The 2012 F1 season was won by Sebastian Vettel during his all-conquering Red Bull years, but people often say it was Fernando Alonso’s biggest year in F1 and that he deserved more of the World Championship, but an extremely slow Ferrari. were influenced by. Somehow despite the hurdle, he managed to take the fight for the world title to the final race. A large part of Alonso’s success lay in his rocket start, which pushed him far beyond his qualifying position on the first lap. Alonso was widely considered at the time and is still considered perhaps one of the fastest starts in F1, but this assumption was based on what we all saw on television and what the end result was, but Now what was happening inside the car.
Rob Smedley, who was in the neighboring garage as chief race engineer to Alonso’s teammate Filip Massa, is today lead engineer in F1 at Liberty Media and his team worked with Amazon Web Services (AWS) on a new insight Which has just finished beginning. The Turkish GP said the – start analysis – aimed to uncover the same data that gave spectators a perception of Alonso’s prodigious talent in a more data-centric way.
“So we can often look at, from a macroscopic point of view, drivers who start well, and how they start well, where, sometimes those of you get a sense of whether or not they spin the wheel, or which Also and always people will get down in the first corner first and you know, find that hole in traffic, you know, they get to suck. So that’s the really exciting part of the grand prix. It’s as exciting as qualifying as a whole. Because you have 20 cars on the track and they’re all going into that first corner,” Rob Smedley said in an exclusive interview with carandbike.com.
“Now, what we wanted to do was we wanted to get under the skin of that, so instead of having this big, wide-open macroscopic view, and doing that from TV imagery, or interpreting it from TV imagery, we wanted to get under the skin.” Get under and look at zero 100 times, which is largely the time when all the actions start, when the drive is really, when they have a lot of concurrent actions,” he said.
F1 and AWS have broken down “start analysis” under three buckets – reaction time, initial acceleration phase and how fast the car and driver are in the latter part of launch. The response time bit is fairly self-explanatory and AWS Smedley reveals in the AWS blog, “From the time when the start light goes out to when the driver arm responds by leaving the clutch in the middle (semi-busy/semi-slippery) position.” ”
Similarly, the initial acceleration phase relates to how a driver “handles the wheelspin, throttle position, fully releases the clutch pedal, etc.” And finally, there’s the second part of the launch which deals with “using the slipstream, the overall drag of the car, holding position (making as few changes in direction as possible), etc”.
Former Williams and Ferrari engineer now chief engineer in F1 to help with AWS-powered F1 insight
But Smedley is quick to point out that data based only on the Turkish GP will not be the perfect barometer for deciding who is the fastest starting driver in F1 on average. To find out who it is, it will take data from a few races – ideally 4-5 races. He also pointed out that during the Turkish GP, due to the wet conditions, there was another element of variation. He also noted that in P2 the car will usually be fastest off the grid because it has the advantage of slipstream and less traffic at the same time. At times in P2 the car also gets a clean side of the track and has less stressful braking zones.
“So instead of really focusing on who’s fastest on Sunday, I think it’s more important, for the spectators to start forming a picture in their mind in the 2.3…4 race , who is the fastest, who has the fastest reaction time, over 2…3…4 races, who is the fastest from zero to 100. And there is a picture that will start to emerge because there is always a picture And so what it does is pulls, pulls the fan in, because then when you’re looking at it, you’re thinking, well, I know that particular driver I’m interested in whether he’s 11th on the grid or six on the grid, or second on the grid. And I know it’s the start of a race where reaction time is super important or not an overtime is super important. So I really I’m going to look into that,” Smedley explained to carandbike.
How exactly this is happening, the data is being received from multiple sensors mounted on the cars and then the model is on the AWS cloud for a long period of time.
“Telemetry is the data that we extract from the cars. In addition, we use the timing data coming from the position of the F1 system around the track. The transponder (sensors on the cars), triggers the specific loop receiver when it passes, assigning it the specific car ID and time of day to 0.0001s. There are typically 25 timing loops placed around the circuit,” Smedley writes on the AWS blog.
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“We should note that the overall duration described above is longer than just considering the deviation of the speed from 0kph. This is due to the fact that before the car can move, the inertia needs to be overcome. This method is used for any wheel Also removes speed sensor jitter or error around zero minimum. For all intents and purposes, this should give us a more accurate overall picture of performance,” he said.
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