Fastest possible lap times through data and mathematics

The highest resolution data we're modeling comes from Superbike Racing. These sensors are important for capturing a number of different variables including speed, lean angle, brake pressure, tire temperature and exact location on track for any given action. Data capture can be as high as 200 events per second. When combined with track configuration and weather variables the analysis that can be done to influence setup and rider behavior are astounding. In order to process and analyze so much data, sophisticated system architecture is key. Working with large amounts of very high-resolution data requires adaptive parallel computing and machine learning in order to identify patterns of behavior that can decrease lap times by 100ths of a second. These performance adjustments can be used for setup changes and rider decisions, which can make the difference between winning and losing.

It’s all about going faster and finishing the race.  The number of factors that go into making each turn’s entry and exit consistent and fast is staggering but can be captured and analyzed through data science.

Our mission is to bring high resolution analysis showing the strengths and development necessary for optimal bike setup and rider decisions across every inch of the track.

Our goal is to accelerate the improvement of lap times and for riders to finish the race ahead of the pack.