A Rolling-Horizon Stochastic Optimization Framework for NBA Franchise Management with Distributionally Robust Risk Constraints

TL;DR

This paper presents a comprehensive stochastic optimization framework for NBA franchise management, integrating multiple decision modules to optimize long-term value while controlling downside risk under uncertainty.

Contribution

It introduces a novel rolling-horizon stochastic mixed-integer program with distributionally robust and CVaR constraints for NBA franchise management.

Findings

Framework successfully models franchise decision interactions.

Robust optimization reduces downside risk effectively.

Case study on New York Knicks demonstrates practical applicability.

Abstract

NBA franchise management is not a sequence of independent tasks, but a single dynamic control problem in which roster construction, cash-flow discipline, media strategy, external market shocks, and player-health uncertainty interact over time. Using the New York Knicks as a case study, this paper develops a unified decision architecture for franchise management under competitive, financial, and regulatory constraints. The core layer is formulated as a rolling-horizon stochastic mixed-integer program augmented with distributionally robust optimization and conditional value-at-risk constraints, so that long-run franchise value can be optimized while downside exposure remains explicitly controlled. On top of this core layer, we construct coordinated modules for transaction execution, league-expansion shock transmission, media-rights regime transition, and injury-triggered re-optimization. This integrated design reframes multiple managerial mechanisms inside one research problem: how should an NBA franchise allocate resources and update decisions when performance objectives and commercial objectives are jointly determined under uncertainty? The manuscript is organized around problem formulation, model architecture, empirical validation, robustness analysis, and managerial interpretation.