Measuring Tire Cornering Predictability with Data

In our Summer 2015 XC tire comparison test (to be published tomorrow), we started to record lap times for each tire tested. Though there was a small sample of lap times for each tires (5 test riders riding each of the test tire combos once), I decided to also take the standard deviation of the recorded times to see if I could see any patterns in whether the lap times for each tire were more clustered around an average or were, conversely, more spread out. 

[Definition of Standard Deviation:  The standard deviation is a measure of how spread out the numbers in a data set are. A smaller standard deviation means the data is more closely clustered around the average of the data set, while a larger standard deviation means the data is more spread out.]

What I found was that there indeed was a difference between tires in how spread out their lap times were. Based on a synthesis of the subjective feedback and quantitative performance ratings with the standard deviation of the lap times, my interpretation of the standard deviation of the lap times recorded for each tire is that it is a measure of how forgiving a tire is of less-than-perfect riding.  In this context, tires that are more forgiving will have a lower standard deviation score. I’m defining forgiving as the ability to:

·         Communicate the amount of available traction

·         Quickly regain climbing traction after traction is lost

·         Recover easily and quickly from the front tire sliding

·         Slide the rear tire predictably

The opposite of a forgiving tire will be one that tends to break away quickly and without warning. Forgiving tires will tend to have moderate to high traction limits and tend to lose traction predictably thus allowing riders to more confidently explore their traction limits.

The reason why I think the standard deviation is a measure of how forgiving  or, put in another way, how accessible a tire’s performance limits are is because I believe a more forgiving tire should result in less variance in lap times due to more gradual breakaway characteristics that can be caught and corrected more easily.  Tires that break away more unpredictably might be expected to have a larger time difference between a “good” run and a “bad” run.


Your thoughts?