Week 14 GFT NASCAR AI Driver Rankings: Elliott P1, Byron P2, Chastain P3 after Kansas
16 May 2022
By Bob Francis, Managing Editor
Go Full Throttle Racing News
It was a tough day at Kansas for Chase Elliott, but with win at Dover and his massive points advantage, Elliott holds onto the top spot in the Go Full Throttle Week 14 NASCAR AI Driver Rankings. Elliott’s Hendrick Motorsports teammate William Byron holds onto P2 and Trackhouse Racing’s Ross Chastain holds at P3. Kyle Busch moves up 2 spots to P4.
The Big Movers
With the win at Kansas, Kurt Busch jumps 10 spots up to P12. Also gaining spots in this week’s rankings Austin Cindric, Bubba Wallace, and Ricky Stenhouse Jr.. 2nd place finisher Kyle Larson holds at P8. Losing ground this week: Joey Logano falls 2 spots to P6. Aric Almirola and Justin Haley also lost positions after disappointing finishes Sunday at Kansas Speedway.
How do the Go Full Throttle AI models work? (Repeated for context as we have new subscribers joining each week)
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.