论文标题
上下文富玩家对决评估的接缝方法
SEAM methodology for context-rich player matchup evaluations
论文作者
论文摘要
我们开发了用于描述棒球中击球手与投手对决的接缝（合成估计的平均对决）方法。我们首先估计面对投手的击球手（称为经验喷雾图分布）发挥作用的球的分布。许多单独的比赛的样本量太小，无法在预测未来结果中可靠。为了减轻这些担忧，构建了正在考虑的面糊和投手的合成版本。构建了这些合成参与者对这些合成参与者对整体喷雾图分布的影响有多大影响的权重，以最大程度地减少预期的均方误差。我们提供了一个闪亮的Web应用程序，该应用程序允许用户可视化和评估在Statcast时代（特别是2017年至今）发生或可能发生的任何击球手对决。该方法和Web应用程序可用于根据任何输入对决的喷雾密度来确定防御对准，阵容构造或投手选择。可以通过https://seam.stat.illinois.edu/访问此Web应用程序。该方法计算喷雾密度的计算速度允许该应用几乎立即显示任何输入的可视化。因此，Seam提供了依赖对匹配数据的分布解释，该数据在计算上很快。
We develop the SEAM (synthetic estimated average matchup) method for describing batter versus pitcher matchups in baseball. We first estimate the distribution of balls put into play by a batter facing a pitcher, called the empirical spray chart distribution. Many individual matchups have a sample size that is too small to be reliable for use in predicting future outcomes. Synthetic versions of the batter and pitcher under consideration are constructed in order to alleviate these concerns. Weights governing how much influence these synthetic players have on the overall estimated spray chart distribution are constructed to minimize expected mean square error. We provide a Shiny web application that allows users to visualize and evaluate any batter-pitcher matchup that has occurred or could have occurred during the Statcast era (specifically 2017-present). This methodology and web application could be used to determine defensive alignments, lineup construction, or pitcher selection through estimation of spray densities based on any input matchup. One can access this web application at https://seam.stat.illinois.edu/. The computational speed with which the method calculates the spray densities allows the app to display the visualizations for any input almost instantly. Therefore, SEAM offers distributional interpretations of dependent matchup data which is computationally fast.
