Vietnamese Table Tennis and the Data Void: When a Deep-Analysis Template Returns Nothing but Empty Cells

On a Saturday night, I reopened an Excel file called "khung-phan-tich-sau" — the deep-analysis template — that I had built three years earlier while working at a sports data company in Ho Chi Minh City. The file had nine sections. Section one: technique, tactics, equipment. Section two: player data and head-to-head records. Section three: event system and ranking points. Section four: the competitive landscape across nations. Section five: rules and governance. Section six: coaching staff and the talent pipeline. Section seven: the risk surface. Section eight: media and expectations. Section nine: industry transmission.

I typed in the name of an international event that had just ended, then started filling in cells. At the eleventh cell, I stopped.

Not out of laziness. I had nothing to put in. For every cell I knew exactly what belonged there — but the data needed to fill it did not exist. Point-win rate per game, rally-win rate, success rate on the third-ball attack, the distribution of unforced errors versus forced errors, the average length of a rally, the spread of landing points: all blank. I closed the file, opened it again, and realised something that took me another two weeks to dare to write down: my analytical framework was not broken. The data ecosystem around it was.

That void is the real story.

My amateur spreadsheet taught me that data does not need to be glamorous, only correct. But when the moment came to be correct, I found something else: there are sports where a lack of data is not a technical accident but a structural feature. Table tennis is one of them. And in Vietnam, that void is wider than anywhere else I have worked.

Context: a sport running on feeling

I entered the profession with a notebook and a homemade spreadsheet. In 2026, at seventeen, I sat in the stands at Vinh Stadium and logged every pass by a Vietnamese club across ten matches. The result was simple: that team won only two of ten games when its misplaced-pass rate in the opponent's third of the pitch exceeded 15 percent. It was a crude number, but it was correct. From then on I believed that tools do not need to be fancy — they only need to be placed in the right spot.

In 2026, I carried that habit into the World Cup. I watched Croatia and noticed a paradox: their side had very low possession in the group stage, yet went furthest of all. I calculated the running distance and sprint counts of each midfielder to prove something many people called luck. Croatia 2026 was not a miracle; it was the sum of passes everyone ignored. That lesson laid the foundation for how I look at every other sport, table tennis included.

But table tennis differs from football in one fundamental way, and that difference is the origin of the void. Football has a vast public data infrastructure: Opta, StatsBomb, Wyscout, hundreds of providers, thousands of matches logged ball by ball. Table tennis does not. Table tennis has footage, broadcasts, beautiful rallies clipped and reposted — but the low-level data layer, the layer that answers the question "what actually happened inside each rally," barely exists in public form.

This is not a guess. I know because I tried to build it.

The Da Nang database taught me that patience is the easiest algorithm to write and the hardest to run. In 2026, when leagues worldwide were suspended, I spent six months building a transfer database for Vietnamese clubs, collecting more than two hundred deals with contracts, fees, ages, positions and post-transfer performance. That experience convinced me that any sport can be reconstructed through self-collected data. Table tennis tested that belief ruthlessly.

When I decided to shift my focus to table tennis, I followed the proper process: identify the metrics needed, find the sources, standardise, and only then analyse. Step one and step two went smoothly. Step three collapsed. No source provides rally-level data for international events, let alone domestic ones. The International Table Tennis Federation (ITTF) publishes player rankings, match results and schedules. It does not publish a point-by-point breakdown by rally. Nobody does, because no business model exists for it.

Core analysis: nine analytical layers, nine gaps

My deep-analysis framework has nine layers. I will go through each and show exactly which data is missing, why it is missing, and what that absence leaves for Vietnamese viewers.

Layer one — technique, tactics and equipment. In football I have xG to measure chance quality and PPDA to measure pressing intensity. In table tennis the equivalent metrics would be: point-win rate on serve, point-win rate on receive, success rate on the third-ball attack, the ratio of unforced to forced errors, the average rally length, and the distribution of landing points. None of these are published regularly. Television coverage offers a few aggregate figures — winners, errors — but that is the surface layer, enough for commentary, not enough for analysis.

Equipment is an especially neglected layer. Table tennis is a sport where gear directly shapes playing style: rubber hardness, sponge thickness, blade type, even glue. In 2026, the ITTF banned glues containing volatile organic compounds — something many players used to boost spin temporarily. That decision permanently changed how athletes built their strokes. In 2026, the 40+ plastic ball replaced the celluloid ball, reducing spin and altering rally rhythm. These are real, verifiable milestones, yet quantitative data on how they changed point-win rates is almost nonexistent. I want to know: after the switch to the plastic ball, by how much did the share of points ending within the first three shots rise? I have no answer, because nobody collected that number.

Layer two — player data and head-to-head records. The ITTF ranking is the best public data this sport has. But a ranking only says where a player stands, not why. Head-to-head records exist at a basic level: who beat whom, by what score. What is missing is depth: wins on which surface, in which format, at which stage of a career, with which equipment. A player beating a specific opponent three times while that opponent had a wrist injury is not the same as beating him fully fit. The data does not distinguish the two.

In Vietnam, the problem runs a level deeper. Leading players such as Nguyen Anh Tu, Dinh Quang Linh and Nguyen Khoa Dieu Khanh have international records, but their domestic matches are rarely recorded in a queryable way. I tried to reconstruct the domestic head-to-head records of a few players and had to abandon it, because results were scattered across short reports, inconsistent in format, and often missing game-by-game scores. Every player is a set of notes; only those willing to read reach the last line. In Vietnam, anyone who wants to read must first rewrite the notes.

Layer three — event system and ranking points. The international competition system has changed dramatically since World Table Tennis (WTT) launched in 2026 as the ITTF's commercial arm. WTT runs a tiered series — Grand Smash, Champions, Star Contender, Contender — with different points and prize money. In theory, this creates a clear hierarchy for judging player strength. In practice, analysing the value of each event requires data on participant strength, schedule density and ranking impact — computable but not published ready-made. I once wanted to build an index measuring the "real difficulty" of each event, based on the average strength of its field. I abandoned it because I would have to collect every draw by hand.

At regional level, the Southeast Asian Games is a key arena for Vietnamese players, yet it is where data is thinnest. At the most recent SEA Games, I tried to find rally-level data for table tennis matches and found only match scores. To an analyst, a match score is like knowing the result of an election without a single ballot.

Layer four — the competitive landscape. This is the only layer where the picture is fairly clear, though it still rests on observation rather than data. China remains the dominant tier, though thinner than a decade ago. The chasing group includes Japan, with Tomokazu Harimoto the most prominent face of the new generation; Europe, with Sweden's Truls Moregard and France's Felix Lebrun; and individuals from countries without a strong tradition, notably Brazil's Hugo Calderano, who rose into the world's top group with a distinctly personal style.

Notably, China's dominant tier is being restructured from within. Fan Zhendong and Ma Long — two pillars of a decade — withdrew from the world rankings in late December 2026, a move tied to disputes over WTT's mandatory participation rules and penalties. At the same time, a new generation such as Lin Shidong emerged and took the number one spot. It is an observable transfer of power, and it reshaped the top of the competitive landscape within months.

Yet to measure the gap between China and the rest of the world rigorously, I would need data on top-ten seats by period, titles at the biggest events, and the depth of the under-21 generation. I have estimates, not figures. When an analyst has to say "roughly," it is a sign the data infrastructure owes him something.

Layer five — rules and governance. Table tennis has a dense history of rule changes, and every change creates winners and losers. The 40mm ball arrived in 2026, reducing speed. The 11-point format replaced 21 points from 2026, making each point more important and reducing the advantage of a patient defensive style. The solvent-glue ban took effect in 2026. The 40+ plastic ball appeared in 2026. These are four verifiable changes, and four moments that forced the entire playing population to rebuild its strokes.

More recently, changes have been more about governance and commerce than technique. WTT's launch in 2026 was a restructuring of power: a commercial entity controlling the calendar, prize money and points. Tension between mandatory participation and player autonomy escalated to the point where two of the sport's biggest champions withdrew from the rankings. For someone in the transfer market like me, this is exactly the kind of volatility worth tracking, because it affects players' commercial value and career choices. But to quantify it, I would need data on the number of mandatory events, penalty levels and income impact — no public source aggregates this.

Layer six — coaching staff and the talent pipeline. This is where the Chinese model differs most clearly from the rest. They operate a system of provincial teams, a national team and linked training centres, producing an almost continuous flow of athletes. Japan has built an organised youth system that lets teenage players reach the international stage early. Vietnam is different: the pipeline depends on a few centres and a few respected individual coaches. When a good player emerges, the first question is not how they will be developed but whether they can afford to continue.

I want to build an index of pipeline depth: the number of athletes in a given age group with international results, broken down by country. That is the kind of index a professional sport needs to know where it stands. I suspect federations have it internally, but it is not published.

Layer seven — the risk surface. Risks in table tennis can be grouped: competition risk, generational-transition risk, governance risk, media risk and systemic risk. The first four can be identified by observation. The last — systemic risk — is the most worrying and the hardest to measure, because it sits inside the very data void this article describes. A sport that cannot measure itself does not know when it is falling behind.

Layer eight — media and expectations. This is the layer Vietnamese audiences touch most, and the one most affected by the data void. Without numbers, media drives with emotion: a player "in great form," an opponent "awkward to play," a match "destined." Those phrases are not wrong, but they cannot be verified. They produce a concrete consequence: audience expectations form without anything anchoring them. When results fall short, no one can trace back to see where the error was.

I do not believe in destiny; I believe in correlation coefficients. But a correlation coefficient needs two columns of numbers. Here I have only one column — and that column is usually feeling.

Layer nine — industry transmission. The table tennis value chain splits into three tiers. Upstream is equipment and youth development: brands such as Butterfly, DHS, Nittaku, Tibhar and Stiga compete to supply rubbers and blades to national teams and top players. Midstream is events, associations and clubs — where WTT and regional federations operate. Downstream is broadcasting, sponsorship and derivative markets such as private coaching, digital content and merchandise.

Worth noting: the upstream tier — where data on equipment and performance would be most valuable — is the most closed. Big brands know exactly which rubber suits which stroke, but that is competitive property, not public knowledge. An outside analyst like me can only reach the tip of the iceberg.

The contrarian angle: the problem is not a shortage of data

The easiest way to tell this story is to assign blame. Blame the ITTF for not publishing deep data. Blame WTT for chasing commercialisation. Blame Vietnamese media for preferring speed over analysis. Blame federations for not building databases. All those complaints are partly right, and all of them are useless.

My contrarian view is this: table tennis does not lack data because someone was careless. It lacks data because the structure of the sport makes rally-level data absurdly expensive relative to the value the market is willing to pay. A football match has tens of millions of viewers and billions of dollars circulating around it; its data infrastructure pays for itself. A professional table tennis match, even at the highest level, has an audience smaller by several orders of magnitude. The cost of collecting point-by-point data is the same, but the basis for recovering it is not. That is an economic reason, and it cannot be solved by appeals to enthusiasm.

The consequence is that the analytical model I carried from football into table tennis does not simply meet a gap — it meets a structural gap. I can write a complete deep analysis of a football match without asking anyone's permission, because every number is there. With table tennis, I must become a collector before I can become an analyst. Every article is an archaeological dig.

There is an obvious counterargument: if there is no public data, collect it yourself. I tried. In 2026, I attempted to hand-log an international event by rewatching footage and marking every point. I got through one and a half matches in two weeks. An event has hundreds of matches. Simple arithmetic shows that to get data for one event, I need a team, not an individual. And that team needs to be paid by a business model that does not exist yet.

This leads to a more uncomfortable conclusion about myself: perhaps expecting a football-style analytical scene for table tennis is expecting the wrong thing. Not because table tennis is lesser, but because the two sports have different market structures. Applying a football template to table tennis may be my mistake more than the sport's.

But if I fully accept that conclusion, I must accept that table tennis can forever only be commented on through feeling. And that I do not accept. There is a middle ground: you do not need rally-level data to produce better analysis than exists today. You need match-level, event-level and season-level data — things collectable at far lower cost, if someone organises it.

That is where Vietnam can do something many bigger table tennis nations do not need to do: build from the bottom up.

The void in Vietnam, and the opportunity inside it

I once worked on a transfer for a first-division club in Thailand, based on a database I had built over years. I found a 23-year-old striker playing in Brazil's second division with a high expected-goals-per-90 figure, but who had played less than half the available minutes because his club favoured an ageing star. I made the case with performance comparison charts, and three weeks later the deal was completed as a loan with an option to buy. That player scored eight goals in the remaining half-season, lifting the club from sixth to second.

That story taught me something applicable to table tennis: the greatest value of data lies in finding what others overlook. In football, that is an undervalued player. In Vietnamese table tennis, it could be a player undervalued because there is no data to prove otherwise.

Imagine a young Vietnamese player with a very high point-win rate on the third-ball attack, who often loses at international events because he meets a strong opponent in the first round. With no sufficiently detailed domestic ranking and no head-to-head analysis, that player is judged by match results — which depend heavily on the luck of the draw. A good data system would separate two things: actual ability and displayed results. That is the kind of information a national team should have before selecting players.

The problem is that building such a system requires three things Vietnamese table tennis does not yet have at once: disciplined recorders, a unified data standard, and a queryable archive. I have done this alone at small scale and know exactly what it costs. It does not cost much money. It costs persistence.

And here is the point I want to stress: the cost of having no data is not paid in articles. It is paid in decisions. A federation without data will pick squads based on relationships and impressions. A coach without data will not know whether his player lost because the opponent was better or because he made too many errors. A player without data will train by feel rather than by what needs fixing.

I know this because I have been on the other side. When I had a transfer database in hand, I no longer had to argue emotionally with an agent. I put the chart on the table. The conversation changed entirely. The same logic can apply to table tennis, if someone starts recording.

The risk of not measuring

There is a risk few name: when a sport has no data, decision-makers become harder to challenge. Without numbers, there is nothing to cross-check. A controversial selection decision cannot be tested with evidence, and is therefore defended by personal authority. This is the kind of governance risk I have seen in Vietnamese football, and it tends to recur where data infrastructure is weak.

There is a second risk, generational. Without data on pipeline depth, a sport can be ageing without knowing it. Top players keep winning, events keep happening, and no signal shows the space behind them is empty. When the current generation stops, the void appears — and by then it is too late to build.

The third risk concerns media. Without data, news is driven by personal stories and emotion. That is not ethically wrong, but it creates an unbalanced information ecosystem where the most viral content is not the most correct. I have refused to write certain reports because I could not verify the source, and that refusal cost me a few relationships with editors who prioritise speed. I do not think I am entirely right. But I think I can live with that choice.

What can be done now, at almost no cost

I do not believe the solution must be a big project. On the contrary, I believe in what the amateur spreadsheet taught me: a crude tool in the right place can strip bare an opponent's weakness. Here are things that can start now.

First, standardise domestic result recording. Every domestic match should be logged with game-by-game scores, not just the match score. This is the lowest data level, and it answers the most questions per unit of effort. From game scores, one can calculate match tension, the ability to win deciding games, and form trends over time.

Second, log equipment information. Rubber type, thickness, blade, and gear changes over time. This is data brands keep private but viewers can observe and record. It allows questions like: after changing rubber, did a player alter his style? Such questions cannot be answered now because nobody records them.

Third, build a domestic head-to-head database. Who met whom, when, with what result, under what circumstances. This is the kind of data I once built for football in six months, and it can be built for table tennis at a far smaller scale.

Fourth, and most important, record the method. A number without a measurement method is a useless number. If a federation announces "player X is in good form," it should state by what criteria, over what period, and compared with whom. This is discipline, not technology.

I know these proposals sound unglamorous. No app, no artificial intelligence, no beautiful charts. But I have walked this road once and know it works. Croatia 2026 did not win with technology. They won by executing the basics correctly under pressure. Vietnamese table tennis can start from the same basics.

Takeaway: a signal for the next round

My nine-section Excel file is still empty. I have not deleted it. I keep it there as a reminder that a void is not a full stop but a map of things to do.

What I want to know over the next twelve months: whether someone in Vietnam begins systematically logging game-by-game scores of domestic events. It is a small task, but it is the first brick. If that brick is laid, everything behind it becomes possible. If not, we will keep commenting on table tennis with unverifiable phrases, and keep being surprised whenever results differ from what we felt.

I do not believe in destiny. I believe in correlation coefficients. But to have a correlation coefficient, someone must first sit down and record the first number. That is the hardest part, and the part nobody wants to do. The Da Nang database taught me that patience is the easiest algorithm to write and the hardest to run. Vietnamese table tennis is standing at exactly the starting point of that algorithm.