One knows from the Algorithmic Complexity Theory [2-5, 8, 14] that a word is incompressible on average. For words of pattern $x^m$, it is natural to believe that providing $x$ and $m$ is an optimal average representation. On the contrary, for words like $x ⊕ y$ (i.e., the bit to bit or between $x$ and $y$), providing $x$ and $y$ is not an optimal description on average. In this work, we sketch a theory of average optimal representation that formalizes natural ideas and operates where intuition does not suffice. First, we formulate a definition of K-optimality on average for a pattern, then demonstrate results that corroborate intuitive ideas, and give worthy insights into the best compression in more complex cases.