Industrial Generative AI for
next-generation race analytics, powered by Orquestra
Andretti Autosport is upgrading their analytics infrastructure to enhance decision-making and win more races.
The Race Analytics Command Center
Tires wear out quickly going over 200MPH, requiring time-consuming pit stops to change tires. Zapata is working with Andretti to create a machine learning model that can guide strategic decisions around tire changes, such as when a car should swap out tires, which tires should be used, and how often they should change tires based on current conditions. This use case translates to predictive maintenance problems across industries.
The fewer times a car has to refuel, the more time it can save in the race. Zapata and Andretti are applying machine learning and advanced analytics to help drivers optimize their fuel consumption and determine the best timing for refueling. Similar fuel savings solutions have wide applicability in any industry looking to shrink its carbon footprint or time to delivery.
When there’s an accident or debris on the track, drivers are required to reduce their speed and are prohibited from passing other cars. This is a yellow flag, and since cars aren’t going full speed, it’s often a good time for a pit stop. Zapata and Andretti are creating a model to predict when a yellow flag is likely based on track conditions, the status of various cars, the drivers in those cars, and other factors. This ability to predict and preemptively respond to disruptive events has wide applicability beyond racing.
Michael Andretti
CEO and Chairman, Andretti Autosport