We use multivariate change point analysis methods, to identify not only mean shifts but also changes in variance across a wide array of statistical time series. Our primary objective is to empirically discern distinct eras in the evolution of baseball, shedding light on significant transformations in team performance and management strategies. We leverage a rich dataset comprising baseball statistics from the late 1800s to 2020, spanning over a century of the sport's history. Results confirm previous historical research, pinpointing well-known baseball eras, such as the Dead Ball Era, Integration Era, Steroid Era, and Post-Steroid Era. Moreover, the study delves into the detection of substantial changes in team performance, effectively identifying periods of both dynasties and collapses within a team's history. The multivariate change point analysis proves to be a valuable tool for understanding the intricate dynamics of baseball's evolution. The method offers a data-driven approach to unveil structural shifts in the sport's historical landscape, providing fresh insights into the impact of rule changes, player strategies, and external factors on baseball's evolution. This not only enhances our comprehension of baseball, showing more robust identification of eras than past univariate time series work, but also showcases the broader applicability of multivariate change point analysis in the domain of sports research and beyond.

翻译：本研究采用多元变点分析方法，不仅识别统计时间序列中的均值偏移，还检测多维度方差变化。我们旨在通过实证方式厘清棒球发展历程中的不同时代，揭示球队表现与管理策略的重大变革。研究基于涵盖19世纪末至2020年跨越百年的棒球统计数据构建的丰富数据集。结果验证了既有历史研究的结论，精准定位了死球时代、种族融合时代、类固醇时代和后类固醇时代等公认的棒球历史分期。此外，本研究深入探测球队表现的显著变化，有效识别各球队历史上的王朝时期与衰落阶段。多元变点分析被证明是理解棒球演变复杂动态的重要工具。该方法通过数据驱动的方式揭示棒球历史格局的结构性变迁，为规则变更、球员策略及外部因素对棒球发展的影响提供新见解。这不仅深化了我们对棒球演进的理解——相比以往的单变量时间序列研究实现了更稳健的时代划分，更彰显了多元变点分析方法在体育研究及其他领域的广泛适用性。