Most of the time, we have a general sense of the quality of our performance after a race; sometimes we feel that we've given our best, while others we regret that we did not go faster at a critical time during the competition. The best way to understand the quality of a performance is to carefully examine the data that you had collected through your powermeter or heartrate monitor.
Below is data that I collected from an early May race. The day was cold--the temperature was in the mid-forties, and there was heavy rain prior to the race. Because it was so damp and chilly, I cut short my normal warm-up routine; during the race itself, I thought that while I had ridden hard, I could have done better. In order to get a more objective reading of my performance, I downloaded and analyzed my ride's data later that day.
I first looked at my heartrate data:
My first observation is that while my heartrate was relatively steady throughout the entire race, it was low as compared to my anaerobic threshold, which is 166 beats per minute (BPM). The graph above indicates that my heartrate averaged approximately 160 BPM for the entire race, which points to some possible problems: I did not warm-up enough; the cold and wet negatively impacted on my ability to perform at an optimal level; I may have been tired, as I was just completing a three week training cycle.
Also in the graph above, there are four distinct periods in which my heartrate dropped very low. Without looking at any correlating data, it is difficult to determine why this occurred, though the first two dips most likely corresponded to the first two 90 degree turns on the course.
In the graph above, I overlaid heartrate data with speed to see if there was any correlation between the two. Note that in the first two instances, my heartrate drop was matched with a corresponding drop in speed: in both cases, my speed had dropped to below 16mph, which is reasonable for taking a 90 degree turn in extremely wet and cold conditions. For the later two instances, there was a corresponding increasein speed, which suggests to me that I may have been riding downhill. The only way to validate these interpretations, though, is to introduce an additional data parameter, in this case altitude:
In the graph above, the tan overlay represents changes in altitude. For the first two drops in heartrate, note that there were no significant altitude changes, even though my speed had dropped. There are only two reasonable interpretations for this: I had lost concentration and slowed down; I was entering a corner and exercised caution. Because these two drops in heartrate occurred so early in the race, I would suspect the later, rather than assume that I had lost my concentration. This can be verified by adding yet another overlay, this time distance, and cross-referencing the course map.
For the last two drops in heartrate, note that both were associated with significant drops in altitude. As I rode downhill, my speed increased significantly, even though my heartrate decreased. This is a normal phenomena when cycling, and powermeter users also would note a corresponding drop in power output on descents, too. Interestingly, while my speed oscillated during the ride, my heartrate was relatively consistent throughout the entire race, which suggests that the course could be considered rolling or that I may have encountered headwinds on different points of the course.
What to Take Away?
A quick scan of the numbers confirms that Saturday's session was difficult enough to preclude a strong race on the following day, as I had spent almost an entire hour riding in Zone 4 and 5:
Again, data collected is useful only if you analyze it. I hope that the example covered in this issue will help you perform similar analyses of your own training and race sessions.