We present a tractable framework for detecting changes in performance metrics and apply these methods to Major League Baseball (MLB) batting and pitching data from the 2023 and 2024 seasons. We propose a changepoint detection algorithm that combines a likelihood-based approach with split-sample inference to better control false positives, using either nonparametric tests or tests appropriate to the underlying data distribution. These tests incorporate a shift parameter, allowing users to specify the minimum magnitude of change to detect. We demonstrate the utility of this approach across simulation studies and several baseball applications: detecting changes in batter plate discipline metrics (e.g., chase and whiff rate), identifying velocity changes in pitcher fastballs, and validating velocity changepoints against a curated quasi-ground-truth dataset of pitchers who transitioned from relief to starting roles. Our method flags meaningful changes in 91% of these "ground-truth" cases and reveals that, for some metrics, more than 60% of detected changes occur in-season. While developed for baseball, the proposed framework is broadly applicable to any setting involving monitoring of individual performance over time.

翻译：我们提出一个可处理的框架，用于检测表现指标的变化，并将这些方法应用于2023和2024赛季美国职业棒球大联盟（MLB）的击球和投球数据。我们提出一种变点检测算法，该方法将基于似然的方法与分割样本推断相结合，以更好地控制误报，其使用的检验方法可以是非参数检验，也可以是适用于基础数据分布的检验。这些检验包含一个偏移参数，允许用户指定待检测变化的最小幅度。我们通过模拟研究和多个棒球应用案例展示了该方法的实用性：检测击球员击球纪律指标（例如追打坏球率和挥空率）的变化，识别投手快速球的速度变化，以及针对一个精心整理的、由后援投手转为先发投手的投手准真实数据集来验证速度变点。我们的方法在这些“真实”案例中标记出了91%的有意义变化，并揭示出，对于某些指标，超过60%的检测到的变化发生在赛季期间。虽然该框架是为棒球运动开发的，但其广泛适用于任何涉及随时间监测个体表现的场景。