During the application/interview process with the Philadelphia 76ers front office, I was presented the project of predicting three-point shooting percentages for all NBA players this season. From my general awareness of statistical projection systems for baseball, the basis of the model would be to use past historical data to estimate a player’s true skill level. However, there are additional factors that could influence a player’s percentages. For example, a player’s true skill level can evolve over time, and while the direction and extent of that change may vary significantly by the player, there could be some generic trend evident across basketball. In addition, a player’s shooting percentages can depend heavily on the intra-game context of his shots, such as the location of the shots (distance or location along the arc), how open he is, and whether the shots are off-the-dribble or catch-and-shoot. Furthermore, there may be subtle inter-game influences such as whether the games occur at home or on the road and how much travel and rest time the player has had. Of the many different variables that could in theory impact three-point shooting percentages, many of them are either themselves unknown or their average effects may be determined to be minimal. As a result, the goal of this project was to build the model foundation that can predict three-point shooting percentages on its own and that can be extended in the future to include additional variables.
