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Category Archives: Z22

Long-Term Contracts and Predicting Performance in MLB

By Drew Goldstein

In this paper, I examine whether MLB teams are capable of using players’ past performance data to sufficiently estimate future production. The study is motivated by the recent trend by which teams have increasingly signed long-term contracts that lock in players for up to ten seasons into the future. To test this question, I define the “initial years” of a player’s career to represent a team’s available information at the time of determining whether or not to sign him. By analyzing the predictive ability these initial years have on subsequent performance statistics, I am looking to answer whether—and if so for how long—teams can justify signing players to long-term contracts with guaranteed salaries. I also compare the results of the predictive tests with actual contract data to determine the per-dollar returns on these deals for different types of contracts.
I conclude from my analysis that a player’s past performance does in fact provide sufficient insight into his future value for teams to make informed decisions at the time of signing a contract. Teams are able to better predict the future production of potential signees by examining their consistency and relative value in the initial seasons of their careers. Furthermore, the results from examining the contract data coincide with my findings on performance; teams and players arrive at salaries for long-term contracts that divide the future risk between the two parties. The returns on long-term contracts are thus demonstrated to be higher than for short-term contracts, as the overall value of longer deals compensates teams for the associated higher annual salaries.

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Advisors: Peter Arcidiacono, Michelle Connolly, Duncan Thomas | JEL Codes: Z2, Z22, Z23

Determining NBA Free Agent Salary from Player Performance

By Joshua Rosen

NBA teams have the opportunity each offseason to sign free agents to alter their rosters. Using only regular season per game statistics, I examine the best method of calculating a player’s appropriate salary value based upon his contribution to a team’s regular season win percentage. I first determine which statistics most accurately predict team regular season win percentage, and then use regression analysis to predict the values of these metrics for individual players. Finally, relying upon predicted statistics, I assign salary values to free agents for their upcoming season on specific teams. My results advise teams to rely heavily on Player Impact Estimate (“PIE”) when predicting their teams’ win percentage, and to seek players whose appropriate salaries would be significantly more than their actual seasonlong salaries if the free agents were to sign.

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Advisor: Kent Kimbrough, Peter Arcidiacono | JEL Codes: C30, Z2, Z22 | Tagged: Free Agents, Salaries, NBA

The New Landscape of the NBA: The 2011 Collective Bargaining Agreement’s Impact on Competitive Balance and Players’ Salaries

By Nicholas Yam

The National Basketball Association (NBA) passed a new Collective Bargaining Agreement (CBA) in 2011 that introduced many changes to the structure of the league. The purpose of those changes was to improve competitive balance among the league, allowing smaller market teams to better compete with larger market teams. Many of the changes targeted the league’s salary cap and teams’ ability to pay players. This paper aims to determine whether competitive balance in the NBA improved under the 2011 CBA. The paper also determines which types of players’ salaries were affected the most. The results showed that competitive balance did not improve under the 2011 CBA. However, the results showed that higher performing players were paid proportionally more money than lower performing players following 2011 CBA.

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Advisor: Peter Arcidiacono | JEL Codes: Z2, Z20, Z22

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