Modeling screen-based tactical interaction in basketball

Abstract

Modern basketball coaching increasingly relies on quantitative tools to support tactical decision-making and individualized training. However, the majority of methods used to analyze and teach screen-based interactions remain descriptive and subjective, limiting their applicability for objective evaluation and optimization. The purpose of this study was to develop and experimentally validate a continuous-time dynamical model describing the tactical interaction of three agents involved in a basketball screen: the screener, the defender, and the ballhandler. The proposed model represents the redistribution of tactical effectiveness through a system of coupled differential equations governed by three interpretable parameters characterizing reaction speed, screen quality, and offensive decision-making. Experimental data obtained from semi-professional players using positional tracking were used to estimate model parameters and assess sensitivity. The results demonstrate that the proposed approach allows objective quantification of screen effectiveness, identification of dominant tactical factors, and formulation of individualized training recommendations. The model bridges the gap between qualitative coaching knowledge and quantitative sports analytics, offering a practical tool for applied basketball pedagogy. Introduction. The optimization of training processes in sports remains a key challenge due to the complexity of interactions between physiological, biomechanical, and environmental factors. Existing approaches often fail to ensure performance stability under variable training loads. This study proposes a structured approach based on adaptive regulation principles to improve training efficiency and performance consistency. Materials and Methods. The study involved n = 12 athletes and was conducted using an experimental design with controlled training interventions. Biomechanical parameters and performance indicators were recorded throughout the study period. Statistical analysis included descriptive statistics, confidence intervals, and effect size calculations (Cohen’s d) to assess both statistical significance and practical relevance. Results. The implementation of the proposed model resulted in significant improvements in key performance indicators, including increasing the number of team victories in training tournaments. The observed changes were supported by moderate to large effect sizes, indicating meaningful practical impact. Additionally, a reduction in performance variability was observed under load conditions. Discussion. The findings demonstrate that integrating adaptive control mechanisms into training systems enhances performance stability. Compared to conventional methods, the proposed approach provides better regulation of training loads and improved responsiveness to athlete-specific conditions. The results support and extend current theories in sports training and motor control. Conclusions. The proposed model is effective for optimizing training processes and improving performance stability. It can be applied in practical coaching settings to enhance training outcomes and reduce the risk of overload.