When you pick randomly and independently from three random variables $A$, $B$, and $C$, having (cumulative) distributions $F_A$, $F_B$, and $F_C$ and corresponding distribution functions $f_A$, $f_B$, and $f_C$, then by definition of independence the chance that all three numbers are less than some value $x$ equals
$$\Pr(\max(A,B,C) \le x) = F_A(x)F_B(x)F_C(x).$$
Differentiation with respect to $x$ (via the product rule) gives the PDF of the max as
$$f_{\max(A,B,C)} = f_A(x)F_B(x)F_C(x) + F_A(x)f_B(x)F_C(x) + F_A(x)F_B(x)f_C(x).$$
That sure looks like a decomposition corresponding to $A$, $B$, and $C$. Indeed, the first term (by definition) tells us that the chance that $x \lt A \le x+dx$ and $B \le x$ and $C \le x$ is $f_A(x)F_B(x)F_C(x)dx$. Integrating that over all $x$ would then give the chance that $A$ exceeds both $B$ and $C$.
In the present situation, $\int_\mathbb{R} f_A(x)F_B(x)F_C(x)dx = 17/8910 \approx .00191,$ for instance.
The formula clearly generalizes to any finite number of independent random variables having any distributions you please.