  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.
