Week 16 GFT NASCAR AI Driver Rankings: Elliott P1, Chastain Up to P2, Byron Drops to P3 after Coke 600
30 May 2022
By Bob Francis, Managing Editor
Go Full Throttle Racing News
The NASCAR Allstar Race (Week 15 of the Cup Season) was not a “points paying” race and Blaney’s win had almost no impact on the rankings. The Coca-Cola 600, on the other hand, did! Chase Elliott remains at the top of the Go Full Throttle NASCAR AI Driver Rankings by 2.4 points. Ross Chastain, who finished 15th moves up into P2 and Willaim Byron who was one of many cars crashed out of the 600 finishing 32nd, causing him to drop to P3. The rest of the top ten: Kyle Busch, Blaney, Logano, Larson, Truex Jr., Bowman, and 600 winner Denny Hamlin who jumps 5 spots to P10 with the victory.
BIG MOVERS
Alex Bowman drops 2 spots to P9, Kurt Busch also drops 2 spots to P15, and Austin Cindric falls 4 spots to P16. Busch and Cindric were also caught up in crashes and did not finish. Tyler Reddick continues to show strength with a 6 place finish moving up 4 spots. Also of note was the 8th place finish by Michael McDowell who moves up 2 spots to P22 in our rankings.
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.
