Measured vs. Subjective Workout Data
Collecting information from workouts and competitions is at the heart of athletic performance: afterall, it’s the comparison of this data over time that helps refine goals, direct training and track changes in fitness.
The humble stopwatch has evolved into today’s GPS-enabled fitness trackers that measure and report a variety of athletic-performance information. That information falls into two categories (objective metrics and subjective metrics), and it’s important to understand how they differ.
Objective metrics are measured using commonly-accepted standards. Distance, power output, and heart rate are objectively measured during a ride. Other metrics like VO₂ max and lactate threshold are measured in a laboratory setting. The same measurement taken by different people produces the same value, repeatably.
Subjective metrics are those that involve an element of human judgment: they are observable, but not quantifiable with a common standard, and have a degree of variability in reporting. The same workout can feel “easy” one day and “hard” on another day. Seven hours of sleep may be “enough” for one athlete, but “not enough” for another. Sensations of fatigue, mood status, and stress levels are self-reported and subject to an athlete’s perception and biases, but have no universal scale of measurement.
After upload, workout data is inputted to computer models and algorithms, and this is where the objective and subjective intersect. Developed by data scientists, these models analyze objective data and report back metrics like “Body Battery,” fitness level, acclimation to heat or altitude, and others. Large sets of objective data help power these models, but we can’t discount the influence of human judgment involved in their creation. In other words, these models use measured data to report back subjective metrics.
Data review is an important part of the training process, whether on the scale of one ride or years’ worth of workouts. Continue to record and upload your workouts, but be sure to know what data has been objectively measured, and what metrics are the results of an algorithm.
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