We introduce a novel extension of the volume-averaged correlation function (VACF) framework by replacing the traditional spherical smoothing kernels with anisotropic, ellipsoidal windows. This generalized approach enables the study of shape-dependent clustering statistics and captures directional information encoded in large-scale structure, particularly in redshift space where galaxy distribution is distorted by peculiar velocities. We define and compute ellipsoidal VACFs ${\overline{\xi }}_J(r_{\parallel },r_{\perp })$ and the corresponding reduced cumulants $s_J(r_{\parallel },r_{\perp })$, allowing for joint sensitivity to both scale and anisotropy across arbitrary statistical order $J$. Using a suite of comoving Lagrangian acceleration $N$-body simulations spanning a grid of cosmologies with varying ${\mathrm{\Omega }}_M$ and ${\sigma }_8$, we analyze the behavior of ellipsoidal VACFs and cumulants in both real and redshift space. We find that the shape of the smoothing kernel that maximizes the clustering signal depends strongly on the redshift-space distortion regime: spherical in real space, prolate in the fingers-of-god-dominated regime, and oblate in the Kaiser squashing-dominated regime. While the standard VACF amplitude is mainly sensitive to ${\sigma }_8$, the shape-dependence of redshift-space skewness shows a coherent response to the combined growth parameter $f{\sigma }_8$, with a typical sensitivity at the $1-3\sigma$ level between neighboring models. Our results demonstrate that ellipsoidal VACFs offer a computationally efficient and information-rich generalization of counts-in-cells analysis, with promising applications to galaxy survey data, halo catalogs, and cosmological tests of gravity beyond $\mathrm{\Lambda }$ cold dark matter.

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• Cosmological parameters
• Cosmology
• Dark matter
• Gravitation
• Large scale structure of the Universe