Autocross Analysis

NMS #77

When I watch car racing on TV or read about how to be a better driver, one big topic is always how the drivers and teams use data analysis to find out where to go faster. For instance, with a two driver Formula 1 team, the drivers can look at tons of info on how they drive a track, and see differences in brake points, speeds, steering angle, etc. 

McLaren shopping at Long Island Sports Cars!
Just kidding, I just happened to stay in the hotel next door. 

At an autocross event, I can watch other drivers go through the course and copy some of what they do if they approach certain points differently. Another great way to improve is to have someone ride with you and give you tips, or even ride with the fastest drivers to get a better idea of HOW they are going faster. 

I consulted with the Ferrari Pope, and he told me "Data, Schmata. Drive faster!"

Data wise though, I typically will have video of each run that I can quickly watch before trying it again, so that's helpful. More specifically, I use a free app on my phone to record timing and some other tidbits such as start and finish speeds, and top and low speeds at other points along the course. So, here's a look at three runs from my latest event, and just a few observations. 

RUN 1: 42.871 Using this as a base line, we'll look at the next two runs that were faster and see WHERE they were faster. 

RUN 1

Data Point A: START and END SPEEDS: 21.0 and 26.8, which totals 47.8mph. 

Data Point B: Slowest and Fastest during the run: 21.0-40.1 With a top speed of 40.1, not a blazing fast autocross course design, which to me means driving skill comes into play more than just driving a higher horsepower car. 

Data Point C: Fastest 4 spots: As seen on the photo above, this run included some green (fast) speeds of  40.1, 38.6, 38.6, and 38.1. All four adds up to 155.4mph. 

Data Point D: Slowest 4 spots: 24.3, 25.5, 28.6, and 31.2. Most of us probably look at their driving and want to know HOW FAST DID I GO, but it's probably more important to find places to not be so slow! Regardless, adding these four times turned out to be faster than the next two runs, although it's only 0.1 mph faster than run three and 1.2mph faster than run 2. Call this one a tie.  

RUN 2: 41.260

 

RUN 2

Data Point A: START and END SPEEDS: 24.0 and 28.3. This totals 52.3mph,  is a huge 3mph faster at the start, and 1.5mph faster at the end, so no surprise this run was faster than Run 1. 

Data Point B: Slowest and Fastest during the run: 22.7-43.0 Hey, a good improvement in both of these speeds should give a faster run. What this does NOT spell out though is how a faster/slower spot affects the NEXT part of the course. This data point isn't the only impact on the entire run. 

Data Point C: Fastest 4 spots: 43.0, 41.7, 41.0, and 40.1. With all four times in the 40s, this run beat Run 1 that had only a single 40.1 top speed. A total of 164.8mph. 

Data Point D: Slowest 4 spots: 22.7, 23.6, 28.8, and 33.4. In my data here I'm not being consistent with comparing the exact same spots, so this category is not exactly apples to oranges, and the app doesn't always measure a fast or slow speed in the same spots. 

RUN 3: 40.338

RUN 3

Data Point A: START and END SPEEDS: 19.0 and 30.2. This totals 49.2, which is slower than Run 2, making this the slowest run at the start, but fastest at the end by 1.9mph. I'm going to say that this data point proved nothing, since the overall fastest run did not have both the fastest START and END speeds.

Data Point B: Slowest and Fastest during the run: 19.0 and 43.5 A slower start, but that higher top speed of 43.5 had to help the overall time here. 

Data Point C: Fastest 4 spots: 43.5, 41.5, 40.8, and 40.3.  By adding these fast four speeds, this run just barely edges Run 2 by 0.3mph. Not much of a difference, but, faster is still faster! 

Data Point D: Slowest 4 spots: 24.3, 25.5, 28.6, and 31.2. These four speeds add up to only 0.1 slower than run 1, 

CONCLUSIONS: While this is not an exact science, what sticks out to me is that looking at the four slowest points didn't indicate which run was fastest, since the fastest and slowest runs were only separated by 0.1mph. 

However, the four fastest spots clearly put Run three on top, with a total difference of 10.7mph. Apparently you can drop your run time by making the car go faster!

The Data Points for slowest four and fastest four spots didn't really mean a lot, other than in trying to make the car go faster everywhere. 

For all three runs the car was on the same settings of rear wing up, traction control (Porsche Stability Management,) and same driver on same course on the same day with basically the same ambient temperature. The tire pressures were lowered a bit, so that likely contributed to faster times. Driver familiarity with the course also helped.