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We ask if there are alternative contest models that minimize error orinformation loss from misspecification and outperform the Pythagorean model.This article aims to use simulated data to select the optimal expected winpercentage model among the choice of relevant alternatives. The choices includethe traditional Pythagorean model and the difference-form contest successfunction (CSF). Method. We simulate 1,000 iterations of the 2014 MLB season forthe purpose of estimating and analyzing alternative models of expected winpercentage (team quality). We use the open-source, Strategic Baseball Simulatorand develop an AutoHotKey script that programmatically executes the SBSapplication, chooses the correct settings for the 2014 season, enters a uniqueID for the simulation data file, and iterates these steps 1,000 times. Weestimate expected win percentage using the traditional Pythagorean model, aswell as the difference-form CSF model that is used in game theory and publicchoice economics. Each model is estimated while accounting for fixed (team)effects. We find that the difference-form CSF model outperforms the traditionalPythagorean model in terms of explanatory power and in terms ofmisspecification-based information loss as estimated by the Akaike InformationCriterion. Through parametric estimation, we further confirm that the simulatoryields realistic statistical outcomes. The simulation methodology offers theadvantage of greatly improved sample size. As the season is held constant, oursimulation-based statistical inference also allows for estimation and modelcomparison without the (time series) issue of non-stationarity. The resultssuggest that improved win (productivity) estimation can be achieved throughalternative CSF specifications.

An Analysis of an Alternative Pythagorean Expected Win Percentage Model: Applications Using Major League Baseball Team Quality Simulations