The Effectiveness of Sabermetrics in Baseball: Moneyball Revisited

This thesis revisits the principles of sabermetrics as outlined in Michael Lewis's 'Moneyball', investigating whether traditional statistics hold up to their analytical counterpart. By employing univariate linear regression, the study evaluates correlations between advanced metrics such as OPS+, wRC+, and ERA+ against traditional measures like batting average, slugging percentage, and ERA. Data spanning over a century of Major League Baseball (1908–2019) were analyzed to assess the evolving relationship between these metrics. Results indicate that advanced statistics often provide a more nuanced and reliable assessment of player performance, with strong correlations observed in metrics designed to refine traditional measures. However, the analysis also highlights limitations and areas of divergence, such as weaker correlations between Wins Above Replacement (WAR) and traditional statistics like wins and saves. These findings underscore the transformative role of sabermetrics in shaping modern baseball strategy while emphasizing the need for a balanced approach that integrates both historical and innovative perspectives. Ultimately, this study affirms the enduring relevance of sabermetrics in enhancing player evaluation and team decision-making processes, offering insights into the ongoing evolution of baseball analytics.