German Quiroga

Age: 43

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Season Age Series Starts Wins ASP AFP eARP ASP-FP eGR-LR PFAE Avg. PFAE Laps Led Laps Run %LL %LC
2005 24 Lights 1 0 12.00 10.00 11.00 2.00 0.18 -1.00 -1.000 0 40 0.00 100.00
2007 26 Xfinity 1 0 20.00 28.00 21.44 -8.00 -0.35 -6.88 -6.885 9 81 11.11 98.78
2008 27 NASCAR MX 14 2 7.64 6.86 5.70 0.79 0.24 52.79 3.770 249 1279 19.47 97.34
2009 28 NASCAR MX 14 3 7.21 4.79 0.40 2.43 0.27 80.88 5.777 285 1692 16.84 98.66
2010 29 NASCAR MX 14 3 5.00 6.57 -1.57 0.23 51.25 3.661 0 1880 0.00 95.05
2011 30 Trucks, NASCAR MX 16 3 10.19 6.56 6.48 3.63 0.41 110.14 6.884 221 2335 9.46 99.83
2012 31 Trucks 4 0 23.25 18.75 20.69 4.50 0.12 10.04 2.510 0 406 0.00 76.03
2013 32 Trucks 22 0 13.59 15.77 13.84 -2.18 -0.03 -8.52 -0.387 63 3211 1.96 94.69
2014 33 Trucks 22 0 12.73 13.18 11.71 -0.45 0.08 31.19 1.418 27 3060 0.88 93.01
2016 35 Trucks 3 0 10.67 9.67 10.81 1.00 0.06 11.92 3.974 0 527 0.00 100.00
2021 40 NASCAR MX 12 0 6.42 10.33 8.73 -3.92 -0.28 -23.53 -1.961 0 1380 0.00 92.93
2022 41 NASCAR MX 1 0 9.00 8.00 8.24 1.00 0.13 1.54 1.541 5 144 3.47 100.00
Owner Car Season Series Starts Wins Average Start Average Finish Avg. PFAE
Kenn Hardley 42 2005 Lights 1 0 12.00 10.00 -1.000
Jay Robinson 28 2007 Xfinity 1 0 20.00 28.00 -6.885
Monica Morales 5 2008 NASCAR MX 14 2 7.64 6.86 3.770
Monica Morales 2 2009 NASCAR MX 14 3 7.21 4.79 5.777
Monica Morales 2 2010 NASCAR MX 14 3 5.00 6.57 3.661
Kyle Busch 51 2011 Trucks 2 0 22.00 21.00 -0.601
None 2 2011 NASCAR MX 14 3 8.50 4.50 7.953
Kyle Busch 51 2012 Trucks 4 0 23.25 18.75 2.510
Tom DeLoach 77 2013 Trucks 22 0 13.59 15.77 -0.387
Tom DeLoach 77 2014 Trucks 22 0 12.73 13.18 1.418
Tom DeLoach 11 2016 Trucks 3 0 10.67 9.67 3.974
Lorena Valero 11 2021 NASCAR MX 4 0 2.25 13.50 -6.457
Oscar Rodriguez Garcia 01, 69 2021 NASCAR MX 8 0 8.50 8.75 0.288
Oscar Rodriguez Garcia 69 2022 NASCAR MX 1 0 9.00 8.00 1.541

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.