We explore the data analytics behind the Monaco Grand Prix 2015.
It was the decision that cost Hamilton the Monaco Grand Prix, “What happened guys?” he said. “I’ve lost this race, haven’t I?” The experts at Formula One allowed complex algorithms to overrule their common sense which ultimately led to Hamilton’s humiliation. Behind the competitive atmosphere there is a huge amount of analytics to be assessed and analysed, but how is this done?
Wandering around the pits at the Grand Prix’s and you are just as likely to bump into a scientist or engineer as a mechanic. The back rooms look more like a NASA mission control room. There are more computers than people and several engineers running simulations and studying streams of data days before the race has even begun.
McLaren, for example are deploying telemetry systems. These systems use data generated by sensors for real-time analysis, in combination with historical data and predictive models. This gives you an idea of the size of this data, there are 160 sensors on each car, generating 1GB of raw data in each race. This data is then sent back to a control room in Surrey, where the data is analysed and used to direct the drivers.
Stuart Birrell, McLaren’s CIO says “We can run queries like ‘show me all races in last three years where we ran a particular suspension strut at a particular setting’. How could you query that in a traditional database? It would take hours. We use top level tools to model and compare fluid dynamics, wind tunnel and telemetry time-sequenced data. For that query we got a result from 13 billion data points in 100ms.”
Similarly, the Lotus F1 team are running 50 virtual servers on site at each race. This allows them collect and analyse as much as 30 MB of data per lap from as many as 250 sensors on the car. Michael Taylor, IT Director for the Lotus F1 team, says “What we're doing is taking real-time data and trying to extract as much knowledge as we can. The key is how we use this information to make informed business decisions, to optimise strategy, and improve the performance of the car. We're using real-time data and analytics to make decisions that directly and visibly impact the outcome."
Lewis Hamilton hangs his head low and finds it hard to hide his disappointment on the podium.
F1 teams get very little time to test their cars on the track, therefore most will do lots of simulations in their own facilities, meaning that it is critical to collect as much data as possible when the car is actually racing. Tony Jardine, a Formula One analyst for Sky Sports in the U.K. believes “data is so good for drivers that the pit crew could literally coach a driver to the most optimal racing conditions based on a live data feed of everything imaginable”.
In Hamilton’s case the system seemed to have shown “wrong data” and based on this data Mercedes decided to pit as they believed that they had a gap, which in fact they didn’t. In Monaco there is no GPS which ultimately made the decision more complicated for the analysts. Mercedes boss Toto Wolff explained that "we are all humans…sometimes you need to make decisions within a fraction of a second, and this time we made a decision and it was the wrong decision. We have to analyse it properly, see how we can avoid it in the future, apologise to Lewis, and apologise, and apologise."
Data analysis is a science of understanding data to bring insight, make critical decisions and gain competitive advantage. The F1 industry is one of the most competitive sports and is most certainly a data driven industry. Make no mistake the sophistication of the sport is excellent with the speed of race simulations significantly improving over the last few years, ultimately it was one human error that cost Hamilton his top spot position.
Viki Dowthwaite | Principal Consultant
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