Week 9 GFT NASCAR AI Driver Rankings: William Byron Holds P1 after Bristol Dirt Race
20 APRIL 2022
By Bob Francis, Managing Editor
Go Full Throttle Racing News
As with most ranking models, our GFT algorithms apply significant weight to WINS. That was made clear this week as all the data came in from the dirt race Easter Sunday night at Bristol Motor Speedway. William Byron, driver of the Hendrick Motorsports № 24 Chevrolet was at the top of Driver Rankings last week after his win at Martinsville, but even with a disappointing 18th place finish Sunday in Bristol, Byron’s 2 wins in 2022 keep him at the top of the rankings for a 2nd week.
The Big Movers
Bristol Dirt winner Kyle Busch (№ 18 Joe Gibbs Racing Toyota) jumps 7 spots to P6 in the rankings. Busch’s teammate Christopher Bell (№ 20 Joe Gibbs Racing Toyota) moves up 6 places to P15 and Ty Dillion (№ 42 Petty GMS Motorsports) moves up 3 to P22 in the Week 9 version our NASCAR GFT Top 25 AI Driver Rankings. Downward movers included Kurt Busch, falling 4 spots, while Ross Chastain, Martin Truex Jr, and Austin Dillion each fall 3 places in the rankings.
The Top Ten
Four from Hendrick Motorsports
P1 Byron
P2 Elliott
P4 Bowman
P9 Larson
Two from Team Penske
P3 Blaney
P5 Logano
Two from Joe Gibbs Racing
P6 Kyle Busch
P10 Truex Jr
One from Trackhouse Racing
P7 Chastain
One from Stewart Haas
P8 Briscoe
Manufacturer = 5 Chevrolet, 3 Ford, 2 Toyota
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.
