Abstract The selection ratio (SR), i.e. the ratio of proportional use of a habitat over proportional availability of this habitat, has for long been the standard metric of habitat selection analyses. It is easy to compute and directly estimates disproportionate use. Its apparent restriction to habitat selection analyses using categorical predictors led to the development of the resource selection functions (RSF) approach, which has now become the norm. The RSF approach has however led to debates and confusion. For instance, what functional form can be used remains debated, and the concept of relative probability of selection is often misunderstood. I propose a reformulation of the SR demonstrating that it can be estimated in a regression context, and thus even with continuous predictors. This reformulation suggests that RSF can be seen as an intermediate step in the calculation of SR. This reformulation also clarifies some longstanding debates about RSF and data-selection/fitting practices. I further suggest that SR estimates the strength of habitat selection, but that the contribution of selection in determining use, which should be more directly linked to fitness than selection per se, should be estimated by another metric, the selection effect on use (SE). SE could be estimated simply as the difference between proportional use and proportional availability, and can be computed from SR and a density estimation of availability. I conduct a habitat selection analysis of plains zebras to demonstrate the added-value of going beyond RSF scores and using SR estimated in a regression context, and of combining SR and SE. Overall, I highlight the inter-relation between various metrics used to study habitat selection (i.e., SR, other selection indices, RSF scores, marginality). I conclude by proposing that SR and SE can be the unifying metrics of habitat selection, as together they offer a comprehensive view on the strength of habitat selection and its effect on habitat use.