The Importance of Rider Biometrics
Rider Weight: It Really Matters
Rider weight is the single most important biometric for optimizing frame stiffness, particularly regarding drivetrain performance. Rider weight is significantly more important than FTP, W/Kg, fitness, age, leg length, or number of years riding.
This may sound like an overstatement since most bike builders don't ask about rider weight — and certainly don't employ tubeset designs that take advantage of athlete weight nuances.
The "one tubeset for all" mentality is as old as the velocipede itself. Through most of framebuilding history, knowing a rider's weight didn't really matter because there were only a couple of tubeset sizes available. Therefore, it was almost irrelevant whether a rider was 138 lbs or 216 lbs. However, when Seven began and we designed and machined each tubeset one at a time from a wide range of starter sizes, we could finally optimize for rider weight as a significant variable.
Why does rider weight matter? Our goal is to determine ideal frame stiffness and optimal deflection for each rider. Watts output determines frame deflection. Rider weight is the largest variable and biggest determiner of watts output. Excluding gender, no biometric is more critical to tubeset engineering.
Order process: When ordering a Rider-Ready Seven you'll find a very tuned rider weight range. When ordering a custom Seven and completing our Design Guide, make sure to include your weight (along with completing all the other data fields). If you want to provide your target weight instead, that's cool. But, best to supply both current and target. Anything is infinitely better than nothing. Also, we'll build to whatever you want; we just have to know what that is.
When we cannot extract rider weight from a retailer, we have to employ data triangulation to estimate watts output. (This calculation includes body measurements and about a dozen other data points from the Design Guide. We know our weight calculator is accurate when we overlay our 35,000 rider biometric datasets for comparison.) Seven's calculation is significantly better than nothing, but is it not ideal. Provide us your weight; do not let your Seven be suboptimal.