When the Spreadsheet Is Empty: The Discipline of the Null Result in Esports Analysis

That night, my colleague had his finger on the publish button. The scouting report on an esports team about to enter the knockout stage ran four pages — bolded text, full charts, a carefully colored comparison table. But when I opened the raw data file, every cell was empty. Not a single row of input. Not a single match recorded. Not a single metric calculated. The report's conclusion read: “The opponent shows no clear weaknesses.” I held his hand back.

That moment taught me something seven years in the job had never said clearly enough: in sports data analysis, the most dangerous thing is not a wrong number. A wrong number can be caught, cross-checked, corrected. The most dangerous thing is an empty cell dressed up as a conclusion. An empty cell does not announce itself as empty. It lets the reader fill it in with belief.

A null result is not a finding. It is a silence that has not yet been filled.

Context: a data pipeline can go silent

In modern esports analysis, data travels through a multi-stage pipeline. The first stage extracts raw events from match logs: who picked which champion, who secured which objective, the tempo of teamfights, item timing. The second stage takes those events and turns them into analysis: which team is strong in which phase, where the weaknesses lie, which scenarios can repeat.

The problem is that the pipeline can break at the first stage without anyone noticing. An encoding error, a failed retrieval, a corrupted format — and the second stage receives an empty file. But the second stage still runs. The framework still prints. The headline still looks good. The tables still line up. A report born from nothing looks exactly like a report born from real data.

This is especially dangerous in esports, more so than in traditional football. Esports has a meta that shifts with every patch, and each patch upends the power order of team compositions. Sample sizes are thin, match counts are low, and analysts often have to conclude from just a few dozen games. When the data foundation is already thin, an empty cell is even more easily misread as a conclusion.

I once witnessed the same thing at a much smaller scale. In 2026, when I was fourteen, I sat on the sideline of a youth football league in Seoul taking stats for fun. One midfielder had a 92% pass accuracy. Looking at it, everyone nodded. But I counted that across the whole match, he played only three passes forward. That 92% was a disguised empty cell: empty in tactical meaning while full in raw number. I wrote that his midfield control was “soulless.” The coach confirmed it and changed his setup. That was the first time I saw a silence tell a truth the naked eye had missed.

Analysis: three layers of an empty conclusion

Layer one — an empty cell means “unknown,” not “none.” In data logic, the absence of evidence is not the same as evidence of absence. When an esports team's stat sheet records no weaknesses, that can mean the team has none — or it can mean nobody has gathered enough data to find them. These two possibilities lead to opposite tactical decisions. A team with no weaknesses: you play to match tempo and wait for mistakes. A team with an unidentified weakness: you must go find it before the match begins.

There are matches the naked eye cannot see; you have to let the stat sheet tell the story. But when the stat sheet has nothing to tell, the analyst must say plainly that there is nothing to tell yet.

Layer two — formatting manufactures false authority. A table full of “insufficient data” cells still looks very serious. Straight rules, clear headers, bold text. The reader's brain is trained to trust what looks structured. A beautifully formatted report will pass the gut-check before anyone has time to check whether it has any substance. A spreadsheet cannot lie; it is the reader who must learn to listen. The problem is that readers usually listen with their eyes, and eyes are fooled by form.

Layer three — the missing validity gate. This is the most overlooked link. A decent data pipeline needs a gate: if the extraction stage returns empty, the analysis stage must stop and label it “invalid,” rather than keep producing. Without that gate, an empty input flows straight down to the reader as a finished conclusion.

I learned the importance of cross-checking early. In 2026, analyzing Germany's loss to Mexico at the World Cup, I did not just look at the score. I calculated xG: Mexico generated 1.8 against Germany's 0.9. That number showed the win did not come from luck. But to be sure, I also counted counterattacks and the position of Germany's back line. Don't argue with words; let xG speak — but let at least two data sources speak before you conclude.

Two years later, when global football froze during the pandemic, I sat at home calculating PPDA for the entire 2026-2026 K League 1 season. Ulsan Hyundai allowed opponents just 8.2 passes on average before recovering the ball. When I predict, I don't look at emotion; I look at PPDA. Ulsan then went unbeaten in their first five games when the league returned, and my article was republished by a sports outlet. But what I remember most is not the correct prediction. What I remember most is that I stated the sample size, stated the assumptions, and left a cell open for the possibility that I was wrong.

In 2026, as an intern, I built a model comparing strikers on goals, xG, and non-penalty xG. A Suwon midfielder scored 12 goals from 9.4 xG — a finishing rate far above expectation. I presented it with a scatter plot; the club signed him, and the next season he scored 15. But if I had only looked at the goal count and ignored xG, I would have seen nothing.

The contrarian angle: this industry rewards confidence, not honesty

This is the greatest paradox of sports data analysis. A report that says “I don't have enough data to conclude” is almost always sent back by the editor. It has no catchy headline, no prediction to argue about, no clicks. A report that says “the opponent has no weaknesses” is the opposite: tidy, decisive, easy to share.

So market pressure pushes analysts toward fabrication. When the system rewards certainty, people will manufacture certainty even without a basis. I was once laughed at by a senior colleague as an intern, simply because I was a young woman daring to submit a data report. I did not argue. I drew a scatter plot and presented finishing-efficiency metrics. They told a girl not to talk tactics; I drew a chart instead of answering.

But the deeper lesson lies elsewhere. The most frightening thing in an analysis pipeline is not a wrong prediction — a wrong prediction can be fixed. The most frightening thing is a “no-risk” conclusion built from an empty data file, then passed down to decision-makers. A coach who believes the opponent has no weaknesses walks into the match with blind confidence. An investor who believes a team carries no financial risk places the wrong bet. The biggest risk is not inside the data. It is in the gap between the data and the conclusion.

I don't believe in luck. I believe in blocked shots and forgotten spaces. A stray number can be a truth hiding where no one expects — but a stray empty cell can be a lie waiting to be believed.

The takeaway: a signal for the next analysis cycle

Our data pipelines are growing faster than our ability to audit them. Every new season, every new patch, every new tournament pours more data into the system. But without a validity gate, we are only producing prettier reports from emptier files.

The signal I will track in the next analysis cycle is not which team wins. It is which analyst dares to publish their sample size, dares to state their assumptions, and dares to write the words “not enough.” A mature analytics culture is not measured by how many conclusions it produces, but by how many times it dares to stop when the data is not ready.

For readers, I leave one small habit. Next time you read an esports report full of charts and decisive conclusions, ask a single question: how many matches was this table built from? If the answer is unclear, then every conclusion after it is decoration.

Don't argue with words; let xG speak. But before you let it speak, make sure it is actually in the room.