$\def\eqd{\stackrel{\text{def}}{=}}$
Let $\ \frac{X_t}{100}\ $ be the probability of your winning on the $\ t$-th try. Then you will have won on the $\ t$-th try if and only if $\ X_{t+1}=1\ $. Your expected number of wins after $100$ tries is therefore
$$
\sum_{t=1}^{100}\Bbb{P}\big(\text{You win on the $\ t$-th try}\big)=\sum_{t=1}^{100}\Bbb{P}\big(X_{t+1}=1\big)\ .
$$
Now $\ X_t\ $ is a discrete, homogeneous, $100$-state Markov chain, the entries of whose transition matrix are given by
\begin{align}
P_{pq}&=\Bbb{P}\big(X_{t+1}=q\,\big|\,X_t=p\big)\\
&=\cases{1-\frac{p}{100}&if $\ q=p+1$\\
\frac{p}{100}&if $\ q=1$\\
0&otherwise.}
\end{align}
If $\ \pi_t\ $ is the (column ) vector of probabilities of $\ X_t\ $ being in any given state:
$$
(\pi_t\big)_q\eqd\Bbb{P}\big(X_t=q\big)\ ,
$$
then
$$
(\pi_1)_q=\cases{1&if $\ q=1$\\
0&otherwise,}
$$
and
\begin{align}
\pi_{t+1}^T&=\pi_t^TP\tag{1}\label{1}\\
&=\pi_1P^t\ .
\end{align}
Your expected number of wins after $100$ tries is therefore given by
\begin{align}
\sum_{t=1}^{100}\Bbb{P}\big(X_{t+1}=1\big)&=\sum_{t=1}^{100}\big(\pi_{t+1}\big)_1\\
&=\sum_{t=1}^{100}\pi_{t+1}^T\pi_1\\
&=\sum_{t=1}^{100}\pi_1^TP^t\pi_1\ .
\end{align}
Because the matrix $\ P\ $ is sparse, with only two non-zero entries in every row, the recursion \eqref{1} can be used to calculate the vectors $\ \pi_{t+1}\ $ for $\ t=1,2,\dots,100\ $ quite quickly:
\begin{align}
(\pi_{t+1}^TP)_q&=\sum_{p=1}^{100}\big(\pi_t\big)_pP_{pq}\\
&=\cases{\sum_\limits{p=1}^{100}\frac{p\big(\pi_t\big)_p}{100}&if $\ q=1$\\
\frac{\pi_{q-1}(101-q)}{100}&if $\ 2\le q\le100$}\ .
\end{align}
The Magma script given below performs the calculations described above, and obtains exactly the same answer as Number Basher does with the Python script he gives in his answer. To run the script, copy and paste it into the online Magma calculator and click on the "submit" button.
nt:=100;
n:=100;
oldpi:= ZeroMatrix(Rationals(),1,nt);
newpi:= ZeroMatrix(Rationals(),1,nt);
oldpi[1,1]:=1;
expw:=0;
for t in [1..nt] do
for p in [1..n] do
newpi[1,1]:=newpi[1,1]+poldpi[1,p]/n;
end for;
for q in [2..n] do
newpi[1,q]:=oldpi[1,q-1](101-q)/n;
end for;
expw:=expw+newpi[1,1];
oldpi:=newpi;
newpi:= ZeroMatrix(Rationals(),1,nt);
end for;
print expw;