Basketball is a dynamic sport that requires precise decision-making, often based on instantaneous assessments of player intent. Understanding a player's intention to shoot or pass the ball is crucial for analyzing game strategies, predicting player movements, and enhancing automated commentary. This paper explores the application of logistic regression technique for human intent detection in basketball, specifically focusing on the task of differentiating between shooting and passing movements based on various kinematic features.Human intent detection is a vital aspect of understanding and predicting human actions in various domains, and in sports like basketball, it plays a crucial role in enhancing player performance and game strategies. In basketball, detecting the intent behind a player’s movements and actions can provide valuable insights into their tactics, strategies, and overall game plan. This ability to discern intent from motion data can significantly impact coaching, player training, and real-time game analysis.