Waging WAR: MLB Player Valuation Using Advanced Metrics By Alexander Knorr, Sebastian Kadamany and Nick Vedder

Who we are and what motivates us UCD Economic Master’s Students Curious about how goods are valued and priced (Microeconomics) Using the idea of market value to identify disparity between current prices and competitive pricing

Value in the setting of MLB players Identifying players who most outperform their current salaries We estimate what players should be earning to identify price disparity We calculate a straightforward index to identify who outperforms their contract the most in (relative terms)

Data and WAR Used Baseball Reference for all variables: WAR, age and salary Unfortunately was not available for Minor Leagues (we’re too poor to purchase that data!) Used recent career statistics (2010 – 2014) which has 145,109 player – season observations Collapsed and summarized Data Salary as of 2014 Median WAR statistic Age as of 2014

Wins Above Replacement (WAR): Why reinvent the wheel? WAR isolates the individual effect a player has on winning, holding team, season and even stadium factors constant Identifies relative performance to an “average” replacement player

Model Specification (1) Stratified players by position (Fielder or Pitcher) Transformed Salary (dependent variable) using logarithms to normalize and provide relative interpretations Removed players who were earning exactly the 2010 rookie minimum to reduce the skew of salary Log Transform for Fielders

Model Specification (2) Used Linear Regression for estimating “true” salary WAR will drive true value of a player Experience terms will control for player experience as that is not explicitly accounted for in WAR Age start will attempt to temper the power of experience in predicting salary. Controlling for players “past their prime”

Results of Salary Estimation Fielders Pitchers

Aside: Controlling for High Correlation Between Experience and Salary By using the squared experience term, we allowed for players to lose predicted salary as they “passed their prime”

Ranking Simply calculated the ratio of predicted player salary to actual salary as of 2014 Creates the Salary Arbitrage Index (SAI) Rank by SAI

Conclusions Ranking the top 100 players by traditional statistics or even WAR does not identify opportunity The very best players are typically already paid appropriately Management always wants to find efficiency “Biggest bang for the buck” SAI was directly developed to identify opportunity in player prospecting