Central California is littered with great riding terrain. Since I’ve been in San Luis Obispo for the past few months, I’ve explored lots of areas and decided to focus on a small portion of a route I’ve done a few times in the last two months. One of the well-known roads leaving San Luis Obispo and heading north toward Santa Margarita and Atascadero is called “The Grade,” which refers to Cuesta Grade. Climbing the Grade requires riding on Highway 101 alongside semis and other motorized vehicles up a 7% grade. The climb lasts several miles. After my more recent ride up it on Memorial Day, I decided to look back at some of my older data for comparison. It’s good to check in and measure our progress.

I wanted to compare my data in several different formats; the first two with distance as the set parameter and the third with time as the set parameter. Although each graph depicts the same two rides on my same Scott Contessa bike, each data set looks somewhat different. I think it’s a good demonstration of how only focusing on one or two numbers while examining your own data can lead to falsehoods or perhaps misconceptions. It’s good to play with data once in a while to gain new perspectives and interpretations.

Graph 1 – Set Distance for 3 Miles
Location	The Grade (7% climb)
Date	4/05/08	5/26/08
Duration	20:52	15:24
Distance (miles)	3	3
Avg Speed (mph)	8.64	11.72
Avg HR (beats/min)	163	163
Max HR (beats/min)	170	180
Avg Power (watts)	221	209
Max Power (watts)	381	320
Work (kilojoules)	278	192
Avg Cadence	65	88
10 min Peak Power (watts)	233	233
5 min Peak Power (watts)	246	246
1 min Peak Power (watts)	282	262

In this first graph I set the climbing distance at 3 miles, ending my measurement at the very top of the grade (1522 ft). This was easy to see on my graph because the decent thereafter is pretty big with a much higher speed. I think there are several interesting things to point out. First, my times were extremely different. Normally some external factors could play a role in this such as wind, cold, heat, etc., but from my memory these variables were not too extreme. Therefore, I looked through the data more to find another explanation. My power data was almost exact, except the April average was slightly higher due to a couple of higher peak power values in the set. The biggest difference was my cadence. An average of 88 rpm compared to 65 rpm! With this huge difference in cadence I was able to do less work, maintain a similar power output and save over 5 minutes on the same 3 mile climb as I pedaled over 3 mph faster.

In this next graph I plotted the data for the final mile of the climb.

Graph 2 – Set Distance for 1 Mile
Location	The Grade (7% climb)
Date	4/05/08	5/26/08
Duration	7:59	6:28
Distance (miles)	1	1
Avg Speed (mph)	7.58	9.29
Avg HR (beats/min)	167	175
Max HR (beats/min)	170	180
Avg Power (watts)	227	235
Max Power (watts)	278	320
Work (kilojoules)	109	90
Avg Cadence	62	76
5 min Peak Power (watts)	230	235
1 min Peak Power (watts)	242	262

Similar to the first graph, my cadence and speed were significantly higher in May and my time was much faster. Although the power values are slightly different, they are still within a reasonably similar range. My maximum and average heart rate values were higher in May, which implies that I had better fitness and was able to maintain and achieve those higher values. Keep in mind that increased heart rate values can also be affected by heat, caffeine, and dehydration.

Graph 3 – Set Duration for 9 Minutes
Location	The Grade (7% climb)
Date	4/05/08	5/26/08
Duration (min)	9	9
Distance (miles)	1.13	1.42
Avg Speed (mph)	7.62	9.51
Avg HR (beats/min)	167	173
Max HR (beats/min)	170	180
Avg Power (watts)	226	237
Max Power (watts)	278	320
Work (kilojoules)	121	127
Avg Cadence	62	78
5 min Peak Power (watts)	230	246
1 min Peak Power (watts)	242	262

For this last graph, I examined the last 9 minutes of the climb. I wanted to put this in to show how focusing on distance versus duration restructures the data. The basics of the activities are similar to the first two graphs, except the power values show a greater discrepancy. Looking at my 5 min and 1 min peak power values and comparing them between graphs, it is apparent that on my second ride I continued to get stronger toward the end of the ride whereas with the first ride my power dropped a bit over time.

Sometimes I find that after downloading my data from a ride I focus on the given average and peak power values, which is what I did when I first started comparing these two rides. When I realized my May average power output was lower than April I was annoyed. However, this does not paint the whole picture of the day’s efforts (or even this short climb). If I focus on raising my average power during long rides, I have to consider factors such as slowing at lights, sitting in a pack, weather, nutrition, and more and should therefore look at the bigger picture. Since this climb was more of a time trial effort without slowing, stops or drafting, those values are more reflective of my effort than the average for a four hour ride. By breaking down the graphs into smaller segments and analyzing it more closely, I can learn more about myself over the duration of a ride and reveal areas that I need to focus on (i.e. cadence, my power outputs for different durations, etc.). Although it takes extra time, I think it’s good to examine the nuances of rides occasionally to find some weak spots or just to see how I am progressing over time. Keep enjoying the ride and thanks for reading!