  We perform an exploratory data analysis on a data-set for the top 16
professional darts players from the 2019 season. We use this data-set to fit
player skill models which can then be used in dynamic zero-sum games (ZSGs)
that model real-world matches between players. We propose an empirical Bayesian
approach based on the Dirichlet-Multinomial (DM) model that overcomes
limitations in the data. Specifically we introduce two DM-based skill models
where the first model borrows strength from other darts players and the second
model borrows strength from other regions of the dartboard. We find these
DM-based models outperform simpler benchmark models with respect to Brier and
Spherical scores, both of which are proper scoring rules. We also show in ZSGs
settings that the difference between DM-based skill models and the simpler
benchmark models is practically significant. Finally, we use our DM-model to
analyze specific situations that arose in real-world darts matches during the
2019 season.
