There are two sides to this question: the mathematical aspect, and the causal (inferential) aspect.
In the mathematical sense, a dependent variable y is a function f of an independent variable x: $y = f(x), f \subset X \times Y$. In this sense, the set X is the set of all players, and the set Y is the set of home run rates. The function f connects the chosen player and his (or her) home run rate. However, this relation can be inversed by use of the inverse function $f^{-1}$, in the sense of $y=f(x) \Leftrightarrow x = f^{-1}(y)$
However, I understand your question as pertaining to the inferential structure. In terms of your question, what depends on what? Clearly, the home run rate depends on the player, because it is a function of some characteristics of the player - his speed, striking prowess, etc. In that sense, the data itself is imbued by a causal structure: if the player changed - say, went into heavy training or used enhancing drugs - he or she could improve performance.
I hope this goes somewhere in answering your question.