An Empty Data Sheet in the Middle of Transfer Season: Lessons from a VBA Year Without Numbers

1. Forty-seven columns, three cells filled

On August 12, I opened a spreadsheet sent from the office of a VBA club. It had 47 columns: name, birth year, height without shoes, wingspan, height in shoes, standing vertical, three-quarter sprint, minutes played last season, three-point rate, free-throw rate, number of injuries, average days lost per injury, expected transfer fee, proposed salary, contract length, extension clause, and thirty more columns its author had built with obvious care. I scrolled through all 31 rows. Three cells had been filled — all of them in the name column.

The sender wrote exactly one line in the email: "Can you tell me what this roster is missing?" I sat still for two minutes. What is it missing? It is missing everything that turns a spreadsheet into a decision tool instead of a list of names.

Three days later I called back and said it plainly: with this file, I cannot tell you which player deserves a raise, which one should be released, or whether the club is overpaying or underpaying. Not because I lack the expertise. Because there is no comparative data. You cannot weigh a man on a scale with no weights.

That was the moment I understood this transfer window has a bigger problem than any rumour: the entire market is pricing players by feel, and feel leaves no draft to check against.

2. Eight teams and a data system close to zero

The VBA launched in 2026. Nearly a decade on, it has been through several rounds of expansion and contraction, currently sitting at seven to eight teams, running four to five months a year, with each club playing somewhere between 20 and 30 games depending on the format. That is a scale small enough that every bad personnel decision becomes visible to the naked eye within four weeks.

The problem is structural: a small league means small budgets, small budgets mean the analytics department is cut first, and when analytics is cut, the data is never recorded. A self-feeding loop.

I spent the first four months of this year auditing what can be publicly verified about domestic players. The result forced me to lower my own forecast twice.

• Of roughly 150 players I track across youth and professional registration lists, only about 12 have enough of a file to build a basic valuation model: minutes, shooting rates, position, height, birth year, contract status.
• Fewer than 20% of games are published with possession-level detail. Most offer only the final score and individual points.
• There is no official public channel for injuries. A player absent four weeks may have a torn muscle, a disciplinary issue, unpaid wages, or a family matter. Outsiders cannot tell the difference.

Those three lines are not a technical complaint. They describe a market where the cost of information exceeds the cost of transfer. When buyers do not know what they are buying, sellers price on expectation — and expectation runs 30 to 40% above reality in every market I have worked in.

3. The real cash flow of a VBA season

A mid-tier VBA club runs on a seasonal budget of roughly VND 3 to 8 billion, depending on ambition and on whether it hires high-end imports. The typical breakdown:

Player payroll takes 45 to 60% of the total. That share is higher than a professional football club of comparable attendance, because basketball has fewer roster slots but each slot carries more leverage. With 12 to 14 active players, one player 20% better than his replacement can swing five to seven wins a season.

Import costs — salary, housing, flights, insurance — take 15 to 25%. The going rate I have logged over the last three seasons sits between USD 2,000 and 4,000 a month for a mid-tier import, plus accommodation and travel. Contracts are short, two to four months, and this is where I want to slow down later.

Operations — arena, referees, domestic travel, medical, media — take 20 to 30%. This is the hardest line to cut and the one clubs cut wrongly, stripping medical and analytics first, then paying for it in injuries and in the last four minutes of games.

Revenue is almost entirely sponsorship. For most clubs, sponsorship is 60 to 75% of total income. Ticketing, at VND 100,000 to 300,000 per seat and arenas of 1,500 to 3,500 capacity with 45 to 70% occupancy, contributes far less than the crowd on the concourse suggests. Broadcast rights, at the point I measured, contribute under 5% of most clubs' revenue.

That last number matters most. When broadcast is under 5%, the league has no financial incentive to produce data. Data only has value when someone pays to read it — and the only reliable buyers of Vietnamese basketball data right now are betting operators, whom the league does not want to serve. A market with no data buyers produces a market with no data. That is a rule, not laziness.

4. The price of an import and the short-contract trap

The question I get most from clubs during a transfer window is not "is this player good" but "is this player worth that salary". Those are different questions, and the market's answer to the second is damaging its own structure.

Take a simple calculation. An import on USD 3,000 a month, a three-month deal, plus USD 2,000 in housing and flights, costs about USD 11,000 — roughly VND 280 million at the rate I used. Over those three months he plays 15 to 20 games at about 25 minutes each.

If he scores 18 a game on average efficiency, he gets renewed. If he scores 18 and the team loses 12 of 18, he still gets renewed, because 18 points is the only number available. That is the gap: when there is only one metric, that metric becomes the whole truth.

I rebuilt a small sample of VBA imports across three recent seasons by re-watching game footage and coding possessions by hand. The finding is unsurprising to anyone in the trade: the group with the highest scoring average was not the group with the largest impact on team point differential. The second group played fewer minutes, shot less, and appeared in the decisive possessions of the fourth quarter.

On a three-month contract, a club has no time to discover that difference. They sign on a small sample — usually three to five game tapes sent by an agent, always the best three to five. The real cost of a short contract is not the salary. It is that the club never learns whether it bought right until the season is over, and by then the contract has expired.

5. Agents, noise and the information risk premium

Every transfer window carries three kinds of information: what the club knows, what the agent wants the club to believe, and what the public hears. In a well-documented market, the three collide and produce a price. In an undocumented market, the second swallows the other two.

I am not attacking the agent trade. In the VBA, agents are performing better than the data infrastructure allows them to. When a club has no independent performance file, the agent is forced into the role of information provider — and providers always retain the right to select. That is structure, not ethics.

The financial consequence is concrete. Every time a club signs without comparative data, it pays an information risk premium. In the sheets I rebuilt, that premium ran 15 to 35% of a player's fair value. For a club with a VND 3 billion payroll, 25% of the import budget is roughly VND 150 to 190 million a season paid for uncertainty rather than ability.

That money could fund a part-time analyst for four months. It never appears on a balance sheet, because it is scattered across individual contracts and nobody totals it.

6. Valuing a domestic player: four variables and one hole

The hardest part of the Vietnamese transfer market is not imports. It is domestic players. Imports have overseas tape, a prior league for reference, an international agent. Domestic players have only what the local league records — which is very little. The paradox: the group with the greatest long-term value is the group priced least accurately.

I use four variables when forced to put a number on a domestic player without full data.

Minutes over the last two seasons. The best proxy for quality when advanced stats are absent, because coaches have an incentive to win and do not play bad players 25 minutes a night for two years.

Age and remaining seasons before decline. In basketball that threshold usually falls around 30 to 32 depending on position and injury history. A 27-year-old with three prime seasons left is worth far more than a 31-year-old with identical numbers.

Positional substitutability. In the VBA, players 1.90m or taller who can move off the ball and defend two positions are the scarcest group. The scarcity is not height. It is switchable defence. A team with two such players can run two systems in one season.

Contract status. A player with one year left is worth something entirely different from one expiring. This is the variable Vietnamese clubs skip most often and the one that costs most: players bought at the price of two contract years when only four months remained.

Those four variables produce a range, not a number. A range is enough to negotiate with, not enough to conclude with. And the gap between the range and the final contract figure is where this entire industry is dropping money.

I once re-watched three games of a domestic big man I rate highly. In 34 minutes of the second game I counted 11 correct switches, four wrong ones, and nine occasions where the opposing team refused to pass into the area he was guarding. No stat sheet records that. But if I coach against him, I avoid that area — and that is the value.

7. Injuries nobody announces

Of the four information types that move a player's price, injury status has the highest return and is the most concealed in the VBA. The reason is understandable: announcing an injury announces weakness to rivals, to sponsors weighing contracts, and to the player himself in extension talks. In an eight-team league with a thin labour market, that information can cost a deal.

But the cost of concealment is paid by someone else. When a club signs a player without knowing his injury history, it is buying an asset with hidden depreciation. In files I have handled at club level, contracts that failed because of injury rarely failed immediately. They failed in month two, after the money was paid and the replacement options were gone.

I once built an injury log for a club, recording every time a player left the floor, when, and in what situation. After four months it showed a clear pattern: players over 30 averaged 1.8 times as many days lost per injury as players under 25. That figure appears in no official report. It was enough to restructure a contract — lower base, bonuses tied to games played.

No club did it that season. Not because they did not understand, but because that structure reads as distrust, and in a market where relationships outweigh contracts, distrust carries its own price.

8. What Chinese basketball taught me: money does not fix bad data

I was born in China and have followed its professional league since I was a student. It is a system with a budget many times the VBA's, real broadcast revenue, global sponsors, and clubs that pay one import more than three VBA clubs spend in total.

That system still makes the mistakes small leagues make. It still signs on scoring averages. It still overpays domestic players under pressure to have a local star. It still hands out four-year deals that become immovable burdens after eighteen months.

What I learned standing between two basketball economies is not that the big league does it better. It is that data does not generate itself from money. Data generates from someone sitting there and writing it down, and that person must be paid to do work they do not enjoy.

In China, money buys recorders. But money also buys silence, and when the two collide, silence usually wins. In Vietnam there is no money to buy recorders — but there is also much less to be silent about. That is an advantage VBA clubs have not noticed.

A small league holds a privilege the big leagues have lost: it can publish everything and nobody will care enough to react. Posting a detailed injury table on a club page will not create a media crisis in an eight-team league. It will create data. And after three seasons, that data becomes something no other club has.

9. The contrarian angle: an empty field is not a green light

This part is for the people who work with spreadsheets as much as I do.

There is a bad habit I have seen at club level and league level alike: when a data cell is empty, people treat it as no problem. An empty safety checklist means safe. An empty injury list means healthy. An empty record means nothing to worry about yet.

Nothing is more dangerous than that reading. An empty cell is not information. It is the absence of information, and every decision built on absence is a decision made blind.

Risk analysis has four states, not two: checked and clean, checked and problematic, not yet checked, and impossible to check. The first three are manageable. The fourth is where most of the Vietnamese basketball transfer market lives, and it is mistaken for the first almost daily.

When the 47-column file with three filled cells arrived on August 12, my first reaction was not "this club is lazy". It was: if I conclude "no player has a problem" from this file, I will have produced a wrong conclusion with a professional-looking surface. That is the most dangerous output an analyst can generate.

In fact, I have done exactly that. In 2026 I cut a young player from a list of 15 best commercial bets because his sample was too small to model growth. I read "insufficient data" as "not worth tracking". Six months later I had to publicly correct myself and write a piece arguing against my own earlier article. Since then I publish forecasts with a specific date and time, and correct within 48 hours when wrong. Mbappé scored; I was studying my own mistake.

The first step of a number-cruncher is admitting he cannot count everything.

10. The human backstage: the name crossed out

Spreadsheets are cold. People are not.

I once proposed cutting seven veteran players in a 40-page restructuring plan, and I did not care about the tears in the locker room when it was read aloud. The 40-page plan was sunk by a night of rain, but I knew how to swim.

The truth is I did not know how to swim. I only knew how to tread water. Four months after the club dissolved, I sat in Nha Trang with a backup drive of ten years of data and no answer to the question of what those seven men are doing now.

This transfer window, every time a club sends me a cut list, I add a section at the end of the report I call the human backstage. Not to soften the board. Because it has real economic value.

A cut player can become a competent free agent for a rival at zero cost. A player whose pay is reduced can lose 15% of his efficiency for the first three months. A player pushed out without explanation will make three others in the locker room start questioning their own position. All three consequences are measurable in points and none appear in any spreadsheet.

I spent one season tracking a player released mid-season. He signed elsewhere after six weeks, played less, and his new team had a better differential in the minutes he was on the floor. Nobody at his old club re-read that data. If they had, they would not have released him.

11. What happens if the data never comes

I spent four months reaching a conclusion I dislike: the VBA probably will not have a full data system in the next three to five years, because nobody has a financial incentive to build it.

That does not mean clubs must keep buying by feel. There is a shortcut so cheap it is hard to believe: record what happened in your own games, by hand, with one person in the stands and a notebook.

A team plays 25 games a season. Twelve real data lines per game — minutes, correct switches, blown assignments, fourth-quarter turnovers, shots taken in the last four seconds — gives that club 300 rows of data no other team in the league has. The cost: one sports-science student and a motorbike.

Three hundred rows will not build a model. It will prevent three mistakes a season. And in a league with margins as thin as the VBA's, three fewer mistakes may be the highest-yielding investment on the entire balance sheet.

12. Takeaway

An empty dataset is not a verdict. It is a table nobody has sat down at.

The question I leave for VBA management is not "when do we get an analytics system". It is: if next season not a single additional data cell is filled, what will your club decide with, and who is accountable for that decision?

As for me — 27 files on the desk, and what I smell is not risk but tomorrow. Though after seven years here I have learned one thing: tomorrow only arrives for the person who brought a notebook, not the one who only brought faith.