Predicting Postseason Success in Major League Baseball Through Econometric Practices

Abstract

This research aims to use econometric methods to determine which regular season statistics are determinants of postseason success in Major League Baseball (MLB). Millions of viewers watch the MLB Playoffs on a yearly basis, as they enjoy the unpredictable nature of the postseason, as there always seems to be at least one team that surprises the country and takes the pennant at the end of October. The ability to identify a couple of statistics from the regular season that could predict success in the postseason would allow teams to know what areas to focus on during the regular season. The author personally created and organized a data set from various sources, as no data sources compiled its data in a consistent and downloadable manner1. The data set that was created consists of statistics from the 2000-2021 seasons (22 years) in which they transformed into panel data. The variables used consist of eleven independent variables from the defensive and offensive side of the game including two variables that deal with outside elements such as players’ salaries and home attendance, and four dependent variables representing each round of the Playoffs marked with dummy variables. Using three separate models to avoid multicollinearity, the author utilized logit regression models to explore relationships between regular season statistics and postseason success. It was determined that On Base plus Slugging Percentage (OPS), Walks and Hits per Inning Pitched (WHIP), and Regular Season Wins (RSW) from the regular season have the strongest relationship to making it far into the postseason in Major League Baseball.