The Data Gap in Vietnamese Swimming: What the Scoreboard Does Not Say

On the electronic scoreboard of a national swimming meet, the time appears in full: 1 minute 58.34 seconds in the men's 200m freestyle. Right beside it, the fields for 50m splits, stroke rate and breathing count are left blank. The organisers have a result, but not data. For someone who makes a living reading numbers, that blank space draws more attention than the figure that just appeared.

I stayed behind after the session, opened the nine-dimension analysis framework I had built for swimming, and found that most of its fields sat in a state of insufficient information. Not because I was lazy. Because the data-collection system beneath it does not exist. An empty analysis table is not the end of the story — it is evidence that the story was never recorded.

In Vietnam, swimming carries a paradox. It is the sport that produced continental-level names such as Nguyễn Thị Ánh Viên and Nguyễn Huy Hoàng, athletes who won medals at ASIAD and secured Olympic berths. The results exist. But the data that would explain those results is barely stored in any systematic way.

I once worked with GPS data from 29 footballers at a club in Saigon, and found that high-speed running distance rose 20% before a muscle injury occurred. Since then, I have built fitness metrics into every analysis. When I turned to swimming, I realised I had no equivalent raw material. No splits, no stroke rate, no lactate-threshold data. Only the final time and the ranking.

My job is to place a result in its correct position within a probability distribution. To do that for swimming, I need three layers of data: technical data from each lane, performance data across multiple seasons, and contextual data about the competition system. In Vietnam, the first layer has almost vanished, the second exists only in fragments, and the third depends on the memory of the people working in the field.

This is not merely a technical problem. It is a problem for an entire sporting ecosystem. When a swimmer finishes and only the final figure is recorded, we lose the ability to answer the simplest question: why did they win, and can they win again?

I built the nine-dimension framework as a way of checking myself. Each dimension is a question, and each question needs a specific kind of data. What stands out is that when this framework is applied to Vietnamese swimming, most of the fields return empty values. This is a map of the gaps.

Technical dimension. To assess a swimmer, I need the start phase, the underwater speed after the dive, the turn technique and the stroke efficiency. Without splits, I cannot tell whether a swimmer accelerated in the final 50m or faded in the third. Without stroke rate, I cannot calculate distance per stroke. A swimmer who covers 200m freestyle in 1:58.34 can distribute effort in entirely different ways across the four lengths, and each distribution tells a different tactical story. With only the final figure, every distribution looks identical.

Performance dimension. To position a result, I need to compare it against the national record, the SEA Games record and the current-season ranking. But when the database is not standardised, every lookup becomes a manual survey. A result with no reference point is not an achievement — it is a number standing alone.

Competition-system dimension. Swimming lives on a four-year cycle. A domestic meet means something very different in a pre-Olympic year than in a post-Olympic year. The A-cut and B-cut qualification mechanisms determine whether a swimmer earns a place at a major meet, but to analyse that, I need the standards, the deadlines and the quota numbers. This information exists, but it is scattered.

World-map dimension. Who dominates the men's 200m freestyle in Asia? Which Vietnamese event has the smallest gap to the regional leaders? These are questions a data map could answer in seconds, but nobody has built it.

Rules and anti-doping dimension. Swimming has very specific rule boundaries: the 15-metre underwater limit after the start and after each turn, and the permitted number of dolphin kicks in breaststroke. To judge whether a technique touches a rule boundary, I need video and measurement data. We do not have it.

Athlete-career dimension. Swimming is a sport where peak age arrives early and differs sharply between men and women. A 17-year-old female swimmer and a 24-year-old male swimmer in the same event are at two entirely different physiological stages. But if a profile records only results and not age, height and training volume, I cannot draw the career curve.

Risk dimension. The biggest risk to Vietnamese swimming is not its rivals. It lies in the fact that we have no way to detect shoulder injuries early — the most common injury in this sport. Without training-load data, there are no number-based prevention measures.

Public-narrative dimension. Audience expectations tend to follow medals, while reality moves at a deeper level. The gap between these two layers is where trust erodes.

Industry-ripple dimension. A swimming star can pull money into facilities, equipment and youth development. But that flow only lasts if there is data proving the investment works.

There is a temptation I always have to guard against: treating an empty analysis table as a conclusion. When every field is blank, it is easy to conclude that there is nothing to say. The truth is the opposite. A system that returns empty data systematically is not a system without data — it is a system broken at the collection stage.

I distinguish two situations. First, a piece of writing that genuinely contains no information, such as a purely emotional commentary. Second, a piece of writing that does contain information but dropped it during processing. These two situations call for two completely different responses, and confusing them is the costliest mistake.

This is also where I repeat an old principle: correlation is not causation. When a swimmer improves at the same time as a coaching change, it is easy to attribute the improvement to that change. But without weekly data on training load, technique and fitness, I cannot point to a physical or behavioural mechanism linking the two variables. I am only permitted to say they are related.

More dangerous than emptiness is silently accepting it. If an empty analysis table is treated as a valid one, every decision downstream rests on an evidence base that does not exist. In sport, that means selecting athletes by instinct, investing on faith, and failing without understanding why. Every result has a baseline probability. We call it a surprise only when we have not yet checked the table.

Vietnamese swimming does not lack talent. It lacks a system that records that talent as traceable data. The first task is not to buy more equipment, but to standardise how splits, stroke rate and athlete profiles are recorded at the grassroots level.

Ordinary observers look at a scoreboard to understand a single meet. I look at the meet to understand years. I sit far from the pool to see more clearly than the referee, but I only see clearly when there are numbers. And until the data system is built, the biggest question in Vietnamese swimming will remain the one we do not yet have enough information to answer.