Why is Vietnamese sports data still struggling to find its identity?

In a world where every shot, every spin can be quantified into numbers, the question is no longer "are statistics valuable" but "are we measuring the right things?" And the answer, at least in the context of Vietnamese sports, is making many people in the industry uncomfortable.

A recent in-depth analysis exposed a troubling gap: the entire nine-dimensional analytical framework — from technique, tactics, equipment, to competitive mapping, event systems, governance, coaching, risk, public narrative, and industry transmission — all returned "insufficient information to assess." Not because of lack of data, but because the initial data source — what experts call "Stage-1" — returned an empty table with not even a player's name, a tournament, or a specific number.

This is not simply a technical error. This is a manifestation of a systemic problem in how we collect, process, and use sports data in Vietnam.

Data never lies, but readers often deceive themselves. This saying isn't from anyone in Vietnamese sports — it comes from the British analytical tradition, where xG (expected goals), PPDA (passes per defensive action), and numerous other advanced metrics have been the common language of professionals for over a decade. But when bringing these tools to a market where even basic match data is scarce, that saying is no longer a philosophical warning — it becomes a clinical diagnosis.

Seven years ago, when I was a first-year Journalism student in Hanoi, I wrote an analysis of a match between Hanoi FC and SHB Da Nang, using xG to prove that the home team's 3-1 victory wasn't luck. The article had only 32 views, but received a comment from a young coach: "You see the match completely differently from other journalists." That comment has stayed with me to this day, not because it praised me, but because it showed how wide the gap between traditional viewing and data-driven viewing had become — and wider than at that moment.

Seven years later, that gap hasn't narrowed in the expected direction.

People talk about the "data wave" in Vietnamese football, about clubs starting to hire analysis specialists, about some sports media beginning to incorporate statistics into articles. But the painful truth is: most of it is still in the "copying statistics from foreign sites and pasting into articles" stage rather than building independent collection, verification, and analysis systems. This is why an in-depth analysis following proper international standards — with nine dimensions, dozens of metrics, and a risk assessment system — couldn't be executed when applied to the Vietnamese context: because if there's no input, there can be no output.

But this is precisely what's worth discussing. The failure story of that analysis isn't a failure of the method. It's a failure of the data collection process — and that process, in the Vietnamese sports context, is struggling right at its very origin point.

Let's talk about what's actually happening at V-League matches, National Cup, or even table tennis — the sport that analysis was targeting. Matches happen, results are announced, but detailed data on touches, shot positions, possession percentage by the minute — things that international analysts consider "minimum necessary" — almost don't exist in publicly accessible form. This isn't a technology issue. This is an organizational will issue.

In the analysis I had the opportunity to read, there was a notable observation: "The most likely cause is a fetch/parse error at Stage-1 rather than a truly empty article. A genuine table tennis article, no matter how short, usually provides at least one player name, one tournament, or one result." This is an important observation, not because it's technically correct, but because it reflects the reality that even automated analysis systems can't rely on Vietnamese sports data sources.

And here's the paradox: we live in an era where everything can be measured, but the most reliable sports data source in Vietnam is still... the memories of commentators and the feelings of fans.

This doesn't mean the future is bleak. On the contrary, it means there's an enormous space to build. In 2026, when I was 20, I collected data from 64 matches at the Russia World Cup and noticed Croatia had an average running distance of 119 km/match — highest in the tournament — but only ranked fifth in xG created. I predicted they'd reach the final. When that happened, Zing.vn republished my article. It wasn't because I was better than anyone. It was because I had data to bet on, and I accepted being contradicted if wrong.

The lesson here isn't "do what I did." The lesson is: to analyze correctly, you first need correct data. And correct data doesn't appear spontaneously — it needs to be built systematically, from the ground up, with investment in human resources, technology, and most importantly, organizational will from sports governing bodies.

Summer without football is when the truth emerges, no longer hidden by media smoke. That's something I've said many times, and it's true in every context — even when the "truth" revealed is an empty analysis table.

But let me add something that many data enthusiasts: even if we had complete data, are we ready to read it honestly? Because data-driven analysis doesn't just require numbers — it requires humility about what numbers cannot tell us. A player with low xG but scoring decisive goals; a team with poor possession stats but winning through counterattacks; a coach criticized for "poor form" but actually building a new tactical model — these are things any analytical system can miss if it only looks at numbers and forgets the humans behind them.

The past V-League season saw no shortage of such cases. A team underestimated before the season due to "poor historical data," only to finish much higher than predicted. A young player with unremarkable physical stats but consistently scoring in fast counterattacks. A foreign coach with possession-based philosophy opposed by fans for "boring play," but statistics showing his team's defensive-to-attack transition efficiency in the top three of the league. These stories don't appear in analysis tables — they only exist in how we read between the lines of statistics.

And this is where I want to pause to address something many data enthusiasts overlook: not everything important can be measured, but everything that can be measured is important if we measure correctly and place in the right context.

Returning to the "null-returned" analysis. The sad truth isn't that it couldn't be executed — but that it was right not to execute. Because an analysis lacking evidence is better than an analysis full of false evidence. And this is what Vietnamese sports analysts need to learn: humility before data limitations isn't weakness — it's professional discipline.

I've bet my entire reputation on bold predictions based on data. Sometimes I'm right, sometimes I'm wrong. But I've never — and will never — written an analysis without at least three quantitative metrics to verify my position before publishing. That's the oath of a data storyteller.

And that's also what that "empty" analysis is telling us: start from what we actually have, not from what we wish we had. Build data collection systems from the ground up. Require tournaments to publish detailed statistics. Invest in match-tracking technology. Train a generation of analysts who can read both numbers and the gaps between numbers.

Then, nine-dimensional analyses will no longer return "insufficient information." They'll return real stories — about people, about matches, about things data can tell us and things data cannot. And that's when we'll truly understand the power — and limits — of numbers in sports.

Form can be an illusion, but data streams don't lie — only those who read it deceive themselves. And this is the moment for the entire Vietnamese sports industry to ask: where are we deceiving ourselves?