  In sports, an aging curve depicts the relationship between average
performance and age in athletes' careers. This paper investigates the aging
curves for offensive players in Major League Baseball. We study this problem in
a missing data context and account for different types of dropouts of baseball
players during their careers. We employ a multiple imputation framework for
multilevel data to impute the player performance associated with the missing
seasons, and estimate the aging curves based on the imputed datasets. We then
evaluate the effects of different dropout mechanisms on the aging curves
through simulation, before applying our method to analyze MLB player data from
past seasons. Results suggest an overestimation of the aging curves constructed
without considering the unobserved seasons, whereas estimates obtained from
multiple imputation address this shortcoming.
