Nine Empty Columns: When Esports Analysis Runs Out of Things to Say

2:07 AM, Los Angeles time. I had just closed my laptop after a group-stage analysis stream when a notification pulled me back to the screen. A collaborator sent me a 4,000-word file, properly titled, divided into nine numbered sections, with tables, bold text, and directional arrows. At a glance, it looked like a professional analysis piece that any esports newsroom could publish immediately. But by the third line, I noticed something strange: there was not a single proper name in it. No team, no player, no patch version, no tournament, no date. Nine sections, and every single one filled with the same phrase: insufficient information to evaluate. I laughed alone in the dark apartment. Then I stopped laughing. Because I have lived in this industry for ten years, from the day I was a seventeen-year-old girl counting the acceleration bursts of a French footballer to write a blog, to today when I earn my living translating the movements of players into sentences. And I know that the file I received at 2 AM was not an isolated accident. It is a mirror held directly to a disease spreading very fast through the esports content industry: we are producing more and more analyses, but fewer and fewer of them actually have anything to analyze. The skeleton is intact. The organs are hollow. I call it the Nine Empty Columns Syndrome. And I think it is time to speak plainly about it. Over nearly a decade of writing about esports, I have witnessed three major waves of change in how this industry produces content. The first was the era of personal writing: if you had a computer, a feel for the game, and enough patience to type, you could write. I belong to that generation. In 2026, when I was seventeen, prepping for university exams in California but with my head entirely in Paris where Mbappe had just fired past Croatia in the World Cup final, I wrote a piece counting exactly fourteen acceleration bursts of his, calling him a nineteen-year-old hypercarry, and calling N'Golo Kanté a map-opening support with twenty-two ball recoveries. A male account jumped in to mock me: what does a girl know about offside that she can analyze? I did not take the post down. I just attached an Opta stats link and kept my tone. Three days later, the piece had been shared more than two thousand times. That was my first lesson in the power of statistics: a single concrete number can parry a blow better than ten sentences of self-justification. From then on, I built a habit I would later call my shield of numbers. Every argument I make must have at least one piece of statistical evidence standing behind it. Not to show off that I can read a data table, but to prove that my viewpoint is not mere sentiment. Readers may disagree with my conclusion, but they cannot say I invented a number. The second wave was the era of comprehensive datafication. Major tournaments started opening APIs, statistics sites sprouted like mushrooms after rain, and suddenly anyone could look up a player's exact minute-by-minute metrics. This was when I entered the industry professionally. In 2026, at twenty, interning at a sports media company, the pandemic hit, stadiums closed, and leagues returned in a crowdless state. I noticed something no one in the newsroom wanted to say out loud: Liverpool won the Premier League that year, but their pressing metrics had dropped nearly eighteen percent from the previous season. I wrote in an internal memo: a crowdless stadium means losing a kind of morale buff, and Liverpool cannot strike early as before because they lack that buff. My boss, an older man, frowned and asked if I was sure. I proposed a series called Football Meta, explaining football in the language of games. The first episode, comparing defensive counter-attacking to a late-game composition, passed one hundred thousand views in a few days. That was my second lesson: when an old analytical frame can no longer explain a new phenomenon, the writer must build their own frame. I began setting up hypotheses, introducing game-based reference systems, then verifying against real data. I no longer wrote according to the old grooves of traditional sports journalism, but built my own method: state the argument, build the model, cross-check the data, then conclude. The third wave — and this is the most worrying one — is the era of automation. Tools that generate text using artificial intelligence appeared, promising to free writers from heavy labor, allowing content production at a speed no newsroom could match. And just like every other technological wave in journalism history, it did not ask anyone's permission before crashing in. I do not oppose the tools. I oppose how people use them. Because what I received at 2 AM — a structurally perfect analysis skeleton hollow in content — is the textbook product of the third wave when operated the wrong way. Let us read that file carefully. It has nine sections. Section one is patch and meta analysis. It says it will evaluate the direction of the meta, who benefits, who loses, what the key data is. But every cell in the analysis table says: insufficient information. The reason is simple and lethal: no patch notes, no win rate, no pick-ban rate provided. It does not even know which game it is analyzing. And as I stress in every workshop I teach, this is precondition number one of any esports analysis: identify the specific game title. You cannot apply the logic of a MOBA tournament to a tactical shooter, nor the logic of a shooter to a fighting game. Same region, same player age, but if you mix ecosystems across different titles, you produce a logical mess no reader can untangle. The striking thing is that the analysis is self-aware about its own problem. It writes plainly that the cross-title contamination risk cannot even be assessed, because there is no title to anchor to. This is a commendable moment of integrity. A poor text-generation engine would invent a game, a tournament, a player, and keep writing with arrogant confidence. The analysis I received did not do that. It chose to acknowledge its emptiness, even though the repeated acknowledgment nine times makes it read like an indictment of the very process that produced it. And here is the third lesson, the most important one: an empty acknowledgment is not honesty. It is merely emptiness carefully recorded. There is a vast gulf between saying I have no data so I will not conclude, and saying I have no data but I will still output a nine-section document so someone might mistakenly think I did work. That file falls into the second case. It saves no one. It merely shifts the problem from the writer to the reader. I spent that whole night dissecting the file. I wanted to understand what it was, where it came from, and why it existed. Because I believe every defective product in the content industry is a lesson, as long as you dig deep enough. Section two of the analysis is about tournament systems and formats. It intended to evaluate whether the format is fair, the format's effect on upset rates, the effect of schedule density on players' stamina and psychology. But all it could write into the table was: insufficient information. No tournament name, no tier, no team count, no single or double elimination, no schedule, no venue. And it also honestly confessed something frightening: because there was no timeliness assessment in the input data, it could not determine whether the source material was still current. An article about a 2026 format could be processed as if it were today's breaking news. I want to pause here for a moment, because it touches a problem I consider more serious than missing data. That problem is time. In sports in general and esports in particular, timing is an inseparable part of truth. Information true in March can become misinformation in September, not because the information itself changed, but because the context around it changed. An undated analysis is an untraceable analysis. It is like a photograph without a date: you can admire it longingly, but you cannot use it as evidence for anything. I have encountered the consequences of this in my own career. In 2026, when Spain won the Euro, I wrote a piece praising Lamine Yamal after his goal against France in the semifinal. I called him an early-game prodigy with twelve successful dribbles, able to choose the timing of an all-in like a rookie marksman who already has a team-wipe. A middle-aged female reader left a comment: I want to understand this boy, not learn your game slang. My boss added: good idea, but you are burning the piece with terminology. I sat down and rewrote the whole article, keeping only three game comparisons, and added a line explaining that late game means the second half. The new version reached three times the old article's reach. The lesson here is not to abandon game language. The lesson is that game language only has value when it opens a new perspective the original tongue cannot express. I do not use the word hypercarry to show off that I play games. I use it because it describes more precisely than any word in the football dictionary how Mbappe detaches from the system and forces the whole match to revolve around himself. But when I use that term for a reader who has never played a game, I must open a door for them to walk in, not build a wall for them to stand outside. Section three is about teams and players. It intended to assess rosters, positional fit, chemistry, bench depth, the form of key players, and even the coaching staff. All it managed was to fill in a placeholder name: insufficient information. No team named. No position, no role, no in-game call structure. And this, once more, is the symptom of a very common process error I call circular dependency. The attributes section is instructed to identify from the information points above. But the information points section above is empty. The instruction points to itself, an infinite loop with no exit. The writer is locked in a doorless room. I have seen this type of error many times working with automated content systems. It happens when someone designs a perfect template but forgets that a template is only a mold; it needs real material to produce a product. You can have the world's most beautiful cake mold, but if you forget to buy flour, you will have no cake. The file I received was a tray of empty molds neatly lined up, presented as if ready for service. Section four is about the regional landscape. It intended to compare strength between regions, evaluate talent pools, youth development output, ecosystem health. But this time it delivered a warning I consider the most important in the entire document: the same region can hold completely different status across different game titles. A region strong in one MOBA may be only a wildcard in a tactical shooter. Therefore, regional conclusions cannot be borrowed across titles. I want to emphasize this, because it is one of the most common misunderstandings I see in amateur analyses. People tend to think of a region as a fixed attribute of a team. Korea is strong, China is strong, Europe is strong. But the truth is that regional strength is a variable dependent on the title, on the patch version, on the transfer cycle, and on peripheral factors like naturalization policy or infrastructure conditions. A conclusion that this region is stronger than that region without specifying title, version, and timing is not analysis. It is prejudice dressed up with statistics. Section five is about club finance. It intended to assess revenue structure, sponsorship sources, salary budgets, capital injections, and financial risk signs like unpaid wages or dissolution. But everything was empty. And I want to dwell on this section at length, because this is the section that in my experience esports journalism most frequently omits, and also the most consequential. Throughout my career, I have watched more than a few teams that looked famous on the leaderboard drown silently due to financial problems. No one wrote about them until the death happened. And that death always arrived as a sudden shock to the public, even though to insiders it had been foretold for months. One of the things I learned from my first mentor in the industry was this: a team can win a match without money, but no team can survive a season without money. That is the truth leaderboards never display. So when I received an analysis with an entire section devoted to finance but zero numbers in it, I did not read that as a neutral blank. I read it as a deliberate blind spot. Because in this industry, not talking about money is often not because there is nothing to say. Not talking about money is often because saying it is too hard, too sensitive, or simply too rarely read for the writer to bother. Section six is about rules and governance. It intended to assess competitive integrity, transfer and registration rules, contract compliance, minor protection, and governance disputes with the publisher. All empty. But here, the analysis made an observation I found surprisingly sharp, even though it was presented coldly like a technical footnote: in esports, there is no independent arbitration body; the publisher is both the rule-maker and a commercial stakeholder. This sentence is painfully correct. And it means governance analysis is only ever as good as its source documentation. I once experienced this truth directly. A few years ago, I followed a dispute between a young player and his team. The two sides signed a contract that in my reading was extremely ambiguous. A keyword in that contract could be read two ways, and both readings had legal arguments behind them. There was no sports court to rule. No clear precedent to cite. Everything ultimately depended on the power of the stronger side. Since that case, I always check source documents very carefully before writing about rules. Because in a system without independent arbitration, documents are not just evidence. Documents are weapons. Section seven is the risk profile. And this is the section I most want to talk about, because it contains a warning I think any sports reader needs to engrave on their bones. The analysis writes that the entire risk profile in this case is unratable, and the most important thing is: an unratable risk profile must absolutely not be reported as a low-risk profile. The distinction matters enormously. A low rating implies evidence of an absence of risk. This case is an absence of evidence. Those two things are worlds apart. Absence of evidence does not mean no risk. It means no one has looked hard enough. I once read an analysis before a major final in which the author concluded that Team A had no risks, because there was no negative news about them. A few days later, Team A was crushed because their number one star had a wrist injury no one knew about. No negative news does not mean no problems. It only means problems have not leaked out. And in our industry, many problems never leak out until it is already too late. Section eight is about public narrative and expectations. It intended to assess whether the prevailing narrative is sustainable, what heat cycle stage it is in, and whether there is a gap between market expectation and objective evaluation. All empty. But I want to draw a lesson from this very emptiness. Without a source, you cannot judge channel credibility. In esports, narrative heat and factual accuracy diverge sharply by channel. A rumor spreading on a forum has completely different credibility than news published on a publisher's official page. A comment in a streamer's live chat has completely different credibility than an article from a newsroom with an editorial process. If you do not know who is speaking, you cannot know how much to trust. And this leads to one of the most dangerous habits of the modern sports reader: using share counts as a measure of truth. I learned this lesson bitterly. In 2026, when I wrote a piece about Morocco and called their defensive style a survival meta, a waiting strategy, the article generated a huge amount of controversy. Many called it anti-football. I argued back that in esports, defense still wins championships, so why not in football. Coach Walid Regragui was asked about that article in a press conference, and I could not sleep from excitement. But the thing I remember most was not the excitement. The thing I remember most was the fear mixed in with it. I feared I was right for the wrong reasons. I feared I had attracted readers with a beautiful conceptual frame rather than with the truth. And since then, I always ask myself before publishing: am I persuading readers with evidence, or seducing them with words? Section nine, the final one, is about the industry transmission of esports. It intended to draw a transmission map from upstream, the game publishers and event licensing systems, through midstream, the clubs, tournaments and streaming platforms, down to downstream, sponsorships, derivatives, and mainstream cultural integration. But the entire map was empty. And the analysis explained its own reason: the industry transmission dimension is the most title-sensitive of all nine sections. I completely agree. Patch release cadence, revenue-sharing mechanics, and governance structures differ fundamentally between publishers. Running this dimension without a confirmed title guarantees serious category errors. And this is why the section was left empty, rather than filled with generic industry commentary. Reading to the end of the document, I realized I was holding something strange: an analysis aware of its own meaninglessness. It did not pretend to have answers. It only asserted that it had no answers, and asserted it so politely it was almost provocative. Its overall assessment gave all four value dimensions one star out of five: competitive value, industry value, timeliness value, reference value. All one star. An honesty so brutal it hurts. But here is where I have to go against the crowd, exactly the way I do when analyzing matches. Because there is one thing that analysis, whether accidentally or intentionally, got right. And if I stand only in the position of criticism, I will miss it. The thing it got right is this: it refused to fabricate content. In an era when any tool can generate a smooth, rounded, stat-filled analysis that sounds very plausible but is entirely fake, a document choosing to say I do not know is a disciplined act. It is not hypocrisy. It is an incomplete honesty. And this is the point where I want to pause and go deep, because it is the core of everything I believe about this craft. The biggest problem of sports content in the age of automation is not a lack of data. The biggest problem is that there is too much fake data presented with a completely trustworthy appearance. In behavioral economics there is a concept I quite like: people would rather believe a clear, confident answer, even if wrong, than an uncertain admission, even if right. Human nature craves certainty. And whoever sells us certainty, wins. But in sports, certainty does not exist. A match can turn at the last minute, the last second, in a moment no prediction model could foresee. Anyone who claims to know the outcome of a match for sure is lying, or does not understand the very sport they claim to master. So an honest analysis must first be an analysis capable of stating what it does not yet know. And in that sense, the file from 2 AM, with its nine honestly empty columns, achieved something many data-filled articles do not: it did not deceive me. But — and this but is very important — not deceiving does not equal being useful. An honest but hollow analysis is not a finished work. It is half a work abandoned, repackaged as a product. So what is right and what is wrong? I think the answer lies in a place few want to look: in the verification stage, the stage that checks whether the source document actually contains enough content to analyze. The analysis I received failed at that stage, not at the thinking stage. It failed because someone let an empty data packet pass through without a single checkpoint to stop it. And here is what I want you, the reader, to take from this. When you read a sports or esports analysis, ask yourself three questions. First, does the writer clearly state what they do not know? If not, they are pretending they know everything. Second, where do the numbers in the piece come from? If there is no source, they are dressed-up numbers. Third, what time period does the piece address? If unclear, you do not know whether you are reading breaking news or reading a corpse. These three questions sound simple. But I assure you, if you apply them seriously, you will filter out most of the junk content sports readers are force-fed every morning. Since that night, I changed the way I work. I built a mandatory checklist for every piece. Which title. Which version. Which date. Which source. Which three core facts. What information I do not yet have. If I cannot pass this checklist, I do not write. Not because I am out of ideas, but because my idea does not yet have enough bone and flesh to exist. And as I tell my collaborators: a piece without data is not a short piece. It is a dead piece. I know some will read this and think: she is anti-technology. I am not anti-technology. I am anti-laziness before technology. Anyone who has followed my career knows I am not naive about the power of data. My entire shield of numbers is built from data. My entire way of translating a play into a paragraph relies on data. But data is not the destination. Data is the vehicle. And a vehicle with no driver can only crash into a wall. There is one thing outsiders to the industry often do not understand. They think esports analysis is a technical job. They think you only need to read a stats table to do it. The truth is the opposite. Esports analysis, like sports analysis, is a human job. A stats table tells you what happened. But it does not tell you why. And no stats table in the world can answer the question why. To answer why, you need to sit for hours digging through old matches, re-listen to interviews, re-read contracts, talk to insiders, remember that a twenty-year-old player just broke up with his girlfriend a week ago. No data can measure a breaking heart. But a breaking heart can be the reason a player performs badly in the third match of a three-match run in four days. And this is where I return to my biggest belief in this craft. I believe a true sports writer is not the one who makes the most accurate predictions. I believe a true sports writer is the one who helps the reader understand a match more deeply, feel it more fully, see it as a living system of people making decisions, not as a dry string of numbers. Data cannot replace understanding. Data only serves understanding. And whoever reverses this order — treating data as the destination and understanding as the byproduct — is ruining their own craft. Over more than ten years, I have watched many people enter and leave this industry. Those who last, in my observation, are not the best writers, nor those with the most data. They are those who have a system. They know what they know, what they do not know, and what they can learn. They never over-trust a single conclusion, but they also never refuse to draw a conclusion. They hold the tension between those two poles, and that tension is what creates quality in their work. I remember once, when I was very young, an older editor told me something I still carry. He said: people write at their best when they write about what they are not entirely sure of. Because when they are sure, they become jealous of their own truth, and they start defending it instead of exploring it. But when they are unsure, they will go searching. And the journey of searching, not the destination, is the article itself. I think the file from 2 AM is a search journey that stopped before it even began. It had nothing to search for, because it did not even know what it was looking for. And so, it stopped right at the gate, standing there knocking forever while no one was home inside. But I do not want to end this story with reproach. Because if there is one thing my career has taught me, it is that every failure in this craft is an opportunity, as long as you have enough patience to read it to the end. I kept that file on my machine. I gave it its own name. I call it the reminder. Whenever I feel the urge to write something fast, something big, something grand, I open it and reread its nine empty sections. It reminds me that emptiness is not the enemy of a work. Pretending is the enemy. Emptiness can be filled. Pretending cannot. And perhaps this is what I want to send to everyone writing about sports right now, whether you write by hand or by machine, whether you write for a big newsroom or a personal blog, whether you are a former player or a first-timer touching a keyboard. What I want to say is very simple. Let your truth be bigger than your confidence. Never let your skeleton be more beautiful than the flesh inside it. And if you must choose between saying something you know and staying silent about something you do not, choose the second. Because silence at the right time is part of the truth, while noise in the wrong place is part of a lie. As I write these final lines, the Los Angeles sky has brightened. The first sunlight creeps through my home office window, touches my computer screen, touches the file of nine empty columns I still have not deleted. Out there, esports tournaments are preparing for the most intense stretch of the regular season. There will be teams rising in surprise. There will be stars fading in silence. There will be contracts signed at night and there will be injuries hidden in the training room. And there will be hundreds of analyses generated every day, most of which tell you they know everything. I hope that among them, there will be more analyses willing to tell you: I do not know, and here is why I am still searching. Because a mature reader does not need a teacher who is always right. A mature reader needs a companion honest enough to admit they are also finding their way through the fog, just like the rest of us. And ultimately, as in any match, the most valuable person is not the one who talks the most in the room. It is the one who speaks at the right moment, and stays silent at the right moment.