V.League and the Data Void: When Numbers Are Absent, Mistakes Take the Throne

In October 2026, I published a table on my personal blog that I thought would only please a few colleagues. Marseille lost 0-3 at home to PSG. But my expected-goals table showed the hosts created 1.94 expected goals, while the visitors managed only 1.21. I wrote one short line: the result was not wrong, but it did not tell the whole story. Within two days my inbox was full of criticism. "Women don't understand football." "xG is a scam for people too lazy to watch the game." I read it all and answered no one. I sat down, built a data frame of 23 Ligue 1 matches, and proved that PSG that season won big thanks to an abnormally high conversion rate, not through dominance of chances. Three months later, PSG's numbers fell and they lost 1-2 to Lyon. But what I learned was not that "I was right." What I learned was this: to argue with data, you first need data. And that is exactly where the story of Vietnamese football begins.

In V.League, when a team loses 0-3, people argue about the referee, the goalkeeper, about "spirit." Almost nobody argues about numbers. Not because numbers are beyond dispute, but because the numbers do not exist to argue with.

I follow V.League from afar, through screens, through news feeds, through friends who are coaches and technical directors in the country. For many years I have recorded a paradox I have never seen in a league of comparable scale: the league grows more professional in form, yet its data infrastructure remains amateur.

A Ligue 1 match generates thousands of data points: each player's position every second, touches, running distance by speed zone, xG per shot, xG for goals, xG for assists, and PPDA — the passes allowed per defensive action, simply put a measure of how aggressively a team presses. A V.League match, if you are lucky, gives you a scoreline, a list of scorers, and a few raw numbers like shots and corners.

This sounds like a technical complaint. But it is not only a technical matter. It is about money.

Because every decision in football is a decision about resources. How much a club pays for a player, how many more years it keeps him, when it sells him — all are investment decisions. And investing without data is not investing. It is gambling.

V.League has a feature that makes this problem more serious: most clubs depend on one or a few corporate owners. Broadcasting revenue is tiny against total spending. Commercial revenue is not yet large enough to create a healthy transfer market. When money comes from a single owner, transfer decisions tend to come from that person's intuition, or from an agent's recommendation. Not from a model.

I once sat in a meeting where a player was valued 40% above the market, simply because he had scored twice against a strong team in a live televised match. Two goals. One match. An entire season forgotten. That is the smallest possible sample. And I call it by its proper name: a sampling error.

Let us start with the thing I trust most in football: the shot. Not the goal. The shot.

A goal is a shot plus a goalkeeper plus a bit of luck. The shot is the decision.

That is why I begin every analysis with xG — expected goals, the probability that a shot becomes a goal based on position, angle, shot type and number of defenders. A player who shoots five times from the edge of the box has a very low total xG. A player who shoots once from the centre of the goal, eight metres out, has a higher xG than those five shots combined. Over a 26-round season, this difference creates the gap between a real striker and a striker sheltered by luck.

In V.League we have very little public xG data. But there is a substitute, if you know how to read it: the television feed. I often record matches and hand-draw the shot map for key games. Watching many recent V.League seasons gives me an observation that is rarely mentioned: V.League teams shoot a lot, but shoot very inefficiently in terms of position. Dozens of shots from beyond 25 metres every match — the kind with an average xG of about 0.02 to 0.04, meaning you need 25 to 50 such shots to produce one goal. Looking at the stats sheet, people see "Team A had 18 shots" and think it was a fierce attack. Read the shot map, and most of those were harmless long-range efforts.

This is the kind of misunderstanding data is born to resolve. And when there is no data, the misunderstanding becomes the norm.

Let me give a more concrete example. A wide player in V.League I tracked for two seasons. In the first, he scored nine goals and was praised as a "new star." But when I drew his shot map, six of those nine goals came from rebounds inside the box — situations that depend on luck and on teammates, not on finishing skill. The next season his goals fell to three, and the public said he had "lost form." In truth, he had not lost form. He had returned to his real number. The goals found him in the first season, then left. His shot did not change. His shot had never been good enough.

Where the world sees a player in decline, I see a regression line returning to the mean.

Now let us talk about fitness — the threshold I have pursued since the Croatia 2026 lesson. I repeat that story because it applies to V.League more than anywhere. Croatia 2026 ran a total of 318 km in the group stage, the highest in the tournament, but their average speed in the second half dropped 7% against the first. I warned they would collapse in extra time. They reached the final, and against France they ran 11 km less than their opponents and lost 2-4. Croatia 2026 taught me that heroes also have biological limits.

V.League has a schedule I consider harsher than many European leagues in one respect: travel distance. A northern club may have to travel to the far south-west, then to the centre, within three weeks. Add the heat. Add the humidity. Factors that European data models do not factor in. I once saw a team play its third match in eight days, and in the second half their high-intensity running dropped by nearly half. This is not "losing spirit." This is physiology.

And this is exactly where I place my bet on the view I believe matters most for V.League: this league is mispriced by the very people who run it, and that mispricing starts with a lack of data.

Without stable fitness data, no one knows which player is playing beyond his limit. Without xG, no one knows which striker is truly dangerous. Without passing data by zone, no one knows which midfielder truly creates. As a result, clubs buy and sell on the most visible signals: goals on television, reputation, age, and an agent's recommendation. Those signals carry very high noise.

Let me be clearer about what I call "the mispricing of youth." This is my professional view, and I will say it plainly: data models — even good ones — overvalue the potential of young players and undervalue dressing-room chemistry. A 19-year-old with a pretty development index, priced high, pushed into the first team, but who does not understand how the collective works, has a negative real value for a season. In V.League, where the collective and dressing-room relationships matter far more than in Europe — because squads are small, because of culture, because of local ties — this factor is even larger.

I have followed a V.League club that bought three expensively priced young players in two consecutive seasons. All three had decent individual technical numbers. But the club did not improve in the table, and even slipped. Meanwhile, another club kept a 32-year-old many called "finished," moved him into a connector role in the dressing room, and that club rose. I have no xG data to prove this. I have something else: nearly three decades observing the industry, and an intuition trained by data. Data is the only thing I believe after witnessing too many broken promises. But I have also learned that some things do not sit in a spreadsheet cell — and dressing-room chemistry is one of them.

At this point I must argue against myself. Because if I only say "go buy data," I am selling you something whose output I cannot verify. And that is a mistake I do not want to make.

The truth is that data is not immune to bias. It only removes one kind of bias to create another. An xG model trained on European data may not fit V.League, where the pace is slower, where fitness differs, where pitch quality differs. Impose an outside model without recalibrating, and you exchange one mistake for another — only this time with prettier charts.

And there is something I must state even more clearly: correlation is not causation. I once wrote a piece I was very proud of, data-wise, showing that teams that ran more won more over a certain stretch. Very convincing. But then a colleague showed me something: the teams that ran more were often the teams chasing a deficit. They ran more because they were losing, not in order to win. If I had taken that number to persuade a coach to "make the team run more," I would have caused harm. I retracted that piece. I rewrote it with its correct conclusion.

A risk model saves no one, but it gives them a chance. And I will add: a bad risk model can kill a club faster than having no model at all.

So my answer for V.League is not "buy a data system." The answer is: start with the smallest, cheapest, most honest task. Record every shot. Record positions. Record running distance by speed zone. No need for a tracking camera. No need for artificial intelligence. Just one person sitting down after the match and drawing the map. Do that for all 26 rounds. After twenty-six rounds you will have something no V.League club currently has: your own history. And your own history is the only trustworthy starting point.

There is one more counter-argument I must state myself, because I know it is true. Data has never been a prerequisite for playing good football. Teams can win a title without it. But they cannot grow sustainably without it. Data does not help you win a single match. It helps you know why you won, so you can win again. That is a very large gap.

If you want to see the V.League data void in its rawest form, look at the transfer market. This is where I believe noise is systematically drowning out signal.

On domestic forums and news sites, dozens of V.League transfer rumours appear every day. Most have no source, no timestamp, no verifiable element. I sort each such rumour into one of three tiers. Tier one: a named sporting director or agent speaks, with a specific timestamp. Tier two: an anonymous internal source. Tier three: just "I heard." In the V.League market most rumours are tier three, and those are the ones we spend the most time reading.

What is worrying is that clubs read them too. And when an owner starts to fear that his star will leave, he may make a decision — sell, replace, or raise wages — based on a tier-three rumour. This is where data must step in. Not data about players. Data about context: how long a contract has left, what the release clause is, how much wage headroom the squad has, which clubs an agent has met in the past three months. These are verifiable. And they matter more than any rumour.

The transfer market does not buy players, it buys stories. The job of a professional like me is to separate the story from the value. And the job of a club is to pay for the value, not the story.

In V.League, a foreign player who arrives and scores twice on his debut can be revalued at double within a week. I have seen this. But if you read the data from his previous league — minutes played, position, quality of opponents — you can predict far more accurately. There are strikers who scored 15 goals in a lower division against weak defences, and 3 in V.League. That is not "failure to adapt." It is a technical profile recorded beforehand, that no one bothered to read.

This is another version of my "fitness threshold": I am known for digging into running distance, sprint speed, and even small numbers like the count of high-speed bursts in the second half. In Europe these numbers are the standard for deciding whether to sign a contract. In V.League they barely exist. As a result, clubs sign players whose bodies are already at the limit, and discover it only after the contract is activated.

This is not a problem unique to V.League. But V.League has a chance to overtake it faster, because the league is smaller, fewer teams, less data. A title-winning V.League side needs only 26 matches. Recording 26 matches properly is entirely within reach.

If I had to give one signal for the next round of V.League, it would be this: watch the first club to hire a full-time data person — not a match analyst for big games, but someone who sits down after every match and records his own team's numbers. That will be the club leading the rest two seasons later.

And if you ask me what I believe most in football, after all these years reading tables, my answer is still: Numbers have no bias. The bias is in the person who lacks numbers. But I will add, because I learned this at my own cost — a number is only a map. Walking the road is still human work.

V.League is missing a map. Not players, not money, not fans. Just a map. And a map can be drawn. That is the greatest comfort, and also the most worrying thing: once you have a map, you run out of excuses for getting lost.

I still remember sitting in front of the screen in 2026, reading the criticism, deciding not to reply. I do not regret the silence. I only regret losing three months to prove something a properly built data table should have shown me at once. In V.League today, I see myself in the people arguing about football with feelings, because they have nothing else to hold. And I understand that feeling. But I also know where it ends. PSG won that year, but I chose to believe in the shots that did not go in. In V.League, those shots are forgotten every week, and we call it football. I call it a gap waiting to be filled.