Numerous statistics have been proposed for the measure of offensive ability
in major league baseball. While some of these measures may offer moderate
predictive power in certain situations, it is unclear which simple offensive
metrics are the most reliable or consistent. We address this issue with a
Bayesian hierarchical model for variable selection to capture which offensive
metrics are most predictive within players across time. Our sophisticated
methodology allows for full estimation of the posterior distributions for our
parameters and automatically adjusts for multiple testing, providing a distinct
advantage over alternative approaches. We implement our model on a set of 50
different offensive metrics and discuss our results in the context of
comparison to other variable selection techniques. We find that 33/50 metrics
demonstrate signal. However, these metrics are highly correlated with one
another and related to traditional notions of performance (e.g., plate
discipline, power, and ability to make contact)
