The Empty Dossier: Brazilian Football Is Selling Conclusions Without Data

On August 11, 2026, a forty-page dossier landed on the boardroom table of a club playing in Brazil's Serie A. The cover carried the logo of a data consultancy headquartered in São Paulo and a title: "Squad Capability Analysis — Mid-Year 2026 Transfer Window." Inside were twenty-seven tables, formatted exactly the way any sporting director wants to see them: line charts by matchday, heat maps by pitch zone, a market value column, a minutes-played column, a conversion-rate column.

Eighteen tables contained numbers. The remaining nine said "N/A."

It took me four days to trace the dossier's origin. There was no match-tracking file behind it. No event log. No extraction sample from any data provider. The eighteen tables were built by copying player market values from a public website and dividing them by domestic league minutes. The whole calculation took about thirty seconds per player.

The nine "N/A" cells were the only honest part of the document. And the club paid for it.

That is why I am writing this. Not to expose one particular consultancy, but to point at a larger structure: Brazilian football is producing conclusions faster than it produces evidence, and nobody pays a price for the missing evidence.

A new industry: the report industry

Law 14.193/2026, passed by the Brazilian Congress on August 6, 2026, and effective that same month, created the Sociedade Anônima do Futebol — the SAF model. Football clubs converted from non-profit sporting associations into joint-stock companies. With that shift came a new set of obligations: periodic financial disclosure, independent auditing, public disclosure of related-party transactions, and a professional management layer on employment contracts — a finance director, a sporting director, a head of analysis.

Alongside that process, the CBF issued and progressively tightened its Fair Play Financeiro rules, requiring clubs in the national league to control the gap between revenue and wage costs, with penalties measured in points and in restrictions on player registration. FIFA has operated the Clearing House since 2026 to handle training and solidarity payments in international transfers. That entire administrative system, at bottom, demands exactly one thing: verifiable data.

The market responded in the opposite direction.

Three converging forces sit behind the report boom. On revenue, domestic broadcast rights income and shirt sponsorship money have both risen, but not fast enough to keep pace with wage bills. On personnel, clubs have hired sporting directors and analysts on the expectation that tools will replace experience. On media, every transfer window becomes a content event running for weeks, where speed is rewarded more than accuracy.

The result is a new intermediary layer. They do not sell players. They do not sell tactics. They sell certainty.

And certainty is the easiest product to counterfeit in the entire football value chain, because nobody checks it until results on the pitch contradict it — by which point the money is already gone.

Anatomy of a data report in four layers

A serious football data report passes through four layers, and every layer leaves an auditable trace.

Collection is where raw data is recorded. Every in-match event — pass, shot, duel, player position by the second — must be tied to a match ID, a player ID, and a timestamp. This layer cannot be convincingly faked, because it has to reconcile with video. If a report claims Player A completed forty-seven lateral passes in the opponent's defensive third, there must be forty-seven timestamps matching forty-seven moments on tape.

Verification is where the analyst cross-checks at least three independent sources. This is the most expensive layer and the first to be cut when budgets tighten. Verification does not produce attractive charts. It produces one of two conclusions: the data holds, or the data is unusable.

Modelling is where human beings impose assumptions. A player valuation model always contains variables chosen by its author: weights for age, for minutes in a strong league, for resale potential, for nationality. Every weighting choice is a statement about value. No model is neutral, and anyone claiming their model is neutral is selling something else.

Interpretation is where conclusions get written. It is the only layer club leadership actually reads. It is also the only layer that can be produced without the other three.

That is where the gap lives.

The dossier I held on August 11, 2026 had a full interpretation layer. It had none of the other three.

When a blank cell is a fact

The nine "N/A" cells in that dossier initially made me suspicious. In my working habit, a blank cell signals one of two situations: the data does not exist, or the author deliberately hid it.

I contacted three independent sources — two analysts who had worked with the consultancy in question, and one sporting director at the club that signed the contract.

All three produced the same account. The eighteen numbered tables were built from a spreadsheet with two columns: player name and market value looked up publicly on the morning the dossier was assembled. Every other metric — conversion rate, contribution index, season-on-season progression index — was the result of dividing market value by minutes played. One division, applied to every player, blind to position, blind to league, blind to age.

Notably, the nine "N/A" cells were not a sign of sloppiness. They appeared in rows where the spreadsheet had no market value to look up — specifically, young players not yet updated on the public data site, and players recovering from long-term injuries who had been removed from valuation lists.

In other words: the author left blank exactly the places where their source data was blank. They did not invent. They simply did not verify.

That distinction matters more than it appears. Someone who fabricates numbers is a fraud, and fraud leaves traces. Someone who does not verify is merely a relay, and relays leave no traces at all — because nobody in that chain has claimed to have asserted anything.

Across my writing career I have learned that most false information in professional football is created not by liars but by honest relays who are too lazy to verify. They pass numbers along the way you pass along a lost item: without checking the source, without checking the age, without checking the item's original purpose.

One out-of-rhythm number, one career collapses — I only need enough patience to look.

Out-of-rhythm values in the export market

Brazil remains the world's largest exporter of footballers by number of registered international contracts per year. That flow gives me a sample large enough to spot pricing anomalies.

Published transfer fees are usually reported in a two-part structure: a fixed sum paid on a schedule, and a variable sum tied to performance. Vinícius Júnior left Flamengo for Real Madrid in 2026 on a reported fee of around 45 million euros. Endrick left Palmeiras for Real Madrid, completed in 2026, on a reported fixed sum near 35 million euros plus variables. Estêvão left Palmeiras for Chelsea under an agreement announced in 2026, on a reported fixed sum above 30 million euros plus variables. Vitor Roque moved from Athletico Paranaense to Barcelona with a reported fixed sum near 30 million euros alongside a variable package that could take the total close to double. Luiz Henrique moved from Botafogo to Zenit in early 2026 on a reported figure above 30 million euros.

This two-part structure is where anomalies appear that cannot be explained by professional judgement alone.

Following matches in Brazil's Serie A and cross-referencing them with transfer records, I keep finding the same pattern: the variable component in deals for young Brazilian players is typically set disproportionately high relative to the same age cohort in other markets. The average variable share for Brazilian players under twenty in the international deals I collected across the last three seasons sits between roughly 40 and 55 percent of total potential value, while the comparable figure for players of the same age from other European and South American markets is usually lower.

There are three plausible explanations, and I list all three because I do not have enough data to eliminate any of them.

A risk explanation: the buying club accepts a high performance-contingent price because it prices the failure risk of young Brazilian players higher. That is coherent if the player is moving from a lower-pressure environment into a harsher one, and the adaptation gap is priced in.

An accounting explanation: variable sums are typically recognised differently from fixed sums in the seller's financial statements, creating an incentive for both buyer and seller to present different pictures across different accounting periods.

An intermediary explanation: the variable component is the easiest place to allocate commissions among the parties involved, because verifying a future performance condition is far harder than verifying a one-off cash transfer.

These three explanations are not mutually exclusive. They may all be true.

And here is the point I want to stress: no valuation equation in Brazilian football today is required to disclose its weights. A 40 million euro fee can be defended by a good data model, by a relationship between an agent and a sporting director, or by dividing market value by minutes played. From the outside, all three cases look identical.

Records never disappear. They only wait for someone stubborn enough to find them.

Legal traces: contracts that do not pay in cash

While cross-checking those deals, one form of payment structure kept recurring and consumed most of my time.

It is the clause allowing payment in goods or services rather than cash. A club signs a sponsorship agreement, and the partner pays in "advertising services," "media services," or "image rights use across campaigns of unstated value." Contractually, the transaction is valid. Accounting-wise, the true value is hard to establish.

This is the structure I investigated at a major São Paulo club in 2026, when a forty-page document set arrived in my inbox. I spent four months reconciling every figure against three years of publicly filed accounts and found a discrepancy of roughly 3.2 million US dollars. I did not publish until every number had been independently verified.

The principle I took from that work and apply to every investigation since: a non-cash clause is not automatically a sign of fraud, but it is always a sign of an information gap, and information gaps are always priced into the next negotiation.

The relevant legal framework in Brazil has several layers. Law 9.615/2026, the Lei Pelé, governs the employment relationship between player and club and separates contractual wages from image rights. In practice, image rights in Brazil account for a substantial share of a player's total income, and that share creates a grey zone around tax obligations and disclosure obligations. Law 14.193/2026 added a corporate governance layer, requiring SAFs to disclose related-party transactions. At the international level, FIFA banned third-party ownership of players' economic rights in 2026, with a transition period completed in 2026, and has run the Clearing House since 2026 to distribute training payments.

These three layers do not mesh perfectly. A deal can comply fully with FIFA rules on international transfers, comply fully with SAF disclosure rules, and still leave a portion of value untraceable — because that portion passes through a service agreement between two legal entities with no obligation to disclose detailed terms.

Throughout that reconciliation I have kept one rule: every legal or financial term I use must be defined in the article itself, in a single sentence, and never used as a substitute for reasoning. If I cannot define it, I do not use it. That is why parts of my writing read as dry. The dryness is deliberate.

The data supply chain and the capability gap

A significant share of the reports Brazilian clubs buy comes from international supply chains. Event data providers, positional data providers, and scouting data providers run their own collection systems, with tagging teams and internal quality-control processes. Annual subscription costs for a mid-table Serie A club can range from tens of thousands to several hundred thousand US dollars, depending on league coverage and data depth.

That spending is almost always considered reasonable in a budget plan. The problem comes afterwards.

Buying a subscription is not the same as having analytical capability. A club can pay for three data platforms and use them only to look up player market values before each transfer window. This situation is far more common than fans imagine, and it produces a specific consequence: the gap between available data and the decisions actually made becomes the habitat of intermediaries who sell reports.

Based on my experience following matches in Brazil's Serie A and Serie B across several seasons, I notice a pattern among mid-table clubs: when the analysis department is shrunk or outsourced, squad decisions in the transfer window tend to rely more on personal referrals and less on models. That is not wrong in principle, but it changes the nature of the risk: risk shifts from "the model picked the wrong player" to "the referrer picked the wrong player."

That gap is the territory I watch.

Tactics are not born on the pitch. They are born in the numbers people choose to forget.

The rumour market and a source reliability ranking

During the mid-year 2026 transfer window, the volume of information circulating about Brazilian deals was larger than in any period I have tracked. To process that volume, I apply a four-tier source ranking and publish it for readers.

The top tier is official announcements from a club or federation, tied to a numbered document with an issue date. The second tier is journalism with a long track record — sources that can demonstrate a past accuracy rate. The third tier is aggregator pages, where information is copied without an independent origin. The lowest tier is anonymous accounts with no verifiable record.

In my observation, most false information at the third and fourth tiers is not created from scratch. It results from a relay chain: a tier-two source offers a conditional speculation, a tier-three source drops the condition and keeps the speculation, a tier-four source drops the context and keeps the player's name.

The process takes a few hours and nobody in the chain is accountable for the final version.

To counter that pattern in my own work, I always frame projections as probabilities or conditional statements, with sample size and data year attached. In recent pieces on the success prospects of young Brazilian players moving abroad, I present results by age cohort and by destination league rather than offering a single headline rate. The difference between the two presentations is not computational accuracy. It is who carries responsibility when the projection is wrong.

Every transfer is a detective story, and data is the silent witness.

But a silent witness is only worth something if somebody actually calls it to testify.

The cost of an error nobody records

When a deal fails — the player does not adapt, the injury drags on, the resale value lands below the purchase price — the entire cost is booked under "professional risk." Nobody checks which data the decision rested on, or whether it rested on a spreadsheet copied from a public website.

This is the system's largest blind spot. There is no post-audit mechanism forcing an analytical report to prove its data sources after the outcome is known.

The consequence is not in any single deal. It is cumulative. A club making twenty squad decisions per season on reports with uncontrolled data quality will compound its error across seasons, but that error is scattered across so many accounting lines that it becomes invisible on the financial statements.

Numbers never lie. Only the people reading them lie to themselves.

In Brazil, the CBF's Fair Play Financeiro rules can catch a club spending beyond its revenue. They have no instrument to catch a club spending on the basis of an empty analysis, because both cases look identical in the books. Paying for a forty-page report with twenty-seven tables looks more professional than paying for an in-house analyst, but it does not mean it generates more information.

For the investment funds now involved in Brazilian football's SAF model, this is a quantifiable risk. If a club's decision file cannot be reconstructed from raw data, that club's asset value cannot be fully assessed through published financial metrics alone. The missing valuation does not appear on the balance sheet. It appears at the exit.

The reasonable part of the blank cell

I have to say this clearly, because otherwise the piece will be read as a blanket indictment of the football analytics industry — and that is the wrong reading.

In many cases I have examined, the "N/A" cells in a report represent correct practice. An analyst writing "insufficient data to conclude" is protecting the club from a bad decision. I have read reports that were returned by leadership for having too many blank cells, and in most of those cases the author was right and the leadership was wrong.

Football punishes the admission of not knowing very harshly. A sporting director presenting a fully detailed but thinly sourced report to a board will be rated more highly than a sporting director presenting a report that admits its data limits. These two people receive outcomes inverted from the actual quality of their work.

This is why I argue the biggest pressure in the industry comes not from the sellers of poor reports but from the buyers. The demand for certainty is the cause; the empty report is the symptom.

The counter-intuitive angle sits here: solving the empty-report problem requires changing how decision-makers are evaluated, not merely changing data suppliers. A club that adopts a rule requiring every report to include a traceable raw data file would eliminate most of the problem within one transfer window. But it would also receive fewer reports, slower, and more expensive. Very few boards accept that trade.

Conclusion

The dossier of August 11, 2026 was returned. I verified that. The club did not sign any player on the basis of those eighteen tables.

This time the system self-corrected. It self-corrected because one sporting director was patient enough to ask a question that should never have needed asking: where is the raw data file.

But the system does not self-correct every time. Among the reports I have collected over the past three years, the majority were never returned, never checked, and left no trace in the club's decision file.

Viewers see the goal. I see a crack in the story they were told.

If you work at a Brazilian club in any role connected to transfers, the question for you is not whether the report you just read is reasonable. The question is: if every conclusion in it were proven wrong, could you point to the data file that produced them?

If the answer is no, then that dossier never existed.