A Fully Filled Spreadsheet Can Still Be Empty: The Craft of Excavating Context in Vietnamese Youth Football

In August 2026, at the Viettel Youth Football Training Center, I opened a spreadsheet with every column a scout could want. Height, weight, BMI, 30-meter sprint speed, completed passes per match — all neatly filled in for a 16-year-old midfielder named Nguyen Duc Nam. Not a single cell was empty. I read the table, checked it against the national U17 standard, and concluded the boy lacked the physical foundation to step up to the first team.

Three months later, Nam debuted for the first team in the V-League. In his first five matches he recorded four assists. My spreadsheet was still correct on every number, but the conclusion was completely wrong. What I missed was not in any column I had created: Nam had just returned from a cruciate ligament injury and was in a compensatory growth phase. He was not weak. He was recovering.

That mistake taught me a working principle: numbers are the topsoil, and the real work begins when you dig beneath them. In Vietnamese youth football, scouting reports are usually passed around as files packed with metrics. They look complete, with titles, tables, and bolded conclusions. But many such reports are in fact empty — exactly like a spreadsheet filled to the brim yet missing the one decisive column.

The worrying part is that a report which looks complete is easier to trust than one that admits it lacks information. Readers are drawn to the feeling of certainty. Centers such as PVF or Viettel produce hundreds of pages of data every season. When I am invited to review an academy, the first question I always ask is not "what did you measure", but "under what conditions did you measure it".

Over many years as a consultant, I noticed a recurring pattern. The most impressive reports tend to have the highest density of numbers. They make readers feel they are approaching science. But density of data does not equal quality of analysis. A table with fifty metric columns can still be useless if all fifty columns measure the same surface condition and none touches context.

I call my method excavating context. I do not excavate stars, I excavate context. Every young talent stands on a geological layer made of academy, family, curriculum, minutes played, and opponent level. Ignore that layer and every compliment and every criticism becomes meaningless.

Vietnamese youth football has a specificity that European standards cannot be transplanted onto directly. Puberty timing, height, nutrition, and match density differ markedly from academies in France. A 16-year-old in Hai Phong may be shorter than the European benchmark yet far superior in load tolerance under a congested schedule. If I applied the old yardstick unchanged, I would wrongly discard exactly the players best suited to Vietnamese football.

After the Nguyen Duc Nam lesson, I added a column called "biomedical context" to every dataset. That column records the date of the most recent injury, days of rest, the approximate growth peak, and recovery level. It sounds simple, but it changes how I read every other metric. A 30-meter sprint slower than the benchmark no longer automatically means slow; sometimes it only means the boy just returned after a few weeks off the pitch.

In 2026, at the World Cup in Russia, I applied this approach to Kylian Mbappe. Instead of merely counting four goals, I measured eleven successful dribbles in the match against Argentina. But I also pointed out that those dribbles were only effective because Mbappe played on the left channel and was rarely marked. A player is not a number, but a number is where I begin the excavation. Strip a number from space and tactical position and you have a beautiful but wrong report. That report was later used by PVF as teaching material. What I want my students to take away is not "Mbappe is good", but that for the same player in the same match, how you read the numbers decides the conclusion.

My method for measuring efficiency under pressure also changed over time. With Mbappe in 2026, I split dribbles by pitch zone and by degree of marking. A dribble in midfield with no one closing down is worth far less than one at the edge of the box against two markers. If you merge them into a single "successful dribbles" figure, you have lost the entire tactical context — and that is exactly the kind of error that makes a report look professional while its conclusion drifts.

Two years later, when global football paused for COVID-19, I accepted an invitation from Song Lam Nghe An to review their academy. Old data showed striker Tran Van Cong, 18, with a rate of 0.8 goals per 90 minutes, the highest in the academy. But he often cramped and rarely played. With the training ground closed, I interviewed his family online and analyzed archived GPS data. I recommended signing him to a professional contract before the league resumed. When the 2026 V-League kicked off, Cong scored six goals.

The metric I used here was not total goals, but goals per 90 minutes and load tolerance. Total goals are governed by minutes played — something a young player rarely controls. Goals per 90 minutes separates opportunity from luck, while GPS data shows how much match load the body can bear. Compensatory growth is the most beautiful thing the league table cannot measure.

In 2026, I followed the winter transfer window of Hai Phong FC. The loan deal for defender Le Van Son from Ho Chi Minh City FC showed risk signals when I examined three AFC Cup matches. Son won twelve tackles but made three direct errors leading to goals under away pressure. The tackle figure looks impressive, but it hides the problem: he won the ball in safe areas and lost his man in dangerous ones. I advised the club against a long-term signing. Two weeks later, Son suffered an injury and the contract was cancelled.

In the same spirit, when I followed Spain's matches at Euro 2026, I noticed midfielder Pedri dropped 18% in distance covered after the 75th minute. I predicted he would decline if pushed to extra time. The coaching staff did not rotate, and Pedri left the tournament with an injury. Distance covered is often packaged as an effort metric, but ineffective running also produces pretty numbers. What matters is not total distance, but the distance that drops exactly when the match needs it most.

From those wrong and right calls, I distilled a three-layer process for every judgment about a young player. The first layer is the raw number: goals, assists, distance, minutes. The second layer is the conditions that produced the number: quality of teammates, opponent level, tactical position, quality of the preceding pass. The third layer is biomedical and psychological context: injury, biological age, compensatory growth phase, family pressure. Never let a single metric become the backbone of an article.

My analytical tools had to change. In 2026, I realized I was slow to adapt to the high-intensity trend in modern football, so I began studying machine-learning algorithms to supplement my work. But machine learning does not solve the core problem: if the input is empty or lacks context, the output will be dangerously confident.

That is the counterintuitive angle I want to stress. In this profession, the most professional act is sometimes not to deliver a conclusion, but to admit there is insufficient data. When a spreadsheet is filled but lacks the context column, the only correct conclusion is to ask for a re-measurement. The analyst's greatest temptation is to fill gaps with plausible-sounding guesses — a name, a number, a judgment that sounds expert. But every time you fill in recklessly, you are no longer analyzing, you are fabricating.

Emptiness itself is also a signal. When an entire dossier is blank in every column, it is usually not because the player has nothing, but because the data-collection process broke somewhere. An archaeologist does not conclude "this site has nothing" when his spade snaps. He checks his tools first. A data map can point the wrong way if you do not read the terrain.

For Vietnamese youth football, I believe the industry is missing exactly one thing: reports willing to state their own limits. If an academy keeps the habit of recording injury context, biological age, and match conditions for two consecutive seasons, then contract decisions will depend less on gut feeling and will miss fewer talents lying in the sediment layer. An injury does not erase a talent's name, it only pushes that talent down into the sediment.

Nguyen Duc Nam was a lesson I paid for with my own credibility. I once believed a full spreadsheet was trustworthy. Now I believe a spreadsheet that acknowledges what it lacks is the truly trustworthy one. Perhaps the right question for any young analyst is not "what did I measure", but "what am I not measuring".