When the Data File Comes Back Empty: The Silence Trap in Esports Transfer Analysis

June 2026. I was sitting in a third-floor meeting room at a K League 1 club, in front of a spreadsheet that had just been opened. File name: target_midfielder_full_data.csv. Rows of data: none. No error message, no explanatory email from the provider, no note in the final column. Just whitespace running from cell A1 to the bottom of the sheet.

Seven minutes later, the meeting had moved on to the proposed salary. Nobody brought the whitespace up again. A decision about a person was made on top of an empty file, and nobody called it by its proper name.

In sports analysis, football and esports alike, the most expensive mistake is rarely bad data. Bad data still gets argued over, still gets re-checked by someone else. Missing data gets quietly skipped, and that quiet then gets read as a positive signal.

Context

The analysis department of a professional esports club runs on three data layers. The publisher layer supplies raw match logs. The commercial layer supplies processed metrics. The club's internal layer is where I work, where a player dossier has to travel from raw data to a signing recommendation in roughly ten days.

The workload at the third layer is enormous. Each transfer window, two analysts cover dozens of dossiers, each containing match data, video, medical reports and a standardised metric table. When the workload outruns the clock, the process finds shortcuts by itself, and the most popular shortcut is trusting the metric table that already exists instead of going back to verify it.

In esports that shortcut is more dangerous than in football. Every title has its own metric system, and that metric system shifts with each patch. A figure for vision controlled per minute by a support player means something different once the publisher reworks the vision mechanic, and something different again once the champion pool forces supports onto a different lane. Importing football metrics here produces a number that looks highly professional and is entirely wrong in substance.

The evidence chain

In 2026, as a first-year student in Busan, I collected match data on Asan Mugunghwa in K League 2 by hand. The club sat top of the table, yet its expected goals per match was only 1.02, below Busan IPark further down the standings at 1.48. Across six matches, Asan scored six goals from the penalty spot. I wrote on my personal blog that the club would slide in the second half of the season. Asan finished fourth and lost in the play-offs. The post drew two thousand views, an enormous number for a student blog at the time.

The lesson sits in the structure of the number, not in whether I guessed right. A 1.02 figure standing alone says nothing. Low expected goals plus six penalties in six matches produces a sample distorted by the very source of the goals. Read the league table and you see a leader. Read the scoring structure and you see a team living on luck.

Do not trust the table, ask xG. The table tells you the past, data tells you the future. That line only holds when the data exists and when the sample is large enough to speak.

In June 2026, at the World Cup in Russia, I analysed South Korea's 2-0 win over Germany in Kazan. Germany's PPDA was 5.8, meaning they pressed extremely hard. Many analysts used that figure to criticise head coach Shin Tae-yong's approach. I split the data into fifteen-minute blocks and found Germany's running peaked between the 60th and 75th minutes, and that their pressing system broke apart after Kim Young-gwon came on. My rebuttal was attacked. Three weeks later, FIFA published a report confirming exactly what I had written. I was attacked for daring to question PPDA. FIFA confirmed it.

In the summer of 2026, when national leagues had to play in empty stadiums because of the pandemic, I was a master's student. I tracked 214 matches in the Bundesliga and K League 1 from May to August. The home win rate in the Bundesliga fell from 43.2 percent to 37.8 percent, and average goals per match rose from 2.79 to 3.12. Those 214 empty-stadium matches taught me this: home advantage is data, and it can be measured. The short study went up on Medium, an editor at a football analysis outlet got in touch, and I gained access to paid data for the first time.

Back to the empty file on screen in 2026. In esports that situation shows up more often than in football, because the sample is blocked by the structure of the competition itself. A support player on a team that habitually loses early gets few minutes in the late game, where vision-control and teamfight metrics accumulate. His metric table looks empty because his team does not give him time, and reading that table as “no contribution” is a textbook causal error.

Patches sever the comparison chain. Pooling data from two patches into a single average column produces a number that does not exist in reality, only in the spreadsheet. The commercial data layer also has uneven coverage: major leagues are logged in full, while regional qualifiers and test-server matches have almost nothing.

A dossier built on missing data is still presented in the same format, the same font and with the same confidence as a complete one. The reader cannot see the missing part, because the missing part is never printed. When a data source returns empty, the only correct conclusion is “insufficient information to assess”, and that sentence must be written out in words, not left as a blank cell. A blank cell gets filled by the reader's own assumption, and the human default assumption is always “there is no problem”.

The counter-intuitive angle

Most disasters in transfer analysis do not come from a wrong number being stated. They come from a right number being absent and then read as cleanliness.

In a league's compliance file, the line “no violations detected” sounds deeply reassuring and has two different versions. Version one: the full logs were checked, accounts and match history cross-referenced, nothing found. Version two: there were no logs to check. Both versions get printed in identical wording, and only one of them is information.

The same holds for club finances. No news of delayed wages does not mean wages were paid on time. In most esports club collapses I have tracked, the first signal was the absence of a news item: not a single contract extension announced across an entire transfer window.

This industry rewards decisiveness. An analyst who says “I don't know” is rated lower than an analyst who states a wrong number loudly. In 2026 I recommended signing Lee Kang-in for eight million euros, based on data placing him in La Liga's top ten for chances created per ninety minutes. The board rejected it on the grounds that he “does not show defensive ability” — a conclusion built on a blank cell, since that defensive metric did not exist for his role in his previous system. Six months later Lee Kang-in shone and helped his club survive relegation, while my club finished eighth.

Takeaway

In the next transfer window, the competitive edge will not belong to whoever holds more data. It will belong to whoever logs what they do not know and treats that log seriously. A missing-data register, updated per dossier, is far simpler than a forecasting model: it needs three columns — which metric is absent, why it is absent, and what would change if it existed. In your most recent report, how many blank cells are being read as a compliment?