The design of nickelbase superalloys requires to tune the content of different phases with a composition of Ni3A, either to strengthen the alloy (γ′-phase, Ni3Al, γ′′-phase, Ni3Nb) or to influence its grain size and avoid embrittlement (δ-phase, Ni3Nb, η-phase, Ni3Ti). Here, we use a machine-learning-inspired approach to understand the influence of elemental properties on the energy of an alloying element in the respective phases. It is shown that the energy in γ′′, δ, and η can be fitted well using the Bader charge and the volume of the element in a nickel matrix. In the case of γ′, the small lattice mismatch requires a fit not involving the volume, but the bond order instead. We also show that the frequently used Md-parameter can be predicted from the properties of an element in a pure nickel matrix. Finally, the physical basis of the results is discussed in detail.