F1 vs. Academia, round 1: Sharing Information

For the last four years I have been simultaneously doing a PhD in biomedical engineering and consulting for Formula One teams, principally Ferrari. This has put me in a position to directly compare the academic and commercial ways of working. In this post I will discuss one particularly striking difference between F1 and academia: the speed of sharing information.

For the purposes of our comparison we can think of an F1 team as like a department at a university, and individual functions within an F1 team, such as aerodynamics, or engines, as like individual research groups within a university. This is a fairly good comparison, about the same numbers of people work in each and cutting edge research goes on in both. To get a measure of the speed at which information is shared between groups let's use a kind of echo-time – the time taken between having an idea and seeing that someone else has developed that idea. First, let's see what this time is in the F1 case:

  1. An aerodynamicist has an idea (this does happen occasionally) for a new shape of front-wing endplate (for example), first she does some CFD to see if it works in theory; this will take maybe a couple of days.
  2. If that works then the model makers will make a scale model of it which is put into the wind-tunnel and tested against the old endplates; depending on how good it looked in CFD, maybe a week or two.
  3. If the wind tunnel agrees with CFD (which it never does) about how good the new shape is then some real endplates will get built; this normally takes a couple of weeks but in rare circumstances it has  been known to be rushed through in days, sometimes even making solid metal pieces when hopes are really inflated about a part.

    Ferrari engineer films Mercedes pitstop

    Ferrari's Ruth Buscombe films a Mercedes practice pitstop in Abu Dhabi.

  4. The endplates will be taken to the next race where they will be run in practice as test items, and then in the race if a driver likes them – at this point everyone in other teams can see them and the idea has, in effect, been published – F1 engineers are always examining each others cars for good ideas. From here it's a similar development time for other teams who covet the shiny new endplates to make there own ones, possibly a bit shorter because they have a greater conviction that they will be good than ideas they've come up with themselves.

The total echo-time, from the first aerodynamicist having the idea to seeing that another team has developed (copied) it, is between 6 and 10 weeks, assuming it's an idea worth developing! Now for the academic equivalent:

  1. Post-doc researcher has an idea/discovers something, decides to write a paper about it; timescales vary enormously for paper writing, from three days to three years, let's be generous and say two weeks.
  2. Paper is reviewed by supervisor and revisions are made; let's say another week, although it might take that long before the supervisor even reads it.
  3. Paper is submitted to a journal where it is sent for peer review; this is where the time really starts stacking up, it could take absolutely ages, the mean is probably about three months.

    Peer review process cartoon

    Cartoon by Nick Kim, http://www.lab-initio.com/

  4. Let's assume it's a really great paper with really lazy reviewers and no revisions are necessary (which would necessitate a repeat of steps 2 and 3), next step publication in the journal; depends on the frequency of publication, let's say another 3 months.
  5. Now another researcher might see it, immediately have a fully fledged idea, and sit down to write her own paper in reply; repeat steps 1-4 for the minimum time until the original researcher sees that his idea has been developed.

This academic echo time is 27 weeks, and that would probably set a new record! It would be more realistic to make this estimate about 2.5 years. Even at 27 weeks it's between 3 and 4 times slower than the F1 equivalent – at 2.5 years it's between 13 and 21 times slower! The really baffling thing about this discrepancy is this: F1 teams do not want to share ideas with other teams, in fact efforts are actively made to prevent sharing information, whereas academia has constructed a whole publishing industry specifically to facilitate the sharing of ideas! So what's going on here, why is one so much faster than the other? Here are two candidate factors:

Validation: The F1 team can validate its ideas itself, in CFD, in the wind tunnel, and ultimately on track, whereas the academic has to send his ideas off to be scrutinised by some other academics who aren't paid enough to do it quickly. This is an obvious difference in terms of time that raises an equally obvious question: why are F1 teams able to validate their own ideas while academics are not? There are two issues here, practicality and motivation: practically it's easier to test a mechanical or aerodynamic idea (when you've got a wind tunnel and a car to play with) than it is to test a more abstract idea, especially in biomedicine where the true tests for many ideas would take years themselves or are simply not possible. So the best substitute is used instead of real testing, which is asking some other experienced people if they think it sounds sensible, given the evidence available. The second issue is motivation, we know that an F1 team wants to go faster, so it has no interest in putting parts on its car that don't work – if a part is raced on a car, we know that team really thinks it's good. Not only that but if that team wins we have proof that the parts on their car work. Conversely the academic has an incentive to publish papers, good papers are better than bad papers but a publication is, more often than not, better than no publications. Academics can't be trusted to validate their own work because it's not necessarily in their interest to find fault with it.

Competition: It sounds too obvious to be worth pointing out, but F1 is a competition – all the teams want exactly the same thing, and so have exactly the same problem. A good idea for one team is a good idea for all other teams too, and getting it on the car one race earlier will have real tangible benefits. Academia is not (supposed to be) a competition but rather a collaboration – research groups actively avoid overlapping domains with research groups at other institutions. Publishing a week earlier won't bring any real benefit, it's very unlikely that someone else is about to publish the same thing, there is not a fortnightly "best published idea" prize worth hundreds of thousands of pounds to the university.

I could go on about this all day, but this is already quite a long post so I'll wrap it up here and open up to discussion. Given the relative benefits to humanity of medical/scientific research vs. motor racing it's alarming to find that the useless and supposedly secretive one provides a much, much faster environment for sharing ideas than the hugely valuable and purportedly open one. If we want to eliminate diseases and make people healthier for longer then we have to bring academic information sharing up to speed.

RaceTrace™ analysis of the Malaysian GP

Mercedes were finally beaten to the chequered flag on Sunday, to the great relief of F1 fans everywhere I'm sure. In this post I'll show you some details in the RaceTrace™ for the Malaysian Grand Prix which hint at how Ferrari managed to win with probably the second fastest car.

If you haven't seen the RaceTrace™ before, it's a chart showing each car as a line starting at the left at lap zero (the start) and progressing to the right as laps are completed. The vertical axis is time and the horizontal axis is distance (number of laps), so the slope of a line is determined by the laptime of the car. To make things a little bit harder to describe, but a lot easier to see, the time axis is not simply time since the start of the race but rather time relative to an imaginary reference car which doesn't make pitstops. To see the difference have a look at the plots below:

Raw lap finishing times for the Malaysian GP.

Fig. 1: Raw lap finishing times for the Malaysian GP. You can't get much from looking at this!

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RaceTrace™ for the Malaysian GP

Fig. 2: A RaceTrace™ – time relative to a constant-paced reference car: much easier to see what's going on here! The steep drop on the left is the safety car period, where everyone slows down a lot.

As you can see, the raw times don't tell you much whereas the time relative to a constant-paced reference car really shows you what happened in the race. Now if we just focus on Ferrari and Mercedes we can get some insight into how Vettel managed to win the race, fig. 3 shows just the two main protagonists, Ferrari and Mercedes:

Ferrari and Mercedes highlighted.

Fig. 3: Ferrari and Mercedes highlighted.

You can see that when the safety car came out the two Mercedes dropped down the order and got stuck in some traffic while Seb drove off into the distance, and you can see that all three front-runners stopped twice after the safety car. You will also notice that the line gets steeper after each pit-stop and the lines at the end are much steeper than at the beginning – this is due to fuel-effect. Every lap the cars use about 1.8kg of fuel, and each kg of mass on the car slows it down by about 0.03s/lap, so every lap the cars get lighter and faster, by about 0.054s/lap at this track. Meanwhile the tyres are getting worn out lap by lap and making the cars slower, this is tyre degradation or tyre deg, or simply deg (engineers are not verbose people on the whole). So if tyre deg is bigger than fuel-effect then the lines for each stint will tend to curve downwards as the car gets slower each lap. If deg and fuel-effect are equal then the lines will be relatively straight and change slope at the pit-stops (as in Vettel's last stint). The thing is, fuel-effect isn't really interesting, it affects all cars to roughly the same extent and it doesn't really contribute to the strategic picture, so we'd like to get rid of it and see the effects of tyre deg on their own. To do this we simply change the reference car to have its own fuel effect; instead of showing time relative to a car doing constant lap times, we show time relative to a car going 0.054s/lap faster every lap. Fig. 4 shows the fuel-corrected version of the RaceTrace™ from fig. 3:

Fuel corrected RaceTrace™ for Malaysia with Ferrari and Mercedes highlighted

Fig. 4: Fuel corrected RaceTrace™ for Malaysia with Ferrari and Mercedes highlighted. You can see more clearly the curvature of the lines as the tyres wear-out through each stint.

This fuel-corrected RaceTrace™ shows us the tyre deg much more clearly and we can start to see how much gentler on the tyres Seb is than the Mercedes by how little his trace curves downwards compared to the traces of the Mercs. Bear in mind that the Mercs had fresh tyres coming out of the safety car period and so made three stops in total to Vettel's two. Another thing we can discern from this is that Mercedes pit-stop strategy was not as good as Ferrari's, their second stint was too long. We know this from looking at the slope of the fuel-corrected traces just before each pit-stop: Vettel's line has pretty much the same slope before each pit-stop, meaning the same fuel-corrected lap time before each stop and hence the same amount of deg on each set of tyres. The Mercedes on the other hand have a much more downward slope before the second stop than before the third – this means that they used the second set of tyres a lot more than the third set and that if they had done fewer laps on the second stint and more laps in the third stint they would have reached the chequered flag sooner. Fig. 5 zooms in on the post-safety car stints and highlights the slopes of the three lines at the ends of each stint:

White line highlight slopes of the traces before each pit stop

Fig. 5: White lines highlight slopes of the traces before each pit stop: Vettel's are all the same, indicating optimal strategy for tyre usage, Mercedes are all over the place.

This was a very interesting race, strategically-speaking, we have seen from the RaceTrace™ that Ferrari nailed it and Mercedes got their tyre deg estimates wrong. If they hadn't got stuck in traffic under the safety car it would have been a very close race indeed!