Week 8 GFT NASCAR AI Driver Rankings: Byron P1 with Martinsville win
13 April 2022
by Bob Francis, Managing Editor
Go Full Throttle Racing News
It was a dominating night at Martinsville for Team Chevy and specifically Hendrick Motorsports with Pole sitter Chase Elliott leading the first 185 laps and William Bryon, the race winner, leading 212 laps. Our algorithms do take into account Laps Led and Manufacturer as it relates to historical data (wins) at specific racetracks. The result of Byron’s win and 212 laps led sends him to the top of this week's GFT NASCAR AI Driver Rankings. And with 185 laps led, 2 Stage wins, and a 10th place finish, Chase Elliott moves to P2 in the rankings.
Another Chevy powered car, the RCR № 3 of Austin Dillion also lead a lap. When you add it up, 398 of the 403 laps at Martinsville where lead by Chevy teams (Hendrick 397, RCR 1) and 5 laps were led by Ryan Blaney in a Ford.
The Big Movers
Drivers making the biggest gains this week- Aric Almirola jumps 4 spots to P11 and making his first appearance in the GFT AI Top 25 is Cole “The General” Custer, driver of the 41 SHR Ford Mustang at P24. Falling down the rankings this week, Denny Hamlin and Daniel Suarez, each losing 3 positions after tough performances in Martinsville. For Hamlin and Toyota, Saturday was particularly frustrating after his strong performance last week in the win at Richmond. The № 11 went a lap down in Stage 1 and it only got worse from there.
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.
