more data. The problem is that we need to know precisely what is missing, and why. Most debates about “Vietnamese sports data” revolve around collecting more. New technology, new software, new devices. I think that frames the question wrongly. A team with ten full data tables can still analyse badly if it does not know which tables are trustworthy and which are not. A team with one minimum table but a clear understanding of its limits can make better decisions. This is a paradox running against mainstream intuition: less data but clear understanding is worth more than more data but foggy about its origins. I think of a table tennis coach I know. He does not use software. He has only a notebook. In it, he records three things per match for his student: points won on serve, points lost to unforced errors in the deciding set, and a feel for the tempo. Those three, together, give him a clearer picture than a fourteen-column table with seven empty columns. Because he knows exactly what he records and what he does not. He does not fool himself. That is the core lesson: honesty about the limits of data matters more than the coverage of data. So what signals does this situation leave for the next cycle? I think there are three to watch. First, watch how domestic tournaments publish data. If a tournament publishes a full statistics table, ask: how many cells were recorded automatically, how many were entered by hand, and were any estimated. Asked seriously, this question will quickly separate the tournaments doing data properly from those doing it for show. Second, watch how teams behave when data is missing. A serious team will say plainly, “we have no data on this metric, so we draw no conclusion”. A less serious team will quietly skip it and reach conclusions as if the data were complete. Over time, that difference compounds into a gap in quality. Third, watch young players with clean data records. A player who has been carefully recorded since childhood holds a big advantage when reaching the top, because their coach can see trends across years, not just across matches. This is a form of structural advantage few notice. I believe that in the next few years, as Vietnamese table tennis integrates further with the international system, the empty-cell problem will become more urgent. International events will publish more detailed data, and domestic teams will be forced to keep pace — not only in results, but in data quality. A team without data cannot analyse international opponents. A team with data but no sense of its limits will analyse wrongly. Both lose. I still remember that July afternoon. The young coach said “this match is clean”. I did not argue with him, because I understand it is a common way of thinking. But that evening, I sat alone, reopened the match footage, and recorded every serve by hand. It took three hours. I logged forty-two points. Of those, eleven were left blank in the official statistics. Eleven points out of forty-two — more than a quarter of the match. That is the number I keep in my head as a reminder. Not to boast that I work harder than others, but to remember that every data table can be hiding a quarter of the truth, and the hidden part is often the decisive part. I do not think I can change how the whole industry reads data. But I believe in one small thing: if every analyst spent the first ten minutes reading the empty cells before reading the full ones, the quality of analysis would change. Not a grand change, but a change at the foundation — the layer on which every conclusion stands. Table tennis taught me something I carry into data work: after every serve, wait. Wait for the opponent to return. Wait to see which way the spin goes. Do not rush to conclude from the serve. Likewise, after every data table, wait. Wait to hear what the silence says before trusting the number that speaks. And perhaps what is most needed now is not more columns, more software, more machines. It is the courage to look at an empty cell and say honestly: “I do not know yet.” Those three words, in this trade, are sometimes worth more than a thousand rows of data. Because only those who dare to say they do not know have a chance of finding what they need to know.