Solar car race competitions offer realistic conditions to test and demonstrate the state-of-the-art technologies in multidisciplinary fields. In such races the solar panels mounted on the car produce the energy required to power the vehicle. A simulator runs during the race determines the optimal race speed based on the predicted availability of solar energy and other parameters as well as road conditions. The accuracy of the forecasts, especially the solar irradiance forecasts, has a significant impact on the race strategy. Here we report on the experience of providing irradiance forecasts for two races run by the University of Michigan Solar Car Team at the Bridgestone World Solar Challenge 2015 in Australia and at the American Solar Challenge 2016 from Ohio to South Dakota. The probabilistic forecasts of hourly solar irradiance generated from machine learning algorithms were deployed to optimally decide on the race strategy. This work showcases an example of real time decision making based on insights derived from machine learning utilizing big geospatial data -weather models and measurement data from weather station networks.