  We present evidence, based on play-by-play data from all 6087 games from the
2006/07--2009/10 seasons of the National Basketball Association (NBA), that
basketball scoring is well described by a weakly-biased continuous-time random
walk. The time between successive scoring events follows an exponential
distribution, with little memory between different scoring intervals. Using
this random-walk picture that is augmented by features idiosyncratic to
basketball, we account for a wide variety of statistical properties of scoring,
such as the distribution of the score difference between opponents and the
fraction of game time that one team is in the lead. By further including the
heterogeneity of team strengths, we build a computational model that accounts
for essentially all statistical features of game scoring data and season
win/loss records of each team.
