![]() The first thing we need to explain then, is the detail of the changes in question. There are other parameters too, and the model needs to recognise these and forecast the overall net change from one day to the next in order to give us a robust prediction. As this particular parameter can account for more than 1 second of lap time between two days, it is important that we build accurate algorithms to be able to predict this change. 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. The Alpines of Pierre Gasly and Esteban Ocon, Valtteri Bottas (Alfa) and a gutted Norris rounded out the top 10.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. Russell inherited a spot on the front row, ahead of Piastri – but the Australian too was then pinged for track limits, which promoted Hamilton, Alonso and Leclerc ahead of him. Norris jumped up to second, three tenths down, but lost his laptime after running over the white line at Turn 10. On the final runs, Verstappen bailed out into the pits after going wide. On the first runs, Verstappen set the pace at 1m23.778s, half a second clear of Hamilton after Norris lost his first flying lap due to track limits. ![]() Verstappen set the initial pace – ominously on used rubber – at 1m24.758s, until the fresh-tyred McLarens of Piastri and then Norris pushed him back to third.Īfter a wild battle for track position with Ferrari’s Carlos Sainz, Verstappen improved to 1m24.483s to retake P1, two tenths ahead of Norris. In the closing moments, Hamilton grabbed P1 by a tenth. Qatar GP Grand Prix Q1 results: Verstappen fastest from Norris ![]() Leclerc reset the bar on a second set of tyres with 1m25.452s, before Norris took P1 away with 1m25.131s and Verstappen beat them all with 1m25.007s.įalling at the first hurdle were Logan Sargeant (pipped by Williams team-mate Alex Albon on his last lap by 0.092s), Lance Stroll (Aston Martin), Liam Lawson (AlphaTauri), Kevin Magnussen (Haas) and Zhou Guanyu (Alfa Romeo). The track’s condition was improving throughout, with Alonso retaking P1 with 1m25.685s on his first set of tyres. Norris then set the pace, but the McLaren driver’s 1m26.043s was deleted for exceeding track limits. Verstappen set the early pace at 1m26.884s, which was quickly pipped by Fernando Alonso’s Aston Martin with 1m26.715s, then Charles Leclerc’s 1m26.444s in his Ferrari.
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