Algorithmic fairness provides novel methods for promoting equitable public policy using machine learning. Yet the narrow formulation of algorithmic fairness often provides cover for algorithms that exacerbate oppression, leading critics to call for a more justice-oriented approach. This article takes up these calls and proposes a method for operationalizing a social justice orientation into algorithmic fairness. First, I argue that algorithmic fairness suffers from a significant methodological limitation: it restricts analysis to isolated decision points. Because algorithmic fairness relies on this narrow scope of analysis, it yields a reform strategy that is fundamentally constrained by the "impossibility of fairness" (an incompatibility between mathematical definitions of fairness). Second, in light of these flaws, I draw on theories of substantive equality from law and philosophy to propose an alternative methodology: "substantive algorithmic fairness." Because substantive algorithmic fairness takes a more expansive scope to fairness, it suggests reform strategies that escape from the impossibility of fairness. These strategies provide a rigorous guide for employing algorithms to alleviate social injustice. In sum, substantive algorithmic fairness presents a new direction for the field of algorithmic fairness: away from formal mathematical models of "fairness" and toward substantive evaluations of how algorithms can (and cannot) promote justice.