So you want to know the probability of getting all the faces at least once after rolling the die $n$ times. It is convenient to introduce the number $N_k$ of faces that have been seen after $k$ steps. Obviously, we have $N_1=1$. Also, $N_{k+1}=N_k$ with probability $\frac{N_k}{6}$ and $N_{k+1}=N_k+1$ otherwise -- in other words, the process $\{ N_k \}_{k \geq 1}$ is an Markov chain. One can thus easily compute the vector $V_k=(\mathbb{P}[N_k=1],\mathbb{P}[N_k=2], \ldots, \mathbb{P}[N_k=6])$ for $k=1,2, \ldots$ and solve the problem. One finds $V_{n+1} = V_0 \, A^{n}$ where $V_0=(1,0,\ldots,0)$ and $A$ is the transition matrix of the Markov chain:
$$A = \begin{pmatrix}
1/6 &5/6 &0 &0 &0 &0 \\
0 &2/6 &4/6 &0 &0 &0\\
0 &0 &3/6 &3/6 &0 &0 \\
0 &0 &0 &4/6 &2/6 &0 \\
0 &0 &0 &0 &5/6 &1/6\\
0 &0 &0 &0 &0 &1
\end{pmatrix}$$
To find $V_n$, diagonalize $A$ and then compute the powers. This gives
$$V_{n+1} = \frac{1}{6^{n+1}}\begin{pmatrix}1\\-5\\10\\-10\\5\\1\end{pmatrix}^{tr}
\begin{pmatrix}
6^n &0 &0 &0 &0 &0 \\
0 &5^n &0 &0 &0 &0 \\
0 &0 &4^n &0 &0 &0 \\
0 &0 &0 &3^n &0 &0 \\
0 &0 &0 &0 &2^n &0\\
0 &0 &0 &0 &0 &1
\end{pmatrix}
\begin{pmatrix}
0 & 0 & 0 & 0 & 0 & 1 \\
0 & 0 & 0 & 0 & -1 & 1 \\
0 & 0 & 0 & 1 & -2 & 1 \\
0 & 0 & -1 & 3 & -3 & 1 \\
0 & 1 & -4 & 6 & -4 & 1 \\
-1 & 5 & -10 & 10 & -5 & 1
\end{pmatrix}
$$
For example, after rolling a die 7 times, set $n=6$ in the preceding formula to get
$$V_7 = \begin{pmatrix}6,1890,36120,126000,100800,15120\end{pmatrix} / 6^7$$
From left to right, these are the chances of having observed exactly 1, 2, ..., through 6 faces. The chance of having seen all 6 faces is the last entry, $15120/6^7 = 35/648 \approx 0.054$. In general, the last entry of $V_{n+1}$ equals
$$\Pr[\text{All faces seen after } n+1 \text{ throws}] = 1-5\ 2^{2-n}+5\ 3^{1-n}(1+2^n)-6^{1-n}(1+5^n).$$
Because a "completely analytical approach" has been requested, here is an exact solution. It also provides an alternative approach to solving the question at Probability to draw a black ball in a set of black and white balls with mixed replacement conditions.
The number of moves in the game, $X$, can be modeled as the sum of six independent realizations of Geometric$(p)$ variables with probabilities $p=1, 5/6, 4/6, 3/6, 2/6, 1/6$, each of them shifted by $1$ (because a geometric variable counts only the rolls preceding a success and we must also count the rolls on which successes were observed). By computing with the geometric distribution, we will therefore obtain answers that are $6$ less than the desired ones and therefore must be sure to add $6$ back at the end.
The probability generating function (pgf) of such a geometric variable with parameter $p$ is
$$f(z, p) = \frac{p}{1-(1-p)z}.$$
Therefore the pgf for the sum of these six variables is
$$g(z) = \prod_{i=1}^6 f(z, i/6) = 6^{-z-4} \left(-5\ 2^{z+5}+10\ 3^{z+4}-5\ 4^{z+4}+5^{z+4}+5\right).$$
(The product can be computed in this closed form by separating it into five terms via partial fractions.)
The cumulative distribution function (CDF) is obtained from the partial sums of $g$ (as a power series in $z$), which amounts to summing geometric series, and is given by
$$F(z) = 6^{-z-4} \left(-(1)\ 1^{z+4} + (5)\ 2^{z+4}-(10)\
3^{z+4}+(10)\ 4^{z+4}-(5)\ 5^{z+4}+(1)\ 6^{z+4}\right).$$
(I have written this expression in a form that suggests an alternate derivation via the Principle of Inclusion-Exclusion.)
From this we obtain the expected number of moves in the game (answering the first question) as
$$\mathbb{E}(6+X) = 6+\sum_{i=1}^\infty \left(1-F(i)\right) = \frac{147}{10}.$$
The CDF of the maximum of $m$ independent versions of $X$ is $F(z)^m$ (and from this we can, in principle, answer any probability questions about the maximum we like, such as what is its variance, what is its 99th percentile, and so on). With $m=4$ we obtain an expectation of
$$ 6+\sum_{i=1}^\infty \left(1-F(i)^4\right) \approx 21.4820363\ldots.$$
(The value is a rational fraction which, in reduced form, has a 71-digit denominator.) The standard deviation is $6.77108\ldots.$ Here is a plot of the probability mass function of the maximum for four players (it has been shifted by $6$ already):
As one would expect, it is positively skewed. The mode is at $18$ rolls. It is rare that the last person to finish will take more than $50$ rolls (it is about $0.3\%$).
Best Answer
Just solve it using algebra:
\begin{aligned} P(W) &= \tfrac 2 6 + \tfrac 1 6 \cdot P(W) \\[7pt] \tfrac 5 6 \cdot P(W) &= \tfrac 2 6 \\[7pt] P(W) &= \tfrac 2 5. \end{aligned}