When Data Doesn't Say What We Want: Lessons on Classification in Sports Analysis

I remember the first time I sat in the press room with my old laptop, misspelling Nguyen Thi Oanh's name as "Oanh Nguyen" three times in an 800-word article. The national team coach texted me to correct the error that very evening. I blushed, but instead of feeling discouraged, I felt ignited: I reviewed Oanh's entire performance record from 2026, carefully noting every 1,500m race statistic. My old laptop taught me: slow doesn't mean late, it just means telling the story differently. But there is another lesson, deeper, that I learned later: not all mistakes come from humans. Sometimes, automated systems misclassify, and the consequence is a heap of meaningless analysis. I witnessed this when an article about banking and cross-border financial regulation was labeled "tennis" in a sports analysis pipeline. Sports analysis systems are typically built to process articles about specific sports. When an article about politics or finance is mislabeled, the entire specialized analytical framework collapses. In this case, an Associated Press article about President Trump's claim that U.S. banks cannot operate in Canada was labeled "tennis." That article was actually a fact-check about cross-border banking regulation. It mentioned 15 U.S. banks operating in Canada, classified Canadian banks into three categories (Schedule I/II/III), and included expert commentary on market-entry economics. There was not a single piece of content related to tennis. I used to run 800m, and I understand the feeling of preparing for a race but standing on the wrong track. That feeling is exactly like when an analysis system tries to apply a tennis framework to a banking article — every analytical dimension becomes meaningless. The nine dimensions of the tennis framework — technique, data, tournaments, tour landscape, rules, team management, risk, media, and industry — all returned "N/A — insufficient information." The article had no forehands, no match scores, no tournament schedules. Notably, even the numbers in the article — 15 U.S. banks in Canada, $124.6B CAD in assets, over 3,700 U.S. domestic banks — could not be used for tennis analysis because they are financial data, not sports data. This taught me an important lesson about sports journalism: accurate classification is the foundation of all analysis. If we classify incorrectly, everything downstream becomes meaningless. The arena is not big because of the seats; it is big because of the stories that dare to stay — but those stories need to be placed in the right context. A counterintuitive perspective: the "N/A" result — insufficient information — is actually more valuable than a false result artificially fabricated. In a sports world where automated systems are increasingly used for analysis, admitting "cannot analyze" is a sign of integrity, not weakness. I have witnessed analysts trying to force conclusions from irrelevant data, just to have a story to tell. But in sports, as in life, honesty with data matters more than the appeal of the narrative. When I look back at my journey — from a young athlete who misspelled Nguyen Thi Oanh's name to a multi-sport journalist — I realize that accuracy is not just about checking spelling. It is also about placing the right analytical framework on the right content. An empty stadium means applause moves into the heart; football becomes just breath. And when data doesn't say what we want, listen to what it actually says. I lived a year with three seasons: the ball, the esports keyboard, and the contracts. In all those seasons, I learned that data never lies — but it can be misunderstood. When a banking article is labeled "tennis," the problem is not the data. The problem is the classification system. There are calls no one answers but both ends are healing. Similarly, there are articles that don't belong to the analytical framework assigned to them, but they still contain value — just that value lies outside the framework's scope. Nguyen Thi Oanh is not a name — it is a life running forward. And every article, even if misclassified, is still a piece of the larger picture. In the context of Vietnam's rapidly developing sports landscape, with increasing investment in data analysis, the lesson about accurate classification becomes more important than ever. I see Vietnamese football clubs starting to use data for player recruitment, sports academies applying GPS technology to track performance. But if the data is misclassified, every decision based on it will be wrong as well. Behind the tactical diagram is a trembling person, hoping and forgetting how to breathe. I remember Euro 2026, when I requested an interview with an Italian assistant coach via Instagram and he agreed to talk to me for 50 minutes about GPS and data. He said: "Data is never wrong. But it can be read wrong." That sentence has stayed with me to this day. When I wrote the article "Italy doesn't run randomly: What does GPS say?", I learned to listen to data instead of forcing data to say what I wanted. That is the lesson I want to share with young Vietnamese sports journalists: let data lead the way, don't force data to follow your path. And when the classification system gets it wrong — like the banking article labeled "tennis" — treat it as an opportunity to improve the system, not a failure to hide. An empty stadium means applause moves into the heart; football becomes just breath. Similarly, an honest "N/A" result is more valuable than a fabricated analysis. I end this article with a question: in the age of big data and artificial intelligence, are we listening to data — or are we just seeking confirmation for what we already believe? The answer will determine the future of sports analysis, not only in Vietnam but around the world.