A look at the team's strategists behind every Formula One team, who advise on how to navigate each circuit.
The strategy behind the race
It's going to be a busy week for the racing teams taking part in next Sunday's Abu Dhabi Grand Prix as they prepare for the final race of the season. The world title may have already been decided, but the teams still face a major challenge on the brand new Yas Marina circuit, as it's an unknown quantity. But when it comes to dealing with unknown quantities, F1 teams have a little-publicised but very powerful tool they can turn to. It goes by the enigmatic name of Monte Carlo simulation - and it's becoming as vital to success in F1 as having the right driver.
To the uninitiated, a grand prix race is just a matter of cars whizzing around a circuit for a couple of hours until the fastest car passes a chequered flag. In reality, the race is more like a war, with every corner, straight and pit stop a battlefield where the race can be won or lost. From deciding the optimal number of pit stops to dealing with on-the-spot emergencies from cloud bursts to crashes, drivers and their teams face a constant stream of challenges. And responsibility for knowing what to do in any eventuality falls to a shadowy team of advisers known as race strategists.
It's a job title that conjures up images of ex-military types dreaming up cunning plans while puffing on cigars. In reality, race strategists are mainly denim-wearing mathematics graduates who spend most of their waking hours in front of computers. That's because finding the perfect race plan is no job for the unaided human brain - there are simply too many variables involved, from the aerodynamics and suspension of the cars to the performance of other drivers. Yet somehow race strategists must create a realistic model of what is likely to happen and how best to cope, taking account of all the different possible scenarios.
The number of permutations is so vast that not even the world's fastest supercomputer could crank through them all for each race. This is where the Monte Carlo simulation comes in. Instead of plodding through each scenario, race strategists capture its essence using a so-called probability distribution, a set of values reflecting the relative odds of any given event taking place. They then dip into this set of values at random - just like a roulette ball in Monte Carlo - and combine the result with random values taken from all the other probability distributions. Repeated millions of times, the end result is a realistic simulation of the race and the strategy giving the best odds of winning.
Developed in the 1940s by scientists working on thermonuclear weapons, Monte Carlo simulation in F1 was pioneered during the 1990s by Neil Martin, who joined McLaren as a mathematics graduate from Southampton University. Mr Martin's brilliance as a race strategist made headlines following the 2005 Monaco Grand Prix, when a crash prompted rival teams to make pit-stops. Refusing to follow the pack, Mr Martin advised McLaren's driver Kimi Raikkonen to keep driving - ensuring he kept his lead, and won the race.
It was a victory that highlights one of the biggest challenges facing race strategists: adapting to changing circumstances. While teams typically run millions of Monte Carlo simulations for each race, they can still be caught out by totally unexpected events. In such cases, there is nothing else to do but to run new simulations while the race is still in progress. As speed is of the essence, the teams at the race venue keep fast data-links open to their engineering HQs, where rooms full of interconnected PCs crunch through the new data.
Now the head of strategic operations at Red Bull Racing, Mr Martin directs a team of race strategists whose use of industrial-strength mathematics continues to pay off, the team achieving second place in the constructors' championship, well ahead of the rest of the pack. Over the coming days, however, Mr Martin and his colleagues will be focusing on the same task as their rivals: collecting data about the Yas Marina circuit. The problem is that as the circuit has never hosted a grand prix race before, there's no historical data to work with. Yet without solid, reliable input, the Monte Carlo simulations cannot work their magic.
The Renault driver Fernando Alonso told The National last week that he and his teammates expect to spend five times longer preparing for the new circuit than they ordinarily do. Using maps of the circuit, the team has already created virtual-reality simulations to help identify the optimal aerodynamics and suspension set-ups for the cars. With more than 20 corners per lap, the circuit is expected to be pretty demanding on the brakes. So to ease the strain, the team engineers will adjust the car's aerodynamics to boost the amount of downforce generated by the flow of air around. At the same time, however, boosting the downforce tends to increase the amount of aerodynamic drag on the car - which must still have plenty of straight-line speed. So, as so often in F1, the teams need to find the optimum balance, and computer simulations will help them identify where it lies.
In the end, though, even the best simulations won't guarantee victory in next Sunday's grand prix. The race will be won by the team with the optimal combination of car, driver and technical expertise. But when the winning driver goes up to the podium to celebrate his victory, spare a thought for race strategists who helped put him there. Chances are they'll be thousands of miles away, stuck in a roomful of computers - but with big smiles on their faces.
Robert Matthews is Visiting Reader in Science at Aston University, Birmingham, England