  We study the stochastic $p$-Laplace system in a bounded domain. We propose
two new space-time discretizations based on the approximation of time-averaged
values. We establish linear convergence in space and $1/2$ convergence in time.
Additionally, we provide a sampling algorithm to construct the necessary random
input in an efficient way. The theoretical error analysis is complemented by
numerical experiments.
