Opening theory has traditionally been an essential part of chess players' game preparation, particularly at advanced levels of play. In this study, I investigate the effectiveness of chess openings using a dataset of over 1,000 chess-opening variations compiled from high-level online games. Effectiveness is measured by the difference between an opening's average performance rating and the average rating of players who use the opening. I use ordinary least squares regression, elastic net regression, and random forest regression to examine which opening characteristics, such as color, complexity, and popularity, most contribute to an opening's effectiveness. The ordinary least squares and elastic net regression models produced very similar results, and highlight that employing standard, conventional openings is more effective than unsound openings. These findings aim to inform players in selecting openings that align with their strengths and maximize tournament performance.
