The Data Doesn’t Lie – How Data Analytics is Critical in Formula 1.

Auto Racing, NASCAR, F1, Formula 1 article at Knup Sports

Read on to learn about Formula 1’s use of data analysis and how the data doesn’t lie to inform your future Formula 1 betting.

In the present sports landscape, data and data analytics are of paramount importance. Whether it’s the proliferation of advanced statistics on sports websites, professional sports franchises hiring entire departments for data analysis, or being used to help inform betting picks, data and statistics are everywhere in sports. After all, baseball’s statistics led to the creation of the highly valuable fantasy sports world.

In a sport where the stopwatch is paramount and technical development can mean the difference between improving by hundredths of a second and Grand Prix wins, data and data analysis are essential in Formula 1. Over the course of a race weekend, each Formula 1 car generates around 300 GB of data.

Watching a Formula 1 broadcast on ESPN, F1TV, or F1 Live doesn’t take long to see this data in real-time, powered by AWS: overtake predictions, tire strategies, and a constantly updating timing sheet are the tip of the iceberg of Formula 1 data.

In this article, we will briefly explore Formula 1 data analytics. Formula 1 metrics and data analysis helped make Formula 1 safer and the cars faster, contributing to Ferrari’s continued strategy issues.

Tracking Technical Developments

During the 2020 Belgian Grand Prix qualifying session, former Formula 1 driver and current Sky Sports F1 analyst Paul Di Resta stated matter of factly, “the data doesn’t lie, and the numbers tell the truth.” Seemingly every Formula 1 Grand Prix weekend is littered with news and notes of technical upgrades, with teams aiming to improve their performance by tenths and hundredths of a second.

Everything from car design to in-race tire strategy is based on data collected by hundreds of sensors found throughout each car. There are sensors in the driver’s gloves for health and safety purposes. In Formula 1, save for money and driver skill, there is no more important factor for Formula 1 success than data and data analytics.

For those data-hungry Formula 1 fans with a working knowledge of Python, FastF1 allows you to analyze F1’s immense history of data dating back to the 1950s.

Unlike hockey, soccer, football, or basketball, where aesthetic differences and the crowd’s rooting interest make the difference between stadiums, no two race circuits are identical. Goals and points are easy to compare across multiple games where things are identical for all intents and purposes.

However, how do you track season-long performances on tracks of varying lengths, with variable weather conditions and tire compounds? The answer is “super times.”

A Super time is a somewhat simple metric for newer Formula 1 fans and bettors. Simply put, you take each team’s fastest lap in any session in each race weekend, combine these times, and divide the total time by the number of races.

Obviously, there are caveats and imperfections in this data set. What’s important is that this gives us metrics to measure each team’s performance relative to the competition and the ability to determine the efficacy of the teams’ upgrade packages.

Formula 1 Super Times.

Long-time Formula 1 technical designer, technical director, and commentator Gary Anderson has calculated the super times for the first 13 races of the season. Anderson used these times to expose Ferrari’s underachievement relative to their on-track potential.

Unsurprisingly for those following the 2022 Formula 1 season, Ferrari has the fastest overall super time. However, in Formula 1 and data analysis, the details are essential, something Anderson is quick to note.

This super time is constructed from each team’s best time in any session for a race weekend. Because of things like fuel loads and tire and track conditions, most of these times are from free practice sessions and qualifying. The Ferrari has taken 8 of 13 pole positions and regularly has both cars finish in the top 3 in any given practice session. Yet, Ferrari has struggled with race pace and reliability throughout the season.

Anderson and other F1 analysts are always quick to point out the shortcomings or intangibles that cannot be factored into a data set. However, we can learn valuable data regarding a team’s development progress, especially considering betting on Formula 1 top 6 or top 10 finishes or the many prop bets available.

The data doesn’t lie, and regardless of what metrics you use, the times tell the same story: Red Bull has the best overall 2022 race package, Mercedes are inching closer to the fight at the front, and Ferrari continue to disappoint regardless of what data or results you review.

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