There seems to be an idea lurking in some corners that the coefficient of variation in wrapping together SD and mean is somehow superior to either. That is more than can be reasonably expected.
The CV has some merit when one expects, or is checking for, roughly the same relative variability in different datasets. Thus mouse weights and elephant weights might be worth comparing, or the heights of raspberry bushes and redwood trees.
Here I would want to see both means and SDs because I really don't expect age, height and weight to have the same relative variability, even as a first approximation Thus using some plausible values I just made up (use your own if you prefer) I expect a mean age loosely like 25 years, a mean weight loosely like 60 kg, a mean height loosely like 180 cm and (even without knowing much about any sport) I expect relative variability to go in the sequence you mentioned. Thus ages might go from about 16 to over 30 in many set-ups, so relative variability of age is plausibly highest here.
My own rule of thumb is that coefficients of variation being useful goes hand in hand with logarithmic scales being natural for analysis, which seems unlikely here. For much more discussion see How to interpret the coefficient of variation?