![]() ![]() For example, the fuel level that is carried on a Friday and the fuel level that is carried on a Saturday could be different. The former group (those that the team control) are much harder to predict. how they choose to configure their cars between the Friday and Saturday), and some of them are an actual evolution of the circuit. Some of these are within control of the teams (i.e. The problem we were faced with as engineers and data scientists when trying to derive the algorithms that will forecast the qualifying pace is that from Friday practice to Saturday qualifying, many parameters can change. Using machine learning methodologies to ‘predict’ the future is becoming more and more common place, so using it in Formula 1 seems like an obvious choice, and AWS an obvious partner to work with. The ML model, run on Amazon SageMaker, will essentially take the practice data from the vent in question and use historical data of how teams progress between Saturday and Sunday’s races to try to arrive at a data-driven answer to what the qualifying results will actually look like. Instead, with this F1 Insight powered by AWS we will use machine learning and analytical methodology in an attempt to give us that answer in the most mathematically robust way possible. For the latest graphic in our F1 Insights series, powered by AWS, we will be showcasing an insight that forecasts future events using machine learning methodology.Ī key question that is often asked on a Friday evening is ‘Where do you think the cars will be in qualifying based on the practice results?’ There are usually endless hours spent by journalists and fans trying to analyse every inch of the practice session, trying to come up with the answers. ![]()
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