Empty Data, Empty Conclusions: The Fragile Line Between Analysis and Guesswork in F1

At the press centre of a race weekend, people measure time by the laps remaining rather than by the clock. When the last car leaves the track and the brake lights fade after the final corner, an Italian colleague turns to me: “Who wins on Sunday?” I answer: “There is no data yet to answer that.” He laughs. To him, the reply sounds like an evasion. To me, it is the entire job description.

Fourteen years of watching this industry taught me a simple, underrated lesson: the most dangerous moment for an analyst does not come from knowing nothing. It comes when you are pushed to say something while there is nothing to say. It feels like opening the data drawer and finding it completely empty — no title, no source, no figures, no identified entity. All that remains is a single label: “f1”.

I once received exactly such a report. It arrived on a winter afternoon in Turin, before the season began, while the teams had their factories shut. The document was long, with proper headings, a framework, tables — but every cell read “insufficient information, cannot assess”. At first I thought it was a technical error. By the last line, I realised it was the most honest document I had read in years.

A content machine with no brakes

Formula 1 today runs like a content machine with no brakes. A 24-round season generates thousands of hours of interviews, millions of posts, and an almost biological demand: there must always be a conclusion. In that churn, people forget that analysis is, at bottom, a sequence of operations with an input — and the input must exist before there can be an output.

I do not believe in trophies. I believe in the system that operates to produce trophies. A cup is merely the displayed result of an architecture behind it: how data is collected, how it is verified, and how quickly it becomes a decision. When that architecture is empty, the trophy is just a piece of metal.

My trade has one inflexible rule: evidence first, conclusion after. It sounds obvious, yet it is violated every day in the papers. A winter test with a few fast laps becomes the claim “team X will win the title”. A ten-second social media clip is elevated into an “internal crisis”. The value of those conclusions does not lie in whether they are pessimistic or optimistic. They are worthless because they ran ahead of the evidence.

Behind that haste lies a real pressure. Teams need audiences to sell sponsorship, platforms need content to hold users, and journalists need copy to pay bills. Commercial pressure always bears down on the pressure of truth, and most of the time it wins. I have watched analyses trimmed to be “easier to read”, to the point where the most important data was pushed to the margin. The reader receives the feeling of understanding without being handed real understanding.

The architecture of a conclusion

To see why an empty analysis has value, look at the structure of a decent one. It has three layers. Layer one is raw data: sector-by-sector lap times, top speed, tyre degradation, track temperature, pit-stop counts. Layer two is verification: where the data came from, what motive that source has, whether the numbers agree with one another. Layer three is the conclusion. When layer one is empty, the other two cannot exist.

A conclusion with no data behind it is not analysis. It is a forecast dressed up in terminology. That distinction matters more than it appears. In Italian, people separate “analisi” from “pronostico”. Both can appear on the same newspaper page, but one demands responsibility while the other only demands courage. In a major-tournament season, the best-selling item is cheap courage.

I once spent 240 minutes reviewing footage after my opinion was dismissed, and from that I learned that data is used to prove you are right, but also to know when you are not yet entitled to conclude. That is the line between an analyst and a commentator. A commentator is allowed to say “I feel”. An analyst is only allowed to say “the data shows”.

2026 pushes the whole of F1 into exactly the situation of the empty analysis. According to the Fédération Internationale de l’Automobile (FIA) and Formula 1, the 2026 technical regulations shift the power unit to a configuration split roughly 50/50 between the internal combustion engine and the electric motor, remove the MGU-H, run on 100% sustainable fuel, and cut downforce by around 30% and drag by 55% through active aerodynamics. The car becomes lighter, smaller, and in theory, harder to predict.

What is worth noting is that at this moment, no engineer on the grid has real track data for the 2026 car. Every model rests on simulation, and every simulation is a hypothesis. That means every conclusion about the 2026 season issued right now jumps straight to layer three while skipping layer one. They may be right, but right by accident — and something right by accident cannot be reproduced, nor used to learn.

The cost cap applied from 2026 makes the data layer even more important. When budgets are limited, every upgrade must be a calculated investment; every hour in the wind tunnel is paid for with an allocation. The teams that win in this era are decided by their ability to read data, rather than by the size of their budget. They know when a fast lap is real, and when it is merely a soft tyre on a cool track.

During the two years football had to be played in empty stadiums, I rebuilt a dataset to see how external factors affect a match. The result showed the home side loses about 15% of its pressing pressure when there is no crowd. That conclusion could not have come from just sitting and watching, from “feeling”. It only appeared when I accepted spending two years counting. That is the price of a decent conclusion, and most writers will not pay it.

This leads to the concept I hold dearest: information gain. A piece of writing only deserves to exist if it brings the reader a sliver of understanding they never had. If it merely repeats what everyone knows in a new structure, it spends the reader’s time and returns nothing. In an age when machines can generate thousands of articles an hour, what remains to separate a human from a machine is the ability to refuse to write when there is nothing to write.

On the track there are 20 cars, but the race truly takes place between two brains. One brain on the pit wall, one brain in the rival garage. Between those two brains lies a grey zone — where models do not reach, where data does not finish the sentence. The grey zone is not a place short of light. It is where the truest data lives. But to see it, you must already have data.

There is a temptation worth naming. When pushed to conclude, the inexperienced writer fills the gap with a confident tone. They describe a race they did not watch fully, analyse a diagram they did not read closely, and use decisive language to cover a gap in knowledge. Certainty of tone and certainty of data are two different things, and readers are rarely warned to tell them apart.

I understand why it is seductive. A decisive answer is easier to share than a conditional one. A bold prediction sparks more debate than caution. But if the measure of success is share count, then people are measuring quality with something unrelated to quality. That is why I archive every draft of mine with a timestamp — so that a year later, I can set what I once said against what actually happened.

Lewis Hamilton’s move to Ferrari is the clearest example of this principle. A contract signed on paper, then forced through the experiment of the race track. When I wrote about it, I did not deliver a verdict at once. I recorded the conditions that can be checked: how he settles into a new technical system, what the team around him looks like, and what the first data points say. Every new contract is a hypothesis. The race is the experiment.

There is an image I cannot forget: the empty grandstands of the pandemic season. An empty stadium is not abnormal. An empty stadium is an operating theatre. With no crowd noise, you hear the tyres more clearly, the engines, and the sound of wrong decisions too. That environment taught me that much of what we call a team’s identity is really a product of the audience.

In the paddock, rumours about transfers and technical changes have a life cycle of their own. They appear in a café, are repeated on forums, and suddenly are treated as fact. But a rumour is only layer zero. It has never passed through the verification layer. When I am forced to write about them, I always state the source and that source’s motive. A rumour with no motive is just an untested story.

When discipline becomes a shield

There is a flip side I must admit. Data discipline, if left unchecked, turns into a coward’s shield. I have met clever analysts who use the phrase “insufficient data” as a hiding place, so they never have to commit, never have to be wrong. They speak beautifully about method but never offer a judgement. A judgement that always evades is worth no more than a reckless prediction. Both dodge the real experiment.

Between guessing and staying silent, neither is the right answer for someone doing this trade decently. The craft demands something harder than either: say what the data permits, state clearly the limits of that, and accept being judged when the moment of verification arrives. An empty analysis is not meant to show off honesty. It is a reminder that discipline only means something when it leads to action, not when it replaces action.

This is the biggest blind spot of the school I belong to. When you spend a career warning against hasty conclusions, it is easy to slide into the opposite state: becoming someone who forbids all conclusions. Readers do not need a guard standing at the door of data, shaking his head at everything. They need someone who shows them: here is what the data permits, here is what it does not, and here is my judgement — with its confidence level attached.

So I force myself to seek out the strongest counterargument before concluding. Before saying a team will collapse, I must write three scenarios in which it holds firm. Before praising a performance, I must point out which track conditions could have produced the illusion. Self-challenge is not for hesitating; it is for making a judgement stand firmer when tested.

I also learned that a beautiful model can become a prison. My background in technology makes me believe in systems, but that very belief easily leads to forcing a race into a ready-made template. Every time I finish building a model, I ask myself: if this model is wrong, which evidence will tell me first? If there is no answer, that model is just a belief presented as a chart.

The 2026 test

The coming 2026 season will be the greatest test of this question. A new rulebook, new power units, and an old order that could be upended. In the next few months, hundreds of articles will claim to know in advance what will happen. Most of them will not have a single real lap to stand on. Based on my experience following pre-season testing, the fastest teams in testing are usually not the strongest when the season truly begins.

What is worrying is not the wrong predictions, but that they are forgotten. A wrong prediction remembered is a lesson. A wrong prediction forgotten is a debt. When people only recall the times they were right and erase the times they were wrong, they build something resembling credibility but which is in fact an illusion curated by memory. And in a season of 24 rounds, that illusion can only last until the next race.

The measure of an analyst is not how often he is right, but whether he dares record the times he was wrong. I keep a private journal for this. Every judgement is logged with the date, the data used to reach it, and the confidence level at the time. When the season ends, the journal does not tell me how right I was. It tells me how I thought.

The empty analysis I received that winter now sits in a separate folder, like an artefact. It reminds me that in an industry built on speed and noise, the hardest thing to say is still “I do not know yet”. And that an analyst’s discipline is measured by what he refuses to write, as much as by what he dares to write. I still do not believe in trophies. I believe in the system that operates to produce one — even when that system is, for the moment, only a void.

What I await in the new season is not who wins the title. It is the question of who keeps a cool head while everything around them burns, and who cracks first. My theorem does not predict the champion. It predicts who collapses first. And to predict that, I need something no one has at this point: real data. Until then, I choose to stay silent — and prepare for the day I am forced to speak.