Predict Total Race Time for Formula 1

Business Problem

Formula 1 is the pinnacle of motorsports achievement. Twenty drivers compete week over week to set the fastest times and take home the checkered flag in circuits around the world. However, beyond driver skill and car capability, the strategy for each race — pit stop identification, tyre selection – is integral when determining a race result.

Races take place in a different location with different track and weather conditions. All those variables affect qualification — where the driver’s start position will be decided — and the race strategy itself. However, teams need to have a starting plan for their drivers’ race strategies before they take the track — and that strategy must be adaptable to changing conditions throughout the race.

Intelligent Solution

Have you dreamed of becoming a Formula 1 driver or race strategist? This use case leverages a dataset with past races. We will focus on understanding the variables that significantly impact the race and will try to predict the expected race time and make strategic decisions for the race.

F1 teams have a variety of tyres at their disposal for each race, and track conditions and performance drive the strategy’s decision. After running through some basic questions and practices, a team can decide on the right strategy for the race:

1. How is the track? How does it historically perform?
2. What are the forecasted weather conditions?
3. What is the starting position of the driver?

After a quick consideration of these questions, the team must determine their race strategy – how many pit stops to make, when to make them, and the tyre selection for each pit stop. For such critical decisions, and with such myriad potential combinations, this approach can be improved upon with ML-driven insights to lead to better strategy selection.

Using the wealth of data available on historical races and their resulting outcomes, ML-driven models can help the team develop an optimized strategy for getting the best race time. For example, in rainy conditions, planning for additional pit stops or the appearance of a safety car might allow for an overall faster race than the same choices in hot, dry conditions.

Bringing AI-driven intelligence to this strategy selection problem will give one side an advantage in this blink-of-an-eye conflict and potentially swing the balance of power in the contest.

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