Round 10 F1 GFT Driver Rankings: Max suffers damage at Silverstone, but holds P1 in the Rankings
4 July 2022
By Bob Francis, Managing Editor
Go Full Throttle Racing News
In what was the most dramatic F1 race in recent history, Sunday’s drama and action-packed thriller at the famed Silverstone circuit was gripping. The race started with a scary multi-car crash from the grid that flipped Alfa-Ramero driver Zhou Guanyu upside down, sliding through Turn 1 into the catch fence. (see photo and video below) Ahead of that crash Red Bull Racing’s Max Verstappen had already passed Pole sitter Carlos Sainz of Ferrari and taken the lead. However, with the crash and the fact that half the field had not even got through Turn 1 before a red flag, Max Verstappen was forced to relinquish the lead and restart back in P2. The day went from bad to worse for the reigning World Champion when he ran over debris which became wedged under his Red Bull, causing bad aerodynamics and instability. Carlos Sainz went on to win with Sergio Perez P2 and Lewis Hamilton P3. Verstappen would finish 7th.
Here is the updated GFT F1 AI Driver Rankings after Silverstone. Note that in F1’s official Driver Points, Sergio Perez is in 2nd with 147 points. However, in the GFT models, which takes in multiple data inputs, we have Charles Leclerc in P2 primarily based on the weighting our algorithms give wins and laps led.
Above is Autosport’s analysis of the Zhou crash via their YouTube channel.
How do the Go Full Throttle AI models work? (Repeated for context as we have new subscribers joining each week)
Algorithms
The Go Full Throttle AI Driver Rankings is a cloud based predictive analytics system that uses our proprietary algorithms utilizing artificial intelligence and machine learning technology to dynamically tune and improve accuracy over time. Based on our Pre-Season rankings and the results of F1 testing, Practice sessions 1, 2, and 3, qualifying and the race results, our algorithms go to work crunching the data and learning. The complex math also includes inputs such as top speed, lap times, and laps led.
How do predictive models work?
Can artificial intelligence and machine learning algorithms really make accurate predictions? YES! In fact, our proprietary models accurately predicted 8 out of 10 F1 race wins in 2021, including Max Verstappen’s Driver Championship over Lewis Hamilton. Our NASCAR model was equally as accurate correctly predicting 7 of the drivers making the Round of 8 in the 2021 Cup Playoffs as well as predicting Kyle Larson winning the Cup Series Championship. These models will only get “smarter” in 2022 as more data and “learning” will improve accuracy.
Context is key!
“Artificial intelligence refers to the general ability of computers to emulate human thought and perform tasks in real-world environments, while machine learning refers to the technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience and data” according to Columbia University’s School of Engineering. But Microsoft’s definition breaks this complexity down even more: “AI and machine learning enable companies to discover valuable insights in a wider range of structured and unstructured data sources. For better, faster decision-making, data scientists use machine learning to improve data integrity and use AI to reduce human error — a combination that leads to better decisions based on better data.” For us motorsports fans, we might use the analogy that artificial intelligence (AI) is the car and machine learning (ML) is the engine — you need both to win.
Dynamic tuning to improve accuracy
When we say “dynamic” we are referring to “near real time” data continually feeding the models. Besides the obvious final race results, as an example, our system will “watch” a number of data feeds looking for trusted information, such as the F1 live leaderboard with lap times during practice or qualifying results, laps led on race day, and final results.
