Synergistic Optimization of Competitive Performance and Commercial Profitability in Professional Women's Basketball

Abstract

Addressing the operational challenge faced by professional women's basketball clubs in balancing competitive performance and commercial profitability under salary cap constraints, this study constructs an integrated multi-model dynamic decision-making system. Using the Connecticut Sun as a case study, the research first employs the Entropy Weighted Topix method to establish a player evaluation system encompassing competitive contribution, commercial value, and dynamic risk factors, enabling precise stratification of player value. Subsequently, a BP neural network is utilized for nonlinear forecasting of ticket, sponsorship, and merchandise revenues, combined with a Markov model to simulate the cross-seasonal evolution logic of team status. The core process employs multi-objective optimization using the NSGA-II algorithm to identify Pareto-optimal operational solutions, ensuring synergistic enhancement of competitive strength and economic returns while maintaining a 90.01% salary cap utilization rate. Addressing external uncertainties, the study quantifies potential financial risks from key player injuries and macroeconomic fluctuations via Monte Carlo simulations, while graph theory analyzes the win rate enhancement effects of team collaboration networks. Empirical results indicate that retaining the current 12 players yields an annual net profit of approximately $2.29 million and a projected 60% win rate under the baseline scenario. Through data-driven modeling, this study provides robust quantitative recommendations for resource allocation in professional sports.