[EDITED mostly to report on the answer by **Kevin Costello**
(and to improve the **gp** code at the end)]

I thank Nicolas Dupont for the following question (and for permission to disseminate it further):

I have a playlist with, say, $N$ pieces of music. While using the shuffle option (each such piece is played randomly at each step), I realized that, generally speaking, I have to hear quite a lot of times the same piece before the last one appears. It makes me think of the following question:

At the moment the last non already heard piece is played, what is the max, in average, of number of times the same piece has already been played?

I have not previously enountered this variant of the coupon collector's problem. If it is new, then (thinking of the original real-world context origin of the problem) I propose to call it the "coupon collector's earworm".

Is this in fact a new question? If not, what is known about it already?

Let us call this expected value $M_N$. Nicolas observes that $M_1=1$ and $M_2=2$ (see below), and asks:

I doubt there is such an easy formula for general $N$ - would you be able to find some information on it, e.g. its asymptotic behavior?

[Nicely *answered* by **Kevin Costello**:
$M_N$ is asymptotic to $e \log N$ as $N \rightarrow \infty$.
Moreover, the maximal multiplicity is within $o(\log N)$ of $e \log N$
with probability $\rightarrow 1$. I don't recall any other instance
of a naturally-arising asymptotic growth rate of $e \log N$...]

Recall that the standard coupon collector's problem asks for the expected value of the total number of shuffles until each piece has appeared at least once. It is well known that the answer is $N H_N$ where $H_N = \sum_{i=1}^N 1/i$ is the $N$-th harmonic number. Hence the expected value of the average number of plays per track is $H_N$, which grows as $\log N + O(1)$. The expected maximum value $M_N$ must be at least as large $-$ in fact larger once $N>1$, because one track is heard only once, so the average over the others is $(N H_N-1) / (N-1)$. One might guess that $M_N$ is not that much larger, because typically it's only the last few tracks that take most of shuffles to appear. But it doesn't seem that easy to get at the difference between the expected maximum and the expected average, even asymptotically. Unlike the standard coupon collector's problem, here an exact answer seems hopeless once $N$ is at all large (see below), so I ask:

How does the difference $M_N - H_N$ behave asymptotically as $N \rightarrow \infty$?

[*Looks like this was a red herring*: "One might guess" plausibly that
$M_N - H_N$ is dwarfed by $H_N$ for large $N$, but by **Kevin Costello**'s
answer $M_N - H_N$ asymptotically exceeds $H_N$ by a factor $e - 1$,
and that factor is more complicated than
$\lim_{N\rightarrow\infty} M_N/H_N = e$,
so analyzing the difference $M_N-H_N$ is likely not a fruitful approach.]

Here are the few other things I know about this:

@ For each $N>1$, the expected value of the maximum count is given by the convergent $(N-1)$-fold sum $$ M_N = \sum_{a_1,\ldots,a_{N-1} \geq 1} N^{-\!\sum_i \! a_i} \Bigl(\sum_i a_i\Bigr)! \frac{\max_i a_i}{\prod_i a_i!}. $$ Indeed, we may assume without loss of generality that the $N$-th track is heard last; conditional on this assumption, the probability that the $i$-th track will be heard $a_i$ times for each $i<N$ is $N^{-\!\sum_i \! a_i}$ times the multinomial coefficient $(\sum_i a_i)! \big/ \prod_i a_i!$. Numerically, these values are $$ 2.00000, \quad 2.84610+, \quad 3.49914-, \quad 4.02595\!- $$ for $N=2,3,4,5$.

@ A closed form for $M_N$ is available for $N \leq 3$ and probably not beyond. Trivially $M_1 = 1$; and N.Dupont already obtained the value $M_2 = 2$ by evaluating $M_2 = \sum_{a \geq 1} a/2^a$. But for $N=1$ and $N=2$ the problem reduces to the classical coupon collector's problem. Already for $N=3$ we have a surprise: $M_3 = 3/2 + (3/\sqrt{5})$, which has an elementary but somewhat tricky proof. For $N=4$, I get $$ M_4 = \frac73 - \sqrt{3} + \frac4\pi \int_{x_0}^\infty \frac{(2x+1) \, dx}{(x-1) \sqrt{4x^3-(4x-1)^2}} $$ where $x_0 = 3.43968\!+$ is the largest of the roots (all real) of the cubic $4x^3-(4x-1)^2$. I don't expect this to simplify further: the integral is the period over an elliptic curve of a differential with two simple poles that do not differ by a torsion point. In general one can reduce the $(N-1)$-fold sum to an $(N-2)$-fold one (which is one route to the value of $M_3$ and $M_4$), or to an $(N-3)$-fold integral, but probably not beyond.

@ It's not too hard to simulate this process even for considerably larger $N$.
In **GP** one can get a single sample of the distribution from the code

```
try(N) = v=vector(N); while(!vecmin(v),v[random(N)+1]++); vecmax(v)
```

[*turns out that one doesn't need to call* **vecmin** *each turn*:

```
try(N, m,i)= v=vector(N); m=N; while(m, i=random(N)+1; v[i]++; if(v[i]==1,m--)); vecmax(v)
```

*does the same thing in $\rho+O(1)$ operations per shuffle rather then
$\rho+O(N)$, where $\rho$ is the cost of one* **random(N)** *call.*]

So for example

```
sum(n=1,10^4,try(100)) / 10000.
```

averages 1000 samples for $M_{100}$; this takes a few seconds, and seems to give about $11.7$.

The Art Of Computer Programming? That's the first place I'd look (and in particular, in the relevant exercises) for a result... $\endgroup$The Art Of Computer Programming; that's a good idea, but I cannot locate the problem in either the main text or the exercises. Were you able to find it there? $\endgroup$