Professional athletes often draw the ire of the public for getting paid ludicrous amounts of money to play a game for a living.  Baseball players are no exception, with today’s superstars often getting paid upwards of $300 million.  However, this sort of compensation has not always been the norm.  According to a New York Times article, “Chances Are Extra Work in Off-Season Involves Baseball, Not a Second Job”, Hall of Famers Stan Musial and Jim Palmer had to find work in the offseason in order to pay the bills.  In that article, author Andrew Keh explains, “Stan Musial, who amassed 3,630 hits during his Hall of Fame career, sold Christmas trees from a parking lot alongside his St. Louis Cardinals teammates. . . during the late 1940s, when Musial was a three-time World Series winner and three-time National League most valuable player.”  Despite being at the peak of a sensational playing career, Musial still needed a source of additional income.  Similarly, Jim Palmer went from winning a World Series in 1966 to working at a clothing store: “‘I was the youngest player to ever throw a shutout in a World Series,’ [Palmer] said, laughing. ‘Next thing I know, I am selling men’s clothes at Hamburgers.”’  So how have the contracts of Major League Baseball players gone from startlingly little to unbelievably large?  This project will detail the players’ struggle for adequate rights and compensation through an interactive timeline.

After we examine the history of professional baseball contracts, a new question appears:  What factors cause some players to be paid more than others?  Take the free agent class of 1981, for example.  Rick Waits, a thirty-year-old pitcher, re-signed with the Cleveland Indians on a three year, $1.2 million contract.  John Denny, also a pitcher and just one year younger, re-signed with those same Indians for three years and $1.8 million.  According to Baseball Reference, Waits had accumulated 6.2 wins above replacement over the past three seasons (WAR3), while Denny registered a WAR3 of 4.6.  (Wins above replacement is a value that shows how much better a player is compared to a replacement-level player.  For more information, see the “Glossary” section.)  WAR had not been created at this point in time, but one might wonder why Denny got more money than Waits when that statistic suggests Waits was significantly better.  There might never be a clear answer, but it could be because in 1981, the final year of their contracts, Denny recorded 10 wins, 6 losses, and a 3.15 ERA, while Waits finished with 8 wins, 10 losses, and a 4.92 ERA.  Another possible explanation is that Denny had pitched more innings and had a lower earned run average (ERA) over his career.  By those numbers, Denny clearly deserved the greater deal, but judging based on WAR3, it should have gone to Waits.  So this begs the question: What statistics do MLB front offices care about, and how have their opinions shifted over time? 

In 1982, Toronto ace Dave Steib finished the season with a 7.6 WAR.  The next highest finalist was Cleveland pitcher Rick Sutcliffe with a 5.7 WAR.  If that happened today, would Steib have won the Cy Young Award, the award given to the best pitcher in each league? There’s almost no question the answer is yes.  However, WAR had not been developed yet, and Steib finished fourth in Cy Young voting in 1982.  Sutcliffe finished fifth.  The winner was Pete Vuckovich of the Milwaukee Brewers, who accumulated a mere 2.8 WAR.  Steib was held back by his mediocre record of 17 wins and 14 losses, which paled in comparison to Vuckovich’s sparkling 18-6 record.  Baseball fans know now that judging a pitcher off of his wins and losses is not fair, as those metrics are shaped by many factors he cannot control (like how productive the offense is).  But forty years ago, wins and losses could make or break a pitcher’s reputation.  In this project, we will delve into how statistics lead to money and how the perception of those stats have changed as time has passed and baseball has become increasingly data-driven.  The project will also incorporate an interactive model to predict the contract of a player based on his statistics.