For player $A$, we have the following probability, for each game:
$$ P(Win) = p_W = \frac{1}{2}$$
$$ P(Draw) = p_D = \frac{1}{6}$$
$$ P(Loose) = p_L = \frac{1}{3}$$
There is a way to address this problem by considering states.
For example, at a given time, if the last game was a draw (D), then the two players are in the same situation, independently of the previous games, assuming no one as gained before. This corresponds to a neutral state. This neutral state is also attained at the start of the tournament.
We will call :
- $P_0$: the probability of the neutral state
- $P[W]$ the probability of the state attained when $A$ has gained the last game, but not the previous one
- $P[WW]$ the probability of the state corresponding to a final victory for $A$, i.e. he gained the last two games
- $P[L]$ the probability of the state attained when $A$ has lost the last game, but not the previous one
- $P[LL]$ the probability of the state corresponding to a final loss for $A$, i.e. he lost the last two games
Then, it is straightforward to get the following relations:
$$P_0(t=0) = 1$$
$$P[W](t=0) = P[L](t=0) = P[WW](t=0) = P[LL](t=0) =0$$
$$P_0(t+1) = 1 + p_d(P_0 + P[W] + P[L])(t) $$
$$P[L](t+1) = p_L (P_0(t) + P[W])(t) $$
$$P[W](t+1) = p_W (P_0 + P[L])(t) $$
$$P[WW](t+1) = p_W P[W](t)$$
$$P[LL](t+1) = p_L P[L](t)$$
Finally, the global probability $P[WW]$ is obtained by summing all $P[WW](t)$ over (t).
At this point, much work is still needed to get $P[WW]$, but the problem has been simplified.
Note: a figure representing the states and their different relations help understanding the method and the relations above. This figure is relatively easy to draw by hand, I encourage you to do it.
There is sometimes the possibility to address this kind of problem by defining polynomials representing the status of a state at all time, for example:
$$P_0(X) = 1 + P_0(1) X + P_0(2) X^2 + P_0(3) X^3 + \dots$$
and to translate the previous relations in terms of polynomials.
For example:
$$P_0(X) = 1 + p_D\,X(P_0(X) + P[W](X) + P[L](X)) $$
In this case, at the end, $P[Final Win] = P[WW](X=1)$
I will let you explore it if you want.