NeuroPlayNet: a multimodal AI framework for real-time cognitive-aware strategy optimization in professional basketball

The future of professional basketball demands intelligent decision-making systems that transcend traditional performance analytics to enable real-time, context-aware strategy generation. This paper introduces NeuroPlayNet, a novel multimodal artificial intelligence framework that integrates biomechanical sensing, computer vision, contextual game state modeling, and cognitive workload estimation to optimize both individual player performance and team strategy dynamics. Unlike existing systems that rely predominantly on post-game statistical analysis, NeuroPlayNet leverages a fusion architecture combining real-time player kinematics from wearable inertial measurement units, court-wide visual tracking via multi-camera deep vision transformers, historical playstyle knowledge graphs, and neuro-cognitive indicators derived from electroencephalography-based fatigue and stress markers. The core innovation lies in a neuro-symbolic reinforcement learning engine that dynamically balances short-term tactical adjustments with long-term performance optimization objectives. Experimental validation conducted on simulated NBA game environments enriched with real-world broadcast datasets demonstrates superior performance in shot success prediction (12.6% improvement), fatigue-aware substitution timing (18.9% reduction in injury risk), and win probability forecasting (9.4% enhancement) compared to state-of-the-art baseline methods. The proposed framework establishes a new paradigm for human-AI collaborative basketball strategy design, contributing to more intelligent, sustainable, and injury-aware professional sports ecosystems. Keywords: Artificial intelligence, basketball analytics, multimodal fusion, cognitive computing, reinforcement learning, sports technology, real-time optimization.