In this study, we demonstrate how data from the PGA Tour, combined with
stochastic shortest path models (MDPs), can be employed to refine the
strategies of professional golfers and predict future performances. We present
a comprehensive methodology for this objective, proving its computational
feasibility. This sets the stage for more in-depth exploration into leveraging
data available to professional and amateurs for strategic optimization and
forecasting performance in golf. For the replicability of our results, and to
adapt and extend the methodology and prototype solution, we provide access to
all our codes and analyses (R and C++)