Empty Reports: Fourteen Pages of Scaffolding and Three Data Points

In March 2026, a scouting department sent me a fourteen-page report on a central midfielder playing in a second-tier European league. Four major sections, eleven tables, each table with a bolded heading and a notes column on the right. I read it end to end in forty minutes. Full frame, empty core.

Counting it back: twenty-seven instances of the phrase "needs further monitoring", nine instances of "untapped potential", and exactly three appearances of a numeral — all three of which were match counts watched, not production metrics. Not a single expected goals value. Not a single progressive pass rate. Not a single probability interval. The document carried the form of a technical paper and the information weight of a blank sheet of paper carefully framed.

From that day I changed my intake process: count first, read second. Data points per conclusion, sample size behind that conclusion, and the number of scenarios attached — those three quantities predict the quality of a report better than the entire prose section combined.

This industry took nearly a decade to learn how to collect data, and less than two years to turn the presentation of data into a ritual. Every club in Germany now has its own dashboard. Every scouting department has a standard report template. When a format becomes mandatory, it breeds a new kind of product: documents written to complete a format, not to answer a question.

I entered this profession through one near-miss, when I was nearly written off as naive. In 2026, at twenty-three, I published an analysis using expected goals to argue against Hannover 96's decision to sack head coach André Breitenreiter. The editorial board said I did not understand football. Hannover took eleven points in the final five matchdays and survived. A year later I pointed out that Germany's PPDA at the 2026 World Cup sat at 8.7 passes allowed per defensive action, and wrote that Germany would exit in the group stage. The result arrived a few days later. Since then I have abandoned the match-feel style of writing entirely.

Then came the empty-stadium summer. In 2026, with football frozen, I sat through all 263 Bundesliga matches of the 2026-20 season. The home win rate fell from 46% to 29% without spectators. Union Berlin, famous for its supporter wall at the An der Alten Försterei stadium, surrendered 61% of its points compared with matches played in front of a crowd. I built the "Decay Coefficient" to measure how vulnerable each squad was when the competitive environment changed, wrote it into a forty-page report, and a transfer consultancy in Berlin bought the rights outright. From then on I was no longer purely a writer; I was a valuer.

In 2026, when Christian Eriksen collapsed on the pitch, I did not write a single line about emotion. I tracked Denmark's next four matches. Their PPDA dropped from 11.2 to 9.8, meaning they pressed faster rather than slower, and high-speed running distance rose 7%. Every crisis is unlabeled data.

A year later, the same lens decoded Saudi Arabia's 2-1 win over Argentina at the 2026 World Cup: an offside trap that cost Argentina four goals, and high pressing that flattened the midfield. That analysis was later used by a Bundesliga club as internal scouting material.

By EURO 2026, I received a request to value three transfer targets: a breakout star of the tournament who had played only six matches, a Ligue 1 striker holding 0.52 expected goals per match across three seasons, and a defender just back from a long-term injury. I built a regression model over 1,400 data points and chose the Ligue 1 striker. The choice was rated boring. Three months later, the tournament star was injured, the defender's form collapsed, and the chosen striker scored fourteen goals.

A transfer is not the purchase of a person; it is the purchase of a probability distribution. And a probability distribution cannot be written out with seventeen adjectives.

So what separates an empty report from a real one? Not length, not the number of tables, not presentation quality. Three counts.

The first count is data density: how many independent data points stand behind each conclusion. A conclusion needs a minimum of three independent data points, drawn from three different sources, rather than three citations from the same source. That fourteen-page report held eleven conclusions and three data points. A density of 0.27. In my files, the minimum threshold for a scouting conclusion to enter a shortlist is 1.0.

The second count is the sample size attached to the conclusion. Six matches at a short tournament produce a confidence interval so wide it is nearly useless for valuation. Three club seasons, roughly three thousand minutes played, is what it takes to speak of a trend. When I rejected the EURO 2026 star, what I rejected was not the player's talent but the width of the confidence interval around the value.

The third count is the number of scenarios. A single value is a promise. Three scenarios — optimistic, base, pessimistic — constitute a model. Every report I sign must carry all three, and the pessimistic scenario must be written before the optimistic one, because it is the only one nobody wants to write.

These three counts apply to both football and esports. In esports, a player's decay coefficient can be measured through reaction speed, laning efficiency per minute, and early-fight win rate across successive game patches. When professionalization turns players into products off an assembly line and individual play is smoothed flat by digitized training, the empty report becomes the logical endpoint of the whole process: a document that describes the mould, not the person.

A pressing system was once a tactical advantage. Once mid-table teams could all execute it, it became a physical requirement and football moved one step closer to athletics. Through the same mechanism, an analytical framework was once an information advantage; once every department owned one, it was demoted to an administrative procedure. What gets replicated is not the ability to understand a match, but the presentation format for appearing to understand a match.

Empty reports are born from an incentive structure, not from laziness. Scouting writers are not paid to be right; they are paid not to be held accountable when wrong. A strong conclusion with specific numbers is a conclusion that can be challenged. A vague conclusion with a complete analytical frame is nearly impossible to challenge, because it never asserted anything.

This is where the danger sits. A complete analytical framework works like a washing machine: it turns subjective opinion into a document that looks verified. In the data trade, this is white-collar fraud — not altering the numbers, only wrapping them in structure so that nobody is obliged to check. Numbers never lie — only the hearts of readers turn them into lies.

The second counterintuitive point: an honest empty report is worth more than a full fake one. When the sample is insufficient, the technically correct answer is "insufficient data", not a conclusion stamped with a confidence level in bold type. My files contain at least six items closed with exactly that sentence, and three of those later turned out to be the best players we missed. Missing out because of missing data is an acceptable error; buying wrong because of a beautiful analytical frame is not.

One more thing must be said about correlation. Correlation is not causation, and in esports data this trap runs deeper. A player whose numbers rise after a roster change may simply have been placed in an easier role. A team on a winning streak may simply have drawn a soft schedule. When the whole community is celebrating a name, my job is to pull the emotional pendulum back toward equilibrium: being hot and being genuinely good are two separate things, each requiring its own proof.

The same mechanism operates in another market. Reports that look rigorous are used to move betting odds, and live data flowing to betting companies is the darkest side effect of the digitization of sport. An empty conclusion does not merely harm a club; it creates a tradable asset.

The signal for the next round is not on the league table. It sits where nobody files a report: the off-season, the training sessions, the changes in coaching staff. In the empty-stadium summer, I hear data falling drop by drop — and the largest drops usually fall in a stadium with no spectators.

If I keep one habit from this season, it is the count. Before believing a conclusion about any collective, the work to be done is to count how many data points built it, across how many minutes played, and where the pessimistic scenario sits. Some matches end when the referee blows the whistle — and some only begin when the data speaks.