Week 1 NASCAR GFT AI Driver Rankings: Austin Cindric at P1
The Go Full Throttle AI Driver Rankings are out and as it should be, a win can change everything. Austin Cindric, driver of the № 2 Discount Tire Ford Mustang of Team Penske comes in at P1 with his win at the Daytona 500.
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 The Clash at the Coliseum and The Duels at Daytona, our algorithms went to work crunching data that also includes inputs such as top speed, qualifying position, stage wins, laps led, and in Super Speedway races — the number of cars each manufacture has entered (likely drafting partners, especially for green flag pit stops).
The Big Movers
After all the inputs were in the cloud following final practice, the GFT AI models ranked Cindric a distant 32nd. However, with Cup wins given significant weight in our models, Cindric vaults from P32 to the top at P1. Other big movers in the rankings following Daytona include № 42 car of Ty Dillion (P31 to P13), the № 7 of Corey Lajoie (P30 to P17), and № 15 of Ryan Preece (P29 to P16). Big Movers in the Top Ten include № 23 of Bubba Wallace (P21 to P4) and the № 14 of Chase Briscoe (P23 to P5).
How do the GFT AI Driver Ranking 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 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. Yea, yea, that’s great, but how?
For context, “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 NASCAR live leaderboard with lap times during practice or qualifying results.
In 2022 our goal is to continue to improve the accuracy of the current Go Full Throttle AI Driver Ranking models for F1 and NASCAR Cup that will be published weekly, usually on Saturday to contain the most up to date information ahead of the races on Saturday night or Sunday. We are also in the process of building GFT AI Ranking Models for IndyCar and the World of Outlaws winged Sprint Car series.