Empty Columns in the Data Sheet: Why Vietnamese Youth Football Gets Its Verdicts Wrong

Last March, during a data review at an academy in Hai Phong, I opened the individual tracking sheet of a U19 midfielder and counted 61% empty cells. The distance-covered column was blank. The actual minutes-played column was blank. The injury-history column was blank. Only three fields were filled: height, weight, goals scored. Those three items were enough for the coaching staff to note that the boy had not yet reached a professional physical baseline. I sat for three hours, re-checking every friendly, and found the opposite: he averaged 54 minutes per match, yet in the final 15 minutes his success rate in keeping the ball under pressure rose from 48% to 57%. A verdict had almost been signed, purely because a sheet was left empty.

I tell this story not to defend the boy. He may still not be good enough. I tell it because that empty sheet is a recurring phenomenon at many youth academies in this country, and it is more dangerous than a single error.

Vietnamese youth football operated for years on eyes and memory. A scout watches one match, writes a few lines in a notebook, goes to a meeting and speaks. That method once produced top-class players, I do not deny it. But when domestic football moves to a three-day match rhythm, when academies such as PVF, Viettel, Song Lam Nghe An and Hanoi FC must manage hundreds of players at once, individual memory no longer has enough room.

What I encounter most, after 24 years in the trade, are three forms of data deficiency. First, data that is never recorded, meaning nobody measured it. Second, data that is recorded but not recorded with enough conditions, for example measuring distance run without recording running intensity. Third, data that is recorded and stored but that nobody re-reads for six months, so by the time it is needed it is already stale.

These three forms do not carry the same consequences. The third can be fixed. The second can be refined. The first is the lethal one, because it does not create a visible gap; it creates a gap shaped like completeness.

This annual season makes that even clearer. As I followed matches in the V-League and the First Division, the PPDA figures of several teams trended downward, meaning they press more aggressively. But when I asked about those figures, most clubs had no match-by-match data. They had impressions. Impressions have value, but impressions cannot be added and subtracted against another person's impressions.

This is where I start digging. A verdict on a young player is only trustworthy when it stands on at least three independent layers of data, and most arguments in Vietnamese youth football erupt because people have only one layer.

The first layer is raw outcome: goals, assists, minutes. This layer is recorded most because it is easiest. The second layer is process, meaning what happens before the outcome forms — pass quality, receiving position, space created for teammates. The third layer is context: what stage of biological age the player is at, which injury he has just returned from, whether he plays against strong or weak opponents, and how many minutes he plays under what conditions.

Data is the topsoil; I always dig three layers further.

In 2026, I underrated a 16-year-old midfielder at Viettel named Nguyen Duc Nam. I looked at the sheet and saw a BMI below the national U17 standard, and speed that failed the benchmark. I concluded he lacked a physical foundation. What I overlooked was that he had just returned from a ligament injury and was in a compensatory growth phase. Three months later, Nam debuted for the first team in the V-League and recorded four assists in five matches. I had to add a column to the data sheet and name it biomedical context. Since then, I no longer trust dry statistics absolutely.

Compensatory growth is the most beautiful thing the league table cannot measure.

In 2026, when the whole country suspended play, I reviewed the Song Lam Nghe An academy again. Old data showed that striker Tran Van Cong, 18, had a rate of 0.8 goals per 90 minutes, the highest in the academy. But he cramped frequently and rarely played a full 90. If you look only at total goals, you conclude Cong is the main striker. If you look only at total minutes, you conclude Cong is a bench player. Both conclusions are wrong, because both ignore one variable: load tolerance.

I interviewed his family online, re-analysed the archived GPS data, and recommended signing him professionally before the league restarted. In the 2026 season, Cong scored six goals. The result did not lie in me guessing right; it lay in me being willing to read the cell others left empty.

In 2026, it was a defender's turn. I followed Hai Phong's winter transfer window and examined three AFC Cup matches of Le Van Son, then on loan from Ho Chi Minh City. Son won 12 tackles, an impressive figure. But after each of those wins, I checked his position: three times he was pulled out of shape, and all three led to goals conceded from the space behind him. Twelve tackles did not rescue three goals conceded under away pressure. I advised the club not to sign him long-term. Two weeks later, Son picked up an injury and the contract was cancelled.

A goal only means something when we know what he had just been through.

At Euro and the Paris Olympics 2026, I advised a group of young journalists. I measured that Spain's midfielder Pedri dropped 18% in distance covered after the 75th minute. I warned in the report that if pushed to extra time he would decline. The coaching staff did not rotate, and Pedri left the tournament with an injury. I was right in the forecast but wrong about something else: I was slow to realize that my own way of reading data had become outdated against the high-intensity trend. Since then I have begun learning algorithms, not to replace the eye, but to know where I am blind.

The most counter-intuitive thing I have drawn after many years is this: adding data can make a verdict worse, if that data is empty or skewed.

A sheet that is 61% blank is not a sheet short of information; it is a sheet that lies through silence. Its reader tends to trust the filled cells, because a filled cell creates a feeling of completeness. That is why digitalization in youth football needs an intermediate step many academies skip: checking the data before making the decision.

I once sat through a 40-minute meeting where every argument revolved around a single metric, the successful pass count of a young midfielder. Nobody asked where those passes went, under pressure from how many opponents, and in which part of the pitch. We spent 40 minutes analysing one layer of soil and forgot the entire mountain beneath it.

A data map can point the wrong way if we do not read the terrain.

And there is a subtler trap: once people start having numbers, they tend to turn measurement into the goal. A player who runs a lot is praised, even though running a lot is mostly ineffective running. Distance covered and sprint counts are packaged as effort metrics, but ineffective running also produces very pretty cells. A midfielder who runs 11.5 km per match is not automatically better than one who runs 9.8 km but always stands in the right place.

I do not excavate stars; I excavate context. And the context of Vietnamese youth football is full of empty cells nobody has bothered to fill.

The hypothesis I set for myself, testable over the next two seasons: if every academy adds just three columns — actual minutes per match, biomedical context, and the quality of the space in which a player receives the ball — the rate of misjudging young players will fall markedly, even without any machine-learning algorithm. The condition for that hypothesis to hold is that decision-makers must sit down and read the empty cell before reading the filled one.