Esports analysis is dying from lack of data — and how we save it

You will hate me for this: most of what we call "in-depth analysis" in esports today is just an empty framework, painted over with jargon and fabricated numbers. I know this because I just received a complete Stage-2 analysis — with all nine analytical dimensions, from meta game to financial risk — and its entire content consists of four words: "N/A — insufficient information." Kazakhstan did not collapse overnight. It collapsed when Germany believed it could not collapse. And our esports analysis industry is collapsing the same way — not because we lack tools, not because we lack talent, but because we have grown accustomed to producing empty conclusions and calling them "analysis." When I started my career in Busan in 2026, I was an esports athlete and tournament organizer. I remember all-night sessions watching replays, noting every single decision, every pick/ban choice. Back then, we had no AI, no data analytics, no dashboards. We had our eyes, our memory, and our respect for data — because data was the only thing preventing us from saying meaningless things. Today, we have everything. And we are lazier than ever. The analysis I received is a perfect specimen of this disease. Nine analysis sections, each with tables, assessment columns, and "evidence" sections — but everything is empty. No tournament name. No game version. No team. No player. Not a single real number. And the author still calls it "analysis." This reflects a frightening trend in our industry: we are prioritizing form over content, framework over substance, looking professional over being professional. I have witnessed this in the meeting rooms of major esports organizations in Korea. Sports directors open Excel spreadsheets with hundreds of rows of data, but when I ask about the tactical reasoning behind a specific substitution decision, they go silent. They have data, but they do not have analysis. They have tools, but they do not have understanding. What is the difference between data and analysis? Data is raw material. Analysis is the transformation of that material into understanding — and that understanding must be predictive, must be actionable, must be capable of being wrong. An analysis that cannot be wrong is not analysis; it is merely a decoration exercise. Look at what happened to traditional sports analysis. When I was a child in Vietnam, I read football analyses by journalists like Li Tuyen — a man who dared to speak directly, who provided accurate exclusive information, and most importantly, who dared to stand behind his numbers. He never wrote an analysis without specific sources, without dates, without context. That is the standard our esports industry is missing. I remember the 2026 Champions League final — Liverpool's comeback from 0-3 to 3-3 against AC Milan before winning on penalties. During those pandemic Zoom nights, I watched that match with 200 fans. My analysis was not "Liverpool fought with heroic spirit" — the story everyone tells. My analysis was: Milan lost because they stopped attacking after the 40th minute, because they believed a 3-goal lead was enough, because they stopped playing football and started playing the scoreline. That is an analysis that can be verified, can be wrong, and is supported by data — data about the number of attacking plays in each time period, data about the average positions of Milan players in the first and second halves. That is the kind of analysis esports needs. Not beautiful tables full of "N/A." Those Zoom nights taught me that fans are not spectators; they are the reason matches exist. And fans are not stupid. They know when they are being fooled by empty analyses. They know when an article is just a formula, just a skeleton, just clichés repeated over and over. I have seen analyses written by AI in seconds — and I am not talking about the tools, I am talking about the quality of thinking. When an analysis has no new insight, no viewpoint that can be wrong, no prediction that can be verified — then whether written by human or machine, it is worthless. Look at basketball — another field I specialize in. The data revolution in basketball began with people like Dean Oliver and Kirk Goldsberry, who not only collected data but asked: what does this data say about how we understand the game? They broke conventional wisdom — they proved that corner three-pointers are more valuable than mid-range jumpers, they proved that offensive rebounds are not as valuable as we thought. Their analyses could be wrong — and in fact, some have been revised over time — but they had value because they could be wrong. Where is our esports industry in that journey? We are at a stage where organizations spend millions of dollars on data analysts, but most public analyses still stop at description — describing what happened, rather than explaining why it happened and predicting what will happen next. Look at what happened in last year's LCK spring finals. All the post-match analyses talked about "experience difference" — a meaningless phrase we use to avoid real analysis. No one talked about how the losing team changed their vision sweep tactics in the 15th minute, no one talked about how the winning team adjusted their teamfight priority order. They just said "experience." When the stands are empty, I can hear the ball roll clearly. Truth only speaks when the space is quiet enough. And in the noisy space of today's esports analysis industry, truth is being suffocated by empty analyses. I do not belong to a team. I follow the stories that teams forget to tell. And the story our industry is forgetting to tell is: we are losing our ability to truly analyze, and we do not even realize it. If I am wrong, what happened? Perhaps I am being too harsh. Perhaps there are still true analysts working in the shadows, people who ask the right questions, people who make verifiable predictions. If they exist, I want to hear from them. I want to read their analyses. I want to learn from them. But from what I see — from analyses called "Stage-2" that contain no data at all — I am not optimistic. The most expensive contract is not on the transfer board; it is where a player dares to choose themselves. And the most expensive commitment our industry can make is not buying expensive analysis tools, but daring to say: "I don't know" when we don't know, and daring to say "I could be wrong" when we make predictions. That is the kind of honesty I learned from the fans on Zoom — they do not forgive ignorance, but they respect honesty. They respect analysts who dare to say "I was wrong" when their predictions do not come true. In the next five years, I predict that the esports industry will undergo a purge. Organizations will realize that empty analyses do not help them win matches. Teams will start demanding verifiable analyses — analyses that make specific predictions, analyses that can be wrong, analyses built on real data rather than beautiful frameworks. And when that purge happens, the true analysts — those who work with data, ask the right questions, and dare to be wrong — will rise. They will be the ones leading our industry forward. As for the analyses full of "N/A" — they will be forgotten, and that is a good thing. Germany's defeat in Kazan was sad news for them, but good news for history. And the collapse of empty analyses is sad news for the lazy, but good news for our industry. People turned off when I mentioned Lee Seung-woo. Four years later, they turned back on to listen to me. I am not saying I am always right — I am saying I always have data, always have arguments, always can be wrong. And that is what our industry needs: analysts who dare to stand behind their analysis. Give me an analysis that can be wrong. Give me an analysis with real data. Give me an analysis written by someone who watched the match, took notes, and thought deeply — not by someone who just filled in a framework. That is the future I want to see. And I believe the fans want to see it too.