When Data is Empty: Lessons on Accuracy in Sports Analysis

I have spent 32 years reading matches through footage, not through stories. Today, I received a strange analysis request: a chess article supposedly went through an information extraction step, but the result came back as a blank page. No title, no source, not a single number. In my dark editing room in Hai Phong, I sat staring at the screen and remembered the principle that has followed me throughout my career: footage cannot lie, but it also cannot speak for a tape that was never recorded. This incident does not come from any specific match. It comes from a content production process that malfunctioned from the very first step. A chess article — perhaps about a player, a tournament, a transfer, or a rules dispute — was fed into the analysis system, but the information extraction step (Stage-1) returned an empty result. All data fields were blank: no player names, no tournament information, no core viewpoints, no entities identified. As a sports documentary screenwriter, I have witnessed too many times my colleagues trying to fill gaps with emotion. When there is no data, they fabricate stories. When there are no numbers, they use flowery language. But I learned from SEA Games 29 that precision replaces words — and when there is no precision, the only way to maintain credibility is to state clearly: we do not know. Technical analysis cannot be performed because no chess game was described. No player, no opening system, no win rate or ACPL index. I could sit here and speculate about some anonymous player, but that would betray my core principle: every claim must withstand cross-verification. Player analysis hits the same wall — no names, no ratings, no head-to-head history. Even the question of “who stands where on the world chess map” cannot be answered because we do not even know which country the map is about. Interestingly, the emptiness itself is data. It tells us that the extraction system failed — either the original article never existed, or the model could not read the content. This is a signal about process, not about chess. If I were editing a documentary about this incident, I would record the sound of keyboards clicking in the editing room, the sighs of analysts trying to find something to say, and the long silence when they realize there is nothing to say at all. I have been a hurdler all my life, I just have never been timed. In this profession, I have grown accustomed to having my proposals dismissed because “what does a woman know about running.” But I also learned that silence can be the most powerful answer. When I do not have data, I do not argue. I open my data appendix — and if that appendix is empty, I say so directly. There is a counterintuitive angle here: refusing to analyze when data is missing is not a weakness, but a manifestation of discipline. In an age where everyone wants immediate answers, saying “I do not have enough information to conclude” becomes an act of courage. It goes against the modern sports commentary culture, where confidence is often valued more than accuracy. But I have seen too many articles collapse because of hasty claims based on wrong data — just like rushing back from an ACL injury is destroying the second phase of players’ careers. The empty stadium, I turned the sound of spikes into the heartbeat of the film. But when the stadium has nothing — no players, no spectators, no whistle — I cannot create a heartbeat from nothing. The same happens with this analysis. I cannot assess financial risks, career risks, or psychological risks of a player whose name I do not know. I cannot map generational transitions in the chess world when no generation is mentioned. And I cannot measure the social media heat of a story when there is no story to measure. I trust footage more than stories, because footage cannot lie. But I also know that a blank tape cannot prove anything — it only proves that the camera was never turned on. In this case, the analysis system’s camera was not turned on, or it broke before recording. The result is the most honest analysis possible: an analysis that admits it cannot analyze anything. The question for those operating this system is: why did you send an empty article into the analysis process? Is it because you hoped I would fabricate a story to fill the gap? If so, you have misunderstood me. I do not write based on crowd emotions; I always ask: what is this data actually saying in reverse? And when there is no data, the only answer is: there is nothing to say. In documentaries, we have a term called “cutaway” — a cutaway shot to cover a gap in the story. But in sports analysis, no cutaway can hide the lack of information. The only way to handle it is to face it directly, acknowledge our limitations, and wait for real data to arrive. Esports is a sport of people who read matches through reflexes. But even in esports, you need a match to read. No match, no reflexes, no analysis. This sounds obvious, but in the age of generative AI, we are witnessing too much content created from nothing — thousands of words about events that never happened, tactical analyses of matches that never existed. I do not want to be part of that problem. I want to be part of the solution. And the solution begins with acknowledging the truth: this article has no content to analyze. This is not my failure, but a failure of process — and that failure needs to be acknowledged, not hidden. Each documentary is a race: the audience sees the finish line, I live at every starting line. Today, I am standing at the starting line of a race with no finish. I could run around aimlessly and pretend I am moving forward, or I could stop, look around, and say: we need to go back to the beginning. I choose the second option. Because in sports, as in life, honesty about what we do not know matters more than confidence about what we imagine.