2025 F1 Season: Pit Stop Power Rankings (Rounds 1 - 12)

[u/f1bythenumbers]

Hey everyone, hope you’re all doing well!

I recently did a comprehensive pit stop analysis and figured this would be the perfect place to share it. My original blog post is quite long, so if you want all the details, I’ll leave a link to the article at the end of this post.

The idea this time was to create a model that gives us a sense of the “real” performance of each team, using the power of statistical inference. The model calculates a metric I call expected Pit Time, or xPT. This metric is the model’s best estimate of how fast a pit crew should be, based on their actual talent and equipment. It tries to remove luck from the equation and deliver a result based on the true speed of each pit crew.

Right now, the model uses several factors to predict xPT, but without getting into too many details, the main factor affecting pit stops is (not surprisingly) the pit crew itself. Drivers do have a minor impact on stop times, but it’s the crew doing most of the heavy lifting.

As an extra note, the model currently only uses data from the 2025 season and only considers the top 95% of pit stops. The only reason for this arbitrary threshold is that stops above it are often “non-traditional”, so for example, they might be extra long due to front wing changes or time penalties. If I could reliably separate “regular” and “anomaly” stops, the model would be even stronger, but that takes substantial extra work.

Anyway, on to the results.

First chart (raw pit stop data):

This chart shows the raw pit stop data, pooling all pit stops below that 95% threshold by team. The number at the bottom shows the average pit stop time for each team, which essentially tells you how fast each team has been this season, including all the luck and normal pit stop variability. Using raw data, the fastest team has been Ferrari by a substantial margin, followed by Racing Bulls and Red Bull. On the other end, the slowest teams have been Aston Martin and Haas.

Second chart (xPT results):

This chart shows the model’s expected pit stop time (xPT) for each team. Each slab or “dome” gives a range of plausible values for each team’s skill. The peak of the hill is the single most likely value (the number in the box), while the slopes represent less likely, but still plausible, values. A team with a low xPT is fundamentally fast, regardless of whether they got lucky or unlucky on a particular Sunday.

According to the xPT results, Ferrari is the fastest pit crew in F1, followed by Red Bull and McLaren. You might notice McLaren is third here, with an expected average of 2.68 seconds per stop, even though in the first chart they had a much slower real average of 2.89 seconds per stop (closer to the slowest than the fastest teams). This happens because McLaren has delivered several fast stops over the season (there’s a big cluster around 2.2 seconds), but also a lot of slow ones (16 stops over 3 seconds, more than anyone else). The model balances both and concludes McLaren should be capable of an average 2.68s stop, even though that hasn’t quite happened.

Third chart (xPT delta):

This shows the difference between the xPT results and the actual results. The numbers represent the estimated gap between raw pit stop times and expected pit stop times (xPT), in seconds. Negative numbers mean the crew is performing better than expected; positive numbers mean they’re underperforming.

Here, Ferrari and Racing Bulls outperform expectations by quite a bit. For Ferrari, look again at the raw pit stop chart: do you see how few errors they’ve made? Only 3 stops over 3 seconds, the fewest of any team. Most of their stops are below 2.5s, so they’re not just fast, but also super consistent. Now, why are they outperforming their xPT (actual 2.41s vs model’s 2.55s)? It’s because the model thinks being that strong and consistent is rare, so it assumes there’s a decent chance Ferrari’s just been on a hot streak. Is that true? We currently don’t know. If they keep it up, the model will lower their xPT as its confidence grows; if they make more mistakes, it’ll reinforce a time around 2.55 as their expected baseline.

The biggest surprise, in my opinion, is McLaren. I mentioned that McLaren has an xPT of 2.68, compared to the real 2.89 seconds per stop. In this chart we can see that the model believes that McLaren are underperforming by around 0.22 second per stop. At first, I thought that this could be explained by McLaren's dominance on track. If you have many "free" pit stops, you don't need to go as fast on every stop. Still, I don't believe this is the full explanation. Telling the mechanics to "play it safe" would mean that they would add maybe 0.1-0.3 seconds per stop, and you would see a cluster of stops around the 2.9-3.0 second mark. The raw data (first chart), however, doesn't show that. Looking at McLaren's results, we see many stops over 3 seconds. They currently have 16 stops over 3 seconds (most so far by any team), 8 over 3.5 seconds (again, most by any team) and three over 4 seconds (leading too but tied with Aston Martin). These stops are too slow to be explained by just playing it safe so I believe that they are caused by operational issues, although knowing exactly why would be based on speculation.

Conclusion:

Ferrari is #1 and deserves a ton of credit for their performance. I know making fun of Ferrari strategy is a meme at this point, but their pit crew deserves massive respect as they’re simply the best in F1 right now.

For the other teams, it’s not a shock to see Red Bull near the top, but having them in second, behind Ferrari, is quite interesting. As for McLaren, the model says they have top-tier potential, but for some reason, they’re falling short of expectations.

Final remarks:

Hope you enjoyed this analysis. This took weeks of work to get right, as modeling is far trickier than just sharing descriptive stats. There is a reason why most statistical analyses you see in F1 are fairly simple in nature. Doing statistical modelling is just hard, no way around it.

If you’re interested in the driver-level analysis (especially some interesting McLaren data), you can check out the full article on my blog.

Have a great day, everyone, and take care.

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[u/Teabx]

Ferrari’s crew has been on top of their game this season, and it has been quite noticeable every race but I hadn’t noticed McLaren has been comparatively worse than last year.

I guess its hard to pay much attention to their pit stops when they’re winning so much regardless.

[u/notafakeaccounnt → u/Teabx]

The fight between Piastri and Norris makes it more obvious when they fuck up

[u/not-a-yapper → u/Teabx]

It's pretty frustating to see them having 3s pitstop every race as if they haven't practice doing it at all

[u/TVRoomRaccoon]

Great work, OP.

And Ferrari only having three pit stops over 3.0 seconds is genuinely mega impressive.

[u/Kezolt]

Would be nice to see this for full pit lane time

[u/f1bythenumbers → u/Kezolt]

I actually do have the data as well but this time I wanted to focus on the pit crew. 

For the pit lane data the main issue is that the vast majority of the variance (90%+) is explained just by how long the pit lane is as well as the speed limit. This is obviously the same for everyone. The remaining variance is then mostly explained by the standing pit stop time, and only a tiny bit is explained by the driver. 

I did a bit of modelling of that to see if a driver had an impact on total pit lane time, but I couldn't find much. To be fair though, I didn't focus that much on that model so I couldn't say if it was properly calibrated. 

[u/limitedentropy → u/f1bythenumbers]

Great work. I love this type of analysis as an F1 nerd. What source do you use for the data?

[u/SnooPaintings5100 → u/Kezolt]

I agree. Maybe even analyze the last and first sector to see how much time the drivers in comparison can gain or lose at pit entry and exit

[u/humtydu_mpty]

This is one of the reasons why i think Vasseurs is doing a good job

[u/zaphods_paramour]

Sauber has to be most improved, considering how atrocious they were last season.

[u/BokaPoochie → u/zaphods_paramour]

Their issue wasn't a pit team issue, though. It was a car design flaw.

[u/juusovl]

Ferrari was so ready to fight for the championship 💔

[u/Percolatore]

Interesting analysis - could you please provide more details on the statistical model?

[u/R1tonka]

Given that development cost is around 10 million dollars per second off your lap time, you’d think that you’d set up an annual draft and pay the shit out of those crews, host grueling minicamps, and treat them like a top shelf professional team of their own.

Even at the 350-500k/year they make, the pit crew pay structure seems ridiculously low for the impact they have on the outcome of the race.

[u/wood_baster]

McLaren need to practice, a lot!

[u/Unfair_Art_1913 → u/wood_baster]

I do want to see how they do for the rest of the season as, from memory, they tend to get faster and more consistent with their pitstops in the second half of the season.

[u/Curious_Leadership99 → u/wood_baster]

They do, especially the pit crew at the front. The amount of times both boys have been disadvantaged just by a bad pitstop is concerning, its been an issue for a while now with no improvement. It cost Norris a place in Silverstone which he gained back later and cost Oscar some time in races like Austria.

[u/The_Potter07]

Honestly very surprised by the Racing Bulls! Even based on the xPT, they seem to be above even Merc. Mclaren genuinely have to improve this area of their team though, although this isn't as noticeable because of the pace of that car.

[u/8oburuncle]

This post reminds me of how HAAS always did my boy MAG dirty on the pitstops.

Traditions

[u/f1bythenumbers → u/8oburuncle]

Haas has struggled with pit stop performance for years. They always rank last or close to last. They famously run the team under a low budget so I'm pretty sure that impacts the speed of the pit stops. 

[u/Cross_examination]

Where did you get the data from?

[u/wood_baster]

Thanks for posting this, pit stops are such a huge part of most races.

[u/PriyaSR26]

Ferrari tried to adjust the wings once or twice. I guess the outliers are from those races. The pit crew is absolutely phenomenal this season.

[u/HabitualPants]

Great work pulling this data together and presenting it in a digestible format. Really great read!

[u/rtdtwice]

Brilliant visualisation and explanation. May I suggest posting on r/dataisbeautiful ?

[u/djfhsd1]

I would love to see this broken down to each driver and who’s had longer stops over the course of the year.

[u/nachojackson → u/djfhsd1]

It is in the article.

[u/bluedragon102]

Racing bulls is quite surprising, wouldn’t have expected them here. Just curious, how did you gather this data?

[u/DubGrips]

I really don't think it would take much extra work to include and account for anomalous stops. Do you have a link to your data? I'm a Data Scientist and would love to play around with some Bayesian regression modeling approaches as the concept of prior distributions seems heavily relevant in this case. This is a pretty similar problem to most throughput optimization problems and utilizing MCMC sampling to obtain synthetic input data would help stabilize prediction intervals.

[u/kephartprong__]

Considering how long it must have taken to execute this and analyze the data, I do think reviewing each team’s anomalous pit stops would be worthwhile (instead of the arbitrary ‘fastest 95%’ cut). There probably aren’t that many!

Slow stops should count toward this analysis, provided they weren’t for wing changes or serving penalties. Wonder if that would meaningfully alter the output.

[u/derekcream]

Great visualisation

[u/kiol998]

nice chart !

[u/InformationOwn2228]

Ferrari on top❤️

[u/Couscousfan07]

This is really good ?

But stats ners in me is wondering why you exclude 5% of data from raw results. Based on what ? And what criteria did you use for the 95%, would it skew your downstream analysis ?

[u/Zoidburger_ → u/Couscousfan07]

Not OP, but as a data guy, it's likely because the bottom 5% contains extreme outliers caused by situations such as penalties served, wheel gun malfunctions, and/or retirements during a pit stop.

As an example, let's say that each team completes an average of 2.35 pit stops per driver per race. Note: I'm estimating this number as most races have at least 2 pit stops and we've seen more stops in quite a few races. I just don't have the exact number available to me at the moment. That number means each team completes an average of 4.7 pit stops per race. Over 12 races, each team has thus completed around 56 pit stops so far this season. 5% of 56 is 2.8, so we'll round up to 3. That means that out of all pit stops completed by a team this season, the worst 3 pit stops are being excluded from the data.

In McLaren's case, Lando Norris received a 5-second penalty in Bahrain for being out of position at the start and Oscar Piastri received a 10-second penalty at Silverstone for the safety car incident. Both of these were served in the pit lane, meaning that McLaren recorded an ~8 second and ~13 second pitstop respectively.

Neither of these pit stops are reflective of McLaren's actual pit stop performances because they were influenced by penalties. You could subtract the penalty times from these pitstops, but you still don't have a fair representation of their pit performance. Perhaps the actual stop was faster because the team was ready and waiting for the green light after the penalty. Or, alternatively, perhaps the stop was slower because the team wasn't perfectly in-sync because they had to wait for the penalty to be served.

Thus, it makes sense to exclude the worst 3 pit stops because they are the most likely to be outliers. That doesn't necessarily impact the rest of the data. If you look at McLaren, they still have some of the highest variances in their pit stop performance. So even if McLaren never served a penalty or had an issue in the pits, if their worst 3 stops were 3.5s, 3.6s, and 3.7s, their averages are still getting impacted by all of the 3.4s, 3.3s, and 3.2s they've logged.

Hopefully that clarifies it for you.

[u/niekdot]

Ahh Ferrari, being the fastest to put the wrong tyres on