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Scoring in a basketball game is a process highly dynamic and nonlinear type. The level of NBA teams improve each season. They incorporate to their rosters the best players in the world. These and other mechanisms, make the scoring in the NBA basketball games be something exciting, where, on rare occasions, we really know what will be the result at the end of the game. We analyzed all the games of the -10 NBA regular seasons (6150 games). We have studied the evolution of the scoring and the time intervals between points. These do not behave uniformly, but present more predictable areas. In turn, we have analyzed the scoring in the games regarding the differences in points. Exists different areas of behavior related with the scorea and each zone has a different nature. There are point that we can consider as tipping points. The presence of these critical points suggests that there are phase transitions where the dynamic scoring of the games varies significantly.

Basketball game flow and its design can be described as in many others natural systems. The structure, shape and functionality evolve in time and are closely related to performance in several sports. Basketball is a collaboration-opposition sport, thus games present critical points. Non-linear local interactions among players are reflected in the score evolution, the order parameter. Some researchers often presume that scoring in basketball is a random process, meaning memoryless, described using Poisson Model. Scoring cannot be described by a unique distribution. We examined 6130 NBA games and analyzed time intervals between points and scoring dynamic. In the NBA, the most competed games are decided in the last minute, where fouls play a main role (94.02%). Both teams try to keep their advantage solely in order to reach the last minute, where a different game will be played, which can be considered as an example of Red Queen Hypothesis. We also measured the game flow through players real interactions: passes, screens and space creations. Data follow a homogeneous distribution up to a certain value, suggesting that teams resolve the situation with a few steps (diffuse flow). But, if the situation becomes more critical, the dynamics turn into a Power Law Distribution, they modify the game flow spontaneously into a Scale-Free flow (hierarchical flow). These processes take place simultaneously and continuously during game time. Therefore teams would be considered as self-organizing systems.

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