For each , let be the relative rank of among , with the smallest value assigned rank one. Then . The independent relative ranks of a uniform random permutation can be proved directly: for any choices , insert the index at position in the list of previous indices ordered by their values. After all insertions, assign the values along that list. This is a unique permutation with those relative ranks, and conversely every permutation yields one such insertion sequence.
There are possible insertion sequences, all equally likely because the uniform random permutation is uniform. Thus
The ranks are independent and each is uniform on its possible values. It follows that the independent record indicators have
The first indicator is the constant one, which is also independent of the others. For , additivity of expectation and variance additivity for independent random variables give
Here is the harmonic number.
Let be the year index of the second record. For , independence gives
The empty product for is one. There is no second record in the first years with probability
This exposes a finite-horizon qualification in the final request: a permutation of length alone does not specify when a record outside those years will occur. Under the usual continuation by an infinite sequence of independent, identically distributed observations with a continuous distribution, the same relative-rank argument holds for every finite prefix. Then for every , and . These probabilities sum to one, so the second record occurs almost surely, but
The expected number of additional years after year one, , is also infinite. This is the first-sample case of the waiting time for a new record after a fixed sample.
If instead one asks for the mean year conditional on the second record occurring within the stated years, then, for ,
The mean additional wait under that conditioning is this value minus one. These are distinct questions: assigning whenever no second record occurs in the finite horizon also gives an infinite unconditional mean, whereas a censored observation has finite expectation by the tail-sum formula for expectation.