Jump3D-Net: Spatial Biomechanics-Driven 3D MotionTransformer for Personalized Jump Shot Optimizationin Elite Basketball

Abstract In the realm of computational sciences, the integration of biomechanics with advanced modeling techniques is pivotal forenhancing human performance analysis. Traditional approaches often rely on heuristic models that inadequately capture theintricate spatiotemporal dynamics and cognitive strategies inherent in athletic movements. These methods typically lack thegranularity required for personalized assessments, leading to generalized insights that may not effectively inform individualoptimization strategies. Addressing these limitations, we introduce a novel framework that synergizes spatial biomechanics withtransformer-based architectures to model and optimize complex motion patterns. Our methodology encompasses a three-tieredapproach: initially, we formalize the performance modeling as a high-dimensional, temporally dependent sequence predictionproblem, incorporating latent variables to encode both physical and cognitive components of behavior. Subsequently, weemploy a Trajectory-Aware Cognitive Embedding Network (TACEN) that integrates spatial encoding of movement, possessiondynamics, and team structures through a multi-stream neural system with memory-augmented attention mechanisms. Thisarchitecture adeptly disentangles overlapping patterns of strategic positioning and decision-making into modular embeddings.we implement an Adaptive Foresight (AFC) procedure that aligns model predictions with domain-specific performance metrics,embedding knowledge priors and evaluation heuristics derived from expert annotations and strategic game theory. Thiscalibration iteratively refines the predictive engine under various simulation regimes to extract latent performance capabilitiesand identify strategic inefficiencies. Experimental evaluations demonstrate that our approach significantly outperforms existingmodels in predictive accuracy and provides interpretable insights into individual performance optimization. This researchcontributes to the advancement of computational methodologies in human performance analysis, aligning with the journal’sfocus on innovative applications of computer science in interdisciplinary domains.