Problem
Research questions and friction points this paper is trying to address.
NBA franchise management
stochastic optimization
distributionally robust optimization
risk constraints
dynamic decision-making
A Rolling-Horizon Stochastic Optimization Framework for NBA Franchise Management with Distributionally Robust Risk Constraints
🤖 AI Summary
This study addresses the dynamic decision-making challenge faced by NBA teams in simultaneously optimizing competitive performance and commercial objectives under multiple sources of uncertainty. The authors propose a rolling-horizon stochastic optimization framework that unifies multidimensional, interdependent factors—including roster construction, cash flow management, media strategy, and injury risk—into a dynamic control problem subject to competitive, financial, and regulatory constraints. For the first time in this context, distributionally robust optimization and conditional value-at-risk (CVaR) constraints are integrated to maximize long-term value while controlling downside risk. A modular coordination mechanism incorporates real-world complexities such as trade execution, league expansion shocks, and media rights transitions. Validated through a case study of the New York Knicks, the framework demonstrates significant efficacy in systematically optimizing resource allocation and enabling real-time decision updates that balance athletic and commercial goals.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
rolling-horizon stochastic optimization
distributionally robust optimization
conditional value-at-risk
mixed-integer programming
dynamic franchise management
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