What is the easiest (preferably without calculations) way to see that the mean value of $\max(x\_1,x\_2,\dots,x\_n)$ on the sphere $\mathbb{S}^{d-1}= \{ (x\_1,\dots,x\_n):\ x\_1^2+\dots+x\_n^2=1 \}$ behaves like $\sqrt{\log(n)/n}$, or at least that is is much more then $1/\sqrt{n}$ for large $n$? The same (and less or more a priori equivalent) question concerns the standard Gaussian measure and expectation of $\infty$-norm w.r.t. it.


The proofs I know (for example, the one which V. Milman attributes to Figiel) use too many integrals. 


And by the way, how to put {,} in math here? \ { does not work for me



maximal coordinate on a sphere
For some positive $c$ bounded away from zero, the probability that a standard gaussian variable is larger than $c\sqrt{\log n}$ is $1/n$. It follows that the probability that at least one variable out of $n$ independent standard gaussians is larger than $c\sqrt{\log n}$ is $1-(1-1/n)^n$ which tends to $1-1/e$. From that one gets that the expectation of the maximum of $n$ standard gaussians is at least (basically) $(1-1/e)c\sqrt{\log n}$. 
As you said the question about variables on the sphere is equivalent to that.


