Shooting skill in the NBA is almost exclusively defined by field goal percentage (FG%) - the number of makes out of the total number of shots. Even more advanced metrics like true shooting percentage (TS%) rely on FG%, ignoring the spatiotemporal data now available. In this paper we attempt to reduce the variability in predicting player FG% by using optical tracking data and shot trajectory characteristics to model shot-make probabilities. Using tracking data, we model the trajectories of individual shots from the 2014-15 season via a Bayesian Regression. We use these trajectories to create a shot-make probability model based on each shot's depth, left-right accuracy, and entry angle estimated from our modeled trajectories. Next we present a justification for the reduction in error when predicting FG% using our shot-make probability model. By the Rao-Blackwell Theorem, we condition shot-make probabilities on shooting factor information, thereby reducing the variance of our new estimator relative to raw FG%. Finally, we show that our modeled Rao-Blackwellized estimator is better than the raw estimator at predicting future shooting metrics like FG% and TS%.