Evaluating Throwing Ability in Baseball

TL;DR

This paper introduces a Bayesian hierarchical model to quantitatively evaluate throwing ability of major league baseball players, providing a data-driven method to identify significant contributors and detractors.

Contribution

The study develops a novel Bayesian approach to estimate player throwing ability using detailed game data, accounting for varying opportunities across players.

Findings

Identified players with significant positive throwing contributions.

Identified players with significant negative throwing contributions.

Provided a probabilistic ranking of players based on throwing ability.

Abstract

We present a quantitative analysis of throwing ability for major league outfielders and catchers. We use detailed game event data to tabulate success and failure events in outfielder and catcher throwing opportunities. We attribute a run contribution to each success or failure which are tabulated for each player in each season. We use four seasons of data to estimate the overall throwing ability of each player using a Bayesian hierarchical model. This model allows us to shrink individual player estimates towards an overall population mean depending on the number of opportunities for each player. We use the posterior distribution of player abilities from this model to identify players with significant positive and negative throwing contributions.