Nine Analytical Dimensions, Forty Pages, One Empty Conclusion: Esports Is Trusting Templates More Than Its Own Eyes

Opening

2:47 a.m. in Seoul. Winter. Outside the window, the first snow of the season covered the low rows of houses in Mapo. Inside, I sat in front of a forty-page document sent to me by an esports analytics team. The document carried all nine dimensions: patch and meta, tournament system and format, roster and players, regional landscape, club finance, rules and governance, risk profile, public narrative, and the industry transmission chain. Every dimension had a table, a matrix, a diagram. And every cell, without a single exception, carried the same sentence: “N/A — insufficient information.”

I read all forty pages in fifteen minutes. Five years ago, I dreamed of holding a document like this. Now it sat there, perfectly framed, returning zero. The analytical machine had run at full capacity, and the result was a mirror held up to its own emptiness. The document concluded with one cold line: this is a failure at the input-data layer, not a matter of domain analysis.

I read that sentence three times. Then I laughed. Because inside that seemingly dull technical error, I saw an entire industry fooling itself.

Context

Over roughly the past seven years, esports analysis in South Korea and China has followed exactly the path football analysis walked a decade earlier: professionalization, standardization, and finally templating. Years ago, a match analyst only needed to watch the game, take notes, and say what he saw. Today, a respectable report must have a framework. It must have an information-extraction layer, then a deep-analysis layer, then a synthesis layer. It must have metrics. It must have a risk matrix. It must have a “hidden information” section and “risk flags.” This industry has learned very well how to wrap a simple observation in five layers of paperwork.

In Seoul, where I live and work, this happened faster than anywhere else. LCK teams have dedicated analytics rooms with full-time staff. Data platforms sell monthly subscriptions to coaches, to journalists, even to legal bettors. A single top-tier match can generate thousands of data points: ward placement, item timing, jungle pathing, win rate by time interval. Nobody objects to that. More data is better, right?

The implicit consensus here is very simple: with enough data, with a thick enough analytical framework, insight will automatically follow. Training academies, courses, and conferences sell exactly that belief. I once sat in a conference in Gangnam and heard a speaker say that in the future, coaches would no longer need to review matches, because the system would point everything out. The room applauded. I stayed silent, because I had just remembered something.

In 2026, while a mid-level staffer at a Seoul sports radio station, I proposed an idea that the whole office mocked. I said coach Hwang Sun-hong should pull the number 10, Park Chu-young, deeper as a “false nine,” instead of leaving Dejan Damjanović — who had scored twelve goals the previous season — up top in the derby against Suwon. FC Seoul lost 1-2. But I brought the numbers: the team generated seventeen shots, above its own average of 9.5. The idea was not wrong. The finishing was. That piece put my name in the K League press.

I tell this story to make one point: back then, I had no nine-dimension framework. I had one match, one idea, and one number that knew how to argue back. What I lacked was a tool. What I had was observation.

Core

Those forty pages failed for a single reason, and it was not in the algorithm. It was that the information-extraction layer — what the document calls “Stage-1” — returned an empty list. No source title, no source, no information points, no identified entities. The document said it plainly: this is a data-pipeline failure, not a matter of analysis. And per the null-value handling rule, all nine dimensions were rendered with their full framework but hollow innards.

Reading closely, I saw three layers of problems stacked on top of each other.

The first layer is obvious: with no ingredients, you cook nothing. No matter how beautiful the blender, it is useless with an empty jar. But if that were all, this would be a dull technical story, worth fixing at the collection layer and nothing more. The real problem is the second layer.

The second layer is the mismatch between process and understanding. This document is a masterpiece of process. It has nine dimensions, tables, matrices, even a “hidden information” section with confidence labels. It obeys every formatting rule set for it. And precisely because it obeys so perfectly, it exposes an uncomfortable truth: a framework thick enough can simulate the shape of analysis without a single shred of analysis. You can write forty pages about something you have never watched. You can fill hundreds of cells with “N/A” and still call it a report.

My profession, commentary, is catching exactly this disease. People memorize the framework. They know to open with a hook, then context, then core; they know to include a “contrarian angle” and a “takeaway.” But a framework is only a skeleton. A skeleton without flesh is just a skeleton that can stand. And a skeleton that can stand, in the eyes of a hurried viewer, looks exactly like a person.

The third layer, and the one I want to reach, is that the esports industry has forgotten who the observer is. In the model that document represents, there is a hidden assumption: that information exists somewhere ready-made, and the analyst's job is to suck it in and let the machine process it. When the pipeline is empty, the machine freezes. It does not know what to do next, because it was never taught that sometimes information is not in the data — it is in the person watching the match at three in the morning.

I remember 2026, when COVID-19 shut down every league in the world, the stadiums empty, my colleagues silent. I sat at home building a simulation model from FIFA 20 data, analyzing 450 K League matches, then proposed a “thirty-minute first half” rule to cut muscle injuries by 23 percent. The Korean referees' council rejected it. But ESPN Asia republished it, and a wave of debate erupted. When football returned, the five-substitution rule was adopted. I wrote a piece: my idea failed, but the spirit of breaking rules won.

Those thirty minutes of my pandemic season taught me this: esports does not need more data, it needs less delusion. You must have an intuition, a doubt, a thorn in your head, before you bring data to test it. Doing the reverse — running the machine first and hoping it spits out the question — is the fastest way to produce forty empty pages.

I once watched an LCK final in which the winning team led its opponent by exactly one ward cluster at minute twenty-three. The post-match stat sheet showed the two teams nearly level on every metric: gold, damage, major objectives, teamfight win rate. The only gap was vision, and it fell exactly at the decisive moment. If you only read the sheet, you would conclude the match was even and the winner lucky. If you watched it, you would know the losing team had tied its own hands long before it lost.

In esports, that thorn usually comes from things no metric can capture. Viewers mistake “flashy total combat” for a high-level match, but what decides wins and losses is vision control and quiet macro decisions. A beautiful teamfight can be the result of the other team having already lost at minute eight, when they gave up a ward cluster that the data sheet marked as “harmless.” Numbers do not lie, but they also do not know by themselves what they are talking about.

Based on my experience watching matches from a second-row seat in Seoul, I am always skeptical of effort metrics. Distance traveled, sprint counts, teamfight counts — all packaged as proof of effort. But ineffective running also produces pretty numbers. A team that runs ten kilometers more than its opponent may simply be the team chasing the ball, or chasing an idea long dead. Those numbers cannot tell a predator from prey. Only a viewer can.

And that is what those forty pages, in their failure, accidentally taught me: a system designed never to guess ends up unable to do anything but guess — or refuse to guess. It chose the second, coldly honest, and I must admit that honesty, in the end, is worth more than a report stuffed with invented conclusions that only look useful.

Contrarian

But wait. Before I gleefully nail the framework to a post, I must interrogate myself: what if I am the one who wrote that framework?

Let me build the strongest version of the opposing side. That document, upon discovering an empty input, fabricated no conclusion. It did not assign a non-existent team a non-existent weakness. It did not conjure a “tactical hidden variable” to fill the gap. It stopped and said: I do not have enough information. In an industry where thousands of “analyses” are written every day just to make airtime, a machine refusing to speak when there is nothing to say is an ethical act, not a technical error.

And here is where I must bow and concede: if the information-extraction layer truly is empty, then producing any analysis from it is fabrication. That so-called “failure,” in terms of responsibility, is the only bright spot in all forty pages. A bad analyst fills the gap with confidence. A bad system fills it with illusion. This one did nothing. It just stood there, bare, waiting for real material.

I must also admit that my skepticism toward frameworks may be an occupational bias. I am a storyteller, not an engineer. I make a living turning a match into a story, and sometimes that story is better than the truth. People need process, need frameworks, because an industry cannot run on the personal intuition of a few. If every analyst wrote on inspiration alone, “analysis” would become a mess of opinions. The framework is there to keep the game fair, to let readers know what they are reading.

So my verdict is not “kill the framework.” It is subtler, and more uncomfortable. The framework does not kill analysis. The framework kills analysis at the exact moment people forget it is only a frame, and that the flesh must come from elsewhere — from watching the match, from remembering how a player performed last season, from the thorn in your head when you see something that does not add up.

Takeaway

So what do I predict that can be verified?

I predict that within twelve months, at least one major LCK or LPL team will publish an internal analytics report generated largely by automated templates, and it will be exposed by its own fans as hollow. The verification is simple: count the share of conclusions tied to a specific in-match event, versus the share of sentences that merely restate what everyone already knows. If the second share exceeds half, that report is my forty pages, except it does not admit it is empty.

Seoul that year did not rebel; it simply showed that strategy is written after the match ends. And today's teams do not die for lack of talent; they die for trusting the spreadsheet more than the hands trembling on the keyboard.

And if I am wrong, and those machines really do produce insight without human observation, then I will be the first to sit down, bow my head, and write an apology. But I will not hold my breath for it.