In recent years, analytics and performance prediction in basketballBasketball have evolved and have significant domain value for athletes and teams. In organisations like the National BasketballBasketball Association (NBA) and National Collegiate Athletic Association (NCAA), the teams have departments that focus on their athlete traits like running distance during a game, ball possession effectiveness, shooting positions, and dribbling directions to find players who complement each other, maximising team chemistry and achieving better results. This department also focuses on the opponent’s tactics and play style to detect valuable information that the athlete, staff and coaches may have overlooked. Advancements in computing and machine learningMachine Learning (ML) have developed numerous cutting-edge algorithms in sports data analyticsSports data analytics, some of which offer more accurate forecasts than traditional statistical methods. This chapter discusses how machine learningMachine Learning (ML) algorithms can be utilised to predict athlete lineups and identify athlete and opponent strengths. A case study is presented, demonstrating the application of a combination of modest statistical methods and unsupervised machine learningMachine Learning (ML) algorithms over the game statistics of NCAA Division I women’s basketballBasketball teams that participated in the Northeast Conference (NEC) and the Metro Atlantic Athletic Conference (MAAC) in the year 2023–2024. The outcomes include forecasting team ranks, identifying key performance indicators contributing to the team’s competitive edge, and predicting an optimal athlete lineup against a respective opponent. In addition, this chapter emphasizes important contemporary practices in professional and collegiate basketballBasketball, such as player rotation strategies, load managementLoad management, and monitoring player availability—critical components that support performance sustainability and tactical decision-makingTactical decision-making throughout a competitive season.