This image shows GPS data from the AiM EVO4 data acquisition system for Jodi Christie's qualifying session at Autodrome St-Eustache, overlaid on a Google Earth map of the track. This image shows GPS data from the AiM EVO4 data acquisition system for Jodi Christie's qualifying session at Autodrome St-Eustache, overlaid on a Google Earth map of the track.

Trevitt's Blog: Data from Autodrome St-Eustache

Written by  on Thursday, 11 July 2013 13:47

In my last blog, I talked about working with Jodi Christie and the Accelerated Technologies Honda team in Canadian Superbike this year. At the second round of the series last weekend at Autodrome St-Eustache in Quebec, I had my first opportunity to see some data from the http://www.aimsports.com/">AiM EVO4 data acquisition system mounted on Jodi's CBR1000RR. As I mentioned previously, the EVO4 is a full GPS-enabled system capable of monitoring pretty much any aspect of the motorcycle you want.

So far, we are looking at the basics - RPM, throttle position and speed - along with suspension travel, a channel that shows what the Bazzaz Z-Fi traction control system is up to, and the air-fuel ratio. I've addressed some of these channels previously and will look at others later, but here I will specifically address the GPS capabilities of the EVO4.

The above image shows essentially what the system is capable of - using GPS, it can track the motorcycle's progress around the track very accurately. But whereas the GPS system in your car or as part of your phone tracks your position to within a few metres and updates about once every second, the AiM's GPS is much more accurate and updates 10 times every second. The system's software uses that accurate position data to generate not just speed, but also lateral and longitudinal acceleration, two very powerful channels for analysis, and other information.

How can we use that data to best effect? Most data acquisition systems' software allows you to split the track into any number of segments, and see the time for each segment. With position data that is accurate enough to see changes in the riding line from lap to lap, it's possible to literally see which of the lines the rider took over the course of the session gave the best result in each section of the track. In theory, those optimum lines in each segment can be put together for an improved overall lap time.

Of course, in actual practice it's not that simple. A particular line in one corner may result in a better time in that segment but put the rider off-line and slower in the next segment. Large sections of the track must still be considered at once, especially when sequences of corners are strung together with short straights between.

During Saturday's qualifying session, Jodi logged a dozen or so complete laps, including the fastest lap for pole position at 45.144 seconds. In our data software, the track is split into 10 segments of seven corners and three straights. Even on a short track such as St-Eustache, that ends up being a lot of sector-time data to sort through, and you can imagine how easy it is to get bogged down in maps, sector times and scenarios on a longer track or with more laps to consider. And even if that data can be boiled down to one or two areas of improvement, the rider must put all this knowledge into practice - sometimes the most difficult part.

Sector times are one of the root capabilities of a data system, but when combined with the accuracy of GPS become a much more powerful tool than the systems of even just a decade ago. It's worth noting that the AiM Solo GPS lap timer generates the same GPS data as the more expensive EVO4 full system, and with the Race Studio 2 analysis software you can access all the same information from the Solo - sector times, speed, lateral and longitudinal acceleration, and so on.

It was an interesting weekend for me, as I did everything from home and on a three-hour time difference; John Sharrard emailed me the data at the end of each day, and there were a lot of texts, emails and phone calls back and forth. Jodi ended up setting the fastest lap in the race and finished second to current champion Jordan Szoke, the same result as at the first round at Shannonville.

Our first weekend with the data was definitely a learning experience for all of us; hopefully we can put it to good use at the next round at Atlantic Motorsport Park.

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