Gary Klutt

Age: 31

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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
2010 17 Pinty's 1 0 16.00 11.00 13.50 5.00 0.33 2.96 2.961 0 51 0.00 100.00
2013 20 Pinty's 1 0 7.00 7.00 7.00 0.00 0.00 2.57 2.571 0 42 0.00 100.00
2014 21 Pinty's 4 0 14.25 16.00 17.60 -1.75 -0.19 -8.14 -2.034 0 299 0.00 78.27
2015 22 Pinty's 11 1 7.55 9.55 8.16 -2.00 -0.12 7.24 0.659 12 2070 0.58 96.82
2016 23 Trucks, Pinty's 13 0 8.69 10.23 9.37 -1.54 -0.10 -4.75 -0.365 2 2055 0.10 91.82
2017 24 Trucks, Pinty's, Cup 8 0 10.88 12.88 12.24 -2.00 -0.08 -9.94 -1.242 0 541 0.00 97.83
2018 25 Pinty's 3 0 7.67 10.00 8.13 -2.33 -0.13 -2.61 -0.871 11 126 8.73 90.65
2019 26 Trucks, Pinty's 4 0 5.00 10.75 7.68 -5.75 -0.33 -10.95 -2.736 13 196 6.63 95.61
2021 28 Pinty's 2 0 1.50 5.50 3.02 -4.00 -0.18 0.50 0.250 23 81 28.40 100.00
2022 29 Pinty's 15 0 8.20 10.27 8.86 -2.07 -0.06 -11.13 -0.742 1 1770 0.06 87.80
2023 30 Pinty's 5 0 7.60 15.40 8.65 -7.80 -0.19 -26.76 -5.352 31 162 19.14 69.83
Owner Car Season Series Starts Wins Average Start Average Finish Avg. PFAE
Peter Klutt 68 2010 Pinty's 1 0 16.00 11.00 2.961
Peter Klutt 59 2013 Pinty's 1 0 7.00 7.00 2.571
Peter Klutt 59, 61 2014 Pinty's 4 0 14.25 16.00 -2.034
Peter Klutt 59 2015 Pinty's 11 1 7.55 9.55 0.659
Kyle Busch 51 2016 Trucks 1 0 15.00 11.00 2.622
Peter Klutt 59 2016 Pinty's 12 0 8.17 10.17 -0.614
Jay Robinson 49 2017 Trucks 1 0 14.00 24.00 -11.000
Peter Klutt 59 2017 Pinty's 6 0 6.50 8.00 0.656
Jay Robinson 15 2017 Cup 1 0 34.00 31.00 -2.875
Peter Klutt 59 2018 Pinty's 3 0 7.67 10.00 -0.871
Al Niece 44 2019 Trucks 1 0 11.00 12.00 1.761
Peter Klutt 59 2019 Pinty's 3 0 3.00 10.33 -4.235
Peter Klutt 59 2021 Pinty's 2 0 1.50 5.50 0.250
Peter Klutt 59 2022 Pinty's 15 0 8.20 10.27 -0.742
Peter Klutt 59 2023 Pinty's 2 0 9.50 14.00 -2.880
Petter Klutt 59 2023 Pinty's 3 0 6.33 16.33 -7.000

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.