An expected outcome framework for evaluating batting and pitching performance in major league baseball with applications to the "juiced ball" and the "fly ball revolution"

I utilize Major League Baseball Statcast data from 2015-2017 to build batted ball classifiers using state-of-the-art gradient boosting trees in conjunction with hyperparameter optimization techniques. Visual and numeric summaries of the model results are used to glean insights into batted balls in MLB. Further, the model framework is used to create new batting and pitching metrics with demonstrated advantages over previously used metrics. Using the batted ball classifiers and the introduced metrics, I investigate the "Juiced Ball" and "Fly Ball Revolution" phenomena in MLB, quantify the respective impacts of both phenomena, and present a manner for evaluating batter and pitcher performance across different ball environments.