When F1 Data Lies: From Out-of-Sync Sensors to Unanchored Conclusions

When the Sensor Lies for the Second Time

In 2026, I sat on the coaching staff of a major Serie A club and was handed a job nobody wanted: auditing the movement-data set of twenty matches across a season. The expected-goals figure at home was notably higher than away, yet actual goals were level. A small absurdity that many colleagues overlooked. I did not. Frame by frame, I found a sensor in the south-west stand running about two-tenths of a second late, skewing every goalkeeper build-up on the map. The number was not wrong. The clock counting it was.

I tell that story not to talk about football. I tell it because this week, reviewing my own match-reporting process, I found that same old feeling: a data system that looks flawless, presented with glossy graphics, hiding an empty space behind it. Not empty for lack of machines. Empty because nobody bothered to check whether the machine was keeping time.

Data only tells part of the story; the rest lies in whether people know how to listen.

Context: The Vast Measuring Machine of a Grand Prix

A modern race car carries hundreds of sensor channels: engine revs, brake temperature, tyre pressure, steering angle, braking force, lateral and longitudinal acceleration, fuel consumption, estimated tyre wear, and track position accurate to a few centimetres. Every second, data pours into the garage like a river. The strategy engineer sits before dozens of screens and, within seconds, must decide whether to pit the driver or keep him out three more laps. Three laps can be a championship.

The problem with this machine is that it is too good. When a data system runs smoothly ninety percent of the time, people start trusting it one hundred percent of the time. That confidence is the premise of every collapse.

Take something as simple as the timing loop. The system relies on transponders under the car and antennas around the track. When they work, lap times are accurate to a thousandth. But antennas depend on signal, weather, obstacles, and whether the driver runs the right line. A wide corner, a deep puddle after rain, a car parked in the wrong pit-lane spot — any of these can erase a lap from the timing sheet or, worse, assign it to the wrong driver.

I remember a live broadcast years ago. A driver had just taken fresh tyres and suddenly ran half a second quicker per lap. The press room buzzed. Everyone first thought of a technical step forward, a floor trick, an upgrade timed to that race. But when I replayed the final corner camera against the timing sheet, I realised the driver had cut the track edge at a point the antenna missed, so the clock skipped a stretch. He ran faster on the number, not on the asphalt. A whole analysis direction was built in thirty minutes on a device blind spot.

Every collapse has a premise; few bother to look beforehand. In Formula 1, that premise usually hides in the very thing people assume cannot fail: the measuring equipment.

The Data Anchor: Nine Dimensions and the Forgotten Root

At fifty-seven, after forty-one years observing the industry, I built myself a way to frame analysis across nine dimensions: technical and car, race strategy, team and driver, competitive landscape, regulation and governance, driver market, risk profile, public narrative, and industry transmission. It sounds imposing. But all nine lean on a single thing rarely mentioned: the data anchor.

What is a data anchor? It answers a simple question: the number I am using to conclude — where did it come from, under what conditions was it measured, and who verified it.

I set myself a rule I never break: every analysis must carry a note on measurement conditions. Without that note, I do not cite the number. Not because I am over-cautious, but because I have paid for carelessness, and the price was not a bad comment but an entire analysis direction leading readers astray.

Picture a typical technical analysis. A team's car is said to eat its tyres. Degradation over ten laps exceeds forecasts by three-tenths per lap. A conclusion follows at once: poor aerodynamics, dirty air destroying the tyres, a floor change needed. But under what conditions was that data gathered? Did track temperature spike? Was the driver stuck behind another car, unable to use clean air? Was the tyre-temperature system out of calibration? Without answers, the conclusion is just a guess, neatly packaged.

A contract only looks good on paper until someone tries to fit it into a running system. Likewise, an analytical conclusion only looks good on the board until someone puts it on the scales.

Oddly, professional analysts often ignore the anchor more than ordinary readers do. Because they hold more data, they trust it. Once they trust it, they stop checking. And once they stop checking, they become guides to a belief presented scientifically but lacking a footing.

When the Data Source Collapses: A Lesson from an Empty Space

There is one situation I consider the most dangerous in analytical work: a data gap. Not wrong data — wrong data collides with reality and can be caught. Worse is when data does not exist at all, yet the process runs on as if it did.

I have seen this on a small scale. An analysis file passed from collection to conclusion, and in that file the most important part — the list of actual information — was blank. No article title. No source. No information points. No core viewpoint. No entity named. The entire evidence base was a white page titled with the fields to be filled.

What is most frightening is how many people handle such a gap.

The undisciplined fill it in. They invent a driver. They assign a sourced-less figure to a technical department. They build a story that sounds logical, fluent, and compelling — and entirely untrue. Readers cannot verify it, because it is written in a confident voice.

The disciplined stop. And stopping is an analytical act, not weakness. Saying "I do not yet have enough data to conclude" is far harder than rushing the pen.

In the racing world, data gaps appear everywhere, we just do not notice. When a car retires with an engine failure, we have plenty of data on temperatures, pressures, and revs before that moment. But we have no data on the engineer hesitating for two seconds at the first warning. No data on what the driver suspected in the cockpit. No data on what the team principal weighed before deciding. Those gaps are not on the screen. They sit in silence.

An empty grandstand does not kill the race, but it takes away something data cannot measure. By the same logic, a silent interview, evasive answers, long pauses on the radio — all of that is data. Just data not recorded on any sheet.

Strategy, Tyres, and the Limits of the Model

Let us take a concrete example to see how much the anchor matters.

A team runs third. Its model says that if it pits at the end of lap forty, it will rejoin seventh and can pass two cars to finish fifth thanks to fresh tyres. The model is detailed: it accounts for the tyre wear of each car ahead, their average pace over the last ten laps, expected pit time, and the warm-up penalty of fresh tyres in the first three laps.

But the model assumes something it never states: that the driver's mental state is stable.

In the final laps of a tense race, a driver can lose two-tenths per lap not because of tyres but because their head is elsewhere. A radio message misheard. A mistake last lap still nagging. An unresolved doubt about the car. No sensor measures that. The model is not mathematically wrong. It merely lacks an unmeasurable variable.

This is where I see young analysts get stuck. They build beautiful, even accurate, strategy models and are then surprised when reality differs. They blame luck. But luck is just the word we use when we have not yet identified the variable we omitted.

I once analysed a race somewhere, and before it ran, I wrote a prediction based on spatial data rather than time alone. I drew the shape of the compressed, high-defensive block, measured the average distance between its lines, and found a gap I called a vertical rectangle — a no-man's-land between defence and rear. I wrote that unless they changed, a lofted counter would finish them. When it happened, many mocked me for turning emotion into arithmetic.

But I was not guessing. I was reading the map. And the map showed a gap the naked eye misses, because the human eye tracks the ball, not the space.

Every tracking figure belongs on the operating table, not the altar. This is the principle I live by daily. Data is not worshipped. Data is dissected, examined, doubted, cross-checked. Only when it survives that process may I use it to conclude.

The Blind Spot of Precision

This is the most counter-intuitive part of how I work, and the part I most want readers to remember.

The more precise a figure, the more it can breed a dangerous illusion. When people see lap times measured to a thousandth, they assume everything can be measured to that degree. But device precision does not equal truth precision. A fine clock measures what it is told to measure. It does not measure what it is not told to.

The first blind spot is measurement conditions. A fast lap set in clean air, on fresh tyres, at ideal temperature, with low fuel, is one thing. A fast lap set stuck inches behind another car, on worn tyres, in high track temperature, is an entirely different feat. On the timing sheet, the two can look identical. On track, they are two worlds.

The second blind spot is missing data. When a team brings an upgrade and the driver suddenly goes well, almost certainly someone credits the upgrade. But the team may have changed a garage procedure, or the driver found a new cornering approach, or the circuit configuration simply suited them. A variable that does not change does not mean a variable that does not matter. Sometimes the answer lies in what is absent.

The third blind spot is the analyst. Everyone tends to seek data confirming what they already believe. When I analyse a team I like, I force myself to judge them more harshly. When I analyse a team I have doubted, I force myself not to turn doubt into conclusion. Forty-one years taught me that the hardest thing to control is always my own ego.

And here is the biggest blind spot, the one I call the blind spot of source.

When reporting a deal, a dispute, a staffing change, people rarely ask where the source came from. A leak from a team may be true, or a psychological move planted at the right moment to confuse a rival. A team statement may be truth, or a way to lower expectations. Either way, readers get one sentence, while the writer gets a whole matrix of hidden motives.

From the training ground in Milan to the esports screen, the law of the gap stays the same. Wherever there are people, there are hidden motives. And wherever there are hidden motives, analysis must be twice as cautious.

The Silences Not on Any Sheet

There is one thing I always look for in every race and always find, even when it appears on no chart: silence.

I listen to team radio. I hear the engineer's tone when calling the driver in. A steady tone means they trust the call. A slightly raised tone means they hide worry. A silence held too long means they argue inside their own head. None of this is in the telemetry. But it often precedes what happens.

I also watch the grandstands. A packed grandstand presses on a driver in a way an empty one cannot. A grandstand gone silent after an incident can leave a team disoriented for many laps. That pressure is not measured in seconds. But it exists, and it shapes decisions.

Once, a team made a strategic call that looked reasonable on paper. Watching closely, I found something odd: they pitted the driver two laps earlier than any calculation justified, with no data reason. Later I learned that between those two laps, a short exchange occurred between engineer and driver, never published. The driver said something. The engineer listened. The decision changed. No sensor recorded those two sentences, but they changed the entire race.

This is why I always tell young colleagues: do not only read the screen. Read the control room. Do not only watch the data. Watch the people who create it.

Trust the Data, Do Not Trust It Blindly

I want to close with a reminder to myself, and to anyone in this line of work.

My job is to reconstruct the truth from fragments. Every fragment has value, but no fragment is the whole truth. A good analyst is not the one using the most data. A good analyst is the one who knows which fragment must be verified before belief, and which is lying subtly.

In forty-one years, I have seen conclusions that sounded reasonable rise and fall for lack of one check. And I have seen small discoveries, overlooked by many, become the premise for a great change. I have seen it in football, in racing, and even in the esports screens I follow out of curiosity. The law does not change. Everything has a premise. Few bother to look beforehand.

The next race will come. Data will flow again. And in that flow there will be a number out of sync, a device blind spot, a gap nobody noticed. The question I ask myself before every race is not "which team is stronger". It is: the data I am about to use, where did it come from, and what have I checked it against.

The grandstand may be empty. The sensor may drift. The source may be noisy. But the analyst holds something no device can replace: integrity in verification. Keep it, and I can stand on this track a few more seasons. Lose it, and every figure I produce is merely an echo of an empty belief.

Next lap, I will still be there, headphones plugged into the radio, eyes on the screen, hand noting what cannot be measured. Because perhaps the truth of the race lies exactly where no one bothers to look.