Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Real-Time Motorcycle Racing Optimization

Fausto Morales (Arundo), Marty Cochrane (Arundo)

Who is this presentation for?

Data scientists, data enthusiasts

Prerequisite knowledge

High level knowledge of machine learning is helpful but not required

What you'll learn

The audience will learn about some of the hurdles associated with implementing real-time machine learning applications in a fast-paced setting and how we went about the process of overcoming them.


In motorcycle racing, there is a clear trade off between speed and stability. The rider must decide how they will maneuver and accelerate based on weather, competitor behavior, track conditions, tire conditions, and the geometry of the track. Given all these elements, one can hardly expect the rider to make the best possible decision every time. Riders can be distracted, overwhelmed, or fatigued. Making the wrong decision on the trade off will either lead to an accident or loss of competitive edge.

Fortunately, there is a long history of implementing automated systems to enable humans to make decisions more quickly. Machine learning, in particular, affords the potential for achieving reaction times far shorter than that of a human for tasks for which we can provide a suitably detailed data set. We use off-the-shelf logging hardware and custom in-house software to capture key sensor data and act on model predictions.

Our goal in building our optimization system is to support riders by tracking all the relevant variables associated with their riding and make data-driven decisions. Because slip behavior can vary by rider, bike, and track, we use the first several laps to select and train a model. Finally, the completed model runs on embedded equipment on the bike to limit acceleration and speed in real-time.

Photo of Fausto Morales

Fausto Morales


Fausto Morales is a Data Scientist at Arundo Analytics. At Arundo, Fausto works on product development and customer projects. Prior to Arundo, he worked at ExxonMobil where he worked on projects that included environmental remediation, product pricing and water treatment process modeling. He graduated from MIT in 2012 with a bachelor’s degree in civil engineering.

Photo of Marty Cochrane

Marty Cochrane


Marty is the Director for Solution Architecture (EMEA) at Arundo. Prior to Arundo, he led software development at Europe’s biggest renewable energy company (Statkraft). His background is in software engineering, with a specialization in industrial applications.

In his spare time, Marty competes professionally around the world racing superbikes. He has also developing his own technical platform that is used in top teams around Europe to develop riders’ skills.

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