  Numerical simulations in cosmology require trade-offs between volume,
resolution and run-time that limit the volume of the Universe that can be
simulated, leading to sample variance in predictions of ensemble-average
quantities such as the power spectrum or correlation function(s). Sample
variance is particularly acute at large scales, which is also where analytic
techniques can be highly reliable. This provides an opportunity to combine
analytic and numerical techniques in a principled way to improve the dynamic
range and reliability of predictions for clustering statistics. In this paper
we extend the technique of Zel'dovich control variates, previously demonstrated
for 2-point functions in real space, to reduce the sample variance in
measurements of 2-point statistics of biased tracers in redshift space. We
demonstrate that with this technique, we can reduce the sample variance of
these statistics down to their shot-noise limit out to $k \sim 0.2\, h\rm
Mpc^{-1}$. This allows a better matching with perturbative models and improved
predictions for the clustering of e.g.~quasars, galaxies and neutral Hydrogen
measured in spectroscopic redshift surveys at very modest computational
expense. We discuss the implementation of ZCV, give some examples and provide
forecasts for the efficacy of the method under various conditions.
