Weather is a critical factor in motorsports—it affects vehicle performance and shapes race strategy.
While weather has always been a key consideration in racing, forecasts are now more accurate and detailed at the hyper-local level. When accurate, hyper-local forecasts are combined with data science and machine learning, race teams gain a definitive competitive advantage.
Our cloud-based software services combine the power of The Weather Company’s hyper-local weather forecasts with IBM Watson’s machine learning capabilities to deliver real-time information to motorsports customers during 70 race events per year.
Fluctuating weather dictates a dynamic race strategy
In motorsports, weather can be a wildcard. For example, teams who know the exact precipitation start time, duration and amount can choose the best strategy for maximum results. Decisions made with inaccurate forecasts can ruin an otherwise perfectly executed race.
Chevrolet driver Chris Buescher was able to score his first win in the 2016 NASCAR Cup Series race at Pocono due to a well-executed strategy dictated by weather. Knowing that rain and fog were approaching the track from their weather forecasts, Buescher stayed on the track anticipating the adverse weather while everyone else pitted, thinking that clear skies would prevail.
Fog enveloped the track, and NASCAR officials were forced to declare the race complete due to extremely poor visibility. Twenty-two laps short of the scheduled 160 laps, Buescher was declared the winner.
Top vehicle performance requires accurate forecasts and analytics
Weather also has a major impact on vehicle performance. Changes in weather force teams to constantly adjust vehicle setup parameters throughout a race.
Races in the summer months will see track temperatures climb as much as 40 °F above ambient temperature. If those races start during the day and end at night, the track temperature can change as much as 50 °F during the race.
Understanding this variation is critical. Hotter track temperatures mean less grip and slower speeds. As the air and track cools, tire grip and speed increases.
Tuning parameters, such as grille tape and “packer” shims, are used to improve vehicle performance as track temperatures vary. With more accurate and localized weather data, race teams can anticipate the appropriate time and amount to adjust these tuning parameters.
In addition, barometric pressure, temperature, and humidity all affect engine performance. Weather-forecast metrics are used to predict air density so that engine tuners can program the engine control units with the correct air/fuel ratio.
Machine learning means objective decisions and continuous improvement
Today’s successful race teams have the capability to analyze and understand the massive amounts of data generated from past race events, simulation and testing. This analysis improves future strategy decisions and vehicle performance.
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