INISECTORS - Quali

🟡VER and 🟠NOR were impressively quick in the fast S1 snake, leaving nothing on the table for🔴SAI! Their downforce was unmatched

In addition to that, RBR had lower drag➡️Fastest at the end of most straights

🔴Ferrari was quickest only out of T12/18

RACE PACE - #JapaneseGP

✅Improved the most over 2023 (s/lap):
🥇🟢Aston -1.87 (was terrible last year)
🥈🔴Ferrari -1.72
🥉🟠McL -1.52

❌Got slower:
🟣Alpine +0.08

-LEC was quicker than both Mercedes despite stopping one less time
-ALO matched PIA.
-VER: -0.28s/lap adv.

Race pace last year, for comparison
Only 15 drivers finished the race!
As no Williams driver covered over 90% of the race distance, it is impossible to evaluate their pace improvement, as the conditions would be too different for that

Which team surprised you the most today? 🤔

🛞TYRE WEAR (Per Team and Compound)

✅Best wear:
🔴Ferrari and🟢Aston (on both Hards and Mediums)

❌Worst (by far!):
🟢Mercedes on Hards
🟠McL on Mediums

🔵RBR's wear was moderate, and it didn't impact their excellent pace

⚠️The high wear ruined Mercedes' race

I realised that I made a mistake concerning McL: their race pace last year was 97.54s, and NOT 98.11s

So the team improved 'just' 0.95s/lap

All other values are correct, and the two graphs too

Thanks to those who pointed it out!

Gap to Race Winner

Only LEC and SAI led a lap in addition to VER (after the latter pitted)

In the last stint, LEC and NOR had the most laps on their tyres (27), HAM the least (15): that made him much quicker than both

Still, SAI was even quicker! (on 17-laps old Hards)

💡RedBull lifted through the 130R corner in the first 2 stints to preserve the tyres

The graph shows the speed trap (placed right after the 130R corner, which can be taken full-throttle but without DRS) values

You can notice 🟡RedBull’a bimodal distribution (two wide points: the upper one is their ‘true’ speed, the lower one is relative to lifting in the corner)

Haas reached 309km/h thrice there!🚀

Via @JMP_software

Weekly reminder that you can find ALL my socials and extra content through THIS link
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https://linktr.ee/fdataanalysis

SECTOR TIMES - Japanese GP

🔥Sainz's sectors were on fire in his last stint!

HAM was quick on 🟡Mediums (on a fresher set). However, Mercedes lacked consistency: notice the big gap in S1 and S2 between the first and second ⚪️Hard sets!⚠️

Made via @JMP_software

Increasing a car’s fuel efficiency from 5km/l to 10km/l will save you DOUBLE the money compared to increasing it from 10km/l to 20km/l 💰

Similarly, going 10km/h quicker in a slower highway segment will save you more time (and produce LESS additional fuel consumption) than doing the same through a faster segment ⏱

As @brrrake correctly pointed out, the same principle applies to F1 cars. Increasing one’s speed through the fast corners will save you less time, and induce more additional tyre wear, compared to doing the same through the slower corners. Red Bull Racing (and their drivers) seem to have grasped this concept: by lifting through the 130R corner (the fastest one in Suzuka), they managed to mitigate tyre wear for a modest loss of pace. 🛞

Can Machine Learning help F1 engineers predict an undercut attempt?🤔

@HearneLaurence contacted me about it, and later produced a model doing that!🤖

Solid lines: driver's pitting probability🔮
Dashed lines: the real pit.

The model predicted HAM pitting, and LEC covering it!💡

The plot has been taken from the Report of his Final Year Project.

The report is 114 pages long and very interesting: send him a message if you'd like to read it!

And if you're working for an F1 team you might be interested in his expertise.

Weekly reminder that you can JOIN the CHAT channel to discuss my analyses OUTSIDE of the 'comments'!
👇Use this link👇
https://t.me/FDataAnCHAT

Ferrari made TWO strategies work brilliantly: good strategy, tyre wear, and driving!👌

Check out the last stint (on ⚪️Hards): after pitting, SAI was 2s/lap quicker than LEC, who was managing his older tyres
LEC pushed more and more in that stint as the finish line got closer

Who will finish the season on top: Carlos Sainz or Charles Leclerc?🤔

Perez overtook both Mercedes cars in 2 laps, but overtaking Hamilton was way harder!

-HAM reached 303km/h;
-RUS just 292km/h (much weaker ERS deployment🪫).

Due to Russell's bad straight-line speed, PER could get the slipstream sooner, further increasing his top speed.

Remember the Machine Learning model that tried to predict undercut attempts?🤖

Fantastic news: all the scripts and the 114-page report have been uploaded to GitHub!😁

You will find it here👇
https://github.com/laurence9899/F1_Pitstop_Predict_ML

Kudos to @HearneLaurence for creating it!

Sainz's overtake was a real one - and Charles Leclerc didn't concede easily!😬

Sainz gained 0.5s over the straight, thanks to a better corner exit + slipstream and DRS.

Yet, he had to conclude the overtake into Turn 1: Leclerc braked several metres later than him!

(there was no team order to let Sainz by on the straight, only to not fight him hard)

Mercedes extended the first stint, losing ~2s/lap to the two McLarens for over 10 laps: mid-race they were just 8th and 9th.📉

Pace was excellent in the last stint (fresher tyres), but not enough to offset McL's lower tyre deg!🛞

Merc has multiple issues to solve. ⚠️

After pitting for a new set of Hards, Mercedes' pace suddenly improved by over 4s/lap, which is impressive even when taking into account the significant difference in tyre age.

McLaren's performance was less variable both between stints and throughout each stint as well.

Weekly reminder that you can find ALL my socials and extra content through THIS link
👇
https://linktr.ee/fdataanalysis

Weekly reminder that you can find ALL my socials and extra content through THIS link
👇
https://linktr.ee/fdataanalysis

🗒NOTES ON Chinese GP:

-First race in 5 years! First time with 18" rims🛞
-First sprint of 2024
-Similar to Bahrain, where Aston struggled and Merc edged McL. That was 4 races ago, though!
-2 stops are likely
-1.4km full-throttle section🚀

📸 @pirellisport

Your prediction? 🤔