At the UK MotoGP at Silverstone over the August bank holiday weekend, I was fortunate to be invited to meet the Ducati team and its technology partner NetApp to find out just how important data and soft skills are at the pinnacle of motorcycle racing. Ducati has an emphasis on the importance of human performance and working together as a family. So, perhaps inevitably, our conversation ranged from technical performance to human interactions with some perhaps surprising findings.
For those not familiar with MotoGP, it’s perhaps easiest to think of it as the Formula 1 of the motorcycle world with all the same glamour, technology, superstars, huge budgets and enthusiastic audiences. Technically, it’s at the top of the two-wheeled tree – £1 million-plus bespoke machines racing at over 230mph, supported by a huge team infrastructure that travels to 19 different circuits across the globe every year showcasing 23 riders and 11 teams.
As Davide Tardozzie, team manager of Ducati and the man responsible for making all the human parts work effectively together, told me: “MotoGP is not like the old days of TT racing with riders accompanied by a friend as the mechanic. Today, engineers, particularly electronic engineers, are very, very important.” (In racing circles, data analysts and technicians are classified as engineers.)
Over the last few years, the use of data in motorcycle racing has become vitally important. Telemetry and data analysis allow teams to modify the bike around the individual rider’s style. Mapping and electronic management of the engine enablea riding-by-wire, traction control and variable engine braking which allow skilled riders to extract every ounce of performance from the motorcycle.
A typical MotoGP bike has more than 60 sensors that constantly log different parameters. During free practice on race weekends, each of the four Ducati bikes collects more than 8GB of data on average but, unlike Formula 1 MotoGP, the rules do not allow live data transfer between the bike on the track and the team. This creates a problem as teams need to download, analyse and interpret the data, while also discussing the “feel” of the bike with the riders, and then communicating where improvements might be made, all in just a few minutes.
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