Welcome
This is my first post. I hope you enjoy reading it.
PitWallGeek is a personal research project on Formula 1 team and driver performance.
I have always been fascinated by elite human performance—whether in teams or as individuals. What intrigues me most is not just success, but consistency: why some teams or drivers repeatedly outperform their peers, across seasons, regulations, cars, and circumstances. That fascination probably fuels most sports fans, whether they realize it or not.
I won’t tell you who the Greatest of All Time is. The GOAT question is better left for endless, heated conversations at the bar. But what we can do here is explore how greatness manifests itself in the Formula 1 arena—how it appears, how it evolves, and how it survives the noise of racing incidents, strategy, luck, and machinery.
PitWallGeek exists to look beneath the surface of results and narratives, and to study performance with the curiosity of an engineer and the passion of a fan.
Why does this matter? Because true greatness in Formula 1 does not hide in averages. When it appears, it does so through significant, repeatable differences—gaps that are too large, too persistent, and too resilient to be explained by circumstance alone. Greatness is rare, and when it emerges, it stands apart.
The challenge is that Formula 1 surrounds those differences with noise: machinery, strategy, traffic, timing, and chance. PitWallGeek exists to strip away that noise, so that when exceptional performance appears, it does so clearly and unmistakably.
PitWallGeek uses publicly available timing data (via FastF1) and presents it as a race-engineer’s instrument panel—designed to make performance readable at a glance.
Three Instruments
The same race is examined through three complementary instruments:
Race — Real-time lap evolution, exactly as it unfolded on track.
Pace — Performance isolated by normalizing fuel load and tire wear.
Spec — Equalizes car performance using a defined and transparent set of rules.
Each perspective is shown both in chronological time and as a percentile distribution. This dual view helps separate race strategy, traffic, and incidents from repeatable driver performance—the signal beneath the noise.
Across all three domains, time is the common reference. Every lap belongs to the same race, the same sequence, and the same context.
The chronological view preserves causality—what happened, when, and why. The percentile view reorganizes that same information to reveal performance patterns that persist beyond race events. Together, they describe not two different races, but two complementary ways of reading the same one.
PitWallGeek decomposes each race into lap-time distributions, then recomposes those distributions to form a new, performance-centric view of the same race.
Nothing is added or removed. The race is simply reorganized to reveal what persists beneath events.
Even after this reshuffle, the underlying structure of the race survives.
Pace timeline
Pace Lap Time Distribution
Pace Sorted Time
Sorting Laps
When laps are reorganized into distributions, they naturally resolve into four broad quartiles that remain loosely aligned with the chronology of the race itself:
1st Quartile — Entropy
The opening phase, dominated by strategy divergence, traffic, incidents, and race-start disorder.
2nd Quartile— Emergence
Breaking through traffic, gaining track position, and settling into competitive rhythm.
3rd Quartile- Sustain
Sustained pace under stable conditions, where consistency and tire management dominate.
4th Quartile — Expression
Pure speed at the front—clean air, commitment, and performance with minimal external constraint.
These quartiles are not imposed. They emerge naturally when performance is allowed to speak for itself.
Each domain in PitWallGeek is therefore read through the same three complementary instruments.
A real-time race, preserving chronology and causality.
A lap-time distribution, expressing performance in percentile space.
And a sorted-time “race”, reassembled from those distributions and referenced to the winner’s pace.
Together, these views describe the same event without contradiction. What changes is not the race itself, but the way it is observed—allowing structure, separation, and exceptional performance to emerge with clarity.
This analytical structure is shared across all three domains. What distinguishes Race, Pace, and Spec is not how the data is read, but which variables are allowed to act and which are deliberately held constant.
With that framework in place, we begin with the most familiar view:
Race
The Race view presents the event exactly as it unfolded on track. Lap times are shown in real time, preserving chronology, causality, and all the dynamics that define a Formula 1 race—starts, traffic, pit stops, safety cars, and strategy.
This is the race everyone watched. PitWallGeek does not reinterpret it; it simply provides the instruments to observe it with greater resolution.
Pace
In race conditions, no two laps are directly comparable.
Fuel load is the first distortion. Every car starts the race with the same maximum fuel allowance—up to 105 kg—and burns it continuously. Early laps are heavier and slower; late laps are lighter and faster, assuming tires allow it. A lap from the opening stint and a lap near the end of the race describe fundamentally different cars, making direct comparison meaningless.
The Pace view first removes this effect by normalizing lap times for fuel load, aligning every lap to a common reference.
Tire wear is the second distortion. Grip evolves lap by lap as tires degrade, recover, or are replaced. Even at identical fuel levels, a fresh-tire lap and a worn-tire lap are not equivalent. The Pace view accounts for this degradation separately, isolating performance from tire state.
After these two layers are addressed, what remains is a comparable expression of pace. Not an abstraction of the race—but a reorganization of it, where every lap can be meaningfully compared.
The Pace engine rests on an empirical normalization process, projecting every lap onto a common performance reference so they can be compared without losing their relationship to the race.
Why should you care about Pace?
Because results and raw lap times often tell convincing stories that are simply not true. Pace removes the distortions of fuel and tires, allowing performance to be compared meaningfully across the race—and revealing patterns that are invisible in real time but unmistakable in hindsight.
Spec
If fuel and tires can be held constant, one question inevitably follows: what happens if car performance is held constant as well?
The Spec domain is a modeled view of the race, built on a defined and transparent set of rules. It does not claim to represent reality, nor to fully decouple driver from car—an indeterminable problem in Formula 1. Instead, Spec is an instrument: a controlled framework designed to explore what driver performance might look like under equalized machinery.
Under these rules, lap times are adjusted to a common performance reference, creating a speculative but consistent environment where differences are driven primarily by execution rather than equipment.
Spec is not an answer. It is a question—asked the same way, every race.
Why should you care about Spec?
Because it lets you ask performance questions the real race cannot. By applying the same rules to everyone, Spec creates a common reference where driver execution can be compared without being dominated by car differences.
Spec matters because it lets everyone argue from the same rules.
Why should you come back?
Because greatness leaves a footprint.
Not in headlines. Not in single laps. Not in highlight reels.
But in repeatable, persistent separation that survives strategy, incidents, weather, and luck.
Race after race, the same names begin to emerge—not because the data is forced to say so, but because the signal becomes impossible to ignore. Over time, patterns form. Consistency reveals itself. Exceptional performance stops looking like coincidence.
PitWallGeek is not here to tell you who is great.
It gives you the instruments to see greatness forming—clearly, unmistakably, and on its own terms.
If you keep coming back, you won’t just remember races.
You’ll start recognizing performance signatures—the unmistakable shape of a great driver or a great team, written quietly into the data.
That’s what this project is about.
And that’s why this is only the beginning.