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.
翻译:我们开发了SEAM(合成估计平均匹配)方法来描述棒球中击球和投手的比赛情况。 我们首先估算了球球在球手面对投手时被打球打球时的分布,称为实验性喷雾图分布。 许多个人匹配的样本大小太小,无法用来预测未来的结果。 正在考虑的击球和投手的合成版本是为了减轻这些关注。 有关这些合成玩家对总体估计喷雾图分布的影响的加权构建是为了尽量减少预期的平均平方误差。 我们提供了一个光亮的网络应用程序,使用户能够视觉化和评价任何在Stastcast时代(具体为2017年至今)已经发生或可能发生的击球比对。 这种方法和网络应用程序可用于确定防御性校准、阵列构建或通过根据任何输入匹配估计喷雾密度来选择投影器。 可以在 https://seam.stat.illinois.edu/ 上访问这个网络应用程序, 以计算方法计算喷雾密度的速度,使用户能够对SEAAM的任何数据进行直观性分析。