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How Mathematics can make the difference to Formula 1 success

Published: 6 May 2014Origin: Mathematical Sciences

There is more to winning Formula 1 races than having the best driver. Teams also need the best information to make judgements on the day. University of Southampton Mathematics graduate Neil Martin uses his analytical skills every day in his job as Head of Strategy for Ferrari.

There is more to winning Formula 1 races than having the best driver. Teams also need the best information to make judgements on the day. University of Southampton Mathematics graduate Neil Martin uses his analytical skills every day in his job as Head of Strategy for Ferrari.

How did you get involved with Formula 1?

After completing my first degree studying Mathematics with Computer Science, I undertook an MSc in Operational Research in 1994-5. As part of this MSc course I had to apply the OR techniques that I had learned to a real world problem.

The University of Southampton had set up a number of projects with high profile Companies. However I was really interested in Formula 1 and, with the re-introduction of refuelling, I asked my supervisor (Professor Chris Potts): if I could get a Formula 1 team to sponsor a Race Strategy project, would that be of sufficient mathematical complexity for a project? The University agreed so I wrote to two Formula 1 teams, and McLaren International Ltd (as it was then) replied positively.

I was supplied with some information from races, in paper format. I had to type in all of the sector times for every car, for every session, for every race event for analysis – in 1994 it was all paper timing information, OCR (optical character recognition) was only in its infancy and more trouble than it was worth. Models were then formed using only data available prior to each race.

For one race the model showed that McLaren should have one-stopped, something that the Team had only worked out after the event. As a result, they became very interested in the whole area.

Functionality development requests soon followed and after my MSc submission and a couple of extended contracts with McLaren, I finally joined the Team as an employee in October 1996.

Since 2011 you have been working at Ferrari. What’s involved in the job?

I am in charge of a group that, in effect, acts as an internal consultancy to the rest of the business.

The group is composed of a balanced mix of mathematical modelling and software engineering. We look at various areas of the Company to assist where we can by observing the current process or activity in place, modelling it and then, where appropriate, optimising or putting new understandings, processes or software in place to improve.

How have race strategy tools evolved over time?

In 1995, as a student, I created my first algorithms - it was a single car, single track, with parameters of weight effect, fuel load, fuel flow time, pitstop crew time, pitlane time loss and tyre degradation. Essentially it answered the question: Is Strategy A quicker to the end of the race than Strategy B?

It was a standalone application which was run pre-race by Race Engineers at trackside.

Numerous feature request enhancements were then incorporated however the next big breakthrough was the use of Monte Carlo techniques in 1998.

This allowed us to consider all cars in the race, traffic patterns, Safety Cars, overtaking and other random events. The results were interesting because when we move into the stochastic world we produce outputs like: If we undertake a two-stop strategy we give ourselves approximately 20 per cent exposure to finishing 1st or 2nd, but if we get stuck in traffic, we will most likely finish 7th or below; however if we undertake a one-stop strategy we will most likely finish 3rd or 4th position, but unlikely to finish as high as 1st or indeed as low as 7th. So for the first time from the simulation we could visualise the risk associated with each option, and therefore a Team’s appetite for risk could be taken into consideration.

While a huge step forward, by embracing risk we blurred the lines: before the Engineers had the result of ‘a one stop is quicker than a two stop’ which seemed more clear cut, if somewhat crude.

Up to this point, all of the development was still offline and analysis was carried out pre-race. There were no data feeds which we were able to use to run real time, other than manually typing in the time gap to the car of interest on each lap to determine whether we were ahead of or behind our predicted schedule.

Soon I had written so much software that the trackside engineers wanted someone to travel to races to run some of it, so that became me. In 1999 there was no strategy audio communications channel to the pitwall, only paper printouts. I would run across to the pitwall from the garage, printouts in hand, and shout in their ears above the car noise, trying to illustrate one point or another.

The next moment when we were able to make a leap, was in 2001 when the FOM first provided electronic timing data for all cars, live during sessions. This enabled in-race strategy to be undertaken in a more robust way. New screens were developed to take advantage of this real time information. Further, it allowed us to run real time Monte Carlo simulations to answer questions like: ‘Given the race information to the current lap, what is the best action to undertake if our competitor executes a three stop strategy?’, ‘What is the best strategy for our competitors to use?’

Are numbers always telling the truth or is "gut feeling" sometimes stronger?

In the very beginning when I was the 'new kid with numbers', people with years of experience – who were mainly on the pitwall and were at the top of the Company - would remember a certain set of criteria at a certain race and cite this as the reason why the new methods were about to fail.

Atypical events will, by definition, happen from time to time and it is this information which people tend to remember – especially if in that given race you had a bad result as a consequence.

It didn’t take long, consistently saying to them "You can do this, but the likely outcome will be X or Y" and those outcomes subsequently occurring, before they came around.

Overall a healthy level of scepticism is good, to keep us all on our toes and to stress test the solutions, but flawed logic rarely wastes my time these days.

Interview courtesy of

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