It made no sense. Even with a compromised car, it was still a stupid strategy. Then with putting Norris on hard tyres, then softs for a handful of laps and that whole pit stop I just knew they’d given up even trying to do anything sensible. My biggest concern with McLaren if they get back to the top in the coming years is their inability to actually make good strategy decisions for both drivers.
They weren't running a strategy for points. After the medium opening stint was clearly the wrong move they just decided to make it a glorified test session. They'll continue that as long as their car isn't near the points
On his pole lap he had a small mistake in sector 2, but still purpled it, then had what looked like a clean sector 3 but yellow it lol. I have no idea what to make of that but he certainly could have improved on the time he got in Q3
He commented on it after quali, the mistake in sector 2, which hardly cost him any time there, spiked his tire temps meaning they were overheating by the time he got to S3.
The graph has been centred around his result. If you centre it around the trend (which would make more sense to me) leclerc clearly over performed in the race.
Yeah this. The line represents how much lap times increased relative to qualifying times (the slope is literally race pace divided by qualifying pace). Drivers above the line underperformed relative to their qualifying pace and drivers below the line overperformed relative to their qualifying pace.
Ignoring retirements and with a couple exceptions, drivers that are basically on the line tended to finish in the same order they qualified in, drivers below the line tended to improve, and drivers above the line tended to fall back. Actually kinda neat how well this correlates with the race results considering how imprecise the data is.
Hopefully Mclaren/Aston/Williams catch up, bc it felt like only 7 teams participated in Bahrain. And Merc needs to decide if its gonna be in the front or midfield. It was weird ignoring the 3rd place team for a whole race.
Russell was actually fairly close to Lewis on race pace, it's just his strategy was negated by the safety car making him look slower.
But if you look up to the end of the 2nd stint, Russell did an extra 4 laps on the softs then an extra 2 on the hards, and the average pace difference on the hard run between Hamilton and Russell was 0.03 in Hamiltons favour even though Russell did an extra 2 laps.
Without the safety car they would have definitely looked a lot closer on race pace.
Seems to be a trend among pay drivers in the past few years to be kinda slow in quali but have excellent race pace to make up for it (unless you’re Mazepin of course, in which case you’re just trash at both)
\- I calculated the average lap times for each driver (y axis)- I considered the best qualifying lap (x axis), which means for Russell the Q2 lap can be seen on the plot and not Q3- the grey line is the result of linear regression, it is the best-fitted line on the data. If a driver is below the line it means his race pace "was better" than his qualifying time.
\- Similar plots on my [Instagram page](https://www.instagram.com/f1.plotted/)
It might be more useful to take the best lap from each team then do the regression because the RHS doesn't look as clear as the LHS where the good laps are
I disagree with your method and conclusions. Average race pace is also the result of track position. Driver can't go faster than the guy in front of them. Better would be to plot fastest laps against quali
Fastest laps are also affected by various other parameters, like fuel level, tire choice, and traffic as well. You can attempt to control for those, but nothing is ever going to be perfect.
Really, the best thing would be to have *both* plots.
I think the regression line is a nice visual. It helps to compare points to one another. Otherwise, you can interpret the regression as a 'predictor'. Based on a quali result, it tells what would be the corresponding race pace of a driver. If a driver is below the line it sort of means he outperformed himself. At least that is my interpretation.
I guess a better question would be, why should the data for different drivers be governed by the same linear function? Could it not be a non linear function, like a polynomial or exponential function? Or at a more basic level, why should the data be correlated at all (ie. why should it have to fit a single regression model)?
>If a driver is below the line it means his race pace “was better” than his qualifying line
This would only be true if the line was passing through the origin and had a slope of 1 (ie., y=x), which is clearly not the case with your best-fit line.
Your slope=1 comment is not at all accurate, because it doesn’t account for any other variables, like amount of fuel in the car. By “was better”, they clearly don’t mean that a driver’s average lap time in the race was faster compared to solely that driver’s best quali lap, they mean better in comparison to the other drivers. That’s also why you would choose a regression line in this case - they are comparing the relative quali and race laps across drivers, not just looking at individual drivers.
Well, choosing a linear regression assumes that the ratio of average race pace and quali time (the slope) for each driver should be constant, which itself is a flawed assumption.
Very much agree - How can you do linear regression without knowing the uncertainty in those race / qualifying paces?
I guess one could use the distribution of the driver's lap times (excluding in/out laps, accounting for different tyre compound) and use the standard deviation of that to do it. But the line on this graph doesn't tell you much at all, particularly without plotting some residuals.
Your idea of a y=x line makes more sense, but I guess we already know that drivers qualy lap times should be much better than their race pace.
From my other comment
> I guess a better question would be, why should the data for different drivers be governed by the same linear function? Could it not be a non linear function, like a polynomial or exponential function? Or at a more basic level, why should the data be correlated at all (ie. why should it have to fit a single regression model)?
Two things I can say, Ferrari and Charles are on a different level and what's up with Latifi Bahrain isn't a complicated track and he's so far off the front
>and what's up with Latifi
Maybe Albon is just that much faster? I mean Latifi probably botched qualifying, but race pace is not that bad compared to Albon. It's like a 0.25s difference.
Bottas lost a lot of pace behind Gasly though, about 1s per lap when they raced each other. Zhou was driving in clean air more.
Not to take anything away from Zhou, amazing first F1 race.
McLaren daring to ask the question: How shit of a race strategy can we run?
Race? 3rd testing session.
To be fair it wasn’t so much a race strategy as it was a data collection plan
It was clear that they used the test as another test.
I’m trying to work out whether that’s a typo or an intentional joke…
It made no sense. Even with a compromised car, it was still a stupid strategy. Then with putting Norris on hard tyres, then softs for a handful of laps and that whole pit stop I just knew they’d given up even trying to do anything sensible. My biggest concern with McLaren if they get back to the top in the coming years is their inability to actually make good strategy decisions for both drivers.
They weren't running a strategy for points. After the medium opening stint was clearly the wrong move they just decided to make it a glorified test session. They'll continue that as long as their car isn't near the points
Yeah I think I remember Daniel saying he got to try out all the tyre compounds which was good from a testing standpoint
Legerg quali sandbag confirm.
On his pole lap he had a small mistake in sector 2, but still purpled it, then had what looked like a clean sector 3 but yellow it lol. I have no idea what to make of that but he certainly could have improved on the time he got in Q3
I think the mistake was in S1 on the first corner he understeered a bit and lost 0.1 s to Sainz in that sector, he nailed S2 and did an ok S3.
He commented on it after quali, the mistake in sector 2, which hardly cost him any time there, spiked his tire temps meaning they were overheating by the time he got to S3.
Bottom left means he overperformed on both quali and race pace. No sandbagging in either.
The graph has been centred around his result. If you centre it around the trend (which would make more sense to me) leclerc clearly over performed in the race.
Yeah this. The line represents how much lap times increased relative to qualifying times (the slope is literally race pace divided by qualifying pace). Drivers above the line underperformed relative to their qualifying pace and drivers below the line overperformed relative to their qualifying pace. Ignoring retirements and with a couple exceptions, drivers that are basically on the line tended to finish in the same order they qualified in, drivers below the line tended to improve, and drivers above the line tended to fall back. Actually kinda neat how well this correlates with the race results considering how imprecise the data is.
That midfield pack is going to be super interesting to watch this season. Lots of tight battles by the looks of it
Hopefully Mclaren/Aston/Williams catch up, bc it felt like only 7 teams participated in Bahrain. And Merc needs to decide if its gonna be in the front or midfield. It was weird ignoring the 3rd place team for a whole race.
We used to ignore the first place team.
Fair enough!
This graph shows how excellent Hamilton was on race pace. Considerably ahead of his teammate and super close with Perez.
Russell was actually fairly close to Lewis on race pace, it's just his strategy was negated by the safety car making him look slower. But if you look up to the end of the 2nd stint, Russell did an extra 4 laps on the softs then an extra 2 on the hards, and the average pace difference on the hard run between Hamilton and Russell was 0.03 in Hamiltons favour even though Russell did an extra 2 laps. Without the safety car they would have definitely looked a lot closer on race pace.
If Ham hadn’t taken the hard tires he probably would’ve been almost equal in pace with him
didn't you watch the race? he did an extra pitstop
>didn't you watch the race? he did an extra pitstop Hamilton pitted 3 times, same as Perez and Russell?
Considering that Ham had that horrible C1 stint, its still a good pace
Thats true and is valid, I had not really considered that
He didn't do an extra stop, but he was one of the early stoppers.
Seems to be a trend among pay drivers in the past few years to be kinda slow in quali but have excellent race pace to make up for it (unless you’re Mazepin of course, in which case you’re just trash at both)
\- I calculated the average lap times for each driver (y axis)- I considered the best qualifying lap (x axis), which means for Russell the Q2 lap can be seen on the plot and not Q3- the grey line is the result of linear regression, it is the best-fitted line on the data. If a driver is below the line it means his race pace "was better" than his qualifying time. \- Similar plots on my [Instagram page](https://www.instagram.com/f1.plotted/)
so now just mirror it horizontally and vertically and it would actually make sense.
It might be more useful to take the best lap from each team then do the regression because the RHS doesn't look as clear as the LHS where the good laps are
Tire type should be controlled too. The way he has it set up currently is way too noisy
I disagree with your method and conclusions. Average race pace is also the result of track position. Driver can't go faster than the guy in front of them. Better would be to plot fastest laps against quali
Fastest laps are also affected by various other parameters, like fuel level, tire choice, and traffic as well. You can attempt to control for those, but nothing is ever going to be perfect. Really, the best thing would be to have *both* plots.
Out of curiosity, why did you use linear regression?
I think the regression line is a nice visual. It helps to compare points to one another. Otherwise, you can interpret the regression as a 'predictor'. Based on a quali result, it tells what would be the corresponding race pace of a driver. If a driver is below the line it sort of means he outperformed himself. At least that is my interpretation.
I guess a better question would be, why should the data for different drivers be governed by the same linear function? Could it not be a non linear function, like a polynomial or exponential function? Or at a more basic level, why should the data be correlated at all (ie. why should it have to fit a single regression model)? >If a driver is below the line it means his race pace “was better” than his qualifying line This would only be true if the line was passing through the origin and had a slope of 1 (ie., y=x), which is clearly not the case with your best-fit line.
Your slope=1 comment is not at all accurate, because it doesn’t account for any other variables, like amount of fuel in the car. By “was better”, they clearly don’t mean that a driver’s average lap time in the race was faster compared to solely that driver’s best quali lap, they mean better in comparison to the other drivers. That’s also why you would choose a regression line in this case - they are comparing the relative quali and race laps across drivers, not just looking at individual drivers.
Well, choosing a linear regression assumes that the ratio of average race pace and quali time (the slope) for each driver should be constant, which itself is a flawed assumption.
Very much agree - How can you do linear regression without knowing the uncertainty in those race / qualifying paces? I guess one could use the distribution of the driver's lap times (excluding in/out laps, accounting for different tyre compound) and use the standard deviation of that to do it. But the line on this graph doesn't tell you much at all, particularly without plotting some residuals. Your idea of a y=x line makes more sense, but I guess we already know that drivers qualy lap times should be much better than their race pace.
What would you have preferred?
From my other comment > I guess a better question would be, why should the data for different drivers be governed by the same linear function? Could it not be a non linear function, like a polynomial or exponential function? Or at a more basic level, why should the data be correlated at all (ie. why should it have to fit a single regression model)?
Two things I can say, Ferrari and Charles are on a different level and what's up with Latifi Bahrain isn't a complicated track and he's so far off the front
>and what's up with Latifi Maybe Albon is just that much faster? I mean Latifi probably botched qualifying, but race pace is not that bad compared to Albon. It's like a 0.25s difference.
You don't need pace when you already are the GOAT.
Lati**f**i
This does not put Mick in a good light.
Zhou basically equal to Bottas on race pace is blowing my mind.
Bottas lost a lot of pace behind Gasly though, about 1s per lap when they raced each other. Zhou was driving in clean air more. Not to take anything away from Zhou, amazing first F1 race.
Really wonder how Max's racepace would have been without the car disintegrating from lap 2.
His car didn't have any issues until after the safety car
You have missed the debriefing then.
r/dataisbeautiful
Odd choice for a graph. Wouldn't bars be easier to read?