The Empty Data Table in a Major Tournament Season: When an Analyst Must Choose Between Truth and Illusion

Four in the morning in Liverpool, and I reopen the data table from the round of matches that has just ended. The xG column — expected goals — is empty. The PPDA column — the number of passes an opponent is allowed before each defensive action — is empty too. The matches were played out over a full ninety minutes, with goals, with cards, with moments that made the stands erupt. But the data pipeline broke somewhere between the goal line and the server. When I asked the system to return the analysis, it returned an empty file, along with cells reading "insufficient information to assess." After many years in the trade, my spreadsheet fell silent for the first time.

That silence arrived in the middle of a major tournament season — a moment when every ball, every transfer, every refereeing decision is examined under the microscope of numbers. Supporters watch football through flags and emotion; I watch it through algorithms and columns. And yet an empty spreadsheet taught me more than a blazing match ever could. Among the countless numbers of modern football, the hardest thing is not calculation, but knowing when to stop and admit you know nothing.

When I entered the profession in 2026, in the sports department of a television station, football had no xG, no PPDA, no machine-learning models reading every phase of play. People counted goals, counted passes, and trusted the eye. Thirty-five years later, I sit in England, covering the Premier League for Vietnamese readers and the English market, and I realize the eye is still necessary — but it must be verified. Data does not replace feeling; data forces feeling to be honest.

This year's major tournament compresses all the emotion of international football into a few weeks. National teams have no time to gel like clubs; they must lean on system and preparation. That is precisely why data becomes a compass. But a compass is only useful when the person holding it is willing to look at the needle, rather than at what he wants to believe.

Based on my experience watching matches across many seasons, I once stood before a table of numbers and felt I was witnessing a miracle at Anfield. In the 2026 season, as Juergen Klopp took Liverpool into the top four with 78 points through a frenzy of pressing, I sat down and calculated the team's average PPDA: 8.2 — the lowest in the league. Conservative sides such as Manchester United were reaching 15.7. That figure said something the naked eye struggles to measure: Liverpool did not defend by dropping back, they defended by closing down the moment they lost the ball. The lower the figure, the higher the pressure. 8.2 was a tactical manifesto.

I wrote a long piece on gegenpressing and published it on my personal blog. The response was not gentle. Many said I was mechanical, turning football into spreadsheets, killing the beauty of the game. I understood that feeling. But then came the 4-3 win over Manchester City on 19 January 2026, with breathless counter-attacks and a Kevin De Bruyne exhausted in the effort to haul his side back. That night, I understood that data does not lie — it simply needs to be read correctly. A match can make people forget the number, but the number never forgets the match.

Data whispers, and those who know how to listen will hear the miracle. But I also learned that the whisper is not always right.

In 2026, aged 43, I was invited by a sports website to write a special series on the World Cup in Russia. I built a homemade xG model, analysed all 64 matches, and predicted France would win from the group stage because their chance-creation index was the highest — an average of 2.4 xG per match. At the time, Kylian Mbappé had just emerged as a phenomenon, and my model rated his ability to break through defences very highly. The article was mocked, especially when I argued that Croatia had reached the final on "low xG but good luck."

I was emotionally drained as I watched Croatia reach the last match. I hid in a library for two weeks, re-examining all the data, and found my error: the model ignored corners. A serious omission, because international football lives on set pieces. From then on, I wrote more humbly. I added a section titled "Limitations of the analysis" to the end of every piece, and never made absolute predictions — only probabilities.

xG is a revolution, but every revolution needs time to be accepted. And every revolution has its first casualties: those who believed too early, or believed too much.

In 2026, as the pandemic swept the world, football stopped. Liverpool were then 25 points clear of Manchester City, all but certain of the Premier League title, when the season was suspended. I lost my faith deeply. If data could not predict a pandemic, what did data mean? I wrote three drafts and deleted them all.

When football returned in empty stadiums, I discovered something no model had prepared me for: when the context changes, the metrics change. The home win rate fell from 46% to 39%. Home advantage — seemingly immutable for decades — turned out to depend on the crowd. I had to spend weeks alone redefining my model, and since then, every metric I read has come with a question: what circumstances produced it?

An empty stadium does not falsify data, but it makes the truth feel hollow. That is a lesson I will never forget.

By 2026, aged 46, I was writing a series on the Euros and happened to connect with an Italian tactical analyst on social media. He shared internal training data from the Italy national team: they ran an average of 112 km per match — not the highest in the tournament — but their ball-circulation index was outstanding. Thanks to that relationship, I wrote "The Italians are not a defensive team — they are a movement machine," and the piece was shared more than 10,000 times. For the first time, I felt the joy of data analysis with a community and colleagues who trusted me.

I tell these stories not to boast, but to say that every number I once trusted has at some point made me pay a price. Those who are right before their time always pay in solitude. The early pioneers of xG were called dreamers; the first to use PPDA to criticize a big club were seen as disrespecting tradition. Being right in the numbers does not mean being recognized.

So when the data table is empty tonight, I do not panic. I recognize that I stand before a familiar temptation: the temptation to fill the gap with guesswork. In my trade, that is the line between analysis and illusion.

The transfer market is where that line is thinnest. Every transfer window, I receive hundreds of rumours, dozens of price tags, countless promises from agents. The bubble in the value of young players is inflating to absurd levels: a player who has not played 50 top-flight matches can be valued at 100 million euros. When the data on that player's playing sample is still too thin, that 100 million figure is not a forecast — it is a naked gamble. And the analyst's greatest temptation is to use a few good matches to justify a price that has no basis.

I have watched players like Erling Haaland be revalued every week on the strength of a single goal. A goal can push a value up, but a goal does not make a season. Every number in the transfer table is a destiny waiting to be written — and the writer's responsibility is not to turn that destiny into a bet.

To understand why player prices inflate, one must look at the money behind them. Premier League broadcasting revenue rises with each cycle, and every new deal pumps more liquidity into the market. When more money flows in than there are genuinely world-class players available, prices are driven up not because quality has risen, but because supply cannot keep pace with demand. A 19-year-old striker who scores 12 goals in the second tier can be quoted at the price of a centre-back who has won the Champions League. That distortion lies not with the player; it lies in how the market values potential through belief rather than evidence.

VAR is the same. I have followed VAR since its early days and have always believed the space for subjective judgement within it is larger than people think. The phrase "clear and obvious error" sounds like an objective standard, but it is itself a vague clause. Two referees looking at the same slow-motion replay can reach two opposite conclusions, and both are defensible within that wording. When a penalty is overturned in the 88th minute, the cause is rarely the technique of the shot. It lies in who reads the context, who clings to the letter, and who has the courage to say he is not sure.

I once watched a match in which three different decisions were given for the same collision in the box — once the on-field referee waved it away, once VAR intervened, and once the crowd in the stadium reacted furiously. None of them was entirely wrong. That is the nature of a system trying to turn human judgement into a process. Data cannot erase ambiguity; it merely pushes it to another layer.

That is why I am writing this. Not to tell the story of an empty data file. But to say that emptiness, in a world stuffed with numbers, is itself a valuable signal.

When the data is empty, the most honest thing is not to fill it, but to record that it is empty and go find another source. In the world of a long season, the awakened can only rely on their own spreadsheet — but it must be an honest spreadsheet.

I think back to that time in the library in 2026, when I discovered my model had omitted corners. That day, I learned that the limits of data are nothing to be ashamed of. What is shameful is hiding those limits. A good analyst is not someone who never errs, but someone who builds a system to detect his own errors.

In this major tournament season, as the whole world turns toward national teams, I see many analyses written with absolute confidence. Everyone has a model. Everyone has a prediction. But very few pause to ask: what if my data is empty? What if my source is broken? What if what I believe is merely the echo of what I want to believe?

Such questions do not weaken analysis. They strengthen it.

I have lived long enough in this trade to know that football will always break every model. A team can win with high xG and lose with low xG. A player can shine in one match and vanish the next season. A referee can be right by the law and wrong by common sense. Data gives us probability, not destiny.

I learned at Anfield that belief is itself a variable. The belief of the crowd changes the result. The belief of the player changes the shot. The belief of the analyst changes how he reads the number. And so the honest analyst must constantly examine his own belief.

There is an image I always keep in mind: a father sitting before the screen, watching football with forty years of emotion; his son beside him, watching through a table of numbers on a tablet. Both cry out when the ball hits the net. Both fall silent when their team loses. The difference between them is not generation, but the way they believe. And if a data table can bring those two people closer together, that is already a small miracle.

The data table is still empty tonight. I have no answer yet for the round just played. But I have something to do: call the person responsible for the data source, check the feed, and if necessary, sit down and watch every match again with my own eyes. Because sometimes the most honest way to understand a number is to accept that you do not yet have it.

Perhaps that is the greatest lesson of a major tournament season. When everything is compressed, when the pressure of international football bears down on every ball, we easily forget that football is not only played on the pitch, but also played in the minds of those trying to understand it. And in those minds, humility is a skill, not a weakness.

I will not write an analysis based on empty data. I will write a piece about why empty data matters. Readers deserve to know when a writer truly has evidence, and when he is merely guessing. That is the most basic respect between a storyteller and a listener.

And if the data source returns tomorrow, if the columns fill up once more, I will sit at the desk again, calculate again, whisper with them again. But this time, I will remember that every spreadsheet can one day be empty. The awakened prepare for that day in advance.

Data whispers, and those who know how to listen will hear the miracle. But the wise also know how to listen to the silence. Because in silence, sometimes, the truth is waiting to be acknowledged.