Jack Harvey

Age: 31

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Season Age Series Starts Wins T5 T10 ASP AFP eARP ASP-FP eGR-LR PFAE Avg. PFAE Succ% Laps Led Laps Run %LL %LC
2014 20 Lights 14 4 14 14 3.14 2.86 3.30 0.29 0.12 18.48 1.320 78.6 133 567 23.46 100.00
2015 21 Lights 16 2 12 15 2.88 4.56 3.69 -1.69 0.00 -3.55 -0.222 56.3 40 655 6.11 95.34
2017 23 IndyCar 3 0 0 0 21.33 21.00 21.05 0.33 -0.13 -13.99 -4.664 0.0 0 209 0.00 60.58
2018 24 IndyCar 6 0 0 0 19.33 17.33 19.00 2.00 -0.06 -16.51 -2.751 16.7 0 602 0.00 89.19
2019 25 IndyCar 10 0 1 4 14.40 14.20 15.33 0.20 -0.07 -12.83 -1.283 40.0 0 874 0.00 90.10
2020 26 IndyCar 14 0 0 6 8.79 12.29 10.43 -3.50 -0.16 -19.17 -1.369 50.0 1 1878 0.05 98.84
2021 27 IndyCar 16 0 2 6 12.69 13.00 12.55 -0.31 0.00 -7.16 -0.447 43.8 6 1789 0.34 92.94
2022 28 IndyCar 16 0 0 1 16.56 17.31 16.99 -0.75 -0.10 -46.74 -2.921 31.3 0 1992 0.00 98.61
2023 29 IndyCar 14 0 0 0 20.36 19.64 20.73 0.71 -0.10 -34.92 -2.495 28.6 0 1630 0.00 90.91
2024 30 IndyCar 14 0 0 0 21.79 20.00 17.71 1.79 -0.03 -37.09 -2.649 28.6 0 1757 0.00 87.15
Owner Car Season Series Starts Wins Average Start Average Finish Avg. PFAE
Schmidt Peterson / Curb-Agajanian 42 2014 Lights 14 4 3.14 2.86 1.320
Schmidt Peterson / Curb-Agajanian 42 2015 Lights 16 2 2.88 4.56 -0.222
Michael Shank Racing/Andretti 50 2017 IndyCar 1 0 27.00 31.00 -8.500
Schmidt Peterson Motorsports 7 2017 IndyCar 2 0 18.50 16.00 -2.746
Michael Shank with SPM 60 2018 IndyCar 6 0 19.33 17.33 -2.751
Meyer Shank Racing / Arrow SPM 60 2019 IndyCar 10 0 14.40 14.20 -1.283
Meyer Shank Racing 60 2020 IndyCar 14 0 8.79 12.29 -1.369
Meyer Shank Racing 60 2021 IndyCar 16 0 12.69 13.00 -0.447
Rahal Letterman Lanigan Racing 45 2022 IndyCar 16 0 16.56 17.31 -2.921
Rahal Letterman Lanigan Racing 30 2023 IndyCar 14 0 20.36 19.64 -2.495
Dale Coyne Racing 18 2024 IndyCar 3 0 23.33 23.33 -5.797
None 18 2024 IndyCar 11 0 21.36 19.09 -1.790

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.