A set of ordered points $(x_i, y_i)$ on the plane are connected end to end, and each iteration takes the midpoint and connects to form a new polygon.
$$ (x_{k,i}, y_{k,i})=\left( \frac{x_{k-1,i}+x_{k-1,i}}{2}, \frac{y_{k-1,i+1}+y_{k-1,i+1}}{2} \right) $$
The last point needs to be connected with the first point to calculate the midpoint:
$$ (x_{k,n}, y_{k,n})=\left( \frac{x_{k-1,n}+x_{k-1,n}}{2}, \frac{y_{k-1,1}+y_{k-1,1}}{2} \right) $$
After enough iterations, the graph approximates an ellipse.
How to find the semi-major and semi-minor axes $a, b$ and the rotate angle $\alpha$ of this ellipse?
Is it possible to calculate the shape parameters of the final ellipse directly from the initial points without iteration?
Update 1
A similar transformation is required to zoom in when drawing, to prevent the precision from being reduced to zero, the code is as follows.
next[{xs_, ys_}] := Block[
{x, y},
x = ListConvolve[{1 / 2, 1 / 2}, xs, -1];
y = ListConvolve[{1 / 2, 1 / 2}, ys, -1];
(* Move to origin, won't change the shape *)
x = x - Mean[x];
y = y - Mean[y];
(* Similarity transformation prevents exponential shrinking *)
{x, y} / Max[Max[x] - Min[x], Max[y] - Min[y]]
];
drawPoints[this_] := Graphics[
{
PointSize[0.02], Blue, Point /@ this,
Black, Line@Join[this, {First@this}]
},
PlotRange -> 0.6,
ImageSize -> {300, 300}
];
drawAnimation[points_, nests_] := Block[
{seq, w = 2, h = 1},
seq = Transpose /@ NestList[next, {RandomReal[{-w, w}, points], RandomReal[{-h, h}, points]}, nests];
drawPoints /@ Rest[seq] // ListAnimate
];
drawAnimation[25, 200]

In certain sense, there is no final ellipse. The apparent ellipse will contract for a factor $\cos\frac{\pi}{n}$ in each iteration. At the end, all vertices will converge to a single point, the original vertex centroid of polygon.
Choose a coordinate system where the original vertex centroid is origin. Let $u_0 \in \mathbb{C}^n$ be an $n \times 1$ column vector with entries $z_k = x_k + y_k i$ at $k^{th}$ row. After $t$ iteration, we will use $(z_k)_t$ and $u_t$ to denote the location of vertices and corresponding "u" vector.
In each iteration, the "$u$" at step $t$ and $t+1$ are related by a matrix equation ${}^{\color{blue}{[1]}}$ $$u_{t+1} = \Lambda u_t\quad\text{ where }\quad \Lambda_{jk} = \begin{cases} \frac12, & k - j \equiv 0, 1 \pmod n\\0,& \text{ otherwise }\end{cases}.$$
Start from a polygon with vertex centroid at origin and repeat apply $\Lambda$ to "$u$". After enough number of iterations, $u_t$ will be dominated by eigenvectors of $\Lambda$ which are not orthogonal to original $u_0$ whose eigenvalues are largest in magnitude.
The largest eigenvalue of $\Lambda$ is $1$. The corresponding eigenvector $v_1 \propto (1,1,\cdots)^T$. Since we start with vertex centroid at origin, the original $u_0$ is orthogonal to $v_1$ and $v_1$ will not affect the asymptotic behavior of the polygon.
The next two largest eigenvalues in magnitude are degenerate. They and corresponding eigenvectors have the form:
$$\lambda_{\pm} = \frac12(1 + \omega^{\pm 1}) \quad\longleftrightarrow\quad v_{\pm} = (1, \omega^{\pm 1}, \omega^{\pm 2}, \cdots, \omega^{\pm(n-1)})^T $$ where $\omega = e^{i\frac{2\pi}{n}}$.
If the vertices of the polygon are chosen randomly, then with probability $1$, the original $u_0$ will have non-zero projection on both $v_{\pm}$. So for large $t$, we have
$$u_t \sim A_{+} \lambda_{+}^t v_{+} + A_{-} \lambda_{-}^t v_{-}$$ for some coefficients $A_{\pm}$. Since $|\lambda_{+}| = |\lambda_{-}| = \cos\frac{\pi}{n}$, you will find the polygon contract for a factor $\cos\frac{\pi}{n}$ at each iteration.
With probability $1$ again, both $A_{\pm}$ will be non-zero. If you work out where the $z_k$ are positioned, you will find they lie on an ellipse. This explains why the polygon approaches an ellipse for large $t$.
To get the semi-major/semi-minor axes of the "ellipse" at large $t$, you need values of $A_{\pm}$. They are simply the "projection" of $u_0$ onto $v_{\pm}$. ie.
$$A_{\pm} = \frac1n \sum_{k=0}^{n-1} \omega^{\mp k} z_k $$
I will leave the actual computation of semi-major/semi-minor axes to you.
Note