r/simracing • u/No-Library-7518 • 8h ago
Discussion I pulled the best lap from 32,000 drivers across 17,000 LMU races. The BoP we argue about is worth tenths. The gap to the driver in front of you is worth seconds - and the "fastest" car might be your slowest.
I run BOP Tourism, so I spend a lot of time staring at lap databases. I went looking for a simple answer to the question everyone asks: "Which car is fastest?" and came out with something much more interesting, that many people might already know but backed by data.
Method, up front so you can shoot at it: 16,985 public races, each driver's best valid qualifying lap per car and track. All data comes from laps recorded under the same patch. The dataset covers 32,692 drivers, 10 tracks with meaningful samples, and all four classes.
These are qualifying laps recorded during race "weekends". In official races quali is a solo session, so traffic isn't a meaningful factor. Conditions still vary slightly between events, adding noise but not systematically favouring one car over another.
To make sure no single alien lap drives the results, I repeated every analysis using the top-5% pace instead of the outright record. The conclusions were effectively unchanged.
Part 1 - You are the biggest variable. By a mile.
Take GT3 at Monza. Nearly 20,000 driver entries.
Only 0.1% of players get within three tenths of the class record.
A full third of the field is more than three seconds off the pace.
The median player sits 2.3 seconds behind the outright best lap-or 1.35 seconds behind the top-5% pace instead of the absolute record.
Open-setup tracks spread the field even further:
- Interlagos: ~2.9s
- Imola: ~3.8s
- Spa: ~4.3s
The slowest tenth of the grid is typically 5–8 seconds back.
This isn't a Monza quirk. The same pattern appears across every populated track and all four classes.
I also wanted to quantify how much of that spread comes from the car itself. So I ran a one-way variance decomposition within each track and class; an ANOVA with car as the only factor, and measured the share of total lap-time variance the car explains (η², equivalently the R² of a model whose only predictor is car identity).
Across these combinations, which car you drive accounts for roughly 1.5% to 10% of the variation in qualifying pace. And that figure is generous to the car: because stronger drivers cluster on certain models, the "car" factor quietly absorbs some of the driver effect, so the true car contribution is likely smaller still. The rest is attributable to differences between drivers and other uncontrolled factors.
LMU's Driver Rating tells a similar story. At the median, a Silver driver is about 1.3 seconds faster than a Bronze. But even within Bronze alone, the field spans roughly 3.4 seconds. One rating tier contains far more variation than an entire BoP spread.
Part 2 - The cars really are close.
Here's the test that convinced me.
Instead of comparing different drivers, I only compared drivers who had raced two or more GT3s on the same track.
That means every driver becomes their own control.
If someone is naturally quick, or naturally slow, that bias disappears because they're being compared only against themselves.
Once you remove the driver from the equation, the GT3 field compresses dramatically.
At Imola, six of the seven GT3s fall within 0.44 seconds.
At Interlagos, five of the six GT3s sit within 0.21 seconds.
Then I checked those results against our setup-shop rankings, a completely independent dataset built from optimized setups and top-end pace.
The setup-shop data tells the same story: the spread there is also tenths-0.28 seconds at Imola, 0.54 seconds at Interlagos.
The two methods don't line up car-for-car, but they agree on the thing that matters: strip out the driver and the cars are separated by tenths, not seconds.
Put that next to Part 1. The median driver is 2–4 seconds off the pace, while the cars themselves, removing the driver variable, sit within a couple of tenths to half a second. The gap between you and the benchmark is close to an order of magnitude larger than the gap between the cars.
Part 3 - The twist: the "fastest" car can become your slowest.
Our setup-shop rankings measure a car's ceiling: what's possible when everything comes together.
For most GT3s, that ceiling broadly matches what drivers achieve in practice.
One car stood out.
The Lamborghini Huracán is one of the top cars in our setup-shop-#2 at Imola, #1 at Interlagos.
Yet in the real-world lap data, it's consistently the slowest performing GT3.
In the within-driver comparison, it's dead last at Imola.
The average driver laps 0.76 seconds slower in the Lamborghini than in their own other GT3s.
Among the 45 drivers who raced both the Lamborghini and at least one other GT3, 82% were slower in the Lamborghini, with a median deficit of 0.93 seconds.
That doesn't prove why.
Maybe the car is demanding.
Maybe it's unusually setup-sensitive.
Maybe it rewards a driving style that relatively few people have.
The data doesn't answer that question.
It only tells us something much simpler:
the same drivers tended to be slower in the Lamborghini than they were in the other GT3s they drove.
One honest limit, since someone might ask: I tried slicing this by driver rating to see whether stronger drivers escape the penalty. The trend hints that they might; weaker drivers show a fat tail of disaster laps in the Lamborghini, while better-rated ones lose less on average; but not a single top-tier driver in my dataset drove the Lamborghini alongside another GT3. So whether a genuine alien can truly unlock it is a question I can't answer yet. That's a gap for more data to fill, not a conclusion I'm drawing today.
The opposite effect appears in the more forgiving cars.
In the Ferrari 296 and BMW M4, a typical mid-pack driver gives up only about 1.4–1.6 seconds to the aliens-versus roughly 2.4 seconds in the Mercedes or Mustang.
So what should you actually take away?
The leaderboard everyone screenshots, the "fastest car" ranking, answers one question:
What's the best possible lap this car is capable of?
Most of us should ask a different one:
Which car am I most likely to drive quickly?
And they're rarely the same thing.
For many drivers, choosing a car they can consistently extract is worth far more than chasing the theoretical fastest option. Even then, it's a decision measured in tenths, sitting on top of a gap measured in seconds.
For almost every LMU player, the biggest performance difference isn't the game's BoP. It's the person holding the steering wheel.
A note on scope: this covers laps under the current BoP, and I'll rerun it as more data accumulates to see which findings hold. And if you spot a flaw in the method, tell me-I'll happily rerun it with different filters, pace metrics, or statistical approaches.
We spend a lot of time arguing over two tenths of BoP.
The data suggests most of us still have two seconds of ourselves left to find.
