Formula 1 & Machine Learning

There aren’t many things in the universe that can’t be predicted. Anything that can be quantified can be accurately predicted with data processing and artificial intelligence. The world of sports is rich in quantifiable features, making it ideal for the application of artificial intelligence. Artificial intelligence systems in sports have become commonplace in recent years. Given the positive influence they’ve had as their talents have grown, they’ll continue to make inroads into the world of sports.

Formula 1

Sensors, routers, connectivity, centralized communications, and cybersecurity solutions are becoming increasingly important for Formula 1 teams, allowing data processing to improve cars and raceday strategies.

Formula 1 is a data-driven sport: 120 sensors on each car generate 3 GB of data during each race, and 1,500 data points are generated per second. Formula 1’s data scientists are using Amazon SageMaker to train deep-learning models on 65 years of historical race data to extract critical race results statistics, make race forecasts, and provide fans with insight into the split-second decisions and strategies used by teams and drivers.

McLaren’s Electronic Control Unit (ECU)

About 750 million data points will be received and sent by the ECU during a two-hour race. That is twice the number of words that each of us would say in our lifetime.

Succeeding in Formula 1 is now all about the cycle of racing, calculating, analyzing, designing, and then repeating this process, according to Zoe Chilton, head of Technical Partnerships at Aston Martin Red Bull Racing, with the team making about 1,000 new prototypes between each race on the calendar, or 30,000 for the season.

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Unpredictability

The weather is arguably the most volatile aspect of a race. Even if teams have access to live weather reports, it is difficult to determine precisely what will happen; the 2020 Hungarian Grand Prix was expected to rain, but it did not, affecting tyre and pit stop plans for each driver.

The consistency of the predictions is often called into question when discussing problems such as:

• Crashes — Since the first World Championship Grand Prix at Silverstone in 1950, where there was no medical backup or protective precautions in the event of a collision, safety conditions have changed. When investigating the latest crash involving Romain Grosjean, it was discovered that Grosjean’s car hit the triple-guardrail barrier at 192kph (119mph) with a peak impact of 67G. The article also went into depth on how the badly damaged Haas car caught fire. As a result, no one can guess when a crash will occur.

• Penalties incurred during the race — Drivers can face penalties for a variety of offences, including starting too soon, speeding in the pitlane, causing an accident, blocking unfairly, or avoiding flags of any colour. The majority of these fines are imposed on the driver. As a result, predicting drivers’ instant decisions is impractical.
• Grid penalties — The penalties were ten grid places for the first use of a sixth of any component and five grid places for the use of a sixth of any other component. If seven or more units were used, the procedure was repeated. Any unused grid penalties were also rolled over to future races.
• Mechanical/technical failures — During the 2006 racing season, the team had a record-breaking zero chassis failures. Nonetheless, the nature of the sport, with production motivated by strong competition, means that our capacity to manufacture pieces has a potential to outpace our ability to properly understand them. Regardless of the procedures in place to ensure the reliability and efficiency of the vehicles, there will still be a slight chance of failure. What the systems do is keep that probability at an appropriate and achievable level while still allowing us to fix faults easily and professionally when they do occur.

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• Pit lane incidents — Pit stops have become an art form for Formula One teams. Red Bull still holds the record for the quickest time to change all four tyres on a car, 1.923 seconds. In 2012, McLaren completed a five-wheel pit stop (all four tires and the steering wheel) in 3.3 seconds. And non-fans are awestruck at how quickly everything is completed. However, in such a high-pressure environment, mistakes are unavoidable.

Deep learning can be used to predict when mechanical failures will occur to solve this problem, but how accurate is it? Pit stops often take 20–25 seconds, which means that a wrongly timed/misjudged stop may cost the driver a podium and valuable championship points; the accuracy must be as accurate as possible.

Tracks Redevelopment

Since races are continually being reintroduced/removed from the schedule, data for a given Grand Prix will not always be applicable. The Dutch Grand Prix at Zandvoort is returning after 35 years — this data would be significantly out of date, particularly because the track is being reconstructed.

Before and After

Changes to tracks between seasons don’t help either, since the track will be “different” any time it changed, particularly if the track distance was affected. To do so, teams will need to build a model of each circuit that integrates the addition/removal of different elements, as well as an algorithm that estimates an average lap time.

Predictions

Some fans are using machine learning to make their own predictions, and others are building visual dashboards to see which factors are more likely to influence the results themselves. Now that the significance of qualifying positions has been identified, the likelihood of winning depending on starting place must be examined, given all other factors are equal — i.e. the driver qualifying first incurs no grid penalties.

Predictability of race circuits

The Baku circuit in Azerbaijan is the least predictable, but considering the race’s short history and outcomes, this is not surprising. The driver in pole position has only won once. In 2017, the winner started from the 10th row, demonstrating how chaotic F1 races are offered the possibility of winning from outside the front row. Clearly, this shows the importance of the circuit in reliably predicting the results of a competition, when not all circuits are as straightforward to forecast as some.

Overall, the large quantities of data available to teams enable teams to study different facets of a race separately, but the difficulty of the variables that make up a race means that, for the time being, using ML methods to predict race strategy is inaccurate. Despite these issues with the results, there is no doubt that ML and AI will soon overtake the sport — the only concern is how long it will take and how accurate it will be.

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