The basketball analogy carries the intuition well. The one place it quietly breaks is worth flagging for a beginner series: the shooter knows which direction to adjust because they watched where the ball landed, while a network in the middle layers has no direct feedback at all. Credit assignment is the actual hard part - working out how much each weight several layers back contributed to an error measured only at the output - and that is precisely what the chain rule is doing. Framing it that way also makes the later topics land more easily: vanishing gradients stop being a mysterious phrase and become the obvious consequence of multiplying many small numbers on the way back.