Formula Data Analysis
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๐Ÿ’กRedBull 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 ๐ŸŸกRedBullโ€™a bimodal distribution (two wide points: the upper one is their โ€˜trueโ€™ speed, the lower one is relative to lifting in the corner)

Haas reached 309km/h thrice there!๐Ÿš€

Via @JMP_software
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https://linktr.ee/fdataanalysis
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SECTOR TIMES - Japanese GP

๐Ÿ”ฅSainz's sectors were on fire in his last stint!

HAM was quick on ๐ŸŸกMediums (on a fresher set). However, Mercedes lacked consistency: notice the big gap in S1 and S2 between the first and second โšช๏ธHard sets!โš ๏ธ

Made via @JMP_software
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Formula Data Analysis
๐Ÿ’กRedBull 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 ๐ŸŸกRedBullโ€™a bimodal distributionโ€ฆ
Increasing a carโ€™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 ๐Ÿ’ฐ

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 โฑ

As @brrrake correctly pointed out, the same principle applies to F1 cars. Increasing oneโ€™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. ๐Ÿ›ž
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Can Machine Learning help F1 engineers predict an undercut attempt?๐Ÿค”

@HearneLaurence contacted me about it, and later produced a model doing that!๐Ÿค–

Solid lines: driver's pitting probability๐Ÿ”ฎ
Dashed lines: the real pit.

The model predicted HAM pitting, and LEC covering it!๐Ÿ’ก
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Formula Data Analysis
Can Machine Learning help F1 engineers predict an undercut attempt?๐Ÿค” @HearneLaurence contacted me about it, and later produced a model doing that!๐Ÿค– Solid lines: driver's pitting probability๐Ÿ”ฎ Dashed lines: the real pit. The model predicted HAM pitting,โ€ฆ
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.
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Ferrari made TWO strategies work brilliantly: good strategy, tyre wear, and driving!๐Ÿ‘Œ

Check out the last stint (on โšช๏ธ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?๐Ÿค”
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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 deployment๐Ÿชซ).

Due to Russell's bad straight-line speed, PER could get the slipstream sooner, further increasing his top speed.
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Sainz's overtake was a real one - and Charles Leclerc didn't concede easily!๐Ÿ˜ฌ

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)
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Mercedes extended the first stint, losing ~2s/lap to the two McLarens for over 10 laps: mid-race they were just 8th and 9th.๐Ÿ“‰

Pace was excellent in the last stint (fresher tyres), but not enough to offset McL's lower tyre deg!๐Ÿ›ž

Merc has multiple issues to solve. โš ๏ธ

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.
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๐Ÿ—’NOTES ON Chinese GP:

-First race in 5 years! First time with 18" rims๐Ÿ›ž
-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 section๐Ÿš€

๐Ÿ“ธ @pirellisport

Your prediction? ๐Ÿค”
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The Chinese GP features a 1.4km full-throttle section!โžก๏ธTop speed: crucial for lap time and to attack/defend๐Ÿ’ก

Image: top speed achieved in the last qualifying, during each team's best lap.

๐ŸŸขAston has solved their drag issues, contrary to โšซ๏ธMercedes and ๐ŸŸ McL.

๐Ÿ”ดFerrari will likely run the same rear wing used in all the races so far.
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Chinese GP: Medium-high load wings

โœ…Downforce CRUCIAL from T1 to T13.

โŒThe 1.4km full-throttle section will punish excessive drag, especially if caused by the (fixed) lower plane!

๐Ÿ”ตRBR maximizes DRS effectiveness through an unloaded lower plane + very loaded upper plane.
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Formula Data Analysis
Chinese GP: Medium-high load wings โœ…Downforce CRUCIAL from T1 to T13. โŒThe 1.4km full-throttle section will punish excessive drag, especially if caused by the (fixed) lower plane! ๐Ÿ”ตRBR maximizes DRS effectiveness through an unloaded lower plane + very loadedโ€ฆ
Significant differences among the teams: Mercedes. Ferrari, Alpine and Williams have a less loaded wing compared to Aston Martin, for example.

Compared to RBR, McL has a less loaded upper plane and more loaded lower plane, which should make their DRS less powerful.
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MINISECTORS - FP1 ChineseGP
๐ŸŸขSTR was quickest on the main straight, and out of T3/T8/T12
๐ŸŸ PIA fast in the twisty S1 and S3
๐ŸŸกVER quickest in S3 (long straight)

Laptimes still very slow (almost 5s to last pole)โžก๏ธHard to guess the running order!
Aston: 324km/h (+4km/h vs McL)
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FASTEST SECTORS - FP1 ChineseGP

Mercedes/ALO on Hards
Others on Softs

๐ŸŸ NOR quickest in the twisty S1 by far:
-McL is showing excellent grip
-Most teams are probably still far from the limits
He aborted the lap while in Sector 3

๐ŸŸขSTR: quickest in S2
๐ŸŸกRBR: quickest in S3
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Visualizing the effect of flexible wings!๐Ÿ‘€
(Collab with @justf1car ๐Ÿค)

Downforce and drag are proportional to the speed squared: they are very small at low speeds, and become huge at high speeds.

This is a problem: cars have less grip at lower speeds (when they need it the most) and face a lot of air resistance at high speeds (on the straights)!

The clever F1 engineers invented flexible wings to get a better compromise:
-As much downforce as possible at lower speedsโžก๏ธCornering improves;
-Reduced drag at high-speed thanks to the flexion of the wingโžก๏ธTop speed improves!
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