FALL 2022
Predicting Career Wins Above Replacement from Rookie Stats in Baseball: Addressing the Esteban German Dilemma
AUTHORS
Jack Blumenstein, University of Pennsylvania (Moneyball Academy)
Josh Braverman, University of Pennsylvania (Moneyball Academy)
Lekh Murthy, University of Pennsylvania (Moneyball Academy)
Wilson Wendt, University of Pennsylvania (Moneyball Academy)
ABSTRACT
Esteban German had a great rookie season, which would seem to predict future success, however it often does not. Data was obtained from FanGraphs, and a model was created to predict rest of career WAR using rookie metrics and a multivariate linear regression model. Important features in our model included: power, strikeouts, stolen bases, quality of contact, defensive ability, age. We found that career WAR can be projected well from rookie metrics using our model. Some players that have a strong start to their careers usually regress because of unsustainable measures in their underlying statistics.
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