Formula Data Analysis
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All the analyses of the worldโ€™s most famous page on Formula 1 telemetry: join NOW to understand F1 better!๐ŸŽ๐Ÿ“ˆ
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๐Ÿ“ŠBEST SECTORS
(Everyone on Softs, except for BOT/ZHO/SAR on Mediums)

๐ŸŸกRBR was quickest in every sector!
Next quickest car:
S1: ๐Ÿ”ดFerrari (+0.03s)
S2: ๐Ÿ”ดFerrari (+0.02s)
S3: ๐ŸŸขMerc (+0.08s)

๐ŸŸ McL was quick in S1, but their low top speed held them back in the fast S3
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Verstappen's best FP1 lap was just 0.015s away from the ideal lap (VER S1 and S3, PER S2)โฑ

NOR and STR didn't combine their best sectors into their best laps:
๐ŸŸ NOR would have finished P8 (instead of P10)
๐ŸŸขSTR P11 (instead of P15)

Weak points:
โšซ๏ธMerc S1
๐ŸŸ McL S2
๐Ÿ”ดFerrari S3
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๐Ÿ—’Notes on Japanese GP Quali!

๐Ÿ”ตVER gained 0.68s over his last pole: LEC/ALO/HAM wondered how they could be so far after good laps!
๐ŸŸขAston: in Q1, ALO was P2, STR P16. Better lineup needed
๐Ÿ”ดFerrari has lost their quali pace since FP3
๐ŸŸ Solid P3 for McL, but was P2-3 last year

Who surprised you the most? And WHY?๐Ÿค”
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AERO PROPERTIES
โžก๏ธHigher mean Speed (more performance)
โฌ†๏ธHigher top speed (lower drag)

๐ŸŸ McL got P3 with the lowest top speed on the grid! Crazy downforce, but they need less drag for a front row. Same for๐ŸŸขMercedes

๐Ÿ”ดFerrari lost their top-speed edge๐Ÿค”

โšช๏ธHaas=Rocket๐Ÿš€
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BEST SECTORS - Japanese GP Quali
๐ŸŸกRBR was quickest in all sectors, but as in FP1 PER beat VER in S2

๐ŸŸ McL's excellent downforce helped them massively in S1: virtually as quick as RBR!

๐Ÿ”ดFerrari was TERRIBLE in S3: slower than HUL, ALB, both RBs...

What's your opinion?๐Ÿค”
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THROTTLE USE - Quali

๐ŸŸกRedBull got pole by being able to stay full-throttle for 2/3 of the lap: they put the power down better than anyone else

Similar for๐ŸŸขMerc/๐ŸŸ McL, but their high drag caused a loss of pace on every straight

๐Ÿ”ดFerrari: no grip, and 'meh' top speed!
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INISECTORS - Quali

๐ŸŸกVER and ๐ŸŸ NOR were impressively quick in the fast S1 snake, leaving nothing on the table for๐Ÿ”ดSAI! Their downforce was unmatched

In addition to that, RBR had lower dragโžก๏ธFastest at the end of most straights

๐Ÿ”ดFerrari was quickest only out of T12/18
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RACE PACE - #JapaneseGP

โœ…Improved the most over 2023 (s/lap):
๐Ÿฅ‡๐ŸŸขAston -1.87 (was terrible last year)
๐Ÿฅˆ๐Ÿ”ดFerrari -1.72
๐Ÿฅ‰๐ŸŸ McL -1.52

โŒGot slower:
๐ŸŸฃAlpine +0.08

-LEC was quicker than both Mercedes despite stopping one less time
-ALO matched PIA.
-VER: -0.28s/lap adv.
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Formula Data Analysis
RACE PACE - #JapaneseGP โœ…Improved the most over 2023 (s/lap): ๐Ÿฅ‡๐ŸŸขAston -1.87 (was terrible last year) ๐Ÿฅˆ๐Ÿ”ดFerrari -1.72 ๐Ÿฅ‰๐ŸŸ McL -1.52 โŒGot slower: ๐ŸŸฃAlpine +0.08 -LEC was quicker than both Mercedes despite stopping one less time -ALO matched PIA. -VER: -0.28s/lapโ€ฆ
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? ๐Ÿค”
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๐Ÿ›žTYRE WEAR (Per Team and Compound)

โœ…Best wear:
๐Ÿ”ดFerrari and๐ŸŸขAston (on both Hards and Mediums)

โŒWorst (by far!):
๐ŸŸขMercedes on Hards
๐ŸŸ McL on Mediums

๐Ÿ”ตRBR's wear was moderate, and it didn't impact their excellent pace

โš ๏ธThe high wear ruined Mercedes' race
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Formula Data Analysis
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 surprisedโ€ฆ
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!
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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)
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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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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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๐Ÿ‘‡Use this link๐Ÿ‘‡
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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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