Rao-Blackwellizing field-goal percentage

Date created: 
Bayesian regression
Optical tracking
Shot trajectories
Variance reduction

Shooting skill in the NBA is typically measured by field goal percentage (FG%) - the number of makes out of the total number of shots. Even more advanced metrics like true shooting percentage are calculated by counting each player’s 2-point, 3-point, and free throw makes and misses, ignoring the spatiotemporal data now available (Kubatko et al. 2007). In this paper we aim to better characterize player shooting skill by introducing a new estimator based on post-shot release shot-make probabilities. Via the Rao-Blackwell theorem, we propose a shot-make probability model that conditions probability estimates on shot trajectory information, thereby reducing the variance of the new estimator relative to standard FG%. We obtain shooting information by using optical tracking data to estimate three factors for each shot: entry angle, shot depth, and left-right accuracy. Next, we use these factors to model shot-make probabilities for all shots in the 2014-15 season, and use these probabilities to produce a Rao-Blackwellized FG% estimator (RB-FG%) for each player. We present a variety of results derived from this shot trajectory data, as well as demonstrate that RB-FG% is better than raw FG% at predicting 3-point shooting and true-shooting percentages. Overall, we find that conditioning shot-make probabilities on spatial trajectory information stabilizes inference of FG%, creating the potential to estimate shooting statistics and related metrics earlier in a season than was previously possible.

Document type: 
Graduating extended essay / Research project
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
Luke Bornn
Science: Department of Statistics and Actuarial Science
Thesis type: 
(Project) M.Sc.