When the F1 analysis returns 'N/A': Lessons on data, credibility, and responsibility for sports journalism

Munich, August 13, 2026. The August evening is unusually hot, and I open a file named “Comprehensive Assessment”. The file is long, structured into nine sections, with risk tables, information ratings, and monitoring signals. It looks like an in-depth motorsport analysis prepared for a broadcast channel. I pour coffee, sit down, and read. When I finish, I realize I do not know what I have just read. No race is named. No driver is named. No team is named. There are no technical parameters, no pit-stop strategy, no driver standings, no transfer story. Every key cell says “N/A”. It is like a beautifully designed museum with no artifacts inside. I have lived with sport for 54 years and worked in this craft for nearly four decades. I started in the days when I heard F1 engines on the radio, before the internet, before Opta turned numbers into a new religion. I have seen rushed articles designed only for clicks, and I have seen analyses that shamed an entire footballing nation. But rarely have I seen a document so confident in its structure yet so empty in its content. In the 2026 transfer window, audiences are flooded with rumors. Every day there are dozens of articles about a driver leaving a team, a sponsorship deal worth tens of millions, or a title race reshaping the market. The noise is so loud that people forget a simple truth: a story only has value when it can be verified. The assessment I read today is a perfect example of how a system can create a professional-looking shell without a single sports fact. It rates information value on a scale from one to five stars: sporting value, industry value, timeliness, and reference value. Result: one star out of five. It flags high risk, not because a team or a driver is in danger, but because its own input is empty. That may sound technical, but to me it is a deeply human story. There are days when a sports writer has no information, no data, no source. The real question is not how to write a long article to hit a deadline. The question is whether we are brave enough to say “I do not know yet” before publishing. I remember Germany losing 0-2 to South Korea in Kazan in 2026. I wrote that Joachim Löw had turned the world champion into a tactical museum. Germany had 72% possession but only three shots on target, and zero in the second half. The article was fiercely mocked. People called me a shock merchant. I had data, though. I had Opta numbers, match rhythm, and context. Two weeks later, Kicker cited my analysis. Not because I was absolutely right, but because I used data to challenge consensus. Today's analysis has nothing to challenge. No data, no consensus, no shock, no new insight. From a technical viewpoint, it lacks lap times, top speed, tire degradation, and track statistics. From a strategy viewpoint, it lacks pit-stop windows, soft and hard tire phases, and a Safety Car scenario. From a team viewpoint, it lacks standings, teammate comparisons, and operational health. No chain is connected. If I were running a sports newsroom, I would treat that document as a signal, not as a product. A signal that the system is detecting something missing. But if someone publishes it as a finished analysis, it creates a form of information pollution: not immediately harmful, but slowly corrosive to readers’ trust. I have been wrong before, and I am not afraid to say it. In June 2026, I wrote an article titled “Haaland will break Pep’s pressing structure”. My argument was that a classic striker would slow down Manchester City’s ball circulation. Erling Haaland arrived from Dortmund for 60 million euros, and I believed Pep Guardiola would have to change his system. Result? Haaland scored 36 goals in 35 Premier League appearances. I was wrong. But I did not delete the article. I did not defend it stubbornly either. I sat down, watched the matches again, and analyzed how Guardiola turned Haaland into the first line of defense, how the team reduced passes but increased efficiency, and how opposing defenders were stretched to create space for the players around him. I wrote a series called “Sweet Mistakes” to dissect my own incorrect prediction. Strangely, that series was received better than the original hot take. Why? Because audiences are not looking for perfection. They are looking for honesty in method. An analyst can be wrong in a prediction, but cannot be wrong by claiming to know what they do not know. If I publish an empty F1 analysis, I am deceiving readers with structure. I am giving them a beautiful frame with no painting inside. The assessment I read today also reminds me of the summer of 2026, when the Bundesliga returned after the pandemic. Dortmund played Schalke at Signal Iduna Park with no spectators. For the first time in my life, I clearly heard coach Lucien Favre shout “Schieben!”, goalkeeper Roman Bürki directing the defense, and tires gripping the grass. I did not need a commentator to scream about a beautiful move; I needed to listen to what the naked eye could not catch. Empty spaces, silences, small sounds drowned out by the crowd. I realized that in sport, the most overlooked thing is not technical detail, but silence. Here, “N/A” is that silence. There are silences on a field that speak louder than any blockbuster contract, and there are empty rows in a data table that speak more clearly than any conclusion: the system has insufficient information. If we know how to listen, we will not rush to publish. If we know how to listen, we will tell readers that we do not know yet, instead of pretending we know everything. For years, I have studied how F1 drivers save energy, read the rhythm of a race, and protect their mentality in the final laps. I have also spent years understanding one thing: the person with the most data is not necessarily the best writer. The best writer knows which data is reliable, which data is noise, and which data is still missing. Emotion is also a rare form of data, but emotion cannot replace fact. My view may seem harsh. A preliminary analysis with all fields marked N/A may simply be an intermediate step in a workflow. After adding information, it might become useful. Perhaps I was too quick to call it a poor piece of journalism. Yes, I could be wrong. Silence is sometimes just a pause before the answer arrives. But because I was once labeled a “shock merchant”, I understand the power of holding to principles. A sports analysis cannot begin with an empty answer and fill it in later. It must begin with the right question: what are we analyzing, why does it matter right now, and which numbers could change how we understand the issue? Without that question, every analytical tool is only a microscope placed over a blank piece of paper. Today’s assessment gives reference value one star out of five. That makes me think about the 5,324-word articles we produce every day. Length is not the measure of truth. A five-thousand-word analysis can say nothing; a short answer can carry the full weight of a problem. When I see a long document with too many N/A cells, I do not see professionalism. I see a trap. The trap makes readers believe that because the document is systematically presented, it must have value. In motorsport, we say data cannot lie, but the person selecting the data can. A table of lap times can hide the quality of the car, the driver, and the team strategy. A possession statistic can hide a lack of creativity in the final third. Today’s all-N/A analysis does not lie in that way, but it causes a different misunderstanding: it makes people believe everything is being handled. I do not want to be part of that misunderstanding. I want to be the one asking the first question: where is the source? What is the original article? Who is the author? When was it published? Can it be verified with on-track data? If those three questions cannot be answered, I do not need to read further. When I was young, I thought writing about sport was writing about moments: a 90th-minute goal, an overtake on the final lap, a penalty in a shootout. But at 54, I understand that those moments exist only on a foundation of discipline. Without the right data, the right source, and the right preparation, no moment can be told truthfully. The emotion of the crowd is real, but if a writer uses emotion only to fill an analysis, the crowd will soon be gone. In a transfer window, this is even more true. A rumor about a driver can come from the driver’s own media company, from a team trying to pressure a rival, or from an anonymous account with nothing to lose. If I share a rumor without showing its origin, I am no different from someone spreading gossip. If I say I do not know and need more information, I am building a long-term relationship with the reader. Today’s N/A analysis is a reminder to myself. Sometimes I want to make a shocking statement to attract attention. Sometimes I want to write something long and ornate to prove I still have the touch. But each time, I remember the empty cells in that analysis. I ask myself: if I do not have the data, do I have the courage to say so? The answer is not easy. But I believe it is the most honest question a sports journalist can ask in the age of AI, automated analyses, and exaggerated headlines. We can let machines write for us, but we cannot let machines think for us. Machines can fill a blank cell with the letters N-A, but only a human understands that “N/A” means we should stop. I stop here, not because I have nothing to write, but because I want my next words to be based on evidence. In sport, as in life, knowing what you do not know is a strength. The analysis I read today may not help readers, but it helped me remember that every sports story begins with an honest question, not with an empty answer wrapped in a perfect structure.