Nine Layers of Analysis and an Empty Report: The Data Gap Inside Esports

A nine-layer analysis of esports landed on my desk. The cover page carried a date, a file code, a department name. Inside, all nine layers said the same thing: insufficient information. No game title, no tournament, no team, no player, not a single number. A report thousands of words long with nothing to read. The notable part is that it was still correct. And because it was correct, it exposed a problem far larger than one data-pipeline failure.

I have followed esports since 2026, first as a competitor, then as a tournament organiser. Nearly two decades later I keep one habit: before I trust a number, I ask where it came from. That habit made me read the empty report differently. To others it was a failure. To me it was a mirror.

Nine layers, one condition

The esports industry has built a nine-layer analytical framework, and it did not come from imagination. It came from a real need: a single esports match is shaped by at least nine groups of variables, and missing any one of them is enough to make a conclusion wrong.

The first layer is patch and meta. A small update can reverse the strength order of teams. The second is tournament system: format, series length, qualification path, schedule density. The third is team and player: paper strength, role fit, chemistry, bench depth. The fourth is the regional map: who is strong, who is falling behind. The fifth is club finance: sponsorship, revenue distribution, salary bill. The sixth is rules and governance. The seventh is the risk profile. The eighth is public narrative. The ninth is transmission across the whole industry.

A framework like that sounds solid. The problem lies elsewhere: the nine layers are only worth something when each has real data poured into it. And that is where esports is running out of breath.

I have sat in enough meeting rooms to see this repeat. A transfer analysis arrives with ten pages of charts, but when I ask where the transfer fee came from, the answer is “per the media”. A club financial report boasts a surging sponsorship line, but nobody asks what that sponsor actually sells. A regional strength forecast is drawn from feeling, not from international results.

Why esports analysis runs on empty numbers

Esports is one of the most data-rich sports: every match generates thousands of data points, from champion win rates and pick-ban rates to the timing of the first fight. Yet when it reaches high-level analysis — team valuation, player valuation, revenue forecasting — the industry often runs on empty data.

The first reason is that match data and business data are two separate worlds. Win rates are public. Salary bills are not. Transfer fees are murky. An analyst can state precisely that Team A wins 68% of its laning phases, but cannot say what Team A pays its star player. Conclusions about strength are full; conclusions about money are empty.

Every valuation model is wrong. The question is: wrong in whose favour. When business data is missing, people replace it with assumptions, and assumptions always have an owner. A team that wants to sell a player picks assumptions that push the price up. A team that wants to keep a player picks assumptions that pull the price down. The same player, two sets of numbers, two conclusions.

The second reason is content-production pressure. Communications teams need a steady stream of articles, leadership needs a polished report before every meeting, sponsors need a number for their own internal decks. Nobody in that chain is rewarded for saying “not enough data”. The person who says it is treated as incompetent. So the blanks get filled with language. A report with no figures gets written in adjectives: “huge potential”, “strong growth momentum”, “market-leading position”. Reading it, people feel informed, but hold nothing.

I once built a player-valuation model based on social-media follower growth combined with performance metrics. A young midfielder had follower growth three times that of peers with similar professional metrics, yet his commercial value was almost entirely untapped. Leadership dismissed it, calling it a fan game. I wrote the report anyway and built three more model versions. What I learned was not that I was right. What I learned is that when data is not allowed on the table, decisions get made by the gut of whoever holds power.

The third reason, and the most overlooked, is timing. Esports data decays fast. A champion win-rate figure from an old patch can be meaningless after one update. An analysis that does not state when the data was collected cannot be verified. Within the nine-layer framework, this is the most fragile layer, because it sits in no layer at all — it sits in the footnote people forget.

There is one type of number I always slow down for: broadcast-rights revenue and brand value. A sponsorship is announced at several million dollars, but the payment terms — by year, by impressions, by performance milestone — are rarely stated. Broadcast revenue is the prettiest number when you never ask where it comes from.

I keep one rule when reading any report: every number must answer three questions — who measured it, when, and who paid for that measurement to happen. If it answers none, the number is decoration.

Esports is not football’s rival. It is the mirror that exposes the whole industry’s spending habits. Football had centuries to build auditing systems, published accounts, standardised contracts. Esports grew up in fifteen years, with investment money arriving faster than its institutions could be built. The result is an industry with the revenue of professional sport and the data infrastructure of an amateur league.

The counterintuitive angle

That empty report, in the end, is the most honest document in the drawer. It refuses to fill in numbers where there are none. It does not invent a game title, a team, a transfer fee. In an industry where fake reports are everywhere, honesty takes the shape of failure.

The real danger does not lie in empty reports. It lies in reports that look full. A document with all nine layers, all the charts, all the jargon, where every number cannot be traced to a source, will be read as truth and used to make decisions. At that point, the emptiness shows up not as absence, but as excess.

I once watched a transfer window where three parties cited the same fee figure, and all three were wrong, because the original number came from an unsourced post. Nobody checked. Everyone needed a number for the slide.

A club does not need a full stadium to make money. It needs to know what the empty stadium is saying. For esports, the data laboratory is not in the most crowded matches, but in the gaps: the field nobody fills, the number nobody sources, the footnote nobody reads.

What is left after stripping the unsourced numbers

The nine-layer framework is still right, and still necessary. It did not break because of one empty dataset. It only reminds us that every layer of analysis stands on a single condition: data must be real, traceable, and brave enough to say “not enough” when it truly is not enough. Esports can keep producing plenty of beautiful reports. The question I keep for myself: if you strip out every number that cannot be traced, how many pages are actually readable?