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Corner Analysis

F1 Chief Technical Engineer Rob Smedley helps break down each of the new F1 Insights powered by AWS in a series of blogs during the 2020 Formula 1 season

A Blog by Rob Smedley

As the 2020 Formula 1 season moves full steam ahead with races coming thick and fast, so do the intros of the F1 Insights powered by AWS graphics. Using F1-owned data, these insights continue to offer an engaging backstage view of Formula 1 for the fan. Using analytical methods derived and developed within the Formula 1 teams and AWS machine learning services, we are offering fans data and analytics to help tell the story of Formula 1 as it’s never been told before. We have already introduced both Car Performance Scores and Fastest Driver as part of the 2020 series of F1 Insights powered by AWS, and we now move to an even greater level of detail to bring the next analytic which centers around cornering performance.

Since the very first days of Grand Prix racing, lap time can be divided into two simple areas: time on the straights, and time on the corners. Teams work relentlessly to increase downforce to reduce the amount of time that cars spend in the corners and it is this aspect that we want to explain better through our new graphic, Corner Analysis. We want to be able to show how one car compares to the other with the various phases of the corner. We also want to show how drivers compare to one another in driving the same corner. There are often many nuances between drivers and what we call their ‘driving style.’ With the introduction of the new Corner Analysis insight, we hope to give a much clearer and data-driven understanding of what was previously a difficult to explain topic. Once again, Formula 1 and AWS are bringing the data-driven story to the fans to help explain the big performance stories in much more clarity.

Explaining the Graphic

Car performance is made up of many different aspects. What we want to try and convey with this latest F1 Insights graphic are the major building blocks that make up car performance; namely cornering performance, straight line performance and car balance or handling. The teams work relentlessly in trying to improve these three aspects. For good cornering performance, you need a lot of down force to have centred the tyres in the correct working window and to have a car that is well balanced throughout the different phases of the corner. Straight-line performance is instead much more about engine power and aerodynamic drag. These are the main building blocks that the teams will be working hard on as they get to Australia in order to maximise each area of the car performance.

Within this graphic we want to convey all the aspects mentioned above. In microscopic detail we intend to use the car data in order to understand the speed of the car through a given corner. We then split this between high, medium and low-speed cornering at certain thresholds for both the low, medium and high-speed cornering aspect. This will give the fan key insight as to where a car is stronger or weaker than the opposition. For example, last year’s Mercedes was incredibly strong in the low speed corner but was less so in the high-speed corner when compared to the Ferrari and in fact the trend completely reversed when we considered straight-line performance. Although this was clearly evident to those of us that understand car telemetry analytics, it was less so to the fan and therefore in my opinion, we missed telling a key story of the season.

Just as with the high, medium and low-speed cornering performance, the straight-line performance also takes key car data and uses that to understand where a particular car is strong in a given, normalised section of the track (i.e. the straights). This will help us understand who has the best engine or lowest aerodynamic drag package which are two of the key performance attributes that the teams and engine manufacturers will be working on relentlessly as we speak.

The final aspect of this graphic is to consider the analysis behind the car handling or balance. All cars handle slightly differently, and it is the job of the designers and engineers to try to build a car that handles perfectly through every phase of the corner. At the turn in phase (the very initial part of the corner) the rear grip is extremely important as this helps to give the driver a lot of confidence in turning the car in. As the car speed reduces and the vehicle moves towards the mid corner, the front grip becomes more dominant and more important. If a car washes out or doesn’t have enough grip on the front axle, we call this understeer. If instead the rear axle doesn’t have enough grip, and therefore the rear axle tends to slide more than what is required to execute the corner, we call this oversteer. On the corner exit, the importance in car handling then swaps to the rear axle. As the driver picks up the throttle and requests power through the rear tyres, he requires a commensurate increase in rear grip to keep the rear stable. The underlying models within the graphics will convey to the fan if the car is understeering or oversteering in the low, medium and high-speed corners and will so help to appraise which car handles better than the others.

All of the above will help to frame the car performance for the fan and they will be able to understand in microscopic detail who has the best car in the low-speed corners, the medium speed corners and the high-speed corners, in a straight-line or who has the best handled car. These are all the main attributes which are needed to make up a fast racing car. So how did we do it? Let’s look at the math and physics of the underlying models.

Here are some examples: Two drivers in a very similar car can have two very different driving styles. This is something that is often talked about but rarely ever explained through the data. Now, with the Corner Analysis graphic, we are able to do that. Let’s say , both drivers brake at exactly the same point on the track with exactly the same rate of deceleration, but one driver comes out of the brakes much earlier and carries more speed into the corner, whereas the other driver continues to brake in a straight line, slows the car down more, before lifting out of the brakes and turning in. These would give two very different trajectories and therefore two very different speed traces. The first driver who has lifted out of the brakes earlier and carried more speed into the corner would almost certainly not be able to accelerate as quickly, and their time would be made up in the turning and mid-corner phase; whereas the second driver in our example, their time would be made up in the corner exit phase.

The example and explanation above shows how this new graphic can delve much deeper into the data and explain the difference in cornering performance between say, the Red Bull and the Mercedes cars, or the difference between two drivers in the same team, for example Sebastian Vettel and Charles Leclerc. For the first time, we are able to use the data to give a very clear picture as to the reasons behind these differences. In the next section, we explain exactly how the models are derived.

The Modelling

High/Low Speed Corner
Some cars are quicker in different phases of the corners.
In fact we can split the corner into three phases:

1) Straight line braking. This is the phase in which the driver starts the braking from the previous straight to decelerate the car at a maximum rate in order to bring the speed down enough to start the next phase.

2) Turn in phase. This is the phase where the driver starts acting on the steering wheel in order to set the trajectory of the corner.

3) Mid Corner Phase. This would be the phase in which the car is traveling at constant speed at maximum lateral acceleration. This phase is extremely short in a F1 car and, in fact, can be considered as an instant for most of the corners. In a F1 car the driver is always in acceleration or in braking and they almost never keep the speed at a constant value.

4) Exit phase in grip limited. This is the phase where the driver is accelerating out of the corner but he needs to control the throttle as the torque from the engine is higher than the tyre capability.

5) Exit phase in power limited. This is the phase where the driver is accelerating out of the corners at full throttle
In fact, increasing the speed during phase 4) we have two phenomena:
a. the aero-loads increase and push more the tyres down
b. the torque of the engine gets lower as consequence of the speed increase

These two factors contribute to change the limit for acceleration from the tyre to the engine so the driver can go full throttle without the need of modulating it.
Taking as an example Turn 1 (T1) in Belgium (Figure 1) we see that all the phases are present, a part of the mid corner that lasts for an infinitesimal duration. That corner is a low-speed corner and the driver needs to brake in a straight line in order to bring the car at a speed that is capable to sustain the lateral loads due to the curvature (phase 1).
Then the driver needs to set the trajectory still decelerating (phase 2) up to when he reaches the apex (phase 3).

Once the driver has reached the apex, then he can accelerate the car modulating the throttle until the limit of the tyres (phase 4).

After awhile the driver reaches the moment in which he can keep the throttle at 100% (phase 5).

Figure 1 – Belgium T1

Taking as an example for high speed T10-11 in Belgium (Figure 2) we see that the car speed is already low enough to start the turn in phase (phase 2) so there is no need to brake in a straight line. Because the mid-corner speed (phase 3) is high, the limit in exit is from the torque given by the engine immediately after the apex, (phase 5) so that the driver doesn’t need to modulate the throttle.

Figure 2 – Belgium T10-11

As we have seen, these phases change depending on the corner topology, but it also depends on the car and driver. In fact, depending on the driving style and on the car strength, the driver can decide different trajectories that means they will change the relative length of each phase.

A given driver will naturally minimize the phase that is less suited to the particular car/driver. Of course it will be impossible to not pass through a phase if the topology of the corner requires it, but the driver will be able to spend less time in that phase, and will have to increase the length on some, or all, of the other phases.

The different phases are set by thresholds in the car body accelerations both longitudinal and lateral and by looking at the throttle signal. This is an idealization as the reality would be that the driver will pass between phases with continuity while the model identifies the different phases in a more defined way. Using this idealization we are able to compare different cars/drivers in terms of phase duration that can give insight of how the car/driver optimize the performance through one particular corner.

Uncovering the stories through data
Looking at the data in this way, it allows us to answer some typical questions when looking at the car on track: how driver A is able to gain laptime over driver B in this specific corner or sector? Are they driving differently?

Looking at the sector lap time we can understand if a driver is quicker in a high-speed or low-speed sector, but it’s not possible to understand what led to these differences. For example, comparing two drivers out of a low speed corner followed by a straight line we might notice one of the two is scarifying the entry in order to be able to accelerate a bit earlier out of the corner with the goal in mind to be quicker in the following straight to pass the car in front, or have more chance to resist from a possible attack from the car behind.

One more thing we may understand is how the driving style changes through the race as a consequence of tyre degradation. From the Car Performance Scores graphic we can understand if the car is becoming more oversteering or understeering. With Corner Analysis now we are also able to understand how the driver is managing the different phases of the corner because of tyre degradation: is he carrying less speed into the corner since he doesn’t feel the front as before, or maybe is he going on the throttle later because the rear traction is worsening? We can now understand this and comparing two drivers we might also have some additional information to predict what will happen later.

Below is a comparison between Charles Leclerc and Lewis Hamilton in the Formula 1 Rolex Belgian Grand Prix, lap 12 in Turn 1. In this specific corner, from the distance 200m to 500m, Leclerc has been quicker than Hamilton by 0.14 seconds, and looking at the speed trace and identifying each phase of the corner, we can understand how Leclerc has been able to gain some lap time over Hamilton. We can clearly see Leclerc is braking later than Hamilton. This braking phase is shorter since he was more aggressive on brakes and he was also approaching the corner at slightly lower speed. The entry phase instead is very similar between the two as highlighted from the yellow bar at the bottom; in fact at the beginning of the entry phase the speed of the two cars were already very similar leading to a similar approach. The exit phase has also a similar length for the two drivers, despite Leclerc seeming to have better traction since he is accelerating faster than Hamilton.


Then the lateral and longitudinal speed of the car can be calculated, we call them vxCOG Estimated and vyCOG Estimated. As final step, using the lateral and longitudinal speed of the tyre calculated through the yaw rate, it’s possible to calculate the slip angle

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