If I mention the phrase ‘Big Data’, most would associate it with the business sector and industries such as retail, banking and healthcare etc - not necessarily the world of sport. Perhaps surprisingly, few sports use data analysis as comprehensively as Formula 1. With Formula 1 cars capable of speeds more than 200mph – near the speed I reach running for the train in the morning – teams need to be equally fast to stay ahead of the technology curve, and this is where data plays its part.
We have to go back to the 1980’s, when David Bowie and Madonna were dominating the charts, to see the introduction of telemetry being deployed in Formula 1. Telemetry being the collection of data using instruments at a remote point i.e. the racing car, that is transmitted back to receiving equipment for monitoring i.e. the pit/garage. Even then the system would have to be physically taken off the car and back to the garage for analysis. What’s more, data would be limited to only a lap’s worth due to storage constraints and so drivers would often have to give a signal to the team in order to turn the telemetry system on.
By the end of the decade and early 90’s, with major advances in technology, ‘burst’ telemetry enabled teams to send signals from the car to the garage for analysis in real time. With the tech still not running as smoothly as the wine in Monte Carlo, teams would often lose coverage at certain parts of tracks such as Monaco and Spa where the race would pass through trees and buildings, a bit like losing signal on your mobile in the underground… very inconvenient.
Fast forward to the present day and a Formula 1 car has hundreds of sensors attached, picking up data not just on one lap of a race but on qualifying runs, practice laps, tests, the grand prix and much more. Such information is collected on elements like the engine, speed, oil and water levels, exhaust and tyre temperatures, clutch fluid pressure, G-force, and driver biometrics. It doesn’t stop at the car and driver either, in 2016 the Formula 1 team Williams used biometric sensors on pit crew members to measure heart rate, breathing, temperature and recovery times with the aim of improving their fitness to reduce pit stop time and ultimately improve the team’s performance. These fine margins can be the difference between winning or losing a race or a championship, and demonstrates further how much of an important role data can play in determining the fine margins that define success or failure in Formula 1.
In terms of volume of data, a Formula 1 car can transmit 2GB of data per lap whilst a full race could see 3TB of data gathered. The accumulation of this data across 21 grand prix races a season results in a huge amount of information being collected by race teams.
Don’t forget, this is all data that needs to be analysed, not only by the mechanics and engineer’s trackside in real time but also by the team members at the HQ.
Naturally, with this comes the issue of storage, which is why cloud computing is so important to the sport. In 2012, Formula 1 team Renault began a relationship with Microsoft adopting their Microsoft Dynamics 365 for Operations to enable them to analyse and store their data without lugging servers around the world. A far cry from the one lap’s worth of data.
At the end of the day, when it comes to Formula 1, the best performing teams are those that spend the most money, and with the top teams piling in money to better their car, it creates a gap between other teams that haven’t got the budget to close it. Formula 1 bosses are attempting to level this somewhat uneven playing field by aiming to impose a $150m annual spending cap on teams in the not too distant future. A cap that will place even more significance on the use of data in the sport as teams will need to turn their focus towards efficiency. You could argue Formula 1 is a sport driven by money, and although I would say this is, in part, true – I think now more than ever it is data driving Formula 1.