Abstract: Watching basketball is nearly the same as watching repeating coin tossings! By analyzing recently available data from recent National Basketball Association (NBA) basketball seasons, basketball scoring during a game is well described by a continuous-time anti-persistent random walk, with essentially no temporal correlations between successive scoring events. We show how to calibrate this model to account for many statistical season-long metrics of NBA basketball. As further fillustrations of this random-walk picture, we show that the distribution of times when the last lead change occurs and the distribution of times when the score difference is maximal are both given by the celebrated arcsine law--—a beautiful and surprising property of random walks. We also use the random-walk picture to construct the criterion for when a lead of a specified size is "safe" as a function of the time remaining in the game. The obvious application to game-time betting is left as an exercise for the interested.