Week 1 — F1 GFT AI Driver Rankings: Charles Leclerc P1 after Bahrain win
Clearly the chatter about the greatly improved Ferrari performance was valid as Charles Leclerc and Carlos Sainz finish 1–2 in the season opener at Bahrain. That combined with mechanical problems suffered by both Red Bull cars created quite the shuffle in the rankings as we head into Week 2 at Saudi Arabia.
More News Names in the Top Ten
The surprise of the Bahrain qualifying session was the performance of the Hass driver “K-Mag” Kevin Magnussen at P7. But Magnussen backed up that great qualifying run with an excellent race performance finishing 5th. Haas Team Principal Guenther Steiner said “we scored more points in two hours than it had scored in two years.”
How do the Go Full Throttle AI models work? (Repeated for context)
The GFT AI Driver Rankings use 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 for the Bahrain GP, our algorithms went to work crunching the data and learning. The complex math also includes inputs such as top speed, and qualifying position.
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.
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.