You show up on a weekend morning in your shorts and GRM T-shirt, check your tire pressures, wait for the drivers meeting and work assignments, and eventually hop in your car to make some runs. Isn’t that what autocrossing is supposed to be: accessible, laid-back, inexpensive and fun? And it is.
But most of us are there because we’re racers. We like to compete and, let’s face it, we like to win. We’ll sink big money into tires, shocks and every go-fast piece we can find as we chase down a tenth here and there. So why not use data acquisition like every other serious racer does? More of us are doing just that.
We tested several different setups at events ranging from local to regional to the SCCA Solo Nationals. Here’s some of the knowledge we gained.
Grabbing the Basics
[Editor’s Note: This article originally appeared in the June 2019 issue of Grassroots Motorsports.]
After every autocross run, you get one piece of data by default: your time. That time says if you went fast or slow.
But to figure out why you went fast or slow, you have to rely on your memory or some potentially subjective feeling you had about the run. What if you could have more data points? For example, what if you also got a split time halfway through your run? You’d start to know where you improved or slowed down. Four splits would be better. One split per section and turn would be better still–and so on.
Of course, just about any data-acquisition tool today can give you more than just a few split times. It can show your speed through each part of the course, your lateral and longitudinal acceleration, the distance you traveled, a track map, and much more if you want it.
So, if one piece of data isn’t enough, how much more do you really need? This is a tricky question, especially at an autocross where you may have just a few minutes between runs to review your performance. You want enough information to get an objective, measured view of the run, but not so much that you can’t make sense of it quickly.
The primary answer for us turned out to be speed-over-distance graphs. Our strategy was to overlay two runs and compare our speeds at the same points of the course–for example, the same number of feet traveled from the start. In doing so, we could see whether we were faster or slower in each section, and whether going faster or slower in a section helped us or hurt us in the next section.
The Tools for This Exercise
There are myriad data tools on the market today that range from free phone apps to full-blown systems that cost thousands of dollars. We sampled several tools, then narrowed the field to two usable examples from each end of the spectrum.
On the low-buck end, we tried a few free apps and quickly realized their limitations in quality and repeatability. We then decided to spend a little money and settled on RaceChrono Pro, a $17.99 download for our iPhone ($19.99 for Android). Almost all of the free and inexpensive apps recommended using an external GPS, which will have a higher sampling rate than a smartphone chip. We paired RaceChrono Pro with a Garmin GLO Bluetooth GPS ($99) that we attached to the console with Velcro ($2.97).
On the full-blown end, we went with a Race Technology DL1 Club data-logger kit ($870 base price) and an OBD-II interface ($220) from the same brand. The kit includes a data-logger box, external GPS antenna, a mini SD memory card, and some wiring bits to make connections to sensors and other external inputs.
This isn’t intended to be a product review, so we won’t go into much more detail about either of these systems. However, we can say that the low-cost app and the full system differ the most when it comes to expandability and the depth of the analysis software. While an app can be coupled with external cameras and some additional sensors, a full system can connect to a lot more inputs. Plus, software for the full systems is significantly more developed and can really let you drill deeper.
The majority of tools out there, including the ones we used, are actually intended to record multiple laps on race tracks. While they adapted pretty well to autocross, we did struggle a bit to make them work repeatably with single laps and separate start and finish lines. Nonetheless, they certainly helped us speed up.
Using the Data
We were initially enamored with each system’s track mapping feature, but we ended up referring to simple numbers and graphs in order to go faster. We did use the maps to correlate course sections to graph sections, but once we’d done that, we only looked at the graphs after each run.
Our key strategy was to establish a baseline, then compare it to subsequent runs to find opportunities for improvement. If we had a co-driver, the faster driver would set the better baseline. The slower driver usually learned more from the faster driver, but we found the faster driver getting some use out of the slower driver’s data as well. Without a co-driver, our first run served as our baseline.
The more we practiced comparing runs to a baseline, the faster we could interpret the data and put it to use. Here’s how we put our strategy in action.
LOW-BUCK DATA SETUP:
data-logging tools: iPhone 5 in driver’s pocket w/RaceChrono Pro app connected via Bluetooth to Garmin GLO GPS
cost: $119.96 (not including phone)
car: 1999 Street Touring R Mazda Miata
venue: local autocross
In this case, one driver was quite a bit faster than the other, so the slower driver compared his data to the faster driver to see where he could improve.
1
This screenshot shows a typical view of the RaceChrono Pro app. The top half is a track map, while the graph at the bottom shows speed versus distance. The vertical red line corresponds to the pointer on the track map. In this case, the slower driver (heavy blue line) noticed that he needed to speed up a lot in the early parts of the course. He was surprised to see that he performed pretty well on the long straight, a section where he assumed he’d lost time.
2
Studying the data comparison paid off quickly for our slower driver: His next run was much improved–by seconds. He still needed to make a bit more progress to catch his co-driver, but he knew where to chase it.
3
The heavy line represents his second run. Arrow 1 points to a bobble: The driver didn’t get back on the gas fast enough, causing him to carry less speed through the section. Arrow 2 points out another misstep: lifting off the gas too early.
4
This setup really helped the slower driver, but what about the faster driver? He compared his first run to his second run, which was about 1 second faster on the 90-second course. When he overlaid them, however, he could see two clear places where he successfully ran harder in the first run.
On his next run, he fixed those issues and shaved several more tenths off his time. Data had helped him pinpoint the easiest gains.
5
So far we’ve revealed how our drivers used data to speed up during the autocross. But studying that information postmortem can lead to further gains. As the slower driver looked over the data from his runs, a pattern started to emerge. Comparing his runs to the faster driver revealed where he was consistently braking too soon and coasting into turns.
This realization gave him something to work on for the next event: staying on the gas and stabbing the brakes a little past his comfort point.
HIGH-END DATA SETUP:
data-logging tools: Race Technology DL1 data-logger kit, Race Technology OBD-II interface
cost: $1090.00
car: 1967 CAM-T Ford Falcon Wagon
venue: national-level autocross series
To test our high-end data-logging setup, we headed to Indiana’s Grissom Air Reserve Base to run our classic Ford Falcon at the Tire Rack CAM Challenge Powered by SCCA.
1
In this screenshot of our first run, the track map is pretty clear. Each of the peaks and valleys in the graph corresponds to a section of the course. We edited the screenshot with another program to add the labels; the data analysis software offers its own labeling options, but we never found time to use them during an event. Instead we took advantage of the interactive features, like clicking a spot on the map to highlight the same spot on the graph. Our first run took 54.524 seconds.
2
After reviewing our baseline performance, our main strategy was simple: Go a little deeper into each corner without scrubbing off any speed coming out of it. The arrows labeled “A” note where we succeeded on our next attempt.
That second run taught us a couple more lessons, and they’re marked “B.” Lesson one: We entered Turn 5 slower than before, allowing us to exit faster–slow in, fast out. Lesson 2: Going faster into Turn 6 and apexing later caused us to scrub off quite a bit of speed through Turn 7–fast in, slow out. Nonetheless, we were rewarded with a lap time of 53.592, an almost 1-second improvement.
Honestly, data or no data, we would have pushed harder that second time in an effort to find the edge of the Falcon. However, that extra info did help us set a strategy for our third run: We decided to keep our earlier gains and also correct the mistake on Turn 6. Since the data gave us relevant and objective information, we could tangibly put it to use on the third run.
3
The data showed a nice improvement, and we were rewarded with an even faster time: 52.848 seconds.
We’re Just Getting Started
We successfully used data in the heat of the battle, yes, but more gains came from poring over it afterward. The more data we had, the more useful it became, allowing us to locate our biggest issues–and then, hopefully, fix them.
Which is the better tool? We absorbed the data much faster with the RaceChrono Pro app, but we could go much deeper with the DL1. In some cases, we’ve tracked our performance with both systems simultaneously, using the RaceChrono Pro data during the event and the DL1 data for postmortem analysis. If budget is your limiting factor, the app is the clear choice.
This comparison has changed our autocross morning routine just a bit. We still show up in our shorts and GRM T-shirt and check our tire pressures, but now we run and review data, too.
