Week 20 GFT NASCAR AI Driver Rankings: Elliott holds P1, increases lead after 2nd place at Road America
3 July 2022
By Bob Francis, Managing Editor
Go Full Throttle Racing News
ELKHART LAKE, WISCONSIN — The results are in, and we didn’t need our massive array of cloud computing and proprietary AI models to tell us that Chase Elliott’s 2nd place finish at Road America would keep him at the top of the rankings for another week. In fact, after our models crunched all the data from Sunday, Elliott increased his point lead by 2 in our rankings from 7 to 9 points.
The Big Movers
As you would expect, Sunday’s winner Tyler Reddick jumps 6 spots from P18 to P12. Larson moves up 2 spots to P4, and Ricky Stenhouse Jr moves up 1. Kevin Harvick falls 4 spots to P17. Bubba Wallace drops to P25 and in breaking news Joe Gibbs Racing (who fields the pit crews for 23XI Racing) is swapping the crews of Bubba Wallace and Christopher Bell.
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. Data feeds into our system include our Pre-Season rankings and the data available from NASCAR and other trusted NASCAR Cup Series sources that include (but not limited to) inputs such as Driver points, Stage points, Stage wins, top speed, lap times, and laps led from all practice sessions, qualifying and the races.
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.
