By building statistical models, this project is to show how age is a big factor for NBA players. Using the trend for former players to predict the active NBA player's career future. 36 valid candidates have been chosen who completed at least 1200 games and 15 seasons (excluding the short season or players who did not complete more than 20 games) in their career. Doing Z-Standardization and removing any standard deviation, means bias before creating each age model. Combining the Z statistics of each player to make sure equal weights for players' Career Average. The purpose of this project is to see how age affects a player's total playing time and total points received in each season add on. To predict active duty NBA players' career future, authors calculated the combo average of three categories (point average per game PPG, minutes played per game MPG, and points per minute PPM) with 36 sets of data and concluded the golden age range for players. By using statistical analysis software JMP and Minitab, the authors partitioned 36 players into 7 clusters, and the multi correlation has been studied. Contrasting the correlation of players having the same position in each cluster, to see if the value is close to their cluster average, the project is to show how two players are similar to each other and to predict out the active duty player’s future performance by using the former player career trajectory.