The Colorado Rockies play their home games in an environment unlike any other team in Major League Baseball. The altitude effect present in Denver, CO potentially plays a role in why hitters thrive for the Rockies and pitchers struggle. Player’s statistics are altered by playing in Denver, and these performance indicators’ significance diminishes, especially for Rockies players. Statistics are influential in determining player compensation in professional baseball. The statistics of Rockies players, however, are biased because they play 81 home games at high altitude each season. The results of this paper support the notion that the Rockies capitalize on their unique effect by paying players differently than the rest of the league.
Previous research has demonstrated how the Great Recession affected attendance for Major League Baseball. The purpose of this study is to show that high levels of unemployment had the most significant impact on attendance during the recession. Data was collected from 2006, 2008, and 2011 to create a linear regression model that includes attendance for all thirty Major League Baseball teams, the unemployment rates in the corresponding Metropolitan Statistical Areas, and various demographics. Using OLS estimators, the results suggest that unemployment did not have a statistically significant effect on attendance. However, attendance was higher in areas with larger populations of women, African Americans, and people between the ages of 35 to 54. Thus, this study is useful because it provides the framework for an effective marketing plan Major League Baseball teams can use to maintain attendance rates during the next recession.
Previous research has effectively shown how in connection with race, and free-agency, performance statistics have affected the value of the baseball card market. However, examination has not been done on how a player's performance directly relates to their popularity and card value. This study attempts to determine which performance statistics for pitchers and batters significantly affect their rookie card value in the card trading market. The abundance of statistical data from the MLB allows an evaluation through regression based analysis to determine what attributes from Hall of Fame players are determinants. The implications from this research used correctly can aid collectors in determining which cards to invest in.
This thesis is designed to explain the unordinary amount of left-handed hitters found in Major League Baseball (MLB). The focus of this study is to determine the appropriate amount of left-handed hitters a MLB team should employ in order to maximize their success. The driving force behind this study is that the average amount of lefties in MLB is substantially higher than the amount of lefties found in everyday society. The hypothesis is that a team should employ between 33% and 55% of their hitters to be lefthanded in order to achieve a team's optimal rate of success. This study will include all 30 MLB baseball teams over the span of ten years including more than 4100 hitters. Two models will be used to link the effect left-handed hitters have on the total number of runs a team scores, and also a team's season long winning percentage. The regressions produced R-squared values of .91 and .45 respectively. While the model was able to prove several different variables do significantly affect runs scored, and winning percentage the results were inconclusive in relating left-handed hitting to either dependent variable. For that reason the research could not support the hypothesis that MLB teams should employ between 33% and 55% left-handed hitters.