Posted by: drracing | July 5, 2018

Short update

Hi everybody!

Quite some time since my last post here, hopefully this trend will be inverted in the next few weeks.

2018 has been indeed a very busy year till now. And a very exciting one too!

As maybe somebody reading here already knows, since January i am contributing a bit to theRacingLine, a very nice website dealing mainly with Sportscars and GT racing.
I mainly write tech articles there, trying to keep them as easy as possible, in order, first of all, not to bore too much the people who are crazy enough to read them and, secondly, to make them interesting for a broader audience than the even more crazy people who, from time to time, comes here to read something.
Till now we dealt with the basics about tyres, here and about aerodynamics, here, each with a series of 6-7 articles.
Next up will be a longer series about handling.

Of course, i highly recommend anybody interested in race car technology to pay a visit to theRacingLine! it is, in general, a very nice website!

In the mean time, i was also very busy on the simulation side.
I was lucky enough to come into contact with a motorsport legend, Jeff Braun and i started to support him and his current team, CORE Autosport, on the simulation side.
CORE Autosport takes part in the IMSA WeatherTech Sportscar Championship in North America, competing in both the GTLM class (as a Porsche work team) and DPi/LMP2 class, with an FIA spec. Oreca 07. Jeff is engineering the LMP2 car, with the two “resident” drivers being Jon Bennet and his son Colin Braun. For the long races, anyway, they also get some help from another legend, Romain Dumas and for the 24 Hours of Daytona they also had another great driver in the car, Loic Duval (ex Audi LMP1 driver and now competing for the german brand in DTM).
We built up a very very detailed vehicle model of their car, basing on my LMP2 model and went on refining it constantly, both to improve correlation with real data and to add new features that the team would like to test.
The first thing we did was to include a tyre model representing their tyres into my original model. After that, we used their logged data to check the correlation and to improve it further, by adjusting some small bit and pieces.
The team also carried over some specific tests and measurements on the real car to reproduce some areas of the vehicle better in simulation environment.
I will write about this project more in details in a different article.
Anyway, to describe very briefly what we are doing, we currently mainly focus on the preparation of races and test days, testing in the simulator a range of solutions to help Jeff and the Team to know upfront what to expect, on what to focus more and to understand which direction seemed to work better and what was probably not worth any real testing time.
As always, one of the biggest advantages of using a driving simulator is that it gives a chance to evaluate the results of each test both objectively (analyzing simulations results and data and comparing several sessions and/or logged data) and subjectively, also to understand not only how the performance potential of the car changes with specific setup changes, but also which impact on the driver they have.
I guess it is not necessary to say how much fun this makes and how much one can learn from a similar experience, both about simulation and racecar development and engineering.
Beside this, working with a guy like Jeff is something extremely special. He is not only an engineering legend (see here his Linkedin profile), who won more or less any important race in North America (he worked more or less on any car, including Indycar, Prototypes, GT and much more), but also a unique person, who somehow manage to make also the most challenging projects extremely easy and from whom anybody can learn a lot. I certainly do!
I am always amazed to see how such a smart, experienced and successful person like Jeff is, at the same time, so easy, nice and positive not only about his work, but about life and personal relationships in general. I always admired him as an engineer, but to know the guy behind the mind has been an even nicer surprise!
This is surely one of the most funny and exciting projects i have been involved in many years.
On a further plus, last weekend CORE Autosport scored a pole position in Watkins Glen and finished second after six hours of racing, but only because of a yellow flagged that come out with about 30 minutes to go and completely destroyed a 26 seconds lead that they had at that time, also putting them in a bad position with the fuel strategy. After that FCY and pit stop, they came out in 8th position but in less than half an hour recovered finishing second.
All of this, although after about 90 minutes into the race they were in 13th position.
Without that neutralization, CORE could surely win the race.
The car was really good!


Beside this, i got to support two drivers who compete in the WEC Superseason and both raced in Le Mans this year.

One of them is Giedo Van der Garde, who races with the duch team Racing Tam Nederland this year, aboard a Dallara LMP2 car. He immediately impressed at his debut Race in Spa, in May, when he overtook more or less the whole LMP2 field, taking the lead of the race in the early phases like it was the easiest thing in life! And all of this after more than one year since he last drove in a titled race, when he won the ELMS title in 2016.
I was lucky enough to get in contact with him and being asked to prepare a vehicle model that he uses for his training in his own simulator.

VdGarde Dallara Spa

The other one is Mathias Beche, who competes in LMP1 with Rebellion and finished third overall in Le Mans (or, we should say, “first of the others”, as Toyota was clearly in another class, but this is a different story).
Mathias is an extremely talented driver but also a very nice guy, very respectful and very open and extremely passionate about what he does.
He regularly trains in his own simulator and, since some time, he uses a vehicle model that i prepared for him.
Beside being an extremely fast driver, Mathias has a very good understanding of racecar engineering and tuning and believes strongly in the advantages of proper simulator training. He his also extremely accurate when he works on car setup with his engineers and he translate this skills also in his simulation experience.
This makes working with him extremely interesting, because his technical understanding and dedication is reflected very well in the detailed feedback he can give about the model and about its setup. It is extremely interesting to hear his thoughts and reactions not only about the model itself, but also about the setup changes I did, during the development phase, to make the car handle in a way i would think being realistic.
For now, Mathias used the model to mainly train for Le Mans and, apparently, this helped him a bit in achieving a podium results, together with his crew mates. Not bad!

rebellion 2018 LMù

That’s it for now. These projects are keeping me very busy and that’s why i didn’t write much here in the last few months. Anyway, I hope to be able to post some new articles soon, going back to tech topics.

Posted by: drracing | April 25, 2018

ELMS – Paul Ricard Race Analysis

Hi everybody!
Sportsacar 2018 race season has finally started in Europe, with the ELMS having its first race of the year at the circuit Le Castellet in Paul Ricard.
The championship is in great shape, with fourtyfive cars starting this first race and an incredible number of LMP2 (19) on the grid.
Some new teams debuted in the class and indeed one of them won the race with merit, with the championship still offering a nice chassis variety and, now, also a tyre war, with both Dunlop and Michelin supplying tyres to the teams, with the latter now finding place on five cars.
As usual, in this race analysis we will not focus on the race itself, but we will go through an analysis of the performances of the main actors.

The first interesting note is that a team mounting Michelin tyres took the pole position on saturday, in a session inerrupted by a red flag after about five of the ten available minutes, for a problem encountered by one of Autosport Ligier.
The race, anyway, was at the end dominated by a team using Dunlop rubber and the well established Oreca 07, that seems to retain an advantage (at least on this track) on the Ligier and the Dallara, despite the joker the two manufacturers have been allowed this year to reduce their gaps to the french cars.
The track itself has gone back to the configuration we had last seen in 2016, with the long Mistral staight employed fully, without chicane, allowing top speeds well in excess of 300km/h.
As usual, the track has been divided in three sectors, as shown in the following picture.

Track PR

The first sector is composed by two shorter straights, followed by low and medium speed corners: a chicane (Turn 1 and 2) and a series of second and first gear turns (T3, T4 and T5). The second sector is mainly composed by turn 6 and the mistral straight (T7 is a full throttle corner for an LMP2 car), with the T6 being extremely important because the exit has an significant influence on the speed that the car will carry on the long straight that follows.
Finally, the third sector includes the super fast turn 8 and then a long right hander (T9-T10) where the drivers carry a lot of speed in the entry and slow the car progressively to take the apex on the second part of the corner. They are followed by a series of slower corners, where also mechanical grip is crucial.

As we mentioned, the race was dominated by a debuting team (but with long and succesful experiences in GP2 and Formula 2), Racing Engineering, who fields a single Oreca 07. Our analysis shows that the spanish squad won on merit and this is even more remarkable if we think that also one of their drivers was at his first race with an LMP2 car. Although Dragonspeed car n.21 took initially the lead of the race (in the hands of the Lapierre, before retiring after an accident with a GT car with Hedmann at the wheel) and built some gap to the following cars, our analysis shows that Racing Engineering Oreca n.24 had averagely the best pace throughout the whole race.

We will analyze the first five classified cars plus some other representative crews, like Dragonspeed, who led the race in the initial phases, AVF car n.30 (best placed Dallara), Panis Barthez Competition car n.23 (best placed Ligier, running Michelin tyres), Idec car n.28, that took the pole on saturday.

Let’s first start analyzing the pit stops.


Pit Stops


This race was the first one where the new pit stop rules, approved by the ACO-FIA during the winter break, became effective, with the teams now allowed to change the tyres while the car is being refueled, this arguably taking out a bit the appeal of running different strategies and/or try to save time in the pit lane by not changing the tyres.
The first thing we notice is that the car ending the race in second position (TDS Oreca 07 n.33) is the one that spent less time in the pit lane, while the winning team didn’t do as good and spent in the pits some thirty-one seconds more, despite being on a five pit stop strategy as well.
Also G-Drive was particularly effective during the pit stops, allowing their car n.26 the second best pit time of the race.
It is also interesting to notice how car n.29 (Duqueine Engineering Oreca 07) spent a much longer time in the pits than car n.24 but still managed to end the race on the podium, because of a very good pace. The french team came in fact six times into the pit lane (once more of the winning car), with the first pit stop being a drive through/penalty costing about 25 seconds.
Considering they ended the race with a gap of about 7.9 seconds to the winning crew although spending some 36 seconds more in the pit lane, we could think they did indeed a very interesting race.

Looking at the performances, there is no doubt that Racing Engineering crew deserved the win. As the following table shows, the Spanish team obtained not only the best race lap time overall, but also the best results if we considers the average of the best 20, 50, 100 and “all clean” laps best times.


Average lap times - Table


Car n.29 was the one getting closer to car n.24 in terms of pace, above all if we do not consider the best 100 lap times of each crew.
Interestingly, car n.21 was close to car n.24 pace, but never really at the same level, although Lapierre was able to take the lead and built some gap during the first portion of the race.
Car n.33, who finished in second position, was sensibly slower than car n.24, with average gaps increasing as we look at more laps (we move between 0.2 seconds to more than 1 second in our table). Anyway, TDS crew spent 31 seconds less than car n.24 in the pit lane.
As we saw, the first seven positions have been all taken by Oreca cars, despite both Ligier and Dallara being allowed to introduce new parts for this season, to close their gap to the benchmarking vehicle. The best placed Ligier ended in eigth position (running Michelin tyres), with a pace gap of about half a second per lap compared to car n.24.
The best placed Dallara ended only in eleventh position and had an average performance gap of about 1.4 seconds compared to car n.24.
The question is of course how good prepared the teams came to this first race. Some of them could well have run the car for a limited time. On the other hand, anyway, the team who won the race was also at their first race in the ELMS championship.

To get a better feeling about the pace difference between each crew, we can look at the plots showing the best 20, 50 and 100 lap times for each of the car we are considering.


Av best 20 laps


Av best 50 laps


Av best 100 laps

As also the table suggest, the first thing that the plots tell us is that car n.24 and car n.29 had the best pace, with the first being slightly quicker up to the 40th best lap, while the seconds seems to be slightly better in the second half of last plot (if we exclude the very right portion of it).
Dragonspeed car n.21 is constantly a bit slower than car n.24 and car n.29 with the gap opening dramatically in the second half of the second plot.
Car n.23, the best placed Ligier, maintains more or less a constant gap from car n.29, with its lap times represented by a line that runs more or less parallel to the one of Racing Engineering.
Second placed car n.33 pace is not too far from the one of cars n.24 and n.29, up to the 20 mark, but gets worse as we consider more and more laps.
The n.30 AVF Dallara is constantly and substantially slower than the competition, if we exclude the performance drop of Dragonspeed and the car n.28, that is relatively quick in its best 40 laps, but its performance drops significantly on the right part of  the last plot.
This first analysis confirms how Racing Engineering won on merit, mainly because of a better pace than all the other cars. Third placed car n.29 was extremely close though and we should probably ask ourselves if they could fight for the victory, without spending so much time in the pit lane.

We can now dig into a more detailed analysis to understand where each car was stronger or weaker and if any team went for a significantly different setup choice, by looking to sector times and top speeds.

Starting from Sector 1, the table relative to the best times and the average of the best 20, 50, 100 and “all clean” laps times gives us a first hint about the differences between some of the cars and the different choices that each team took.


Average sec1 times - Table


First thing to notice is that the winning crew (car n.24) doesn’t stay on top in any of the metrics of our tables, with the best times being taken by car n.28 (Idec Sport Oreca 07, with Michelin tyres) and car n.29. Car n.28 anyway seems to fall down on performance as we consider more and more laps. On the other hand, car n.24, although not being the fastest, is always extremely close to the best times, with differences always well below one tenths, if we exclude the best sector 1 time overall.
Ligier n.23 (Michelin tyres) is also pretty competitive, staying very close to the best times in the average of the best 50, 100 and “all clean” laps metrics.
On the other hand, AVF Dallara n.30 is constantly 0.3-0.4 seconds slower than the winning car n.29.
If we look at the plots of the best 20, 50 and 100 sector 1 times, we pretty much get a similar picture.


Av best 20 sec1


Av best 50 sec1


Av best 100 sec1


The first plot shows very well how competitive car n.28 was, if we only look at the best 10 sector 1 times. Anyway, the pictures changes if we consider the second and third plot, with car n.24 and n.29 establishing themselves as the references and car n.23 remaining pretty close to them. It looks like this first sector suites the Ligier relatively well, while the AVF Dallara struggles.
Also car n.33 is constantly 0.2-0.3 seconds slower than Racing Engineering and Duqueine crews.
Just by looking at this first sector we could come to the conclusion that car n.24, n.29 and n.23 could behave similarly or have a similar aerodynamic performance. Anyway, if we move to sector 2, we immediately recognize how wrong this conclusion is.


Average sec2 times - Table


Sector 2, pretty much composed by one corner and a very long straight, is all about corner exit and straight line speed. Car n.24 and car n.29 are clearly the best performers here, with car n.24 holding a small advantage.
Car n.23 has a bigger gap to the best crews than in sector 1. Being this sector dominated by top speed performances, we can already suspect that the Ligier is not as slippery as the Oreca 07.
Interestingly car n.30 is much closer to the best performance cars in this sector than it was in sector 1, with times being only marginally slower and a gap that reduces if we consider more laps.
Looking at the plots relative to the best 20, 50 and 100 sector 2 times, we get a better feeling about the performance situation in this portion of the track.


Av best 20 sec2


Av best 50 sec2


Av best 100 sec2


It is even more evident how car n.24 was clearly the fastest in this sector, with car n.29 following close up to the 30-40 marks, but then falling a bit behind. AVF Dallara was particularly good in this sector, where top speed is crucial.
Car n.33 is again not particularly fast, while car n.21 is nowhere close to the best pace.
This seems to anticipate what we could find out by looking at the top speeds, since the speed trap was located at the end of the Mistral straight and of sector 2.


Average TS - Table


The first thing to notice is that car n.30 is without doubt the fastest in straight line, although car n.24 and car n.29 remain close. If we put together this detail with sector 2 performance, it is interesting to notice how, despite having the best top speeds, AVF Dallara n.30 was a bit slower than car n.24 in sector 2, this probably signalizing how Racing Engineering Oreca was more effective at the exit of turn 6.
Ligier top speeds were significantly lower and this is something we must keep in mind when we will analyze sector 3 times: they tell us the Ligier is probably more draggy than the Oreca and the Dallara (at least in the configuration chosen by the Panis Barthez Team for car n.23). We will try to understand if it has also more downforce or not. By looking at sector 1 times, we concluded that in slower corners the two cars were not too far from each others, although the best Oreca still had a small advantage.
The plots relative to the best 20, 50 and 100 top speeds pretty much confirm what we mentioned.


Av best 20 TS


Av best 50 TS


Av best 100 TS


These plots and the data relative to sector 2 also show where Dragonspeed car n.21 was particularly penalized, in terms of performance, with the american team having the lowest top speeds among the cars we consider and loosing significant time in this portion of the track.

Sector 3, the longest of this track, is a mix of medium and slow speed corners, where both mechanical grip and downforce are very important.
Also in this sector, car n.24 and n.29 were clearly among the fastest car, with the Oreca in general obtaining not only the best sector 3 time of the race, but also staying always on top also if we consider the average of the best 20, 50 and 100 best sector 3 times.


Average sec3 times - Table


Beside this, the previous table also shows how the AVF Dallara was particularly slow in this sector, where downforce surely plays an important role.
The best Ligier was a bit closer, with a delta compared to car n.24 moving between 0.2 and 0.4 seconds.
Interestingly, the best sector 3 time was obtained by Dragonspeed Oreca n.21, with the American Team also staying on to in the best 20 laps average. This confirms what we saw analyzing sector 2 times and top speeds: most probably, Dragonspeed opted for an higher downforce setup compared to other teams running Oreca cars, thus having an advantage in this sector. Anyway, it is clear that what they lost in the previous two sectors (above all the second) was not compensated by the advantage they had on other cars in this part of the track.
The plots relative to the best 20, 50 and 100 sector 3 times gives a better feeling about the relative difference between the cars we consider.


Av best 20 sec3


Av best 50 sec3


Av best 100 sec3


The first thing we notice is that the Dallara is heavily penalized in this sector. It would be interesting to understand if the Italian car was so slow in Paul Ricard because of wrong setup decisions or because of a structural lack of performance. It will be one of the topic of next races, for sure, with a Dallara now also taking part to the WEC.
Beside this, the first plot shows clearly how car n.21 had an advantage on the other crews, as long as Lapierre was at the wheel.
Car n.21 and n.29 were pretty much on a similar performance level and indeed still very fast, with Race Engineering crew having a small advantage up to the 70 mark and falling a bit behind afterward. We could suppose the two newcomers run their cars with similar aerodynamic configurations, as they pretty much show the same trends in each section of the track.
One of the most interesting point, though, is also to notice how close the n.23 Ligier was to the two best Oreca. Above all on the long distance, Panis Barthez crew was among the fastest teams in this sector, this probably signalizing that the Ligier has a good downforce but lacks a bit of efficiency, if we also consider what we learnt looking at sector 2 times and top speeds. Again, it would be very interesting to understand if this comes from car’s design or team choices, also considering both the Ligier and the Dallara had a chance to improve their cars during the winter to close the gap to the Oreca.
It will be exciting to follow the next races and see if the gap will reduce in other tracks.

Posted by: drracing | February 9, 2018

2018 non-hybrid LMP1 – What’s the story?

In 2017, sportscar world was thrilled by the unknowns related to the introduction of all new LMP2 cars, which proved to have amazing performance, both compared to their predecessors and to LMP1 cars.
In 2018, we will live a very similar situation, although this time the revolution will happen at the very top of sportscars ladder.
With the shock followed by Porsche retirement announcement last summer and, initially, also with the uncertainty related to Toyota’s plans, sportcars world had to react trying to attract as many private teams as possible, in order to have a proper grid for 2018. To do this, the regulations ensured some incentives to allow teams and manufacturers with a small budget, compared to the one of an OEM backed team, to achieve performance that are close to that of an Hybrid LMP1 car, although using a standard powertrain.
With Toyota remaining the only LMP1 hybrid team to compete in the top class, the focus is all on the privateers and to what their level of performance could be.
The rules will ensure to non-hybrid LMP1 cars a series of breakthroughs in several key areas compared to what thy hybrids are allowed, to try to compensate for the absence of an hybrid system and the performance disadvantage that follows.
Private entered vehicles will have more freedom in terms of aerodynamics, with different rules regarding for example the design of front splitter and rear diffuser; moreover, LMP1 non-hybrid cars will have a minimum weight of 833 kg (3 kg of which reserved for the on board camera system), while hybrid cars will stay at 878 (again, 3 kg of which reserved for the on board camera). The difference of 45 kg between the two has a pretty deep impact on performance.
Finally, according to the latest agreements between FIA/ACO, Manufacturers and Teams, privateers will be allowed to use internal combustion engines only, but with a fuel flow of 110kg/h, which is nearly 10% more of what they were allowed in 2017. In terms of powertrains, the question remains of course how much power and what level of performance each of the available LMP1 engines will achieve. With the rules stating how much fuel can be used, the focus is all on efficiency, as improving this parameter means either having a higher performance or being able to drive longer with a full tank, both cases bringing their advantages in an endurance race.

At the moment of writing, five private teams have announced their intention to take part to WEC Superseason using four different engines, three of them being supercharged (Cowsorth/Nissan, AER and Mecachrome) and one being a normal aspirated (Gibson). The three turbo engines are all six cylinders; one employs a single Turbo (Mecachrome) and two a twin turbo layout; they are often referred as the favourites, both because supercharging should allow an higher efficiency (although i honestly don’t know if this is true) and because it should help producing a more favorable torque curve. In an attempt to optimize even further how each drop of fuel will be used, Mecachrome also opted for a direct injection approach, moving slightly away from the solutions used in the engine they used as a base for their LMP1 product, namely FIA Formula 2 2018 power unit.
Normal aspirated engines, on the other side, could probably struggle in getting the same energy efficiency as supercharged ones (again, not sure about this) and it is not a case that Gibson opted to increase its engine capacity, probably also aiming to have a more favorable torque curve and keeping the rotational speed lower to limit friction losses. Anyway, reliability could be a key word, above all in Le Mans, where the pure pace is really only one of the necessary ingredients to succeed. Beside this, a normal aspirated engine should be easier to “package” into the car, because of the less demanding requirements in terms of cooling and ancillaries (although Gibson V8 will probably be bigger than the two V6 competitors).
The question that keeps a lot of fans busy, at the moment, is really how much power 2018 LMP1 engines will have.
The easiest way to get to a power figure basing on the allowed fuel flow is to use Brake Specific Fuel Consumption (BSFC), a parameter that indicates basically how efficiently each gram of fuel is used to produce power. This parameter depends on a lot of others, but it is somehow a measure of how good an engine is in converting energy, which surely depends directly on the goodness of the combustion process and on the level of technology that has been developed.
According to the rumours circulating about 2018 engines and to the information that could be collected, it looks very much likely that more than one engine will be able to produce a maximum power in excess of 700hp, with some sources saying that one engine could even get close to 720hp. These values are pretty impressive, not only in absolute terms, but also if we consider the BSFC numbers they would correspond to. Assuming an engine would produce about 700hp with a fuel flow of 110kg/h, it would have a BSFC close to 210 g/kWh, which is an extremely interesting value.

With all of this in mind, one questions still remains to be answered. Which level of performance could we expect for 2018 privateer LMP1 cars?

To try to answer, I started a small project, building a “LMP1-like” vehicle model, using a validated WEC-Spec 2017 LMP2 one as a base and running some Driver-in-the-Loop simulator sessions on some of WEC Superseason tracks, including Le Mans. A driver supported this project, taking over all driving duties.
Starting from a WEC 2017 LMP2, I changed all the parameters that differentiate an LMP1 and an LMP2 vehicle and about which I could find trustable data.
First of all, car overall weight was reduced by about 100 kg (LMP2 cars have a minimum weight of 930kg) and also the inertial properties (mainly moments of inertia) were scaled down accordingly.
The vehicle model was also fitted with four LMP2 rear tyres, because LMP1 cars are allowed to use the same wheel size on both axles (i had to slighlty adjust suspension hardpoints, mainly because of the bigger tyre diameter, but nothing having a dramatic influence on final performance).
I then assumed an engine torque curve based on data relative to an engine similar to one of the four we mentioned, scaling the power up to about 700hp. Interestingly, this engine has a different torque curve than the LMP2 one, but produce its maximum power at a similar rotational speed.
As the engine has much more power than an LMP2 one and the car is much lighter, gear ratios also had to be changed, making them longer to fit the new model. LMP1 cars will most probably have seven gears in 2018, anyway for this study I still assumed a six gears box.
The aerodynamics stayed untouched, and this should be a conservative assumption. LMP1 rules should allow to achieve better aerodynamic performance than what LMP2 rules concede. The only difference we need to mention in this area has more to do with car setup, as with the LMP1 car the best performances has always been achieved with a slightly higher downforce (and more rear biased) setup than what suited better the LMP2 on the same circuit.
Basing on this premise about aerodynamic, if LMP1 engines will really have the power figures we used for our model, LMP1 cars could be even quicker than what has been derived with this study.
Beside these generic data about the car, anything more specific can be shared because all the information are confidential.
The tracks we run the simulations at are Spa, Silverstone and Le Mans. In the following analysis, I will use the performance we achieved with the LMP2 model at each track as a reference and compare it to what the driver could do with the LMP1 one, trying to derive a trend that could help to predict what are the lap times that we could expect for 2018.

Let’s start with Spa, as the Belgian circuit will host the first race of 2018/2019 WEC Superseason.
After running some sessions with the LMP2 car model on this circuit, the best laps the driver could run were in the region of 2’02”7.
As soon as we switched to the LMP1 model the difference was evident, not only in terms of pure speed, but also in terms of handling. The much lower weight allow an higher minimum speed more or less in every corner and makes the car extremely agile and very reactive. Moreover, the four equal sized tyres helps significantly also during brakings and further improve car overall grip also in slow corners.
The best lap time out of a few runs was a 1’56”5, that means 6.2 seconds quicker than with the LMP2 car.
Looking at the logged data, focusing on the speed trace first, the reader can immediately recognize how the car flies through the Eau Rouge – Radillion section and achieve a top speed close to 313 km/h at the end of the Kemmel straight. Also the minimum speed at Pouhon, a long and fast left double corner, is impressive, with the driver being able to negotiate the turn at about 241 km/h. The car had a very predictable behavior in slower corners too, with minimum speeds also slightly above the values achieved with the lmp2. Of course, the amount of power available made necessary some work on the throttle pedal, to avoid excessive wheel spin, above all exiting first and second gear corners, like La Source or the last chicane. This seems to suggest that working on the traction control could be very important, above all when the tyres start to degrade.


Spa speed


All of this is well reflected in the following plot, showing lateral and longitudinal acceleration traces. Focusing our attention on the lateral acceleration first, we immediately notice how the car reaches peak values well above 3g at Pouhon, but also has substantial grip in slower corners.
Brakings are also showing higher decelerations than the LMP2 car, thanks to higher speeds and lower weight.


Spa acc


Also in Silverstone, the difference between LMP1 and LMP2 was pretty substantial, with the driver being able to lap in 1’38”67 in the LMP1 and 1’44”1 with the LMP2, which means a gap of about 5.4 seconds between the two. With the track offering shorter straights than Spa and requiring a higher downforce setup, the top speed (at the end of the Hangar straight) is now slightly above 300 km/h. What impress more, anyway, is the speed the driver can carry in some iconic, fast corners like Abbey and Stowe. In the first one, he only slightly reduces throttle opening to help the car turning in and has a minimum speed above 275km/h. At Stowe, a corner with a smaller radius, the minimum speed is anyway above 245 km/h.


Silv speed.PNG


This is, again, well reflected by the lateral acceleration trace, that tells us the car could achieve peak values above 3.5 g at Abbey and above 3 g at Stowe.
Maximum decelerations are again above 3 g, as in Spa.


Silv Acc.PNG


Last, but probably most interesting, are the results obtained in Le Mans.
The driver tested once again the LMP2 model first and, after some sessions, he lapped in 3’26”89. We then switched to the LMP1 vehicle model and, again after some training time, he was able to lap in 3’17”21, which is an impressive 9.7 seconds quicker than with the LMP2. This is probably the most interesting result, as we will have a chance to explain later on.
Analysing the most important metrics, focusing on the following speed plot first, we immediately notice the top speed of 349 km/h that the car achieves before the Playstation Chicane.
Beside this, we identify a minimum speed at Tetre rouge of about 215km/h and of more than 265 km/h at the first of Porsche curves.


LM speed.PNG


Accelerations are good indicators of car overall grip potential and it is interesting to see how car’s lateral acceleration exceeds several time the 3 g mark, as for example at the Porsche curves.


LM acc.PNG


Once again, top longitudinal accelerations are close to 3 g.

We can summarize the results of this study, in terms of lap times, with the following table:


LMP1 vs LMP2 sim


Before digging into the analysis of our simulation results, trying to predict what 2018 LMP1 performance could be, we should summarize once again under which assumptions the above mentioned results have been obtained.
I basically up-tuned an LMP2 car, using 2018 LMP1 minimum weight, four LMP2 rear tires, maintaining nearly all chassis related parameters unchanged (see aerodynamics, suspensions main metrics, mass distribution and only adapting the setup according to needs) but changing gear ratios to match new powertrain performance. Among all assumptions, engine power (and power curve) is probably the most open question: I assumed an engine producing a maximum power of about 700hp, which is what many sources seemed to suggest being realistic for these cars. But of course, there is no certainty this will really be the case.
Another very sensible assumption, in terms of performance, is the aerodynamics. I could imagine, assuming carry over LMP2 performance, I should have underestimated what LMP1 cars will be able to achieve. According to the information I have, LMP1 should be able to perform significantly better on this side, compared to LMP2; since two of the four current LMP2 chassis manufacturers will also produce LMP1 cars, I think we can expect they could achieve much better results with their cars, compared to their LMP2 products. Anyway, much will depend on the focus that each constructor will have on Le Mans, as it could come as no surprise that some of them could give to creating a package suiting the Circuit of La Sarthe more importance, even sacrificing some performance on sprint circuits.
The first point we can conclude is that the new privateers LMP1 should be significantly quicker than LMP2 cars, both in terms of lap times and top speeds. This is maybe no surprise, but could indeed play an important role in race and traffic management, as LMP1 cars should be able to overtake LMP2 cars with relative ease.
Anyway, what is probably more interesting, is the comparison between Toyota 2017 performance and what we could expect for 2018 LMP1 private cars.


LMP1 vs LMP2 2018 pred


The table above shows again our simulation results, including deltas between LMP2 and LMP1 vehicle models, but expands this a bit, assuming we could apply the same deltas to 2017 LMP2 best qualifying lap times, to come to a realistic prediction for LMP1.
As the reader can see, the gap between 2017 Toyota and a 2018 LMP1 privateer, with a car reflecting my assumptions, should be about 0.8 seconds in Silverstone and 0.9 in Spa. Anyway, what is probably more shocking (or interesting) is what comes out analyzing Le Mans performances: if we compare 2017 stunning pole position time (done by Kobayashi) and a prediction based on the delta we obtained with our simulation, we have a tight 0.88 seconds gap (on a 13.6 km long track). I also reported the second best lap done by any Toyota driver (actually again by Kobayashi) to show that Kobayashi’s pole position was something really extraordinary. The second best lap is already above the 3.17 mark and comes very close to our results. Kobayashi’s time was also the results of a track in perfect conditions, as the streets representing the non-permanent part of the circuit had not be reopened to traffic before Thursday in 2017.  As far as i know, Toyota’s team itself didn’t expect to see such a performance, which was also achieved in a lap where they found nearly no traffic.
Moreover, we have to keep in mind two important points:

  • Toyota uses in qualifying the whole energy stored in the batteries or, in other terms, they discharge them completely, not caring about how much energy can be harvested with regenerative braking, because in qualifying only a few laps must be completed. This is probably true in all circuits, but we don’t know exactly if we should expect the same influence on lap times everywhere. Anyway, this means the drop in performance between qualifying and race is pretty big for the Japanese cars, which has not only to manage fuel and tyres, but also face a lower powertrain performance. The non-hybrid cars don’t have this issue, at least if they will not use any special engine setting for qualifying; in any case, the drop in performance should be lower than an hybrid car.
  • LMP1 non-hybrid cars top speed will be much higher than Toyota’s ones. This is particularly true in Le Mans, but will be also the same in some other circuits. In Le Mans, anyway, the difference will not only be bigger in absolute terms, but it will probably also count more because the cars will also spend much more time traveling on straights; this should allow the privateers to take advantage not only on lap times, but also in close fights.


We could expect Toyota still having an advantage coming from their hybrid system in tracks where there are more accelerations starting at a lower speed. In general, in sprint circuits they should still have an edge on the privateers and, depending on their effort, could achieve interesting performances on that side.
Anyway, what is extremely interesting, is really the small gap it seems we should expect in Le Mans. Now, surely Toyota will come with an improved car in 2018: even if staying on the same base of 2017, we can expect they will try to improve their package.
Anyway, as we mentioned already, we should not forget that LMP1 regulations should allow to achieve better aerodynamic performances than what we assumed. It looks like engines performance remain the most challenging open question, both in our analysis and in determining 2018 LMP1 privateers final lap times.
It also looks like Toyota probably came to similar conclusions, because in the last few days media have reported about the privateer being a serious challenge for the Japanese team. It is interesting to see these comments, also because Toyota took part at the discussions to define LMP1 non-hybrid rules and agreed with them, as far as we know. It would be sad if they would somehow not show their cards before Le Mans, with the aim to let the FIA/ACO recheck privateers rulebook.
In any case, we could have a very interesting season indeed!

Posted by: drracing | January 18, 2018

New Projects and Suspension kinematics excel tool – Part 2

Hi everybody,

and happy new year!

First post of 2018! Again a very short one and again about my suspension kinematics excel tool.

Anyway, before to dig into the main topic of this post, i am very happy to announce that my writing duties will expand in 2018, as i started cooperating with a new website about sportscar, that promises to be very interesting: theRacingLine.

Here you find the first of a series of article about the basics of race car technology and physics, that will appear in theRacingLine beside my race analysis and some portion of other articles that i will post here.

Exciting times ahead!

Back to the suspension kinematic tool now.

During the Christmas break i worked a bit on it, among other projects and more or less added all the feature i wanted to have from a pure calculation perspective.

I already described in my previous post how each hardpoint position is calculated, basing on user input (ride, roll or steering, which i added now).

The interesting thing about such a project is that, the more you go on, the more you find some small issues on your way that you need to solve and this is always a nice exercise. There is always something new to learn or to think about!
Sometimes you need to get your head around how to calculate certain metrics or motion in the most efficient way, sometimes you simply have to write something to pick up the right solution among some. Sometimes, you really have to first be sure about how to define a certain calculation, from a mathematical perspective.

As i anticipated, i added the steering motion too; it was pretty straightforward but is still a pretty useful feature.
Beside this, now all the components are in, including pushrods, rockers/bellcranks, springs/dampers and third element unit.
I also included the possibility to decide if the pushrod/pullrod is attached to the lower arm, to the upper arm or to the upright.

I am now able to calculate many useful metrics, including motion ratios, significant point positions, toe, camber, caster, king pin inclination, scrub radius, caster trail, spring length, third spring/dampers length, track width variation, etc.
One of the output is rocker rotation; i added it, in order to calculate suspension motion ratios also for suspension using torsion springs, like in Formula 1, GP2 or in many LMP cars.

I also added a small macro, that allows to iterate between two specified wheel travels using user defined steps, collecting all the data and allowing to generate typical suspension curves, like bump steer, camber gain, motion ratios, etc.

Now, suspension kinematics is nothing extraordinarily complex and there are nowadays tons of tools that are probably much cooler than mine and still relatively cheap. Anyway, doing things yourself is always a nice way to learn something new and to define your tools exactly the way you want them. Of course, it is not always possible, as time is never enough. But this was something i had in my head for a long time and i am pretty happy i finally did it!

By the way, it is interesting to think that a 230 kb excel files can do pretty much all you need in terms of suspension kinematics and produce exactly the same results as much more powerful tools, like expensive multibody packages, at least if you only consider suspension kinematics (no compliance, no elastic elements like bushings). To validate my work i checked the results against one of these expensive software and it was a perfect match.

Once the kinematics part is over, i will probably add a small module to calculate the loads acting in each component, for a given loadcase at the contact patch.


Susp Kin 20180115


Susp Kin 20180115-2

Posted by: drracing | December 8, 2017

Suspension kinematics excel tool

Hi everybody,

this is just a short entry, to share a small project i am working on in the last few days. Since it is something i wanted to do since a long time, i really want to spend a few words about it here and, maybe, trace how it progresses.

As the title of this post reveals, i am working on a simple suspension kinematics tool in excel, which is able to calculate the position of each hardpoint, basing on the defined suspension movement and all the most important metrics.
For now i am only focusing on double wishbones, as this is the scheme which is mostly used on the race cars i am interested in, but it should be pretty easy to adapt it to be used for a generic five links geometry, in the future.

The method i used is based on the intersections of spheres in space. Each moving point of a suspension is derived as the intersection of three spheres, with the center of each seating on an hardpoint whose position is fixed or has been already identified in a previous step; the radius of the sphere is either a link length or the distance between the point we want to locate and another point of the same component (see for example a wishbone or the upright).
For the very first point (namely the outer point of the lower wishbone, in my case) we can use two close points (for example the two connection points of the same wishbone to the chassis) and a reference point, which can be placed everywhere and will be afterward used also as the input for each suspension movement. In my case, this point will only move in z (vertical direction), as this is all what we need to simulate how a suspension moves.

The tool can currently calculate the result of each heave and roll movement and of any combination of the two. The approach i used, mainly for convenience, is to assume that the chassis remains fix with respect to the global reference system, while the floor moves with respect to it, either translating, rotating or both.
Roll is assumed to be an equal and opposite movement of the two wheels of an axle, this avoiding any philosophical debate/headache about around which point/axis the chassis rotates.
Each metric is anyway always referred to the “new” ground position, so if for example i input a roll movement, all will be calculated with respect to the ground now seating with an angle with respect to the global reference system.

As the tool is still “work in progress”, it still doesn’t depict any of the components seating “above the control arms”, see for example the pushrod, rocker/bellcrank, spring/damper unit, antiroll bar and third elements. Also, for now i am not yet done with steering, but this is relatively easy to add as an horizontal movement of the steering rack and will most probably be the very next step.

This is how it looks like as of today:

Susp kin tool 1


Susp kin tool 2


Susp kin tool 3


As you may notice, the Roll Center is one of the metrics i identified, but there are also the instant centers (they are anyway necessary to determine Roll Center position) and, more importantly, the n-lines slope. The n-lines are the lines connecting the IC to the Contact patch and, effectively, the lines defining the only direction through which the suspension linkage can carry forces. The instant center can be really thought as the pivot about which the upright rotate around the chassis.
I thought and read a lot about anti roll effects recently and i am more and more convinced that the Roll Center is not really the best way to define how the suspension geometry contributes to the antiroll moment. Although the roll centers are pretty effective and relatively easy to use as a metric to just have a picture about the geometry’s antiroll contribution, if we really want to understand and describe what happens in a real cornering situation, they fall short, above all when they migrate significantly. That’s why n-lines look like a much more effective tool to understand what happens in certain situations. More on this will probably come in a future post.

For now, i simply enjoy my new tool!
I will publish an update to track the progress (hopefully) soon.

Posted by: drracing | November 28, 2017

WEC – Bahrain Race Analysis

Hi everybody!

Here we are again with the latest race analysis for this year, as after ELMS now also WEC 2017 season came to the end with a very interesting race in Bahrain.

Although the LMP1 title was already assigned in China, the race was anyway very interesting.
Toyota showed again a top form, above all with car n.8, who really dominated the event. Anyway, as we will see, the situation was a bit less biased on their side than in China in terms of performance, with Porsche who wanted to fare well the championship in the best way possible and who was not lucky in more than a situation.
Again, tyre degradation and strategy played a central role, both in LMP1 and LMP2.

Particularly exciting was anyway the battle in the LMP2 class, with the race victory deciding also the title winner. Both the crews fighting for the championship victory (Rebellion car n.31, who won in the end and DC Racing – Jota Sport car n.38) had some issues during the race, with the final stints of Bruno Senna slowed down by the power steering not working anymore and with car n.38 race in part defined by a refueling problem, not allowing them to complete the same number of laps per stint as their direct competitors and forcing them to stop once more than Rebellion.

The track itself is probably not as fascinating as other facilities around the world, but offers anyway some tricky points, like corner 9 and 10, where the cars have to brake while cornering and a couple of relatively fast sections (turns 6 and 7 and turn 12).



As we mentioned, Toyota n.8 dominated the event in Bahrain, leaving all the other contenders at least one lap down. This was Toyota’s third win in a row and fifth victory this year, meaning car n.8 won more races than anyone else in 2017.
Toyota’s car was extremely competitive at the beginning of the year and seemed to work extremely well again after the race in Texas, where Toyota was anyway not too far off from Porsche.
At the light of this strike of victories, it is really a pity that Le Mans proved to be so bitter for the Japanese Team, since that race not only is the most prestigious and well known of the WEC season, but also assigned double points.

In Bahrain, the crew of car n.8 did again everything right, with the car showing the best pace in the field and the team working the strategy perfectly.
Car n.8 was the only one who didn’t experience any issue or accident during the race and hence the only one doing only 6 pit stops, spending some 50 seconds less in the pit lane than car n.2, who finished second.


LMP1 - Pit Stops


In terms of performance, car n.8 was clearly the fastest car throughout the race. Although they didn’t sign the overall best lap (that went to car n.1), the following table (relative to the best laps and best 20, 50 and 100 lap times averages) clearly show their advantage on all the other competitors.


LMP1 - Best laps average table


Although car n.1 obtained the best lap in the race, the average of the best 20 lap times sees car n.8 already at the top, with the gap to all the other crews increasing as we look at more and more laps (average of the best 20, 50, 100 and “all clean laps” lap times).
Interestingly, car n.7 didn’t show the same pace and was faster than Porsche n.2, but slower than Porsche n.1 (who had an accident during the race, costing them time and a penalty). Although car n.8 could probably have been anyway faster than car n.7, it is worth to mention that Kobayashi had an accident about half way through the race; this produced a not well defined damage to the car that could have led a pace deterioration for car n.7, despite the reparation done in the garage.
The trends we just described are also easy to recognize if we take a look to the plots relative to the best 20, 50 and 100 lap times.


LMP1 - Best 20 laps


LMP1 - Best 50 laps


LMP1 - Best 100 laps


It is interesting to notice how, despite car n.1 being able to produce the best 10-11 lap times of the race, it completely falls behind car n.8 on the long run, with a pretty significant performance drop after the 12 mark.
Still, beside car n.8 being clearly the fastest one on the long distance, we cannot notice how car n.1 was still the one getting closer to car n.8 pace, with much better lap times than car n.7. Different story for car n.2, which was constantly out of pace.

If we look at how the lap times evolved during the race, we pretty much see the same trends in terms of relative performance of each car with respect to the others.
The plot below compares car n.8 to car n.1 and gives maybe also an indication about what could be a tendency in car n.8 to better preserve the tires than the Porsche.


LMp1 - All laps - 8 vs 1


Beside noticing that car n.8 line is nearly always slightly below car n.2 one, we can also identify a slightly bigger performance drop for the Porsche in certain stints, like for example the third one. It is anyway very hard to come to any clear conclusion, since there wasn’t apparently any dramatic performance degradation for any of the two cars.
The following two plots, referring to car n.2 and car n.7 performances compared to car n.8, also confirms how much quicker car n.8 was during the whole race and during some stints in particular.


LMp1 - All laps - 8 vs 2


LMp1 - All laps - 8 vs 7


As we mentioned already, particularly interesting was the pace difference between the two Toyota. Anyway, we have to always keep in mind that car n.7 had an accident with a GT Porsche at the beginning of lap 96, that costed them a puncture, a long time in the pit and, most probably, a damage to the car that was not completely repaired. It is not a case that, despite car n.7 being already slower than car n.8 during the third stint (which could be related to the typical Toyota split tyre change strategy: car n.7 changed the tyres during the first pit stop and was indeed pretty fast during the second stint, while car n.8 did it during the second pit stop and was quicker than car n.7 in the third stint), a bigger performance gap between the two cars seems to build up after the 100th lap, so immediately after car n.7 accident.

Let’s now break down our performance analysis considering each track sector and the relative performance of each car.
As usual, the track was divided in three sectors, as shown in the image below.


track map


The first sector is all about straight line speed and longitudinal acceleration, being composed by a big part of the main straight and three pretty slow corners, with two of them coming after intensive braking and followed by strong acceleration.
The second one contains a couple of relatively fast corners (turn 6 and 7) and the tricky corners 9 and 10, with the cars braking for turn 10 while negotiating turn 9. It also presents some intensive corner exit accelerations, above all the one after turn 10, which is followed by a pretty long straight. Here the hybrid deployment surely plays a role on the final performance.
The third and final sector has “only” three corners, but one of them is the fastest of the track (turn 12). Again, there are at least two corners exits where hybrid power deployment is very important, because leading to pretty long straight: turn n. 13 and turn n.14.

Sector one offers immediately the first interesting “talking point”, as car n.1 is clearly the fastest here, with car n.8 getting a bit closer only on the long distance.
This is indeed what the table below (relative to the average of sector 1 best 20, 50, 100 and “all clean laps” times) shows us, with car n.1 producing the best value for each one of the considered metrics.


LMP1 - Sec1 best times


As we mentioned, the gap between car n.1 and car n.8 gets a bit smaller as we analyze more and more laps. Car n.2 and car n.7 are relatively close to each other and both relatively far off the pace of their sister cars.
Looking to the plots relative to the best 20, 50 and 100 sector 1 times, we can get a better feeling about each car’s performance.


LMP1 - best 20 sec1


LMP1 - best 50 sec1


LMP1 - best 100 sec1


The plots give a better feeling not only about car n.1 performance advantage in sector 1, but also about car n.8 getting closer as we move toward the 100 mark. The gap between car n.8 and car n.1 reduces from about 0.2-0.3 seconds on the fastest lap of each car, to only about half of a tenth on the longer distance.
As we mentioned while describing the track, top speed and drag levels play surely a role in determining how fast a car can go through this first sector.
The trap speed data seems to confirm that having a better inline speed is important for sector 1 times, as car n.1 is constantly the one with the highest top speed.
Interestingly, car n.2 has very similar top speed to car n.8 but is significantly slower and this could indicate an advantage for Toyota during the acceleration phases (hybrid deployment).


LMP1 - Best 20 TS


LMP1 - Best 50 TS


LMP1 - Best 100 TS


The three top speed plots above (best 20, 50 and 100 top speeds achieved by each car), seems to confirm not only the advantage of Porsche n.1 on Toyota n.8 (the difference is about 4-5 km/h and we know this can also depends on how long each car coast at the end of the main straight), but also the difference between car n.1 and car n.2, which is pretty significant, while the two Toyota are closer together.
As we mentioned, interestingly car n.8 and car n.2 have pretty much the same top speeds.
As we saw already in other races, this seems to suggest, on one side, how the two cars of the same team may be running with different aerodynamic configurations and, on the other side, also how the Porsche seems to have less drag than the Toyota.

Sector 2 sees a completely different situation.
Toyota n.8 is definitely and constantly the fastest car, with a pretty significant margin on the closest competitor which is (no surprise) car n.7.
The table below, relative to the best laps and to the average of the best 20, best 50, best 100 and “all clean” sector 2 times gives already a pretty clear impression about the impressive pace of car n.8 compared to all the other crews.


LMP1 - Sec2 best times


Car n.8 is not only the fastest in sector 2, its advantage to the closest competitor (car n.7) also increases as we look at more and more laps, with more than 0.2 seconds gap on the “all clean” metrics.
It is also extremely interesting to see how far off Porsche’s pace was compared to Toyota’s one in this part of the circuit,  with a gap between car n.8 and car n.1 that moves between about 0.2 seconds for the average of the best 20 times, and more than 0.3 seconds for the “all clean” metrics. Again, it is important to notice how the gap between car n.8 and the other crews increases on the long distance, while the gap between car n.1 and car n.8 in the first sector showed exactly the opposite trend.
A picture tells more than thousand words; a plot is even better. So let’s take a look to the plots relative to the best 20, 50 and 100 sector 2 times of each car:


LMP1 - best 20 sec2


LMP1 - best 50 sec2


LMP1 - best 100 sec2


Above all by looking at the 100 best laps plot, one can really appreciate how much quicker than the other crews car n.8 was, in particular on the long run.
This plots helps also to see the performance drop that car n.7 had, maybe also because of the consequences of the accident and, in general, how much faster car n.8 was than the two Porsche.

This is also particularly important because, as we will see shortly, the performances of car n.8 and car n.1 in sector 3 were much closer to each other.
Already by looking at the table relative to the best average sector 3 times, we can see how, in more than a metrics, the difference in terms of pace between the two cars can be measures in terms of hundredths of a second, with car n.1 remaining always on top.


LMP1 - Sec3 best times


It is pretty impressive to see how close car n.1 and car n.8 were, above all when looking at the average of the best 50, 100 and “all clean” sector 3 times, with the latest showing a difference of only 12 thousandths of a second!
This well recognizable also by looking to the best 20, 50 and 100 sector times plots.


LMP1 - best 20 sec3


LMP1 - best 50 sec3


LMP1 - best 100 sec3


The first plot, relative to the best 20 sector 3 times, shows at best that car n.1 has actually the best sector 3 times overall. Anyway, if we look to the third plot, relative to the best 100 sector 3 times, we can really appreciate how the lines relative to Porsche n.1 and Toyota n.8 lie one on the other.
Car n.7 is constantly slower than all the other crews in this sector and this doesn’t seem to be linked to the accident, since we cannot really identify any point of the plot where their line lies below the ones of the other cars.
Sector 3 is indeed a mix of high speed straight and strong accelerations out of slow corners, which seems to require a combination of the strong points of each car that we already identified: Porsche has probably a top speed advantage on the penultimate straight, while Toyota could have a small edge on accelerations because of the hybrid deployment.

Combining the tendency of the three sector plots, we can come pretty easily to the trends shown by the best laps plots, with Porsche n.1 being faster up to the 11 mark or so and with Toyota n.8 becoming significantly faster afterward.

Closing with LMP1, this latest race was clearly dominated by car n.8, but it is always interesting to notice how each team interpret the rules and the car setup/configuration, producing better performances in certain tracks or section of a track and being a bit penalized in others. This was again the case in this race, with Toyota being so much stronger in sector 2 thus to allow them to compensate for a worse pace and sector 1 and a pretty much identical pace in sector 3, compared to the Porsche.
Hopefully, the new generation of LMP1 cars coming next year will offer the same technological interest and the same tension from a performance perspective.



The LMP2 class offered probably the most exciting show, with two of the cars contending the race victory also fighting for championship win. Rebellion’s car n.31 arrived in Bahrain with a tight advantage in the championship standings on DC Racing car n.38 and could, up to a point, managed the race. But the situation during the race changed many times, with both cars leading in different phases and with both the cars suffering some technical issues toward the end.
This had a direct impact on the pit stop strategy of car n.38, that was apparently not able to fill the tank completely in the last pit stops, this forcing them to progressively shorter stints and to a stop more than the direct competitors.
It must anyway be said that car n.38 also reacted very quickly to an announced FCY phase (because of a cat walking on the side of the track) that was then canceled and never started at all. The car anyway was already in the pit, with an early stop that could also have led to the one more pit stop at the end.


LMP2 - Pit Stops


The table above tells us that car n.38 spent about 35 seconds more in the pit lane than car n.31. If we consider that the gap between the two cars at the end of the race was about 10.7 seconds, we have something to think about!
Of course, car n.31 also had problems and could not push too hard at the end of the race because of a powering steering issue, with Bruno Senna offering probably one of the best performance of his life.
It is also worth to mention how car n.36, who finished fourth, used a different strategy, trying to triple stint the tyres on one side of the car, by changing only two of them during one of their early pit stops. This lead them to the shortest time in the pit among the cars we consider, but, as we will see, they also dropped down by several position during the stint immediately after this “ambitious” decision.
We cannot know exactly how much each crew pushed during the race, but the data we collected seemed to show that car n.38 had a small edge in terms of performance, although the difference between them and car n.31 was really small.
Our analysis will include more cars than usual, since I could identify more or less two “performance groups”, a faster one and a slightly slower one.
The following table, relative to the best and average of the best 20, 50, 100 lap times and “all clean” lap times, gives already an idea about each car performance during the race:


LMP2 - Best laps average table


Interestingly, the best lap overall has been obtained by car n.37, which was also very close to car n.31 up to the average of the best 20 laps metrics, but fell down on the long distance.
Beside the best lap overall, it is clear anyway that on the long run car n.38 had the best pace, with a gap on car n.31 (the closest contender) reducing a bit as we analyze more and more laps.
It is also worth to notice that car n.13 seemed closer to the sister Rebellion crew than in previous races, above all if we consider the average of the best 100 laps and the “all clean” laps.
Car n.26 was very competitive if we look at the best lap and to the average of the best 20 laps, but falls progressively down on the long distance.
Car n.36, despite its “alternative” strategy, had a good pace and was close to car n.31 in terms of performance up to the average of the best 50 laps metrics, but the gap beween the two cars performance increases a bit on the long distance.

The plots relative to the best 20, 50 and 100 lap times of each car help, as usual, to visualize better each crew’s pace relative to the others.


LMP2 - Best 20 laps


LMP2 - Best 50 laps


LMP2 - Best 100 laps


The first plot shows that car n.37 was indeed very fast if we only look at the best 3-4 laps and still pretty competitive if we consider the best 20 laps, but falls significantly down afterward.
Car n.38 is with no doubt the fastest on track, with car n.31 being close if we look at the best laps overall, falling a bit behind between the 5 and the 45-50 mark and then getting again very close on the long distance.
It is interesting to notice how, after the 50 mark, three cars (car n.31, car n.38 and car n.13) had pretty much the same pace, with only a small advantage for car n.38.
Car n.36 is a kind of special case in this race, partly because of the strategy the team adopted: they probably didn’t have the absolute pace of the best cars, as the first plot seems to suggest, but were anyway pretty close. Their line, in the second and third plots, lies pretty much on the car n.38 one between the 25 and 50 mark. After this, there is a significant performance drop and Signatech car finds itself between the bests in class and the “second group”, on the long run performance.

To better understand how the race evolved, let’s take a look at all the lap times of each car comparing them to car n.31 ones.
We start to the main contender, car n.38.


LMP2 - All laps - 31 vs 38


As we have seen, car n.38 was a bit faster than car n.31 and this is confirmed by the above plot. DC Racing’s crew is clearly faster than Rebellion’s one during the third, the fourth and the last two stints. We know anyway that Rebellion performance was compromised in the closing race phases by the power steering issue. We can also identify how the two cars stopped pretty much simultaneously the first three times, but this trend was broken starting with the fourth stop. From that stop on, car n.38 started coming into the pit lane always before and performed always shorter stints. The results was car 38 stopping once more than car n.31: this was probably one of the race’s deciding factors.

The comparison between car n.31 and car n.13 helps to confirm how close the two cars were in terms of pace, differently to what we have seen in other races.


LMP2 - All laps - 31 vs 13


More interesting is the comparison between car n.31 and car n.36, because it helps to highlight the impact of the strategy / tyres change decision that Signatech took, when they let Lapierre to triple stint the tires on one side of the car.


LMP2 - All laps - 31 vs 36


Although we all know the value of Lapierre as a driver, we cannot ignore the dramatic performance drop that the team experienced during the third stint, that also costed them many positions. This is particularly important considering that Signatech’s crew was in many phases as quick as Rebellion car n.31 if not quicker (like during the third, fifth and seventh stints). Of course, this strategy saved them some pit time, but the performance lost was too big to be compensated.

Let’s now break down also LMP2 teams performance, analyzing each track sector times.

Car n.38 was pretty much the fastest in sector 1, but the gap between DC Racing’s crew and Rebellion car n.31 is nearly nonexistent, above all if we consider more laps. The table relative to the best sector 1 time, the average of the best 20, 50 and 100 sector 1 times and of the “all clean” sector 1 times pretty much underlines how close to each others the performances of the two main contenders were.
Beside this, the most interesting note is how close car n.13 was to the the first two classified crews.


LMP2 - Best sec1 average table


Car n.31, car n.13 and car n.38 form the fastest group, while all the other cars are a bit slower and pretty much close to each other.
A look at the plots relative to the best 20, 50 and 100 laps helps to better understand the relative pace of each car compared to the others.


LMP2 - Best 20 sec1


LMP2 - Best 50 sec1


LMP2 - Best 100 sec1


As we may see, car n.38 line is constantly the lowest, if we only exclude the fastest sector 1 times of the race, up to about the 5 mark in the plots above.
Car n.13 and car n.31 are very close to each other, above all past the 19 mark.
Car n.36 is close, but still a small step behind the two Rebellion cars and DC Racing one, while the other three cars are pretty close together and a further step slower.
As we mentioned already during the LMP1 analysis, sector 1 includes the biggest part of the main straight, where top speed plays a very important role and two pretty hard brakings, leading to two slow corners that are followed by hard accelerations.
To have an indication about the reason why car n.38 is so fast in sector 1 and if there is any difference in the aerodynamic setup each team used, we can take a look to the top speeds data (the speed trap was placed at the end of the main straight).


LMP2 - Best 20 TS


LMP2 - Best 50 TS


LMP2 - Best 100 TS


As we would expect, car n.38 has the highest top speeds and this seems to (at least partially) explain why they were so quick in sector 1. They were probably running a setup with lower downforce/drag than other cars.
It is anyway interesting to notice that car n.31 has constantly lower top speeds than car n.38, still obtaining sector 1 times that are very close to car n.38 ones. It is also worth to notice that car n.13 and car n.31 seem to have similar performances in terms of top speed too.
This is not the case for car n.38 and car n.37, with the latter having significantly lower top speeds.
Car n.36, on the other hand, has slightly higher top speeds than car n.31, but is anyway a bit slower in the first sector, as we saw.

Sector 2 is more twisty and combines low and medium speed corners and a tricky braking point, where the cars brake while cornering.
By looking at the table relative to the best and average of the best times for this sector, we cannot really identify any dominant car, as nearly each metrics has a different crew signing the best result.


LMP2 - Best sec2 average table


It doesn’t comes as a surprise that some of the cars that had a lower top speed are pretty fast in sector 2, above all if we consider only up to the best 50 times: car n.37 signs the best sector 1 time overall and remains on top also if we consider the average of the best 20 times. On the average of the best 50 sector 2 times car n.37 remains on the same performance level of car n.38 and car n.31 but is beaten by car n.36, which was also one of the crews with lower top speeds and was close to car n.37 performance also in the average of the best 50 sector 1 times.
On the long distance, car n.31 and car n.38 return on top, followed closely by car n.13.

The following plots, relative to the best 20, 50 and 100 sector 1 times help us to visulazize the situation we have just described.


LMP2 - Best 20 sec2


LMP2 - Best 50 sec2


LMP2 - Best 100 sec2


Car n.37 is the fastest if we only look at the best 17 sector 2 times. Car n.36 took the lead afterward and remains the quickest up to the 44 mark. Past this point, car n.31, car n.13 and car n.38 come back on top and are clearly the fastest cars, all three with very similar sector times.

Sector 3 is again all about car n.38. Jota’s crew pretty much dominates in terms of performance in this track section, only missing the overall best sector time (anyway for less than 6 hundredths of a second) that was taken by the sister car n.37.


LMP2 - Best sec3 average table


The table above gives a first feeling also about how much faster car n.38 was compared to the direct championship competitor, car n.31. In each of the metrics we consider, the gap between the two crews is always slightly bigger than 0.1 seconds.
Car n.37 has the best overall sector 3 time, as we mentioned already, but falls behind on the long distance.
Car n.36 is relatively fast and pretty close to car n.31 performances, at least up to the average of the best 100 sector times.
Since each car sector 3 times are pretty much similar, the plots relative to the best 20, 50 and 100 sector 3 times are a better mean to get a feel of each team’s relative performance.


LMP2 - Best 20 sec3


LMP2 - Best 50 sec3


LMP2 - Best 100 sec3


These plots confirm that car n.38 is the only one really holding a gap on the rest of the crews, with its line lying constantly below all the others.
Car n.31 gets closer only on the very right part of the third plot, so namely if we consider the “slowest of the best 100 sector 3 times”.
The plot also helps, once again, to identify how extreme was car n.37 performance drop.
Interestingly, car n.26 is very close to car n.31 in this sector, if we consider the data relative to their best 45 sector 3 times.
Car n.36 seems to have potential but their performance also experience a significant deterioration past the 51 mark.

Closing, the analysis relative to the LMP2 class seems to show that, looking only at the performance side, race end result could have been more favorable to car n.38. Considering they stopped once more for refueling than the direct competitors, car n.31, we could ask ourselves how the race could have ended if they could run the same strategy.
On the other hand, we don’t have to forget that also car n.31 experienced some problems, above all during the closing phases of the race.
In any case it was an extremely tense battle, something really enjoyable for every fan. Rebellion succeeded in winning the first season of this new LMP2 era, which is even more remarkable if we think that they didn’t run in the (extremely competitive) LMP2 class last year and that they always showed extremely good performance, beside very very consistent race management.

Posted by: drracing | November 12, 2017

WEC – Shanghai Race Analysis

Hi everybody!

Here we are again with another race analysis. This time we will deal with the penultimate round of the World Endurance Championship, that took place in Shanghai last weekend.

Before we dig into the analysis, anyway, i want to share with you a video about one of the last simulation session we run, a few days before the Chinese race and on the Shanghai circuit.
The vehicle model in use is again the 2017 WEC Spec LMP2 and the simulations we did also helped to evaluate the effects of certain setup choices on final performance.
Here a link to the video.

I think it was all around a very exciting race, with a lot of interesting topics that can be analyzed and a lot of interesting battles/dramas happening on the track, above all in LMP2.

Tyres degradation has been, as always, an extremely important point, since the teams have to always double stint their tires to respect the given allocation. In Shanghai it seems it was even more important, because of the track being particularly demanding with this respect.
Also, the race was neutralized with a FCY only once, this giving even more room for proper wheel to wheel fights and with the teams having to take care even more of their hardware.

In the LMP1 class, the race was dominated by Toyota, who seems to have found much more pace than immediately after Le Mans and that has brought some very effective Aero updates in China. It could have been an 1-2, if car n.7 didn’t have an accident with a GT car nearly at the end of the race.

LMP2 was all about Rebellion’s car n.31, who took the pole in Qualifying and, excluding the pit stops phases, never lost the lead. Senna in particular seems to be living an amazing moment, with a form and performances that no other driver on the grid seems to be able to beat currently. Behind them, anyway, a lot of interesting battles took place, with car n.38 (DC Racing – Jota Sport) who had some tense moments and left China loosing the championship lead, now in the hands of Rebellion n.31 crew.

One very interesting point during the race was how several times LMP1 cars overtook LMP2 vehicles at the beginning of the long straight before the last two corners and then LMP2 cars overtook LMP1 cars again before the braking point, because of their significantly higher top speed (LMP1 cars were coasting at the end of the same straight).

Before we start to analyze the available data in details, i have to give a word of notice about this Shanghai race analysis. For a reason I ignore (I may suppose a problem with the time detecting system), a lot of the sector times (mainly relative to sector 2 and 3) were missing in the list I found in the official website. This means, although the trends we will identify should remain trust-able and realistic, the absolute values of the averages we will look at could well be wrong.
Unfortunately this is something that goes out of my control and that seemed to hit LMP1 data more than LMP2 ones.



As we mentioned, Toyota won with merit in China, their car working extremely well during the whole race both in terms of performance and tyre management. Also strategically, it looks like they did everything right.
If we take a look to the time each car spent in the pit, we see anyway that Porsche pit stops were actually slightly quicker, which give even more significance to the performance advantage shown by Toyota:


pit times


The table above actually indicates how all cars except car n.7 (who spent a long time in the garage for reparations after the accident with a Porsche GT car) did six pit stops; Porsche, anyway, spent less time in the pit, with the winning Toyota n.8 staying in the pit lane about two seconds longer than Porsche n.2 and nearly thirteen seconds more than Porsche n.1.
The advantage on pace was big enough to not only compensate these gaps but also to build a comfortable lead already short after the start.

It is no surprise to see this confirmed by the following table, listing for each car the best lap time achieved during the race, the average of the best 20 lap times, the average of the best 50 lap times, the average of the best 100 lap times and the “all clean laps average”:


LMP1 - Lap time average table


The best results for each metrics are written in red.
This table makes clear, on one side, that the two Toyota had indeed consistently more pace than the Porsche, with the gap between the two brands in each of the considered metrics staying constantly between 0.4 and 0.5 seconds, if we exclude the best laps and the “all clean laps” average.
Another interesting point is how Porsche n.2 was significantly slower than car n.1, while inside Toyota’s team, it is actually car n.7 that seemed to have the best pace, at least if we consider the best 20, 50 and 100 lap times. The gap between the two Japanese cars gets smaller and smaller as we move from the average of the best 20 laps to the “all clean” line and this is indeed what also the plots relative to the best 20, 50 and 100 lap times for each car tell us.


LMP1 - best 20


LMP1 - best 50



LMP1 - best 100


Some major takings, coming from these plots are:

  • car n.2 is by far the one with the worst pace throughout the race, but the question is if they really wanted to take any risk, knowing that they could bring home the championship win on race before the end of the championship
  • Porsche n.1 best lap times are indeed close to the ones of car n.8, but they get slower on the long distance
  • Car n.7 is clearly the fastest car, if we consider the best 20 and 50 laps plots
  • The two Toyota are very close to each other after the 45 mark, with car n.8 holding a small advantage
  • All cars seems to have a similar “gradient” (meaning with this how the lap times grow as we move from the best to the 100th best lap time) for their lap times curve with the exception of car n.8, with its curve staying pretty flat between the 15 and 60 mark, signalizing indeed how good Toyota’s performance was.


It is very interesting also to look immediately at the comparison between all cars’ lap times during the race, focusing in particular to car n.7 and car n.8:


LMP1 all laps - 8 vs 7

It is easy to see how actually during different phases of the races, one or the other car was quicker, with n.8 crew showing better pace during the first, third, fifth and seventh stint. Coincidentally, Toyota used a split strategy with their tyre changes, with one car changing tyres at a certain stop and the other at the following one. No surprise that each car has better performance than the other immediately after a tyre change, with car n.8 changing tyres at the second stop and then again at the forth and sixth (so before each stint where it had better pace than car n.7).

Porsche didn’t use the same approach and indeed the plot relative to the lap times throughout the race doesn’t show the “switching” feature we saw in Toyota’s one (simply car n.2 is most of the time slower than car n.1):


LMP1 all laps - 1 vs 2


Let’s now break down our analysis looking at each sector and at the speed trap data.
As often happens, the car was divided in three sectors, as shown in the following picture:


track map


The first sector includes a big part of the box straight and the first corner, which is a very tricky and long right turn where LMP cars brakes when already cornering, making it an extremely demanding and interesting point of the circuit. It is not uncommon to see different cars following completely different lines here. Beside the first corner (Turn 1 and 2 in the track map above), the first sector is composed by a second very slow left turn, followed immediately by a more open one that is seen by the drivers simply as a part of a single long bend. After a short full section section there is again an hairpin (turn 6) driven at very low speed. We can imagine, hybrid energy deployment plays here a very important role on performance.
Sector 2 is a very interesting mix of high and low speed corners: turn 7 is a very fast and long left one that lead to a slower right turn (turn 8), where the goal is to find the best line to prepare the braking for the following left one (turn 9). It leads into a relatively short straight through a second left bend (turn 10) which is run in full acceleration by LMP cars. Follows a pretty hard braking to turn 11, which is a pretty slow corner but leads into a very long right bend, where the car progressively increase their speed exiting in fourth gear and entering the longest straight of the track: this means, the exit speed of 13 is extremely important in defining final lap performance. Also in this sector, hybrid energy deployment is very important, but downforce also counts in defining how quick a car can go through turn 7 and 8.
The third and final sector is in big part composed by the longest straight of the track, where top speed and straight line acceleration play an extremely important role. The straight leads with a very hard braking into turn 14, again a very slow (right) hairpin. It is followed by a very short straight and the last corner (turn 16), a left bend which proved to be very tricky as many drivers were kept under investigation because of track limits infringements.

Let’s start with the first sector.
This is without questions Toyota’s hunting ground, with both car n.7 and car n.8 being clearly faster than both car n.1 and car n.2 and car n.7 showing a better pace than car n.8, above all if we consider the average of best 20 and 50 laps.
This is what the following table, relative to sector 1 best and average times, tells us, also underlining the small advantage of car n.7 on car n.8.


LMP1 - sec1 average table


As for the overall lap times, the gap between Toyota’s two cars reduce as we move from the best sector time to the average of the best 100 sector times and to the “all clean laps” average, with the two crews producing more or less the same results for these two metrics.
Porsche is again closer to Toyota if we consider only the best sector time, with car n.1, car n.2 and car n.8 obtaining more or less the same time, but looses ground as we move to the average of the best 20, 50, 100 and “all clean laps” sector times.
The situation appears even clearer if we look at the plots relative to the best 20, 50 and 100 sector 1 times:


LMP1 - sec1 - best 20


LMP1 - sec1 - best 50


LMP1 - sec1 - best 100

As we said, similarly to what we have seen analyzing the overall lap times, Toyota n.7 is faster than car n.8 if we look at the left portion of our plots, up to the 35 mark. On the right side of this point, car n.7 and car n.8 are indeed extremely close to each other.
It is also clear how both Porsche were 0.2 – 0.3 seconds slower than the two Toyota, with car n.1 performing a bit better than car n.2. Interestingly, anyway, the gap between the two Porsche remains constantly bigger than the one between the two Toyota.

As always, it is difficult to isolate the reason why Toyota was faster than Porsche in sector 1, as LMP1 cars are so technologically complex that this could also depend on more factors (aerodynamics, hybrid energy deployment, tyre management, etc). Looking to sector 2 numbers, it seems possible that downforce also played a role but this track is also particular demanding in terms of hybrid system recuperation and deployment and it would be no surprise if also on this side Toyota had an advantage

As we mentioned, sector 2 is (even more than sector 1) a sector where downforce can make a difference, since it includes two pretty fast corners (one at the beginning and one at the end of the sector, leading into the longest straight of the track) and one medium speed one. There are anyway also two very slow corner, with one in particular (turn 9) leading into an strong acceleration zone, where also the hybrid system can contribute significantly to the final performance.
And again, as for sector 1, in sector 2 the two Toyota were clearly faster than the two Porsche.


LMP1 - sec2 average table


The above table, relative to sector 2 best time and to sector 2 best 20, 50, 100 and “all clean laps” times average, see the two Toyota again on top, with car n.7 and car n.8 sharing equally the lead in each of the four metrics we consider. This time, anyway, the difference between the two crews is constantly smaller than in sector 1, with the gap between the best Toyota and best Porsche staying always between 0.2 and 0.3 seconds.
This time, the two Porsche were slower than both Toyota also if we look to the best sector 2 times overall.

The plots help again to visualize each car’s performance relative to the others.


LMP1 - sec2 - best 20


LMP1 - sec2 - best 50


LMP1 - sec2 - best 100


What these plot tell us is:

  • Both Toyota are faster than the Porsche.
  • Car n.7 show again a very high potential, above all if we consider the laps before the 35 mark.
  • After the 35 mark, car n.8 is a bit faster than car n.8, while in sector 1 they were more or less on the same performance level.
  • Porsche n.1 was sensibly faster than Porsche n.2, although car n.2 had actually a better sector 2 best time.


The summary of what we have seen in this first two sectors seems to suggest that Toyota could handle the more twisty / high longitudinal accelerations part of the track much better than Porsche.
This is particularly interesting thinking about the aerodynamic updates that Toyota used in China (and what could have been if they had them already before during the season), but it looks realistic to think that also on the hybrid side Toyota had some more flexibility.

Sector 3 anyway shows a pretty different situation. Sector 3 is in big part dominated by the long straight, followed by a very hard braking and a very slow hairpin. Top speed, hybrid performance and low drag play surely an important role in defining how fast a car is in this sector, but as the sector starts in a point where the cars are already traveling at high speed (exit of turn 13) and probably have already deployed a big part of their electric power, i suspect a lower drag can bring here a greater benefit.
If we look at the following table, showing the best sector 3 time and the averages of the best 20, 50 and 100 sector 3 times, we see for the first time a Porsche on top at least in some of the metrics:


LMP1 - sec3 average table


Porsche n.1 is indeed the fastest car if we consider the average of the best 50 and 100 sector 3 times, being anyway extremely close to Toyota n.7 also in the average of the best 20 sector 3 times.
It is also interesting to notice how also the performance of Porsche n.2 are pretty close to the fastest times in this sector, with n.2 crew taking the lead in the “all clean lap” average.
Porsche being here much more competitive than in the first two sectors is confirmed by the plots relative to the best 20, 50 and 100 sector 3 times:


LMP1 - sec3 - best 20


LMP1 - sec3 - best 50


LMP1 - sec3 - best 100


The first thing catching the eye here is that car n.8 is significantly slower not only than the sister car n.7, but also of both Porsche.
Toyota n.7 signs the best sector time and its performance is comparable to that of car n.1 up about to the 30 mark, but it drops a bit and also car n.2 becomes quicker on the long run.
Indeed Porsche were much more competitive in sector 3 than in the rest of the circuit.
Why this?
A small help can come from the Speed Trap data, as the TS was located at the end of this straight. As always, we have to be careful when we analyze this data for the LMP1 class, as the coasting strategies that each team uses can mix the cards a bit. Nonetheless, i think we can still get some important hints to understand each car performance:


LMP1 - TS - best 20


LMP1 - TS - best 50


LMP1 - TS - best 100


There are two things that we can immediately take, looking at these plots:

  • both Porsche were generally faster, in terms of top speed, than the two Toyotas
  • Toyota n.7 seems to be quicker than car n.8 up to the 40 mark and then drops down a bit. This seems to match also with the general performance trends of car n.7, who showed better pace than car n.8 above all to the 35-40 mark in each plot.

Top speeds analysis seems to suggest that the Porsche had either a lower drag setup than Toyota (which could also mean lower downforce, if the efficiency of Toyota really stepped up, because of the new updates; this seems to match with sector 1 and 2 performances), or a different hybrid deployment strategy, maybe prioritizing sector 3 and sacrificing a bit sector 1 and 2. Of course, also a mix of these two factors and, maybe, even others is possible. Maybe Toyota’s recovery strategy was also based on a longer recovery / coasting phase at the end of the straight. Really difficult to say.

Closing the analysis of the LMP1 class, China showed an extremely fit Toyota, most probably well helped by the small updates they installed on their cars for this race.
The Japanese cars were sensibly quicker than the Porsche and built their advantage mainly in sector 1 and 2, the most twisty ones and where downforce and hybrid energy deployment most probably play a bigger role.



The LMP2 class offered a very exciting race, with a lot of drama and wheel to wheel fights, involving also the main championship players, like DC Racing car n.38.
As all the teams use the same car (Oreca 07 – Gibson), there are less major performance differences compared to what we have seen for the LMP1 class (and in some previous LMP2 races), but we can still find some interesting points.

If we start considering the overall lap performance of the first four classified cars and of car n.28 (which came to sixth place, but was much faster than car n.25 who finished fifth), the following table immediately shows us that car n.31, who finished first, actually won on merit, not only because of a perfect strategy and because they avoided any mistake, but also because they had a very strong pace:


LMP2 - Lap time average table


Car n.31 was in each metrics in our table either the fastest car or very close to the fastest times. Beside signing the best overall lap time in the race, they are also on top if we consider the average of the best 20 laps and remain extremely close to the best performances also if we look at the average of the best 50 and 100 laps. They are again the quickest if we consider the “all clean laps” metrics.
Another interesting point, anyway, is car n.38 pace. Jota Sport’s crew was indeed extremely fast and, in terms of pure performance, could be beaten only by car n.31. Their race was anyway affected by a couple of mistakes/accidents that compromised completely their final result.
The plots relative to the best 20, 50 and 100 laps help us even more to understand how each team performed:


LMP2 - best 20


LMP2 - best 50


LMP2 - best 100


The first thing to notice, looking at the plots above, is how close all the cars we are analyzing were to each other, in terms of lap performance potential.
Car n.31 was indeed the fastest if we only look at the best 20 lap times plot. Above all their best 10 laps were sensibly faster than the ones of all the other cars.
Anyway, if we move our focus on the best 50 and 100 laps, we clearly see that the line seating below all the other was actually the one of car n.38, who clearly becomes the fastest car past the 10 mark.
It is also interesting to notice how close to each other were car n.31 and car n.36 (who finished second), in terms of performance, past the 25 mark.
Another interesting point is also that all the lines in our plot seem to show a very similar gradient from left to right (from fastest to slowest laps).

With this regard, it is very revealing to look also at how the performance of each car evolved during the race, also comparing each crew to the winning n.31.
If we first put car n.31 and car n.36 together, we see that the Rebellion crew was significantly faster than the Signatech one mainly during the second and the last stint, but also that the two cars were pretty close to each other (in terms of lap times) during the rest of the race. We could even go so far and say that car n.31 seemed to manage the tyres better, as they normally showed a slower increase of the average lap times during two stints (also in LMP2 each team had to double stint the tyres). But this could also be dependent on other factors, like traffic management.


LMP2 all laps - 31 vs 36


A comparison between car n.31 and car n.38 also offers some interesting discussion points.


LMP2 all laps - 31 vs 38

Car n.31 was again untouchable during the second stint, with Bruno Senna at the wheel, while Jota had his silver driver in the cockpit (Laurent), although Laurent is surely one of the biggest sensation of 2017 Season and is really fast.
Anyway, car n.38 produced in several stints a better performance than car n.31 and was sensibly slower only in the closing phases of the race.
This confirms their performance potential and that their race was actually ruined by unfortunate track action situations, but not because of bad pace.

Rebellion second car, n.13, was a step behind the sister crew in terms of performance and indeed one of the slower cars in our analysis (but we mentioned already how close these five cars were to each other in terms of performance), but still managed to close third, overtaking car n.38 in the final phases.
And indeed, if we compare the pace of car n.38 and car n.13 during the whole race, the first thing that catch our eyes is the dramatic performance drop that Jota’s crew had in the final two stints, while car n.13 was able to keep running with a much better performance.


LMP2 all laps - 13 vs 38


Let’s now try to break down each car performance by looking at the sector times.

In sector 1 car n.31 and car n.38 performance are very close, with all the other crews remaining anyway not too far, above all Signatech.


LMP2 - sec1 average table


Car n.31 and car n.38 metrics are always very similar, with the gap only slightly increasing as we consider more and more laps. Car n.36 follows very close behind.
A look at the best 20, 50 and 100 sector 1 times plots helps to get a better feeling about each team’s pace.


LMP2 - sec1 - best 20


LMP2 - sec1 - best 50


LMP2 - sec1 - best 100


Car n.31 seems indeed to have a small edge on the competition in this sector, with car n.38 being very close if we look at the 20 best sector 1 times plot, but loosing a bit of ground on the long run. Car n.13 is constantly slower than the other crews in this first part of the track.
Again, excluding maybe only car n.13, all the cars we consider are incredibly close to each other up to at least the 70 mark; this underlines not only the consequences of using the same chassis but also, most importantly, how good these teams work and how close they constantly are to the limit, after one season of development of their cars, setups and approaches.

Sector 2 offers a slightly different scenario, with car n.38 being constantly very fast and car n.31 not seating on top here, although remaining always among the fastest ones.
As we mentioned already, sector 2 is at least partially downforce dominated, but we better not jump to quick on conclusions and look first to the data of this sector and sector 3 afterward.


LMP2 - sec2 average table


The table above seems to confirm, without any doubt, that car n.38 was extremely competitive in this section of the track, although again the gaps between the cars in each metrics are extremely small.
This sector seems also to suite car n.13 pretty well, on the contrary of sector 1.
Is this also what the best 20, 50 and 100 laps plots tell us?


LMP2 - sec2 - best 20


LMP2 - sec2 - best 50


LMP2 - sec2 - best 100


Car n.38 and car n.13 look very strong, both if we look at the 20 laps plots or we concentrate on the long run.
Again, all the cars are pack extremely closed together and car n.31 is not far, although not shining as they did in sector 1 and in terms of overall performance.
Did they indeed run less downforce than car n.38?

This doesn’t seem what sector 3 data tell us.
In a sector where top speed (and hence low drag) play an important role, car n.38 and car n.31 are very close to each other in terms of performance and indeed have a slightly bigger gap to the other crews we consider, compared to what happened in sector 1 and 2.


LMP2 - sec3 average table


This is what the table relative to the best sector 3 times and to the average of the best 20, 50, 100 and “all clean laps” sector 3 times tell us.
Anyway, the plots relative to the best 20, 50 and 100 sector 3 times give in my opinion a better feeling about the performance situation in this section of the circuit.


LMP2 - sec3 - best 20


LMP2 - sec3 - best 50


LMP2 - sec3 - best 100


In all three plots it is crystal clear how car n.31 and car n.38 lines constantly lie below all the others, with car n.38 having maybe a small edge up to the 50 mark.
Car n.36, that had a very good overall pace during the whole lap, was indeed a step behind Jota’s and Rebellion’s crews in this sector, as was Rebellion second car n.13.

The top speeds data seemed to confirm that car n.31 and car n.38 were running similar drag levels:


LMP2 - TS - best 20


LMP2 - TS - best 50


LMP2 - TS - best 100


Car n.31 and car n.38 have pretty much always the best top speed and this is indeed no surprise, following the analysis of sector 3 times.
Car n.36 is, on the other hand, constantly the slowest one in terms of top speed, which seems to give a reason for their slightly slower times in sector 3 and their pretty good performances in sector 2.
All of this seems anyway to leave open the reasoning behind car n.38 being slightly faster than car n.31 in sector 2: this doesn’t seem to lie on different aerodynamic configurations.
Maybe the two teams were running different mechanical setups, which could also partially explain the differences in tyre management that we discussed before; but the reason of these differences could also simply “depends” on the driving styles of the drivers composing each crew or on the different race situations in which each team found itself during the race.
In general, car n.31 seemed to be a bit more consistent than the other crews (in particular car n.38, that was the closest in terms of performance), but they also had the (well deserved) advantage of being constantly in front, this giving a chance to manage the race instead of having to chase it, as it was the case for car n.38 during many phases; they most probably could not run in “save mode” and maybe had to stress its tyres much more (see for example the accident and relative spin that car n.38 had during the confrontation with G-Drive car, which surely didn’t do any good to the set of tyres they were using during that stint).

Posted by: drracing | November 10, 2017

2017 – 24 Hour Race Technology Article

Hi everybody!

This is just a very short post to share with you a couple of updates about my latest projects.

The most important news is that (not one but) two articles i wrote are featured in this year 24 Hour Race Technology issue.
The articles are about tyres and tyre modeling, with a specific focus on 2017 LMP2 tyres, their performance and the effects of specific parameters and strategy decisions on cars performance. There is a more theoretical part about tyre models and then a more “practical application” example, where my LMP2 vehicle model and my DIL simulator have been used to evaluate numerically the impact of said parameters on final vehicle performance and lap times. The results are then analyzed comparing logged data of different sessions.

Because of this article, i had a chance to come into contact with some very knowledgeable people, working respectively with Dunlop Motorsport, with bf1systems (a company producing very high level sensors, also for LMP1 and LMP2, including systems to monitor tyre pressure and temperature, including carcass temperature) and with a WEC LMP2 team. All of them provided extremely precious information and the WEC team also helped to validate the LMP2 vehicle model and its behavior (again, it was extremely interesting how good simulation can match reality).


mag 1


mag 2


mag 3


mag 4


You should definitely buy the magazine! Beside my pieces, there are also many other (more interesting) articles, including something written by Paolo Catone, father of the Peugeot 908 and, more recently, of the BR01 that raced last year in LMP2.

Beside this, i will start working with a new website, that will be online at the beginning of 2018 and will deal with news and topics related to sportscar, theRacingLine.
Hopefully the time at disposal will be enough to contribute substantially to this exciting project from my friend Dave Ellis.

Stay tuned as i will follow up soon with Shanghai WEC Race Analysis!

Posted by: drracing | October 27, 2017

ELMS – Portimao Race analysis

Hi everybody!

Last weekend offered another very interesting race, namely the ELMS finale in Portimao and we are here again with a race analysis to try to understand how each of the main characters in the show performed.

The race was pretty interesting, mainly because the title battle had still to be decided, although G-Drive had a pretty comfortable lead and only had to play safe to ensure the championship win. Anyway, we still had an exciting competition on track, with the car that won that started only in ninth position and had an issue with its fuel tank, which forced them to stop one time more than the other main competitors and with the car who lead most of the race (car n.32) and finished second in the championship standing having to serve a 55 seconds stop and go at the end of the race because of track limits infringements.

Beside this, the track itself offered a very nice scenario for LMP2 cars, with a lot of height changes, both relatively fast and slow corners and a very long straight, with pretty high top speeds. Beside this, the circuit is pretty bumpy and this poses a further challenge for the teams.

The race was won by Graff Racing, with their Oreca 07. Autosport Ligier JSP217 came to second while SMP Racing obtained another podium with their Dallara, closing in third position. For anybody interested in endurance racing and race engineering, it is always very nice to see such a variety, with three different chassis in the first three positions.
It was a pity that Dragon Speed, that started in second position, could not join the battle for the win because of an incident during the first few laps, that costed them a couple of laps.

Let’s start our analysis by looking at the time each team spent in the pit lane.


Portima PS


Car n.27 was the one spending less time in the pit, confirming the effectiveness that SMP pit crew showed already in the previous races. Anyway, the team opted not to stop during a FCY phase declared at lap 21 and that was used by nearly all the other teams to pit, saving some time at the end. On the other hand, it is interesting to see how SMP seems able to run longer on a full tank than other cars, most probably because they are able / brave enough to use their fuel to the very end (i doubt the car has a much lower fuel consumption than the competitors).
Car n.40 had to run one pit stop more because they apparently could not full the tank completely. This means, on the other end, they always run with a lighter car than the other crews at the start of each stint.
Car n.32 would have been also pretty good with their pit stop strategy, if they didn’t incur in the 55 seconds stop and go we already mentioned.
Car n.22 was not as efficient as SMP with their pit stops, but did only five stops, using the initial FCY at their advantage.
Car n.21 is included in this analysis as, even if closing the race only in ninth position, was indeed very quick, as we will see. The table above show pretty clearly how their race was completely decided by having to pit 7 times and by the issue they had at the beginning of the race, when Hedman went into contact with another car and lost two laps in the gravel.

Anyway, if we look at the pure performance of each car, Dragonspeed had indeed an amazing pace, at least with Lapierre and Hanley at the wheel.
The following table, showing the best lap, the average of the best 20 and 50 laps and the average of all clean laps (with the fastest time in each column shown in red), tells us that car n.21 was indeed the one with the best performance potential and actually the fastest as long as the two professional drivers were driving.


Best laps av


Dragonspeed Oreca is the fastest car if we consider the best lap and the average of the best 20 and 50 laps, with a gap of more than 0.5 seconds on the second fastest car on the single lap and about 0.3 seconds for the average of the best 20. The gap to car n.40 becomes anyway very small if we look at the average of the best 50 laps, with car n.21 falling inexorably down in the all clean laps average, where it occupies the last position among the cars we consider (again because of the heavy influence of their third driver).

Car n.40 is clearly the second fastest car, as we will also see shortly in our best 20 and 50 laps plots, but we have to take in mind also that car n.22 strategy was clearly to avoid any risk and surely they didn’t push to the limit during the race. During the closing phases, Hirakawa was actually coasting before each braking, maybe to save fuel and avoid the risk of having to stop and this confirms that the team was not really aiming at signing super quick laps but was simply controlling the situation.

The relative performance of each car compared to the others can be well visualized looking at the following plots, depicting the best 20 and 50 lap times of each crew.


Best 20 laps


Best 50 laps


Both plots suggest how car n.21 and car n.40 were clearly the fastest on track, with car n.21 edging the rivals although Graff’s car was often running with fuel less in its tank.
The only area of the above plots where car n.40 seems to be a bit quicker than car n.21 is on the very right side of the second graph.
Car n.32, which was contending G-Drive crew for the drivers title, was actually a bit slower than the Orecas of Graff and Dragonspeed, but seemed to be more consistent on the long run, with less performance degradation on the right side of the 50 best laps plot.
Car n.27 was more or less always off pace but still able to close third, helped by consistent drivers and, beside the strange decision of not pitting under FCY at the beginning of the race, by a very good strategy and quick pit stops.
Car n.22 was not really contending on the pace side, as the team didn’t really need it this time.

We can now look into more detail to each team lap performance, by analyzing the sector times.
The track was divided in three sectors, as usual, but on the contrary to many other races that we analyzed already, in Portimao there isn’t really a sector where a certain performance feature (like top speed or downforce or low speed handling) is dominating on the others, as each sector actually presents a good mix of low and medium-high speed corners, with sector one also including the main (and longest) straight.


track map


The times registered for sector 1 are inline with the whole lap performances, with car 21 being always the fastest as long as either Hanley or Lapierre were at the wheel. Dragonspeed’s crew advantage on Graff n.40 car is about 0.15 seconds on the best sector 1 time and stay at about 0.1 seconds if we consider the average of the best 20 and 50 sector 1 times. Car n.40 is on top if we consider the “all clean laps” average sector 1 time, with car n.21 following closely.
Car n.32 stays relatively closed to the best two cars, getting closer in the average of the best 50 sector 1 times or the “all clean laps” average.


Best sect 1 av


The plots relative to the best 20 and 50 sector 1 times gives even a clearer image of how car n.21 and car n.40 were in a class on their own in this track section, with the other three cars closer together but holding a sensible gap from the best two teams.
It is also interesting to notice how car n.21 line lies always below car n.40 one, if we exclude the laps following the 40 mark.
Among the “others”, car n.32 is the one staying closer to the two Orecas, while both car n.22 and car n.27 are sensibly slower.


Best 20 sec1


Best 50 sec1


Sector 1 analysis is particularly interesting also because the Speed Trap is located at the end of the main straight.
If we take a look to the top speeds, we see that, while car n.40 was, as in Spa, particularly fast, achieving the highest top speeds, car n.21 has actually pretty low top speeds, being actually also slightly slower of the sister car n.22.


Best 20 TS


Best 50 TS


This seems to suggest that, while part of the performance of car n.40 in sector 1 could depend on its top speed, car n.21 most probably was able to go quicker through turn 1 (a 4th gear corner), turn 2 (flat out), turn 3 (1st gear) and turn 4. This could mean that, once again, car n.40 went for a low drag setup, while car.21 worked more on the downforce and handling side. One thing to always keep in mind is also that car n.40 constantly had, at least at the beginning of each stint, less fuel on board than the other cars.
It is also interesting to notice how, while all the other teams had similar top speeds to each other, car n.40 seems to be on a different level with this regard.

Sector 2 showed a more balanced situation, with car n.21, car n.40 and car n.32 being closer to each other, in terms of performance, while car n.27 and car n.22 remain a bit behind.


Best sect 2 av


Autosport Ligier is always among the quickest, leading the group if we consider the best sector 2 time and the average of the best 20 sector 2 times. Anyway, it stays extremely close to the top also in the best 50 sector 2 times average and in the “all clean laps” average.
This seems to confirm the goodness of Ligier Chassis on the most twisty sections, with this sector being a mix of low speed corners (turn 6 and turn 9), full throttle bends and a pretty quick right-hander (turn 8), all combined with very suggestive height changes that make some spots blind.
The following plots, relative to the best 20 and 50 sector 2 times, give even a better feeling of how close to each other car n.21, car n.40 and car n.32 were.


Best 20 sec2


Best 50 sec2


Car n.27 is sensibly off pace in this sector, while car n.22 seems to have potential (if we look at the best 7-8 sector 2 times it had a pretty good pace) but is not particularly fast on the long run, the main reason for this being probably that they didn’t need to take any risk.

Sector 3 offers some interesting discussion points too.
As sector 2, also sector 3 is a good mix of slow and fast corners, with some very big height changes. It also include the very long and very quick last corner (turn 16), which puts a lot of load on the outside tires.
Car n.21 has again the best pace potential and signs the quickest time overall, remaining also on top if we consider the average of the best 20 and 50 best sector 3 times. As usual, its average pace drops dramatically if we consider the “all clean laps” average, because of the influence of their third driver’s performance.


Best sect 3 av


It is also interesting to notice how close to each other the two title contenders were in this sector, with pretty much the same result in the 20 and 50 best sector 3 times metrics.
Car n.27, on the other hand, gets very close to them on the long run, namely if we look at the “all clean laps” average.

Again, the plots give us a better feeling about the performance of each car relative to the others and they confirm car n.21 as the quickest, above all if we look at the best 20-30 sector 3 times, falling back a bit after the 30 mark.


Best 20 sec3


Best 50 sec3


Car n.40 is also very fast and follows car n.21 very closely, with its line actually lying below Dragonspeed one after the 30 mark.
It is interesting to notice how close to each other car n.32 and car n.22 are and how car n.27 gets on their performance levels after the 15th mark.
We could also speculate about the reason why car n.21 has a very strong pace if we consider the best 20-25 sector 3 times, but slows down a bit on the right side of the 25 mark. Since the car fell behind after the initial accident with Idec’s Ligier, a reason could be having to deal with traffic more than the other competitors, but this is really only a speculation. I am saying this, because they didn’t seem to have more tyre degradation than the other cars and were pretty much in line with car n.40 performance also at the end of the race.

We can get a feeling about this, by looking at the plot showing all race laps for each car. As usual, it is a very messy plot, but we can still extract some useful info out of it:


all laps all cars


  • car n.21 is extremely slow at the beginning of the race, with Hedman at the wheel
  • car n.21 is clearly the fastest car on track between lap 76 and 101, which are a big part of Ben Hanley’s stints and the beginning of Lapierre ones.
  • car n.32 was particularly competitive at the end of the race, compared to the other crews


If we analyze the plot comparing each car “race history” to the one of the winning team, we can extract even some more interesting information.
We start with car n.32.


all laps car 40-32

This plots shows again how car n.32 was slower than car n.40 pretty much for the whole race duration, with the exclusion of the last 15-20 laps.
This plot also shows how much closer to each other car n.40 pit stops had to be compared to the competition, because of the fuel tank issue and the additional pit stop of car n.32 because of the stop and go penalty.

Let’s now look at car n.27.


all laps car 40-27


As we saw already, car n.27 was in general slower and this is confirmed by its line lying the most of the time above car n.40 one.
We also identify SMP first pit stop happening out of the FCY window, around lap 29.
It is also interesting to notice how much car n.27 performance degrades between lap 40 and 55, before they stopped for their second pit stop: we could think they didn’t change the tires in their first stop and they suffered their degradation.
Another interesting point is that it looks like SMP crew was able to let their lap times to fluctuate less than the car n.40 ones, remaining pretty consistent on their pace, no matter how quick it was.

There is not too much to say about the comparison between car n.40 and car n.22, beside what we told already about the different pit stop strategy. Car n.22 was in general a bit slower, as most probably they crew never really “looked for trouble” during the race.


all laps car 40-22


Finally, a comparison between the two fastest cars, Dragonspeed and Graff.


all laps car 40-21


As we mentioned already when looking at all the cars together, the first very evident point is how much slower car n.21 was at the very beginning of the race, up to about lap 50. As soon as Ben Hanley jumped in the car, the pace increased drastically and get immediately close to car n.40 one. As we also mentioned, car n.21 was clearly the fastest machine on track between lap 76 and 101 and was very close to car n.40 pace in the last stint.
Indeed, car n.21 situation is always pretty interesting, as the potential it expresses is often extremely high, but is compromised by the non-professional driver who, most probably, is also the best business case for the team.
In general, among the three ELMS race we analyzed, car n.21 was the fastest car (or at least the car potentially faster than all the others) in two of them and was not on the best players level only in Spa.

Posted by: drracing | October 19, 2017

WEC – Fuji Race analysis

Hi everybody!

Here we are again after another WEC race, this time in Japan, at the beautiful circuit of Fuji.

Before starting the analysis itself, though, i would like to share again a video of a simulation session we did in preparation for the race, using the 2017 LMP2 vehicle model that was also employed for the ELMS – Spa race preparation video.
You find it here.
Unfortunately, we didn’t really have any chance of any comparison between simulation and reality, because the whole weekend has been affected by a very unpleasant weather, with rain and fog being always a constant theme in every session, in particular during the race.

The race itself has been wet conditions for its whole duration, with a lot of interruptions (in the form of red flags or safety cars) due mainly to scarce visibility. This has made the race more exciting, on one side, with strategy, vision and luck playing an important role on the final result and because it lead to many track fights, but also left (at least for me) some disappointment, since it was not possible to analyze how quick 2017 cars would really be on this very interesting track.
Also the distance covered was much shorter than it could be, with the final red flag signing the end of contentions after “only” 113 laps, with only 83 laps done under green flag.

These are the main reasons why also my analysis will be shorter than the previous ones. Nonetheless, there are a few interesting points that came out crunching the numbers publicly available.

The race was won by Toyota n.8 car, with the sister n.7 coming into second, in a very important 1-2 for the Japanese manufacturer, that has always obtained very good reults on its home track.
Car n.8 victory is even more important, considering that car n.2, which is leading the championship standing, finished in fourth place, plagued by a dramatic lack of pace.

Something similar happened also in LMP2, with Rebellion car n.31 taking the victory in front of an extremely quick Alpine n.36. DC Racing / Jota sport Oreca n.38 finished third and lost some of the advantage they had in the championship, with Rebellion car n.31 now closer.

Let’s take a look at how the performance of each of the main actors of this race was.


As we said, the very difficult conditions seemed to fit the Toyotas much better than the Porsches.
This is a very interesting point, above all if we consider that Porsche’s car n.2, the one who struggled the most in terms of performance during the race, actually signed the best lap time, with a gap of about 0.25 seconds on car n.8 best lap and was very fast at the beginning of the race.
If we also consider the average of the best 20 lap times anyway, Porsche n.2 seems still pretty competitive, being about three tenths quicker than car n.8 and with an even clearer gap to car n.1 and car n.7.
The situation changes completely, anyway, if we look at the best 50 lap times average: Toyota n.8 car is clearly the fastest, with about 0.3 seconds gap on car n.7 and car n.1, which are very close to each other. Porsche n.2 falls heavily behind and is nearly eight tenths slower than car n.8.


LMP1 average times table


The table above shows a summary where, for each average, the quickest time is shown in red.
We will not consider the “all clean laps” average this time, since the race was run for so long under safety car or yellow flag conditions, that it would not add much to our analysis.

The picture we got from the best and average best lap times is confirmed if we  look at the plots of the best 20 and 50 lap times of each car.


LMP1 Best 20 laps


LMP1 Best 50 laps


The first plot in particular (best 20 lap times of each car), shows how car n.2 is indeed the fastest if we only look at the best 13 lap times. Car n.8 follows closely, even in the very left part part of the plot.
The situation changes completely if we look at the second plot, anyway, where we can clearly see how dramatically the performance of car n.2 falls compared to all the other competitors (in particular compared to car n.8), above all if we focus on the right side of the 25 mark.
At the same time, this plot clearly shows how, for nearly the whole race length, car n.8 was consistently quicker the the other three crews, including the sister car n.7. It is also interesting to notice how close the performance of car n.7 and car n.1 was, with the line of each crew’s best 50 lap times intersecting the one of the other several times in this second plot. Car n.7, anyway, still has an edge on the very right side of the plot, confirming how also the second Porsche struggled in the last part of the race compared to the two Toyota.

According to the info i have and to what we learnt watching the race, Porsche (car n.2 in particular) had issues with the tyres, being affected by poor grip and by issues in bringing them up to temperature, above all in the second part of the race. 
The reason behind this seems to lie in the tyres specifications that each team planned/used for the race. As far as i know, there are two main types of rain tyres available for the teams, one fitting better heavier rain and lower temperature conditions (with Michelin being able to also modify them a bit on the track, if needed in the search for quicker heating up) and one adapting better for less extreme rain / less water / higher temperature conditions.
Apparently, Porsche could use the first tyre type at the beginning of the race but was unable to use them after the first pit stops; probably because of a different planning / pre race strategy, they didn’t have enough of these tyres for the race. As far as i know, these were also the tyres they used during free practice and qualyfing, where both Porsche were still extremely competitive.
If this is true, it means they found themselves running with tyres suiting the very special conditions that WEC encountered in Fuji much less than the ones that Toyota had at its disposal.
I think it is fair to say that, from a strategy perspective, Toyota did an excellent job in Fuji or, at least, any mistake.

According also to post-race interviews, Toyota n.8 was also helped a bit by a gamble with the fuel strategy, avoiding a third pit stop that actually both Toyota n.7 and Porsche n.1 did. As a consequence, car n.8 was also the one that spent less time in the pit.


LMP1 Pit times


It is now clear that car n.8 seemed to have a more consistent performance than the other cars. Let’s try to identify if this pace advantage was built on a specific part of the track more than in others.
As usual, the track was divided in three sectors, as shown in the image below:


Fuji track


The first sector is composed of about half of the main straight (which is very long) and the first corner, a very slow hairpin.
The second one includes the two fastest corners of the track, namely turn 3 and turn 4-5 and is (at least in dry condition) a very good section to evaluate of how much downforce a car has and, in general, how good the car’s balance and handling is in high speed corners.  There is then a mid-low speed corner (turn 6) that lead to a quick, full throttle section of the circuit (again, referring to dry conditions).
Finally, the third sector is composed mainly by slow corners, driven in first or second gear and, in some cases, with adverse camber producing very slippery car behavior.

Let’s take a look to Sector 1 times first.


LMP1 Sec1 average times table


The two Toyota are clearly in front here, no matter if we consider the best sector 1 time or the average of the best 20 and 50 sector 1 times.
This is particularly interesting, considering that, although the overall top speed was achieved by car n.7, the two Japanese cars seemed to have less straight line speed than the two Porsches, at least according to the trap speed measurements.
The best 20 and 50 sector 1 times plot confirms what shown by the table above:


LMP1 Best 20 sec1


LMP1 Best 50 sec1


Car n.7 and car n.8 are clearly quicker than the two Porsches, with car n.8 being faster than car n.7 up to the 46 mark and then dropping down a bit.
As we mentioned, this is particularly interesting because the two Porsche had often higher top speeds than the two Toyota, if we exclude for a moment the drop that car n.2 shows after the 25 mark and a big part of sector 1 is actually the final section of the main straight, where top speeds is surely important.


LMP1 Best 20 Top speed


LMP1 Best 50 Top speed

This drives some considerations.

  • we have to be careful, since i don’t know exactly where the TS was located: it was at the end of the main straight, but there is no indication about its exact distance from the start or the finish line. Maybe in certain phases of the race the Porsche n.2 had to brake so early (because of lack of grip) that the top speed measurement was affected (because the TS is in a zone where the car was already decelerating)?
  • For many laps there was a slow zone on the main straight, but i am not sure about how many laps exactly and if and how it affected cars speed
  • with LMP1 cars, there is always the unknown of how and where each car starts its coasting phase (energy recuperation strategy) before the driver hits the brakes
  • in general, it looks like Toyota simply had a better car than Porsche in this very special conditions and both crews were probably able to brake later and/or drive through turn 1 quicker and/or accelerate earlier at the exit of turn 1.
  • with such a bad visibility and the typical rain low grip conditions, i imagine that managing traffic situations was much more difficult than with a dry track, above all approaching such a hard braking zone as the one before turn 1. The car seating in front of the field (above all at the beginning of the race, when the lapping of slower classes has not begun yet), has sure an advantage.


As we are going to see analyzing sector 2, we could even think that the Toyotas had more downforce than the Porsche (and hence more drag too, which would explain the lower top speeds), but with a wet track one has always to be careful in coming to similar conclusions, as the pace of each car is dramatically affected by many factors, like the ability to have the tyres working in the right window (both in terms of temperature and pressure), mechanical settings (in order for the driver to have confidence but also to avoid typical rain issues like aquaplaning), the ability of each driver to deal with such a difficult scenario.

In any case, both Toyota cars seemed to have an edge on the two Porsche also in sector 2, where, in normal conditions, downforce would play a very important role.
Looking at the best sector times and average of the best 20 and 50 sector times, we see how car n.8 is always on top. Interestingly again, Porsche n.2 was extremely quick too if we look at the best sector time overall and also at the average of the best 20 sector 2 times (although in this later case the gap to car n.8 increases sensibly). On the other hand, we see again a dramatic drop when considering the average of the best 50 sector 2 times.
It is also interesting to notice how car n.7 and car n.1 have very similar performances no matter which value in the following table we look at:


LMP1 Sec2 average times table


Car n. 8 is pretty much in a league of its own, above all if we look at the best 50 sector 2 times average.
This is confirmed also by the plots relative to the best 20 and 50 sector 2 times.
Again, we see how car n.8 is constantly the fastest and how car n.2 had some good pace at the beginning of the race, producing very good times before the 16 mark, but falling down after.
It is also interesting to see how close the performance of car n.7 and car n.1 were, as the two cars indeed fight repeatedly with each other during the race.


LMP1 Best 20 sec2


LMP1 Best 50 sec2


On the contrary to what we have seen till now, sector 3 seems definitely Porsche’s territory, with both cars signing the best times here and with car n.2 being clearly the fastest if we look at the best 15-20 sector times.
The following table, relative to the best sector 3 times and the average of the best 20 and 50 sector 3 times underlines how Porsche had really an edge on the Toyotas in the final part of the track:


LMP1 Sec3 average times table


The picture appears even clearer if we look at the best 20 and 50 sector 3 times plot.
Interestingly, car n.8 was not particularly brilliant in this sector, being often also slower as car n.7.


LMP1 Best 20 sec3


LMP1 Best 50 sec3


Before closing with LMP1 and switching to LMP2, i would like to underline again how, beside a pure performance analysis, it is difficult to drive safe conclusions about each car performance in such difficult weather and track conditions, because a lot of different factors could play into the equation.
For sure, Toyota seemed to have an edge on Porsche and put in place a better strategy (with car n.8 also being helped a bit by the latest red flag) and, probably a better tyre choice/management.



Let’s now take a look at the LMP2 class. The race was won by Rebellion car n.31, followed by Signatech Alpine car n.36 and DC Racing / Jota sport car n.38.
Albeit all the teams using the same chassis, some differences could be identified in how each car performed and in how each team tackled both setup and strategy.

There has been some post race discussions because some teams didn’t have their Silver/Bronze driver in the car at all, because of the red flags and interruptions that reduced significantly the race time with respect to the planned six hours. But this goes beyond the scope of this article, that wants to focus more on cars/crews performance.

As we will see shortly, actually the fastest car on track didn’t win, but Rebellion managed the race very well and was also able to do one less stop than Signatech (who finished second) and than several other teams. The same is true also for Jota Sport car n.38. Interestingly, most of the teams stopped more or less at the same time for the first pit stop, but then differentiated their strategy and/or needed to stop because of driver changes (as happened to car n.36 just only five laps after its second pit stop).
Some drivers’ mistakes also affected how the race evolved: beside some more spectacular crashes, one effective game changer was probably the spin of Nicolas Lapierre at lap 51.

Because of all of this, it is no surprise to see that car n.31 and car n.38 were the ones spending less time in the pit lane, with car n.38 having the shortest pit time.


LMP2 Pit times


To start looking at cars performance, let’s consider the following table first, showing the best lap time and the average of the best 20 and 50 lap times of each of the first five classified cars.


LMP2 average times table


Already by looking to the above table, it is crystal clear that car n.36 was by far the fastest during the whole race. If the difference between car n.36 and car n.31 best lap is only slightly above 1 tenth, the gap increases progressively if we consider the average of the best 20 and 50 lap times, with a massive difference of nearly 0.6 seconds in the latter.

This is well confirmed by the plots relative to the best 20 and 50 lap times obtained during the race by each car.


LMP2 Best 20 laps


LMP2 Best 50 laps


Beside showing clearly how much quicker car n.36 was compared to the others, these two plots also confirms car n.31 being the second fastest car on track and car n.38 and car n.24 being very close to each other, as we also could see in the average times table.
Fourth classified car n.28 is constantly and sensibly off pace instead, but was one of the few crews that led his non-professional driver in the car for a pretty long time, during the first stints.

Let’s try now to break down each car performance analyzing each track sector times and the top speeds.

If we look at sector 1, we immediately notice how, actually, car n.31 is here quicker than car n.36, with all the first five classified crews being pretty much packed together with the exception again of car n.28, which seems to be a bit more off.
The table below, listing the best sector 1 time and the average of the best 20 and 50 sector 1 times, shows car n.36 being in front of car n.31 only if we consider the best time overall; car n.31 is anyway faster in both the best 20 and 50 sector times average:


LMP2 Sec1 average times table


This is pretty much confirmed by the plots relative to the best 20 and 50 sector 1 times of each car.


LMP2 Best 20 sec1


LMP2 Best 50 sec1


On the contrary to what we have seen in the LMP1 class, the information we gather here seems to match with what we see if we look at the top speeds of each car: as we said already, sector 1 is composed in a very big part by the main straight, therefore top speed plays surely an important role in defining how fast a car is in this track section.
Looking to the previous plots we would expect car n.31 achieving higher top speeds than car n.36, most probably because of a lower downforce/drag setup. And, indeed, this is exactly the case.


LMP2 Best 20 Top speed


LMP2 Best 50 Top speed


Car n.31 has the highest top speed in the bigger part of the best 50 top speeds each car achieved during the race. Car n.36 and car n.38 are both slower than car n.31 and that seems to match pretty well with what we saw looking at sector 1 times.
Interestingly, car n.24 is faster on the main straight than both car n.36 and car n.38, but its sector 1 times are generally slower than car n.36 and not dramatically quicker than car n.38.

Sector 2 seems to be the proof that our assumptions about each car aerodynamic setup could be correct, as both car n.36 and n.38 are faster than car n.31 here.
This is what we can conclude by looking at the table showing best sector 2 times and the averages of the best 20 and 50 sector 2 times and also what the graphs plotting the best 20 and 50 sector 2 times of each car underline.


LMP2 Sec2 average times table


LMP2 Best 20 sec2


LMP2 Best 50 sec2


Car n.36 is clearly the fastest in sector 2, with car n.38 following. Car n.31 and car n.24 are relatively close together.
It seems reasonable to conclude that car n.36 and car n.38 opted for an higher downforce/drag setup, while car car n.31 and car n.24 for a less draggy/higher top speed one. 

Sector 3 has a more balanced situation, in terms of performance, between the two contenders, with car n.31 and car n.36 being very close to each other and having a pretty big edge on all the other crews.
Car n.31 has the best sector 3 time overall, but car n.36 is slightly forward if we consider the average of the best 20 sector 3 times. Finally, car n.31 is again on top in the best 50 sector 3 times average metrics, but with the two cars practically achieving the same results (we have a difference of only 0,003 seconds!):


LMP2 Sec3 average times table


In a very twisty track section, where we mainly find slow corners and tricky road cambers, both the mechanical balance of the car and the ability of the drivers in the crew play a major role.
The equivalence of performance between the first two classified cars and their advantage on all the other competitors is reflected also in the best 20 and 50 sector 3 times plots:


LMP2 Best 20 sec3


LMP2 Best 50 sec3


Car n.36 has a small edge on car n.31 on the very left of both plots above, as confirmed also by the best 20 sector 3 times average. Indeed, the best 10-15 sector 3 times of the Signatech crew are slightly lower the ones of Rebellion’s car n.31.
But after the 15 mark the two cars seems to be pretty much a copy of each other.

Closing also this LMP2 analysis, it was interesting to find out how the winning car probably succeeded not because of a better pace, but because of a better strategy/management, while the fastest car was indeed the second classified one.
Also, it is always intriguing to notice how, although all teams use the same chassis, very often each one comes to a different setup choice and how this is reflected by each car’s performance in different track sections.

Older Posts »