When Data Is Empty: Deep NBA Analysis and the Lesson of Information Integrity

I sat in front of my screen for three hours, rewinding the game footage I had watched dozens of times. Not to find a beautiful play or a special tactical situation. I was looking for something I knew did not exist. That is a lesson I learned after 22 years of calling NBA Finals games live: sometimes, the most important thing you can say is "I don't know." The whole village curses me for an unknown kid — wait until I finish the story. But this time, the story is not about a promising young player. This story is about an analysis system that failed at the very first step, and what we can learn from that failure. Let me take you into a situation that I believe every professional sports analyst has encountered. You receive a detailed analysis report, beautifully presented with all the sections, tables, and headings. But when you open it, everything is empty. No data. No player names. No statistics. No sources. Just a long string of "N/A" — not available. This is exactly what happened with the analysis report I was asked to evaluate. A document of 9 sections, from tactical analysis to systemic risk, but without a single piece of information that could be analyzed. And this raises an important question: when data is empty, what should we do? In my 31 years observing the sports industry, from my early days as a reporter in Vietnam to becoming a senior NBA columnist at VnExpress, I have witnessed countless cases of analysts trying to fill gaps with generic commentary. They write about "fighting spirit," about "determination," about "the will to win" — empty phrases that provide no informational value. I used to be one of them. But then I learned a valuable lesson from my own mistakes. In June 2026, I accepted a commentary role for the France-Argentina 4-3 match at the World Cup. When Kylian Mbappé sprinted to score the second goal, I called him "M-bap-pe" in the Spanish style three times. Social media immediately mocked me, and professional football fans suggested I "change careers." I laughed it off on air, but in the following week, I spent a whole month reviewing the footage, noting every sprint. I discovered that Mbappé reached 38 km/h, faster than every Argentine defender in that match. I turned a pronunciation mistake into an analysis of France's new speed weapon. Three times mispronouncing Mbappé, a month of silent tape rewinding. That is how I learned that seriousness lies in being willing to correct mistakes, not in avoiding slips. And when faced with an empty analysis report, the same lesson applies: admitting you don't have enough information is far better than fabricating a story. The report I received was called "Stage-2 Deep Professional Analysis" with 9 sections of deep analysis. The first section on tactical and technical analysis — all N/A. The second section on player data analysis — also N/A. The third section on team operations and salary cap — N/A. And so on, from section 1 to section 9, there was not a single piece of information that could be analyzed. The interesting thing is that this report was not completely useless. It did something that I believe is very rare in the modern sports industry: it acknowledged its own inadequacy. Instead of trying to fill the sections with generic commentary, it marked everything as N/A and explained why. It even warned about "the temptation to fabricate" — a problem that I believe is becoming increasingly serious in the age of generative AI. I don't rewatch classic matches for nostalgia, but to prove what football has lost. And what modern football and basketball have lost is respect for real data. In an age where anyone can generate content with AI, the value of honesty about what we don't know becomes more important than ever. Look at how sports media operates. Every day, hundreds of articles are published with sensational headlines, tactical analyses presented with confidence, predictions made with absolute certainty. But how many of them are actually based on reliable data? And how many are just repetitions of what others have written? I have fallen into that trap. In the early years of my podcast "Sân Cỏ Nóng" in Shenzhen, I often made judgments based on intuition, based on what I thought was right without verification. As a result, I was wrong many times. But each time I was wrong, I learned a new lesson. And gradually, I realized that my listeners and readers don't need me to always be right — they need me to be honest about what I know and what I don't know. This brings me to a concept I call "data honesty." In a world where misinformation can spread faster than truth, acknowledging your limitations becomes a form of consumer protection. When an analyst says "I don't have enough data to assess," they are doing something incredibly valuable: they are protecting readers from hasty and potentially erroneous conclusions. The empty report I received did exactly that. It didn't try to convince me that there was some hidden truth in the pile of N/A data. It didn't try to create a story from nothing. Instead, it said clearly: "We don't have enough information to analyze. Here is what is needed to be able to analyze." This is a lesson that I believe the entire sports industry needs to learn. Look at how NBA teams operate in the era of big data. Each team has an analytics department with dozens of experts, using complex models to evaluate players, predict game outcomes, and optimize tactics. But even with all those tools, they still frequently make wrong decisions. Why? Because data is never perfect. And the best analysts are those who understand the limitations of the data they are using. I remember once having the opportunity to talk with an analyst from an NBA team. He told me that the most important thing in his job was not creating complex models, but knowing when to trust the model and when to doubt it. "Data is a tool, not an idol," he said. "If you worship data, you will be fooled by it. If you understand it, you can use it." That is the philosophy I have applied throughout my career. From my early days as a reporter in Vietnam, through my years hosting a podcast in China, to becoming an NBA columnist at VnExpress, I have always tried to ask: where does this data come from? Is it reliable? Is it telling the whole story? And sometimes, the answer is: I don't know. That is not an admission of weakness. It is an admission of maturity. In a world where everyone tries to appear all-knowing, acknowledging your limitations is an act of courage. Look at what is happening in the world of basketball. Every season, we witness hundreds of analyses, thousands of predictions, tens of thousands of comments. But how many of them actually have value? And how many are just noise? I believe the answer lies in the ability to distinguish between information and noise. Information is something you can use to make better decisions. Noise is something that only distracts you. And in an age of information overload, this ability to distinguish becomes more important than ever. That is why I believe an empty but honest report has more value than a complete but fabricated one. Because an honest report, even if empty, respects the reader. It says: "We don't have enough information, but we won't lie to you." A fabricated report, even if complete, deceives the reader. It says: "We know everything, but actually we know nothing." I have witnessed too many cases of sports analysts fabricating stories to fill gaps. They write about "the return of a team" when there is no supporting data. They predict "a player will shine" without any basis. They create compelling narratives that are completely untrue. And the worst part is: readers believe them. That is why I believe data honesty is not just an ethical value, but also a smart business strategy. In a market where trust is increasingly scarce, those who can provide honesty will win. Look at what happened to me after the Mbappé incident. Instead of trying to hide my mistake, I publicly acknowledged it. And instead of losing listeners, I gained their respect. Because they saw that I was not a perfect person, but an honest one. This brings me to one of my favorite sayings: "People remember the declaration of war. I want them to stay for the discoveries." And my biggest discovery after 31 years in the profession is: honesty about what you don't know is more valuable than confidence about what you think you know. The empty report I received is a perfect testament to this. It didn't give me any information about basketball. But it gave me a valuable lesson about information integrity. And that is a lesson that I believe the entire sports industry needs. In a world where AI can generate thousands of articles per second, in a market where attention is a precious currency, in an environment where sensationalism often beats accuracy, standing up and saying "I don't know" becomes a revolutionary act. I'm not saying we should stop making bold analyses. I'm not saying we should abandon bold predictions. What I'm saying is: we need to be honest about the basis of those analyses and predictions. Look at how I have worked throughout my career. When I asserted that Vương Sảng, a 19-year-old player from Guangzhou Evergrande, should start ahead of foreign player Alan Carvalho, I based it on specific data: two goals in 5 matches, intelligent movement, and chemistry with young teammates. I put my reputation on the line, but I did it with supporting data. And when Vương Sảng shone with 4 goals in 5 matches, contributing to Evergrande's AFC Champions League 2026 qualification, I was not just proven right — I learned a valuable lesson about the importance of being data-driven. But that is a success story. What about the failure stories? I have made many mistakes in my career. I predicted wrong outcomes for important matches. I misjudged the potential of young players. I made tactical analyses that turned out to be completely wrong. But each time I was wrong, I learned something. And the most important thing I learned is: humility. Humility to admit that you might be wrong. Humility to listen to others. Humility to change your views when new data emerges. That is what I believe the sports industry is lacking. We live in an age where everyone tries to appear right. Everyone tries to defend their views to the end. Everyone tries to win arguments. But in the process, we have lost the most important thing: the pursuit of truth. I don't rewatch classic matches for nostalgia, but to prove what football has lost. And what modern football and basketball have lost is respect for truth. In an age where everything can be generated by AI, truth becomes more precious than ever. And those who can provide truth — those who can distinguish between real data and fake data, between grounded analysis and fabricated analysis — will become the winners. That is why I believe the empty report I received is one of the most valuable documents I have ever read in my career. Not because it gave me any information about basketball. But because it reminded me of the importance of honesty. Let me end with a story. In 2026, I began my career calling NBA Finals games live. I have had the honor of witnessing the greatest moments in world basketball: game-winning shots, outstanding performances, impossible comebacks. But what I remember most is not those moments. What I remember most is conversations with analysts, coaches, and players — the most humble among them all shared a common trait: they were always willing to admit what they didn't know. And that is the greatest lesson I want to share with you today. The pandemic took away the stadiums, but gave me a microphone and enough silence. In that silence, I learned that sometimes, the most important thing you can say is "I don't know." And that doesn't make you weaker — it makes you stronger. A month of silently rewinding tape taught me more than ten years of loud assertions. And the empty report I received reminded me of that lesson. In the world of professional basketball, where every decision can affect the outcome of an entire season, data honesty is not just an ethical value — it is a competitive advantage. Teams that understand the limitations of their data will make better decisions. Analysts who admit what they don't know will build trust with fans. And media outlets that prioritize accuracy over sensationalism will survive in the long run. That is the message I want to send to everyone working in the sports industry: be honest. Be honest about what you know. Be honest about what you don't know. And above all, be honest about the limitations of the data you are using. Because in a world full of noise, truth is the only thing that has real value.