Luman: Making small sample defensive metrics less volatile

From Jonathan Luman at Beyond the Box Score on January 16, 2015:

Current fielding metrics have annual volatility approximately as great as BABIP. This creates difficulties in establishing a timely evaluation of a player’s defensive ability.

This article describes a process that can be used to reduce the volatility of defensive metrics. It finds that a modification to the current calculation of DRS, using Bayesian inference, makes a proxy defensive metric about as “reliable” as offensive metrics (e.g., wRC+ and ISO). This process is demonstrated with publicly available Inside Edge fielding data. The crux of this technique is a two-step process: (1) establish an initial estimate of fielding proficiency for a play difficulty category (e.g., Likely) based on out-of-category data (e.g., Routine, About Even, etc.) and then (2) correct this estimate with in-category fielding data. The advantage of this technique is that it makes a single season of defensive data more representative of a player’s true talent level.

Additionally, the article discusses how the re-development of defensive metrics, using a recursive Kalman filter, might reduce their volatility to an even greater extent.

Read the full article here:

Originally published: January 16, 2015. Last Updated: January 16, 2015.