Age: 27
| Season | Age | Series | Starts | Wins | T5 | T10 | ASP | AFP | eARP | ASP-FP | eGR-LR | PFAE | Avg. PFAE | Succ% | Laps Led | Laps Run | %LL | %LC |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2015 | 17 | Lights | 16 | 1 | 10 | 15 | 5.44 | 5.13 | 4.46 | 0.31 | 0.09 | 8.21 | 0.513 | 62.5 | 118 | 622 | 18.97 | 90.54 |
| 2016 | 18 | Lights, IndyCar | 11 | 0 | 3 | 6 | 8.91 | 10.09 | 10.92 | -1.18 | -0.12 | -20.21 | -1.838 | 36.4 | 0 | 534 | 0.00 | 93.68 |
| 2019 | 21 | IndyCar | 1 | 0 | 0 | 0 | 22.00 | 17.00 | 19.50 | 5.00 | 0.24 | -2.30 | -2.296 | 0.0 | 0 | 89 | 0.00 | 98.89 |
| 2020 | 22 | Xfinity | 1 | 0 | 0 | 0 | 24.00 | 20.00 | 22.00 | 4.00 | 0.17 | 4.99 | 4.992 | 100.0 | 0 | 45 | 0.00 | 100.00 |
| 2021 | 23 | IndyCar, Cup | 2 | 0 | 0 | 0 | 31.00 | 31.00 | 34.42 | 0.00 | -0.46 | -13.05 | -6.523 | 0.0 | 0 | 100 | 0.00 | 57.14 |
| 2023 | 25 | IndyCar | 1 | 0 | 0 | 0 | 28.00 | 32.00 | 30.00 | -4.00 | -0.80 | -8.50 | -8.500 | 0.0 | 0 | 75 | 0.00 | 37.50 |
| 2024 | 26 | Xfinity | 2 | 0 | 0 | 0 | 35.50 | 27.50 | 31.24 | 8.00 | 0.23 | -2.82 | -1.410 | 0.0 | 0 | 135 | 0.00 | 96.43 |
| Owner | Car | Season | Series | Starts | Wins | Average Start | Average Finish | Avg. PFAE |
| Schmidt Peterson / Curb-Agajanian | 7 | 2015 | Lights | 16 | 1 | 5.44 | 5.13 | 0.513 |
| Schmidt Peterson / Curb-Agajanian | 7 | 2016 | Lights | 8 | 0 | 5.88 | 8.00 | -1.796 |
| Dale Coyne | 19 | 2016 | IndyCar | 3 | 0 | 17.00 | 15.67 | -1.948 |
| Trevor Carlin | 31 | 2019 | IndyCar | 1 | 0 | 22.00 | 17.00 | -2.296 |
| Bobby Dotter | 07 | 2020 | Xfinity | 1 | 0 | 24.00 | 20.00 | 4.992 |
| None | 75 | 2021 | IndyCar | 1 | 0 | 25.00 | 28.00 | -8.579 |
| None | 15 | 2021 | Cup | 1 | 0 | 37.00 | 34.00 | -4.468 |
| None | 50 | 2023 | IndyCar | 1 | 0 | 28.00 | 32.00 | -8.500 |
| Bobby Dotter | 14 | 2024 | Xfinity | 2 | 0 | 35.50 | 27.50 | -1.410 |
If you see any numbers that are odd, they probably are, especially if it appears a series if missing a lot of laps led or a driver has the same start as they do finish (in aggregate). We have to do a bit of data cleanup to make things useful, and as a result some missing data is covered up with defaults that hopefully won't pervert the results too much.