When the analysis is all N/A: the no-fabricated-numbers lesson for Vietnamese sport

I have just received a sports data analysis in which every cell reads “N/A”. No athlete name, no technical parameter, no result, no date, no event. At first glance, one might throw it in the bin. But I read it as a rare and trustworthy document: it refuses to print anything that has not been verified. In an environment where rumours, edited highlights and emotion often win, an analysis that dares to say “we do not have enough data” is stronger than many pieces that appear falsely certain. After years of watching matches, I have learned that the line between a valuable judgement and a harmful statement usually lies in how we treat missing data. There are tactical models built from one match that collapse in the second because the sample is too small. There are also teams that press well all season but no one bothers to measure them through three independent sources. I once gave a quiet type of pressure the name “Binh Duong pressing”, because back then Becamex Binh Duong allowed very few opposition passes before pressing. The name may sound interesting, but what keeps it alive is cross-checked data, not flashy words. Many people misunderstand that data analysis must always produce numbers. In fact, knowing that you have no numbers is also a skill. I used to treat models as holy scripture. Now they are only a compass — but without them, we get lost. When building a prediction model for a World Cup, I had to compare hundreds of thousands of shots before daring to talk about xG. But xG is not wrong; football is simply irrational. After that tournament, I learned to count the irrational part as well. No model can measure everything, and a good analyst knows how to mark the areas that the model cannot read. So when I receive an “empty” analysis, I do not rush to criticise it. I read it as a map with blank zones. Those blank zones signal that the data collection system lacks sensors, staff, or common standards. In Vietnam, national teams and clubs are paying more attention to data, but the data infrastructure is not yet unified. Some places track minutes played, some only record wins and losses, and some do not save match video at all. In that reality, an analysis full of N/A does not reflect the weakness of the analyst; it reflects the emptiness of the system. Experience gives me three things to do when facing empty data. First, never fill a gap with guesswork. Second, clearly state the sample size and limitations. Third, treat the gap as a signal to invest in measurement systems. Sports writers in Vietnam often face pressure to publish quickly and to have an opinion immediately after a match. I understand that pressure. But I have also seen many articles die young because they made one-sided claims from a single source. In contrast, long-lasting articles are often those that dare to say “the data is not yet sufficient to conclude”. Caution is not avoidance; it is the writer’s courtesy towards the truth. The counter-intuitive point I want to make is this: empty data is not necessarily the enemy. It can be the last protector of the reader. If an article has no verified numbers, leave it empty; do not stuff it with estimates. The transfer market is the only place where people pay for expectation, not for the present. Likewise, many sports articles sell expectation, dreams and fleeting emotion. But a decent article must show where the dream ends and where verified reality begins. When there is no reality yet, silence is a valid answer. Numbers cannot lie, but people always find ways to deceive numbers. The N/A blank space is a mirror reflecting the honesty of the writer. If this week you read an analysis full of N/A, please do not rush to call it a failure. Ask instead: what is missing in our data system? Are our teams recording enough from three sources? Are our athletes in a blank zone because no one measures them? Once you answer those questions, you will see that blank spaces are not frightening. What is more frightening is an analysis that looks complete, reads smoothly, but stands on the sand of numbers that have never been verified.