Using Genetic Algorithm to Optimize the Training Plan and Game Strategy of Basketball Players

Abstract

Basketball teams rely heavily on the effectiveness of their practice regimens and game plans to succeed. The intricacy of the game and the range of skills players must possess make it extremely difficult to develop effective training plans and tactical techniques. The use of genetic algorithms (GAs) as a cutting-edge technique to enhance basketball players’ practice regimens and game strategies is investigated in this work. Inspired by biology and natural selection, genetic algorithms provide a potent optimization method that, via the repeated steps of evolution, can find close to ideal solutions to challenging issues. As chromosomes made up of genes relating to different parameters and tactics, prospective training plans and game tactics are represented by GAs, which enable them to efficiently search across the large solution space and find combinations that optimize desired results. As part of the research approach, the goals and limitations of the optimization issue are defined. Fitness functions are intended to assess each potential solution’s efficacy, directing development toward better solutions across a series of iterations. This study shows how effective genetic algorithms are in optimizing basketball players’ training regimens and game strategy through simulators and real-world tests. In order to continually enhance player growth and team competitiveness, coaches can use GAs to refine and modify tactics based on feedback as well as performance data in an iterative manner. The results of this study have significance for team sports other than basketball, where results are heavily influenced by the interaction of players’ individual abilities, teamwork, and decisions about strategy. In the end, incorporating algorithms based on genetics into sports analytics presents a viable way to improve coaching techniques and reach the highest levels of efficiency in sporting settings.