Javelin Broderick on his Team Dynojet—Zag Racing Yamaha YZF-R6 at Road Atlanta earlier this year.
This summer I’ve been working with Javelin Broderick, who races in the AMA SuperSport series. I first met Javelin several years ago when I was instructing at Jason Pridmore’s star school; he was just 11 years old at the time and I thought showed some promise. Last year I went to the track several times with Javelin and his father Bernie, and provided them with a Racepak data acquisition system — the same as what we use here at the magazine — to give them some help with setup and feedback on Javelin’s riding. Of course, I had an ulterior motive as I was looking for a low-pressure situation in which to get more experience working with data.
This year, things are more-or-less official; the magazine is sponsoring Javelin and the Dynojet — Zag racing team, and there are Sport Rider stickers on his Yamaha R6. Now 16 years old, it’s Javelin’s first year in the AMA after racing for several years in WERA club events, and he and Bernie are traveling around the country. Unfortunately I haven’t been able to make it to the track this year, but my data box is still on the bike and the team takes onboard video almost every session. With the wonders of the internet, I can have data and video within a couple of hours of a practice session, even though I’m as far as 3000 miles away.
Just as I found from instructing students at the Star school, helping someone to ride better is not a simple matter of saying “go faster.” In fact, it can be deceptively difficult when you throw data and video into the mix, as compared to following someone on the racetrack and actually seeing what’s happening. Overlaying speed graphs of two riders, or watching a quicker rider come by on video, it’s almost too easy to point out where the slower rider can go faster. But the difficulty is in first interpreting the data and video to find out how someone can go quicker, and then explaining it in understandable terms. Going quicker often becomes a matter of doing something differently rather than just riding faster or trying harder; in fact, there have been times that I’ve talked to Javelin about going slower to go quicker.
It’s been a steep learning curve for me just as much as it’s been for Javelin, I think. He is a pretty quick learner, and almost as soon as I point something out on video or through the data, he is working away at it and we can move on to something else. As he improves, it gets increasingly difficult for me to find areas to work on; each time he makes a step forward in his riding, I have to make a step forward on the analysis and interpretation side to keep up. Luckily, I in turn have learned a lot over the past couple of years from working with Kaz Yoshima, who is quite knowledgeable when it comes to data acquisition. Yoshima did a lot of research a couple of years ago on riding dynamics, using a data-packed Kawasaki ZX-6R and working with Jeremy Toye at Willow Springs (“Sensory Overload,” Jan. ‘10).
On the one hand, working with Javelin is relatively easy because at his age and experience he has few bad habits or preconceptions about riding. On the other hand, it can be frustrating sometimes because of that lack of experience; almost any change to his riding is uncharted territory. The year so far has been an interesting experience for me, and I have learned quite a bit about riding, setup and data acquisition — exactly what I was looking for when I first approached Javelin and his Bernie about working with them. If you’ve noticed over the past year, Bradley and I have used quite a bit of data and data-related concepts in Riding Skills Series; a lot of that information, and most of the actual data, has come from my work with Javelin. This issue’s piece on rushing into corners uses data from Javelin’s SuperSport race at Barber Motorsports Park, and is a perfect example of going slower to go quicker on the racetrack.
I think overall it’s been a busy and difficult year so far for Javelin. While he is plenty quick at his local tracks, almost all those on the AMA tour are new for him. Learning a new track in the compressed schedule of an AMA event is not easy, a task exacerbated by the huge field that usually necessitates splitting practice and qualifying into two groups, halving the practice time available. Still, he’s broken into the top 15 on a couple of occasions, including at Mid-Ohio, a track he had never been to before, and at the Laguna Seca round, where there were a staggering 55 entries in the SuperSport class. Hopefully my input has helped and not hindered Javelin’s progress, and I’m looking forward to learning more in the future — and putting that knowledge to good use here in the magazine.