Basketball Analytics. Data Mining For Acquiring Performances

Choices of decision makers in a basketball team are not limited to the strategies to be adopted during games. The most important ones are outside the field and concern team composition and talented and productive players to acquire on which the team can rely to raise its game level. In this paper, we propose to use data mining tasks to help decision makers to make appropriate decisions that will lead to the improvement of the performance of their players and their team. Tasks such as clustering, classification and regression are used to detect weaknesses of a team; best players that can help overcome these weaknesses; predict performance and salaries of players. These will be done on the NBA dataset.