Qualifying Pace F1
Rob Smedley
2/3/2022
Date that article was published: February 3, 2022
Summary of Article
Qualifying Pace by the AWS F1 insights creates various graphics during the qualifying and race events to illustrate the predicted difference between the drivers and the teams. The machine learning model runs on Amazon SageMaker to take the practice data from the vent in question and user historical data of how teams progress. The article is split up into two sections: The New Graphic - Qualifying Pace and Modeling.
For the graphic section, Smedley details the process F1 went through to develop the graphics and the graphics they wanted to develop for the qualifying data, and how it compares to the practice data.
For Modeling, this insights use a branch of machine learning known as supervised learning, which is ased on historical data. The first type of model considers the median of each car’s improvement based on historical data, as well as the team’s fuel and power adjustments as a variance around this median value. The second type of model uses a regression technique that takes the difference in the laptimes with respect to the past and learns the actual result in order to be able to forecast the future using the teams, the drivers, the circuit, and the weather conditions (in particular if the session was wet) as variables.
What Do I Think?
I believe that AWS and the insights they provide give another element to the race for the viewers. Now, viewers can understand the differences between the grid, probabilities of driver overtakes, and many other probabilites and predictions that can add suspense to the sport. These stats provide a reference point to understand whether teams and drivers stray from the norm in a good or bad way, and these analytics should continue to influence how teams operate going forward.
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