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

2. 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

3. 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)

4. 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

5. 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

6. 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

7. 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”

8. Results of Salary Estimation
Fielders
Pitchers

9. 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”

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

11. 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