TeamWellX: Leveraging genetic algorithms to optimize team health and well-being toward sustainable game development and strategy

• Published in: SoftwareX
• Main Authors: Sumedh Khodke, Evan Fox, Nafisa Anjum, Dineshkumar Katta, Amritha Subburayan, Sanjay Aravind Loganathan Ravichandran, MD Ahasan Habib, Nazmus Sakib
• Format: Article
• Language: English
• Published: Elsevier 2024-02-01
• Subjects: Roster-formulation; Genetic algorithm; Fitness-function; Machine learning; Team strength
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352711024000062
• ISSN: 2352-7110

Building a well-rounded roster is crucial in sports, as it directly impacts a team's long-term success. However, the process can be complex, with various factors to consider, such as team dynamics, player skills, and financial limitations. This study aims to develop effective strategies for constructing teams in different sports, starting with American Football in the NFL and potentially expanding to other similar disciplines. In this paper, we propose an innovative tool that leverages genetic algorithm, signifying a method that balances between overall team positional strength, fosters collective team success, and encourages team's long-standing well-being. This data-driven approach also presents a generalizable solution that can be employed across similar team sports, transforming the pattern of roster optimisation strategies and team's performance augmentation.