Data Fit - Comparing Heart Rates

Seven rowers, seven barbells, and seven athletes all neatly aligned. Each athlete getting situated, waiting to beat the clock. The competition was fierce and the time to beat was ten minutes. Looking down the row I could see my competition. Everyone pausing to slow down their heart rate in anticipation of the impending amount of work to be completed!

If someone was to write a dramatic interpretation of our workouts it would probably sound something like the above paragraph. We had just finished the bulk of our hour-long crossfit class and were getting ready for, what seemed like, a fun but challenging workout. A friend of mine, Daniel Garo, was in the heat with me and was wearing a heart rate monitor as well! This was the perfect setup to analyze the performance of two substantially different athletes completing the same workout with similar times.
By analyzing both of our workouts, we were able to differentiate our backgrounds, abilities, and performance to look at what really makes a difference in a workout. All measurements that are hard to identify when looking at individual performance.

WOD - Sundown Crossfit
4 Rounds for time
15 Calorie Row
10 Front Squats (135/95lbs)
8 Chest to Bar Pull Ups
6 Shoulder to Overhead

In order to properly analyze our individual performances, I tracked some statistics that helped determine each of our strengths and weaknesses. To incorporate crossfit style workouts, I used 16.1 and Fran as benchmarks because they are similar to the WOD that we did, while different in terms of time domains (4 min vs 20 min) I also had Daniel and Myself rate our abilities on a scale of 1-10 in 5 crossfit applicable categories: Gymnastics, Weightlifting, Mobility, Cardio, and Stamina (*Note, Daniel is always blue and Andrew is always Orange for the rest of this analysis)

Comparison of PR's

Comparison of PR's

Skill Comparison

Skill Comparison

After looking at the numbers, it is clear that Daniel and I have very different strengths and weaknesses. Daniel is much stronger than me, boasting a 12.5% and 28% advantage in the OHS and front squat respectively. His cardio is pretty decent as well (6/10.) This is a significant advantage on Fran because the time domain is low and thrusters can break a weaker athlete. My strengths, on the other hand, were endurance and running (I replaced the rowing section of the workout with running because we didn't have rowing prs.) If you look at 16.1, you can see that both of our advantages even out at certain time domains. Although, mine looks worse because the workout is supposed to be ten minutes and I completed it in twenty minutes, limiting my ability to get a second wind.

But we have looked at the basics enough, lets see what happened during the whole class. I compiled both Daniel and myself's heart rate monitor data and aligned the two so that the times were matching.

Heart Rate Over Full Class

Heart Rate Over Full Class

I compiled the data from both of our heart monitors and aligned them to match the times. It was a tight match, but in the end Daniel held on longer and was able to take the workout by 30+ Seconds. Daniel finished in 9:32 and I finished in 10:06 Now that we have all of this great data, let’s see if we can figure out how Daniel beat me. The first thing we should look at is our efficiency during the workout. To calculate efficiency, I used total heart beats and workout duration as variables.

(Beats / Minute) * Total Minutes = Total Heart Beats
Daniels Total Heart Beats | t=2121 to t=2733 = 1452 Heart Beats
Andrews Total Heart Beats | t=2121 to t=2767 = 1210 Heart Beats
(1452 - 1210) / 1452 = 16.66% More Efficient (242 Beats Less)

Looking at the calculations, you can see that I (Andrew) was more efficient, so why did Daniel win? One possibility could be that he paced his workout better.

To mathematically determine whether someone paced a workout better is a bit complicated. First, we should look at what an “optimal” pace would be. Although this “optimal” pace would be different for every athlete, the “smoothness” of the heart rate curve should show us if the athlete was working at a constant pace. To derive this I determined the logarithmic regression line (line of best fit) for the WOD so that the line was as close as possible to the athlete’s data. Then I used a statistical analysis variable called S, the Standard Error of regression. This variable tells me how far away each data point is from the curve. Basically,(or “In other words,” the closer each of us is to the perfect line, the lower the S value.

Heart Rate vs. Perfect Pace Line

Heart Rate vs. Perfect Pace Line


If you look at the two graphs, it is apparent that my line was significantly closer to the “perfect fit” line, but let’s do the calculation for the sake of Science.

The formula for standard error of estimates is given below

Working this out for each athlete
S_Andrew = 3.26
S_Daniel = 6.86

After doing this calculation, we find that I had a "better" (more smooth) pace throughout the workout. But I was more efficient (by 16.6%) and held a better pace (more than double S value), so what made Daniel win? Looking at his heart rate, he entered a red line zone three times and was able to recover each time. This could be due to holding a movement (most likely front squats) longer than I did.

Collecting more data points on when we finished different sections of the workout could help us understand muscular endurance factors involved (lactate threshhold and VO2 Max comparisons). But, maybe he just wanted it more. The main takeaway is that if you want it more there is always a way to win.

My personal takeaway is that is to stop whining during the workout because there is someone out there going harder than me.

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