In the high-stakes realm of Formula 1 racing, the Silverstone Grand Prix is a testament to a thrilling blend of speed, precision, and innovation. But another unseen race unfolds in tandem – the race to transform raw data into powerful, actionable insights.
Here, we’ll explore four different ways that F1 teams and analysts could use a potent tool like Alteryx to supercharge their performance.
Discovering the Art of the Possible with Alteryx
Over the last decade, the average qualifying lap time at Silverstone has improved by around 2.2%. To the uninitiated, this might seem like a trivial feat.
However, in the world of F1, it’s a huge accomplishment built through insights. With Alteryx, teams could delve into this historical race time data, isolating influential factors like advancements in technology or alterations to track surfaces.
With these insights, teams could target their improvement efforts with laser-like precision, giving them a competitive edge.
Weathering the Storm
Weather plays a capricious game in F1 races; the 2012 Silverstone GP was suspended due to an unexpected downpour.
This is why accurate weather forecasting is so important in race strategy. Alteryx could be employed by teams to integrate historical weather data with real-time meteorological input, enabling them to anticipate weather patterns. These insights could then guide tire compound selection and aerodynamic setup configurations. The potential payoff is huge, as teams could adapt their strategies in response to predicted weather changes, turning uncertainty into an advantage.
The Pit Stop Conundrum
Over the past decade, the average pit stop time at the Silverstone GP has been around 21 seconds. With Alteryx, teams could build a predictive model factoring in the current position, tire wear, fuel load, and rival strategies. This model could then guide teams toward the most beneficial pit stop window. The challenge here is to minimize total race time by carefully balancing time on the track and time spent in the pits – and Alteryx could be key to finding that sweet spot.
During the 2020 Silverstone GP, tire wear was higher than expected. Alteryx could help teams spot such trends using predictive modelling guided by practice session data, enabling teams to adjust their race strategies accordingly.
Alteryx also shines in simulating different race scenarios. Ingesting and analysing historical data could help teams prepare for various race outcomes. Anomalies, like the 2013 Silverstone GP marked by multiple tire blowouts, could be better managed by teams equipped with insights drawn from these simulations.
Hands-on with Alteryx: A Glimpse into a Possible Workflow
To give a sense of how teams could employ Alteryx, let’s build out a simple workflow:
The first step is to bring our data into Alteryx.
Use the Input Data tool and select the CSV file holding our dummy data.
While our dummy data is pretty clean, real-world data often needs some cleansing.
Use the Data Cleansing tool to remove any unwanted white spaces or null values or to correct data types.
Suppose we want to analyze pit stops for a particular driver, say Driver1.
Drag the Filter tool onto the canvas and set the condition as [Driver] = “Driver1”.
Analyze Pit Stop Timing:
Now, we can use the Summarize tool to analyze Driver 1’s pit stop timing. Add fields to Group By for “RaceYear” and “PitStopNumber”.
Then, add “TimeLost” to Average to find the average time lost during pit stops.
Finally, use the Output Data tool to export your data.
You can write your data out to CSV, Excel, or many other formats.
This is a relatively simple example. Alteryx has extensive tools for complex workflows, including joining multiple data sources, performing advanced statistical analyses, and creating predictive models.
Try building this today with Alteryx Designer or Alteryx Designer Cloud.