Raphael Lessard

Age: 23

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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
2016 14 CARS Pro 10 4 8 9 6.20 3.50 4.77 2.70 0.43 56.15 5.615 90.0 157 1216 12.91 99.18
2017 15 ARCA, CARS Pro 9 1 3 5 11.78 12.78 8.40 -1.00 0.02 -0.30 -0.033 44.4 45 917 4.91 73.30
2018 16 CARS Pro 8 1 4 5 5.50 8.25 6.72 -2.75 0.03 1.93 0.241 62.5 67 1037 6.46 99.81
2019 17 ARCA, Trucks, Pinty's 10 1 3 7 9.20 8.10 8.10 1.10 0.22 27.23 2.723 90.0 236 1589 14.85 99.94
2020 18 Trucks 23 1 4 7 13.04 15.26 13.70 -2.22 0.02 -21.03 -0.914 56.5 20 2989 0.67 93.41
2021 19 Trucks, Pinty's 10 2 3 4 12.00 16.80 13.75 -4.80 0.01 -30.49 -3.049 40.0 162 1240 13.06 97.56
2022 20 ACT, Pinty's 9 0 2 4 7.33 11.78 8.14 -4.44 -0.15 -22.15 -2.462 44.4 9 1665 0.54 98.52
2023 21 Pinty's 2 0 0 0 12.50 21.50 17.07 -9.00 -0.61 -18.11 -9.057 0.0 0 90 0.00 78.26
2024 22 Pinty's 3 0 1 2 8.67 11.00 9.10 -2.33 -0.12 0.30 0.099 33.3 0 438 0.00 70.42
Owner Car Season Series Starts Wins Average Start Average Finish Avg. PFAE
David Gilliland 99 2016 CARS Pro 10 4 6.20 3.50 5.615
Cathy Venturini 25 2017 ARCA 3 0 14.67 18.67 -4.008
David Gilliland 99, 99L 2017 CARS Pro 6 1 10.33 9.83 1.954
Kyle Busch Motorsports 18, 51, 51L 2018 CARS Pro 8 1 5.50 8.25 0.241
Mike Bursley 19, 28 2019 ARCA 3 0 6.33 5.67 3.608
David Gilliland 17, 54 2019 Trucks 2 0 11.00 9.50 2.995
Kyle Busch 46 2019 Trucks 3 0 10.00 12.33 -0.192
J.F. Dumoulin 07 2019 Pinty's 2 1 10.50 4.00 5.496
Kyle Busch 4 2020 Trucks 23 1 13.04 15.26 -0.914
Maury Gallagher 24 2021 Trucks 7 0 14.57 21.71 -5.628
David Wight 80 2021 Pinty's 3 2 6.00 5.33 2.968
None 48QC 2022 ACT 1 0 9.00 21.00 -9.182
Ed Hakonson 8 2022 Pinty's 1 0 4.00 2.00 6.091
Jamie Hakonson 8 2022 Pinty's 7 0 7.57 11.86 -2.723
Mathieu Kingsbury 12 2023 Pinty's 2 0 12.50 21.50 -9.057
None 48, 7 2024 Pinty's 3 0 8.67 11.00 0.099

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.