Hierarchical Bayesian Modeling of Hitting Performance in Baseball

A Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics

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 …

Hierarchical Bayesian Bradley-Terry for Applications in Major League Baseball

A common problem faced in statistical inference is drawing conclusions from paired comparisons, in which two objects compete and one is declared the victor. A probabilistic approach to such a problem is the Bradley-Terry model, first studied by Zermelo in 1929 and rediscovered by Bradley and Terry in 1952. One obvious area of application for such a model is sporting events, and in particular Major League Baseball. With this in mind, we describe a hierarchical Bayesian version of Bradley-Terry suitable for use in ranking and prediction problems, and compare results from these application domain…

Bayesball: A Bayesian hierarchical model for evaluating fielding in major league baseball

The use of statistical modeling in baseball has received substantial attention recently in both the media and academic community. We focus on a relatively under-explored topic: the use of statistical models for the analysis of fielding based on high-resolution data consisting of on-field location of batted balls. We combine spatial modeling with a hierarchical Bayesian structure in order to evaluate the performance of individual fielders while sharing information between fielders at each position. We present results across four seasons of MLB data (2002--2005) and compare our approach to other…

Evaluating Throwing Ability in Baseball

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 playe…

openWAR: An Open Source System for Evaluating Overall Player Performance in Major League Baseball

Within baseball analytics, there is substantial interest in comprehensive statistics intended to capture overall player performance. One such measure is Wins Above Replacement (WAR), which aggregates the contributions of a player in each facet of the game: hitting, pitching, baserunning, and fielding. However, current versions of WAR depend upon proprietary data, ad hoc methodology, and opaque calculations. We propose a competitive aggregate measure, openWAR, that is based upon public data and methodology with greater rigor and transparency. We discuss a principled standard for the nebulous co…

好球判罚（Pitch Framing）的分层贝叶斯模型

A Hierarchical Bayesian Model of Pitch Framing

研究提出贝叶斯分层模型估计裁判判好球概率并量化捕手接球影响，将其转化为赛季平均节省分数，并引入新指标提供更客观的接球影响评估。