Jodi Christie’s Accelerated Technologies Honda CBR600RR and CBR1000RR on pit late. GPS data from both bikes shows why the 600 is almost as quick as the 1000 at some tracks. Jodi Christie’s Accelerated Technologies Honda CBR600RR and CBR1000RR on pit late. GPS data from both bikes shows why the 600 is almost as quick as the 1000 at some tracks. Photo courtesy Andrew Trevitt

Trevitt's Blog: 600 vs. 1000

Written by  on Thursday, 10 July 2014 10:27

Time and again on our short Canadian tracks, we have seen 600cc machines turn lap times close to 1000cc bikes, and well-ridden 600s have occasionally made it onto the Superbike podium. Given the huge horsepower advantage of the 1000, the 600 must be making up time somewhere to be so close on overall lap time. By looking at GPS data from both bikes, it’s possible to see exactly where those differences lie.

In the Mopar Canadian Superbike Championship, Jodi Christie uses an AiM Solo lap timer on both his Honda CBR600RR and CBR1000RR. Along with lap times, the solo records GPS data, including speed, lean angle, lateral and longitudinal acceleration, and even the actual riding line. This data provides some interesting insight to the 600 vs. 1000 question.

CSBK rules keep the 600 limited to a maximum of 125 horsepower and a minimum weight of 385 pounds (175 kilograms) in the Sport Bike class, while the Superbike class limits are 190 horsepower and 360 pounds (164 kilograms). This gives the superbike a potential advantage of 65 horsepower and 25 pounds (11 kilograms). At the Shannonville round of the series, Jodi qualified on the 600 with a 1:05.6 lap time, while he posted a 1:04.3 lap time during Superbike qualifying, for a 1.3-second difference.

A big portion of that gap, as you’d expect, is found on the back straight; here the 1000 tops out at 246km/h while the 600 reaches 224km/h. This gives the superbike a half-second advantage on the back straight alone under acceleration. At the end of the straight, Jodi is able to brake harder on the lighter 1000 and gains another quarter second on the 600. The pattern is repeated on almost every straight: The superbike gains a big chunk of time accelerating, and another, smaller bit under braking.

Again as you would expect, the 600 makes up some of that lost time in the corners. That said, the 600 does not actually corner better than the 1000, but rather the advantage is due to the different way Jodi rides the two bikes. For example, in turn 1 at Shannonville, the fastest turn on the track, Jodi’s apex speed on the 1000 is 124km/h compared with 129km/h on the 600. Lean angle and lateral acceleration (the measure of cornering force) are almost identical even though the superbike is on slick tires and the 600 is on DOT tires.

The difference is that Jodi takes a wide, sweeping line on the 600 to keep corner speed up, whereas on the 1000 he uses a tighter arc and sacrifices corner speed to take advantage of the 1000’s acceleration on the succeeding straight. Even with more corner speed however, the 600 gains back just a tenth of a second here; in other corners, the gaps are measured in hundredths of a second.

There are some other aspects of the data that show interesting differences between the two bikes. While trail braking into the turns gives no advantage to either bike, the superbike shows better corner exits with higher combinations of cornering and acceleration – perhaps due to its wider rear tire. In chicanes, lean angle data shows the 600 turns from side to side quicker than the 1000. At Shannonville, the maximum roll rate for the 600 is 160 degrees per second, compared to just 100 degrees per second for the 1000. Through the chicane off the end of the back straight, this translates to an advantage of a tenth of a second to the 600, a considerable amount. Still, it’s not enough to offset the 1000’s gains while accelerating and braking.

Comparing data from both bikes is more than just an exercise. Data from one bike can be used to help find a quicker line through a particular corner for the other bike, or to confirm that both bikes are working as they should. Usually the two data sets show exactly what you would expect, but there has been the occasional find that makes it worthwhile.

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