When Data Goes Silent: Why Honest Football Analysts Must Learn to Say 'Insufficient Information'

On the night of June 21, 2026, after a quarter-final, I opened my data file. Empty. No coordinates, no pass counts, no sprint metrics. Only a few hurried notes scribbled from the stands and a vague sense of the match's rhythm.

On television at the same hour, an analysis board appeared filled with movement arrows, shaded zones and decisive conclusions about the team's tactical identity. The presenter spoke as if he had just rewatched the entire footage in slow motion. I looked at the screen and knew: he had only an empty data file, same as me.

The distance between those two situations — between admitting that the data has gone silent and filling the gap with conclusions — is the subject of this article. In the middle of a major tournament season, when everything is compressed into emotion and flags, honesty with data becomes the scarcest commodity on the market.

Over the past decade, the volume of data in football has grown exponentially. Each Premier League match now generates millions of data points: every player's coordinates each second, sprint speeds, touches, passing angles, goal probabilities. Semi-automated offside technology (SAOT) arrived in the 2026/25 season, promising to cut decision time to a few seconds. Analytics platforms have sprung up like mushrooms. And with them came a new pressure: always having something to say.

In Vietnam, the explosion is even clearer. For every big match, hundreds of analysis pieces are published within hours of the final whistle. Fan pages, YouTube channels and community groups compete to offer verdicts. Speed has become the criterion. Whoever is faster and more decisive wins.

But speed and decisiveness do not equal truth. And that is where my story begins.

I learned the most important concept in my profession not from a coach, but from a data-handling rule. When a data field is empty, you must mark it as insufficient information. You are not allowed to fill in a random value just so the table looks complete. People call this null handling.

In football, this principle is almost entirely forgotten.

An honest analyst is not permitted to fill conclusions into the gaps of the data, because that is precisely the moment analysis turns into fiction.

I remember the 2026 World Cup. At eighteen, a first-year student in Liverpool, I began a twelve-part series on the diamond pivot in Croatia's midfield. I logged twenty-four receptions by Luka Modrić between the lines in the semi-final against England. I measured that the Croatia captain had covered 11.2 km in total, but only three kilometres of that was forward movement. That value appeared in no official statistics table. I had to record his positional coordinates every five minutes myself.

My conclusion — that Croatia's midfield would collapse in extra time due to accumulated distance — came true. Croatia did not create a miracle; they drew a map. But my point here is not that I was right. My point is that I only dared to conclude this after building my own data-symbol system by hand. If my data file had been empty, I would not have written a single word about Modrić.

The article reached 500,000 reads in the Vietnamese football community. But readers should know its value did not come from being shared; it came from every argument having a coordinate behind it. A good article without data is merely a fluent one.

Four years later, at the 2026 World Cup, I tracked all six of Morocco's matches for a Vietnamese sports channel. I charted that their deep-lying 4-3-3 allowed Spain to make 1,020 passes, but only twelve dangerous moves into the central corridor. Morocco's defensive midfield zone occupied 71% of activity time, against Spain's 38%. Morocco did not defend in numbers; they turned space into a maze.

Before the match against France, I predicted Morocco would lose due to accumulated defensive actions. Their total high-speed running was 8.4 km, the most in the tournament. Result: a 0-2 defeat, exactly as scripted. I created an index I called defensive endurance — combining high-speed distance with tackle success rate when fatigued. That index only had value because I had spent six matches collecting raw data.

If someone asks me about a team I have not tracked for at least six matches, my honest answer is insufficient information. That is not weakness. That is discipline. And in this profession, data discipline is the only thing that keeps a writer from sliding into the role of a storyteller.

The concerning part is that modern football itself is pushing in the opposite direction. Semi-automated offside technology draws lines down to the millimetre. A toe protrudes, a shoulder drifts, and a goal is wiped out. The data here is very precise — precise to the point of cruelty. But that precision hides a gap: it cannot answer the larger question of whether that moment was truly an offside offence in the spirit of the law.

A number accurate to the millimetre can still be an empty conclusion. You can have perfect data and still not have the truth.

I have said this many times and will say it again: the millimetre offside line is killing attacking instinct, and referees are being turned into the editors of the match. Technology does not create justice; it only creates a new format for argument. Every formation is a hypothesis, the match is the experiment. But an experiment only has value when the experimenter accepts that some questions it cannot answer.

The same logic applies to the transfer market. In 2026, when I joined a football media startup in Liverpool, I was assigned to cover the summer window. Through a relationship with a scout, I was the first to reveal the loan move of Emile Smith Rowe from Arsenal to a mid-table club. The deal was designed around a double-pivot system. My analysis showed Smith Rowe received 8.7 passes per 90 minutes in the left half-space — a perfect fit for the new shape.

But I only published after checking two things: tactical compatibility, and the source. I learned that there is a kind of transfer news born not from truth, but from the need to fill a news gap. Agents need to create pressure. Writers need a post. And so a player suddenly becomes linked to a club with which no negotiation ever took place. The transfer market does not buy players; it buys problems. And most problems on the market are set up wrongly, because people start from the conclusion instead of the data.

That is also why I look at the young-player price bubble with a sceptical eye. A player who has not played fifty top-level matches yet valued at hundreds of millions of euros is a naked gamble, dressed up in cherry-picked numbers. People take the three best matches, build a beautiful chart, and call it potential. But potential is not a data field. It is a gap given a name.

I also learned the lesson of empty data another way, in 2026. The pandemic left stadiums empty for 112 days. As someone bound to process, I decided to dissect the effect of losing the wall of noise at Anfield. I analysed fourteen Liverpool home matches played without crowds at the end of the 2026/20 season and showed that their high defensive line made 38% more positional errors, because midfielders lacked the auditory signal from the crowd to cover.

112 days without football, and the substitution rule was a lifeline. I was also among the first to analyse the five-substitution rule: high-pressing teams conceded 0.7 goals per match when opponents could make five changes. But to reach that conclusion, I had to accept one thing: some variables I cannot measure. I cannot measure the fear of a defender hearing the crowd fall silent. I can only measure its consequence on the pitch.

That is the boundary of the analytical model. Metrics answer the question what, not the question why. And an honest analyst must draw that boundary clearly rather than blur it. When you say I don't know why, you are protecting your own credibility for the times you say I know.

This leads me to an observation about how we treat players returning from injury. Demanding that a player prove himself in his comeback match is cruel, and it increases the risk of re-injury. We treat the period of absence as a data field to be filled immediately, instead of a region to be read carefully. Before praising the star, measure the gap he left behind. A returning player is not a variable restored to its old value; he is a new model that must be recalibrated from scratch.

Now comes the hardest part. If honesty with data matters so much, why is it so scarce?

The answer lies in the industry's incentive structure. A decisive, confident analysis piece will be shared more than one that says insufficient information. Algorithms reward certainty. Audiences want answers, not hesitation. And writers, under pressure to produce, gradually learn to fill gaps with tone rather than data.

This is the blind spot I call empty analysis: a piece full of terminology, full of arrows, full of figures, but with not a single verified fact. It sounds professional. It makes readers feel informed. But beneath that shell lies an empty data file.

More dangerously, even real data can be used to produce empty analysis. You select the values that support your argument and ignore the ones that contradict it. You take one match as a sample for an entire season. You turn a moment into an identity. Technically, you are not lying. But you are counterfeiting.

This is the trap I remind myself of every day: because I am used to metric architecture, I am easily tempted to use data as proof for a pre-formed judgement. The only way to break that trap is to always find and state at least one contradicting metric before drawing a conclusion. If I cannot find any contradicting metric, it is likely I have not looked hard enough, or I am reading the data with eyes already set.

I must also admit another limitation: my analytical system is very good at describing structure, but weak at explaining fitness and mental state. I have no metric for the mental fatigue of a team that has lost three matches in a row, or for the bewilderment of a defence that has lost its leader. Those things exist on the pitch, but they are not in my tables. Saying they do not matter is a lie. Saying I can measure them is another lie.

Tactics are the only thing that cannot be faked on the pitch. The problem is that on the analysis desk, people fake it every day, and usually with tools that look very scientific. A heat map does not automatically become truth. An arrow does not automatically become a real passage of play. Tools are just tools; judgement is what is hard.

I do not believe in randomness; I believe in repeated passes. But I also do not believe in conclusions that appear before the data appears. My faith lies in between: where data is dense enough to reveal a pattern, and sparse enough to force me to admit what I still do not know.

In this major tournament season, when every match is compressed into emotion and flags, I want to propose a new standard for readers: ask the writer about his data. Not about the conclusion, but about how he reached it. If the writer cannot show you the data file, cannot show you the coordinates, the pass counts, the match sample, then you are reading an empty file decorated with words. And an empty file, however beautifully decorated, still cannot tell you what will happen in the next match.

Football readers deserve more than a false sense of confidence. They deserve a real map, with regions clearly drawn and regions marked as unsurveyed. When an analyst says he does not have enough information to conclude, that is not the moment he fails. That is the moment he is most trustworthy.

The next match begins in a few days. I will open a new data file again, and if it is empty, I will write exactly one sentence: insufficient information. Will you read an analyst who tells the truth, or an analyst who tells it well?