Let $U$ denote the uniform distribution on the $n$-ball of radius $1$.
What is the expected square-length of a vector under this distribution:


$$ \mathbf{E}\_U[\|x\|^2] $$


By standard concentration-of-measure results almost all the probability measure is on vectors of length at least $(1-\nu)$ for any constant $\nu>0$, hence one can bound the above by $[1-\nu,1]$ for any constant $\nu$.
However, is there a more precise statement?


More generally, let $U\_{\epsilon}$ denote the uniform distribution on the set of vectors $x = (x\_1,...,x\_n)$ with $\|x\|\leq 1$ for which $x\_1 \geq \epsilon$ ($\epsilon$ can depend on $n$).
What is the expected square length of such vectors $x$ (i.e. conditioned on being at least $\epsilon$ away from the equator)


$$ f(\epsilon) := \mathbf{E}\_{U\_{\epsilon}}[\|x\|^2] $$


Note that here, concentration of measure does not apply in general because for fixed $\epsilon$ almost all vectors are located around the "equator", i.e. $|x\_1| \leq \epsilon$, as $n \to \infty$.



The average length of vectors on spherical caps
For the first question:
$$
\mathbb{E}\_U(\|x\|^2)=\int\_0^1 {\rm prob}\,(\|x\|>t)dt=\int\_0^1 (1-t^n)dt=\frac{n}{n+1}.
$$


For the second question: integrating at first by $x\_1=x$ and using previous formula we get that expectation equals $$\frac{\int\_\varepsilon^1 (x^2+\frac{n-1}n(1-x^2))(1-x^2)^{(n-1)/2}dx}{\int\_\varepsilon^1 (1-x^2)^{(n-1)/2}dx}.$$
This ratio may be bounded from below by $\frac{n-1+\varepsilon^2}{n}$.


