Four different ways to solve a data science problem - case study

Based on the theory of stochastic processes (Poisson processes) and the Erlang distribution, the estimated number of postings per time unit is indeed 2x the time since last posting. The theory will also give you the variance for this estimator (infinite) and will tell you that it's much more robust to use time to 2nd or 3rd or 4th previous posting, which have finite and known variances. Now if the group is inactive, the time to previous posting itself can be infinite, but in practice this is not an issue. Note that the Poisson assumption would be violated in this case. The theory will also suggest how to combine time to 2nd, time to 3rd and time to 4th previous posting to get a better estimator, read my paper Estimation of the Intensity of a Poisson process by means of neares... for details.