Scoring in a basketball game is a highly dynamic, non-linear process. NBA teams try to be more and more competitive each season. For instance, they incorporate into their rosters the best players in the world. This an...Scoring in a basketball game is a highly dynamic, non-linear process. NBA teams try to be more and more competitive each season. For instance, they incorporate into their rosters the best players in the world. This and other mechanisms concur to make the scoring process in NBA games exciting and rarely predictable. This paper is to study the behavior of timing and scoring in basketball gaines. The authors analyze all the games in five NBA regular seasons （2005-06, 2006-07, 2007-08, 2008-09, 2009-10）, for a total of 6150 games. Scoring does not behave uniformly; therefore, the authors also analyze the distributions of the differences in points in the basketball games. To further analyze the behavior of the tail of the distribution, the authors also carry out a semilog-plot and a log-log plot to verify whether this trend approaches a Poisson distribution or a PL. This paper reveals different areas of behavior related to the score, with specific instances of time that could be considered tipping points of the game. The presence of these critical points suggests that there are phase transitions where the dynamic scoring of the games varies significantly.展开更多
文摘Scoring in a basketball game is a highly dynamic, non-linear process. NBA teams try to be more and more competitive each season. For instance, they incorporate into their rosters the best players in the world. This and other mechanisms concur to make the scoring process in NBA games exciting and rarely predictable. This paper is to study the behavior of timing and scoring in basketball gaines. The authors analyze all the games in five NBA regular seasons （2005-06, 2006-07, 2007-08, 2008-09, 2009-10）, for a total of 6150 games. Scoring does not behave uniformly; therefore, the authors also analyze the distributions of the differences in points in the basketball games. To further analyze the behavior of the tail of the distribution, the authors also carry out a semilog-plot and a log-log plot to verify whether this trend approaches a Poisson distribution or a PL. This paper reveals different areas of behavior related to the score, with specific instances of time that could be considered tipping points of the game. The presence of these critical points suggests that there are phase transitions where the dynamic scoring of the games varies significantly.
