Replication Data for: Estimating Major League Baseball Team Quality through Simulation: An Analysis of an Alternative Pythagorean Expected Wins Model

Background. Contests are games in which the players compete for a prize and exert effort to increase their probability of winning. For sport contests, analysts often use the Pythagorean model to estimate teams’ expected wins (quality). We ask if there are alternative contest models that minimize error or information loss from misspecification and outperform the Pythagorean model. Aim. This article aims to use simulated data to select the optimal expected wins model among the choice of relevant alternatives. The choices include the traditional Pythagorean model and the difference-form contest success functions (CSF). Method. We simulate 1,000 iterations of the 2014 MLB season for the purpose of estimating and analyzing alternative models of expected wins (team quality). We use the open-source, Strategic Baseball Simulator and develop an AutoHotKey script that programmatically executes the SBS application, chooses the correct settings for the 2014 season, enters a unique ID for the simulation data file, and iterates these steps 1,000 times. We estimate expected wins using the traditional Pythagorean model, as well as the difference-form CSF model that is used in game theory and public choice economics. Each model is estimated while accounting for fixed (team) effects. Result. We find that the difference-form CSF model outperforms the traditional Pythagorean model in terms of explanatory power and in terms of misspecification-based information loss as estimated by the Akaike Information Criterion. Through parametric estimation, we further confirm that the simulator yields realistic statistical outcomes.