Races these
days tend to create a huge amount of data. Having not been at Sebring, I have
no particular access to anything private, but there is still a huge amount of
information out there, and for teams that gather telemetry information, the trick
is not in gathering the information, but in working out how to use it to draw
sensible conclusions.
The trouble
is that the data never tells the whole story. There are always other
parameters; other factors, that influence what is going on, which it is often
extremely difficult to get to the bottom of. Things like engine mappings,
tyres, driver technique and so on, can have a massive effect on a car’s lap
times (or even sector times), and from the outside you can end up tying
yourself in a knot, trying to get to the bottom of it all.
With that in mind, there are some observations about the individual driver performances in the Audi Sport Team Joest cars that time did not permit me to investigate in the dailysportscar article, that I thought were worth sharing. As always, I would welcome your comments.
With that in mind, there are some observations about the individual driver performances in the Audi Sport Team Joest cars that time did not permit me to investigate in the dailysportscar article, that I thought were worth sharing. As always, I would welcome your comments.
In the winning
Audi, number 1, Oliver Jarvis’s fastest lap time was almost 1.9 seconds slower
than Benoît Tréluyer’s fastest lap in the same car. The best that Marcel Fässler could manage, having set pole position, was to lap within a
second of the flying Frenchman, but he was still 0.8 seconds quicker than Olly.
Taking the
average of the fastest 10 laps of each driver shows a similar pattern:
Tréluyer -
1m 45.544s
Fässler - 1m
46.724s
Jarvis - 1m
47.643s
And the average
of the fastest 50 laps of each driver is no different either:
Tréluyer -
1m 46.633s
Fässler - 1m
47.857s
Jarvis - 1m
48.852s
Looking at
the same information for the number 2 Audi, gives:
Average of
fastest 10 laps
Kristensen -
1m 45.681s
McNish - 1m
46.324s
Di Grassi - 1m
47.176s
Average of
fastest 50 laps
Kristensen -
1m 46.951s
McNish - 1m
47.391s
Di Grassi - 1m
48.645s
In each
case, the order matches the driving order in the car, so the fastest driver
started the car, the slowest drove third. Both Audis also copied each other in
terms of driving stints, so let’s analyse each stint:
Audi #1
Stint No. | Driver | Laps | Best lap | Average of best 10 laps | Caution laps |
---|---|---|---|---|---|
1 | Tréluyer | 44 | 1m 45.061s | 1m 45.758s | None |
2 | Fässler | 49 | 1m 46.111s | 1m 46.937s | 5 |
3 | Jarvis | 48 | 1m 46.952s | 1m 47.682s | 4 |
4 | Tréluyer | 78 | 1m 45.211s | 1m 46.098s | 5 |
5 | Fässler | 65 | 1m 46.216s | 1m 47.183s | 6 |
6 | Jarvis | 46 | 1m 47.699s | 1m 48.609s | None |
7 | Tréluyer | 34 | 1m 47.121s | 1m 48.227s | None |
Audi #2
Stint No. | Driver | Laps | Best lap | Average of best 10 laps | Caution laps |
---|---|---|---|---|---|
1 | Kristensen | 43 | 1m 44.870s | 1m 45.704s | None |
2 | McNish | 62 | 1m 45.572s | 1m 46.399s | 5 |
3 | di Grassi | 59 | 1m 46.537s | 1m 47.176s | 4 |
4 | Kristensen | 49 | 1m 45.956s | 1m 46.798s | 5 |
5 | McNish | 69 | 1m 46.227s | 1m 47.390s | 6 |
6 | di Grassi | 44 | 1m 48.356s | 1m 49.146s | None |
7 | Kristensen | 38 | 1m 48.024s | 1m 48.454s | None |
All this seems to suggest quite strongly that both di Grassi
and Jarvis were significantly slower than their team-mates, which, based on their performances in largely similar cars last year, is surprising to say the least. Maybe it is purely that they were less familiar with both the team and the track?
Also, while I would expect Tréluyer to be quicker than Fässler, I am surprised that Tom Kristensen managed consistently to lap quicker than Allan McNish.
Like I said before, the data doesn’t tell you everything. But if there is an explanation for this, then I’d like to hear it.
Also, while I would expect Tréluyer to be quicker than Fässler, I am surprised that Tom Kristensen managed consistently to lap quicker than Allan McNish.
Like I said before, the data doesn’t tell you everything. But if there is an explanation for this, then I’d like to hear it.