Nine Dimensions of Esports Analysis and the Lesson of an Empty Spreadsheet

At three in the morning on November 14, 2026, I sat in front of a spreadsheet with nine sheets. Sheet one was empty. Sheet two was empty. Sheet nine was empty as well. I had a nine-dimension analytical framework ready for a regional esports tournament: formulas, scoring scales, date columns, confidence-note fields. The only thing I did not have was data.

My hands rested on the keyboard. A very specific temptation appeared in my head, one I believe anyone who has worked with data has met: fill it in. Give it one team name and the framework would run itself. Give it one plausible number and the spreadsheet would look convincing. Give it one strong enough conclusion and the article would appear thoroughly researched.

At three forty, I saved the file with a status line: insufficient data, cannot assess. Then I closed the machine.

That was the first time I finished a product whose main conclusion was a blank space. It was also the first time I understood that in this profession, the hardest thing is not finding the truth but refusing to manufacture a false one.

Why a nine-dimension framework needs a null-declaration rule

Vietnamese esports is at a stage where the volume of analytical content grows faster than the volume of verifiable data. Every regional tournament ends and hundreds of opinion pieces appear within hours, most written from the feeling of the match rather than from indicators. Fans read to find agreement, not to verify. That is the perfect environment for a dangerous product: analysis that looks professional but has no traceable origin.

The nine-dimension framework exists to counter that. It splits an esports event into nine independently checkable layers: patch and meta context; tournament system and format; roster and players; regional landscape; club finance and business; rules and governance; risk profile; public narrative and expectations; and finally the industry transmission chain. Each layer has its own dataset, its own units, and its own confidence level.

The critical point lies in a rule I learned from the years I spent recording numbers beside a pitch: every conclusion must be traceable to a specific information point. If there is no information point, the conclusion is not permitted to exist. That sounds simple. But when you sit in front of an empty spreadsheet knowing that one invented line would complete your product, that rule becomes a fight with yourself.

At fourteen I sat on the sideline with a notebook; football did not look at me, but numbers did. At fifteen I analysed Germany's defeat to Mexico using expected goals and discovered that the feeling of total dominance can be entirely overturned by a table of numbers. At seventeen I calculated the PPDA index for every team in the South Korean top division and predicted that one club would dominate the post-pandemic period, based on a figure of 8.2, meaning opponents were allowed fewer than nine passes before losing the ball. In those cases the data existed. I only had to read it.

This time was different. The source I received carried a single label: esports. No tournament name, no team name, no player name, no game version, no format, no dates. Every cell across nine sheets fell into the cannot-assess category.

Layer one: patch and meta

The patch is the foundation of all esports analysis and also the most easily skipped layer. An update can be a minor numerical tweak, a mechanic change, or a full rework of a role. These three magnitudes produce three entirely different consequences: numerical tweaks shift win rates by a few percentage points; mechanic changes can reverse pick-ban priorities; role reworks can erase an entire tactical school within a week.

To assess this layer I need the specific game title, the version number, the change description, win rates before and after, pick-ban rates, and the date the change landed on the tournament server. Without a game title, every conclusion in this layer is speculation. Indicators from a team-based strategy title are not transferable to a first-person shooter, and neither is transferable to a multiplayer survival title. This is a structural constraint, not an opinion.

Layer two: tournament system and format

Format is the variable that decides upset probability. A best-of-one series carries a far higher chance of an underdog win than a best-of-three or best-of-five, simply because a smaller sample means greater variance. A bracket with protected seeding favours strong teams more than a random draw. Schedule density directly affects preparation quality, especially when rest windows are short between rounds.

Without a tournament name I cannot rank how friendly a format is, cannot calculate upset probability, and cannot assess preparation risk. Once again, this is a null result, not a finding that the format poses no problem.

Layer three: roster and players

This is the layer I have worked in most over seven years. Paper strength, role fit, chemistry between lines, bench depth, individual form curves, and coaching capacity. Each item requires a different minimum dataset.

In 2026 I built a striker comparison model for a club in the South Korean top division, using goals, expected goals, and non-penalty expected goals. I found a midfielder who scored 12 goals from 9.4 expected goals, meaning finishing ability well above the quality of chances created. That report had data, a scatter chart, and a conclusion. The club signed the player.

For layer three of the nine-dimension framework, I need at least a team name and a player name. The source provided neither. No subject means no analysis.

Layer four: regional landscape

Regional tiers are a concept entirely dependent on the game title. A region can sit at the top in one title and in the qualifying pool in another. The four indicators I always use are international results, talent pool depth, academy output, and ecosystem health measured by how many teams operate continuously across seasons.

Alongside these sit talent movement signals: the flow of imported players, the direction of contracts, and the quality gap between tiers. All of them require a region name and a tournament name. Without them, the result is null.

Layer five: club finance and business

This is the layer where Vietnamese esports media is weakest. Sponsorship revenue, publisher distributions, salary budgets, capital injection, contract structure, and contract length are six cells that must be filled before any judgment about financial health. I pay particular attention to the ratio of salary spend to total revenue, and to how concentrated revenue is among a few sponsors. When a club depends on a single income source, the dissolution risk does not sit in competitive results but in one contract negotiation. But to say that, I need numbers. The source had none.

One methodological point matters here: the complete absence of financial signals must not be read as an absence of financial risk. A null risk is not a negative risk. This is the most common error in reports that look complete.

Layer six: rules and governance

Competitive integrity, transfer and registration rules, contract compliance, protection of minor players, and disputes with publishers. Each item requires a named party and a specific rule system. Without a publisher, a league, or a disputing party, there is nothing to review.

To be explicit: failing to detect signs of a violation inside an empty dataset is not an exoneration. It is only a null result. That distinction matters enough that I once wrote an internal rule for it.

Layer seven: risk profile

Six risk groups are normally screened: competitive, financial, personnel, rules, public opinion, and systemic. My principle is that risk must be stated before the conclusion, not after. A polished analysis missing its risk warnings is an unfinished analysis.

In this case all six cells were empty. But one risk had already materialised and could be scored: analytical risk. If I were forced to fill nine sheets from an empty source, the final product would be a set of untraceable conclusions. That is a high-level, high-probability, high-impact risk. The only available mitigation is to stop.

Layer eight: public narrative and expectations

Every strong team carries a story: a new dynasty, a last dance, a comeback, a golden generation. That story can be sustainable if it is supported by underlying data, and it collapses if it is supported only by social media heat.

My method is to compare market expectations with an objective assessment across three dimensions: team results, individual form, and transfer moves. The gap between the two is where pricing risk sits. But to measure it, I need to know which team we are discussing. The source did not say.

Layer nine: industry transmission

Finally, the chain from upstream to downstream: publishers changing patches and licensing events; clubs, organisers, and streaming platforms in the middle; sponsorship, derivative products, and mainstreaming at the end. Each link carries a different lag. A patch change can move viewership within two weeks, but it takes several seasons to change sponsorship structure.

Without a publisher name, a platform name, or a sponsor name, this chain cannot be traced. Even betting market signals, which are used only as expectation indicators, cannot be cited when no data exists.

The counter-intuitive point: the biggest error is not a wrong conclusion

People assume the most serious mistake an analyst can make is reaching a wrong conclusion. I would argue the more serious mistake is reaching a conclusion with no traceable origin, because a wrong conclusion can still be corrected when more data arrives, while an invented conclusion has already contaminated a chain of belief.

There are matches the naked eye cannot see; the table of numbers has to tell them. But there are also matches whose numbers were never recorded, and declaring that we have finished analysing them only creates an illusion of knowledge.

The biggest blind spot in this profession in Vietnam today is the pressure to have an opinion. An article saying there is not enough data to conclude will be judged weak. An article daring to say a tournament has no problems will be shared. That asymmetry rewards confidence rather than accuracy.

I once chose differently. At fifteen, when a male reader commented that girls should not speak about tactics, I did not argue. I published a new piece with an expected-goals chart, counter-attack counts, and defensive line positions, and let the numbers answer. That is how I handle every dispute: replace words with data.

A spreadsheet does not lie; the reader is the one who must learn to listen. And the writer must learn to endure a blank page.

Looking forward

I believe that over the next few seasons, what will separate a serious esports newsroom from the rest will not be the speed of publication but the ability to say "we do not know yet." Data is becoming cheap. The ability to refuse false data is becoming expensive.

The nine-dimension framework I use today will become outdated when a new title changes how we measure. But the null-declaration rule will not become outdated, because it does not belong to any game. It belongs to the writer.

A stray number can be a truth hiding where nobody expects it. But an honest empty cell may be the only thing preventing us from building an entire analytical industry on a false foundation.

The question I leave for younger colleagues: in your most recent piece, how many conclusions can actually be traced back to a real line of data?