  We ask if there are alternative contest models that minimize error or
information loss from misspecification and outperform the Pythagorean model.
This article aims to use simulated data to select the optimal expected win
percentage model among the choice of relevant alternatives. The choices include
the traditional Pythagorean model and the difference-form contest success
function (CSF). Method. We simulate 1,000 iterations of the 2014 MLB season for
the purpose of estimating and analyzing alternative models of expected win
percentage (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 win percentage 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. 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. The simulation methodology offers the
advantage of greatly improved sample size. As the season is held constant, our
simulation-based statistical inference also allows for estimation and model
comparison without the (time series) issue of non-stationarity. The results
suggest that improved win (productivity) estimation can be achieved through
alternative CSF specifications.
