From SABR member Brandon Heipp at Baseball Prospectus on October 30, 2012:
This article does not discuss the various definitions of replacement level, the arguments made for and against its use for various purposes, or any such topic with a practical application. While the title might suggest that it will discuss replacement level through baseball history, it sadly does not do that either. Rather, it attempts to briefly describe the history of replacement level as a sabermetric concept up to the mid-1990s or so, when it came to the forefront of most analytical systems.
This history is incomplete because it will surely leave out some notable uses due to the limitations of my library and internet records. It is primarily focused on Bill James’ use of replacement level, since this is fairly easy to track through the annual Abstracts. Whether the concept of replacement level originated with James is something that I cannot answer, but it can be traced back to James at the very latest.
I felt it necessary to write this overview because one will occasionally see references to Keith Woolner as the originator of replacement level. This is simply not the case, which is not in any way intended to be a slight to Woolner. His VORP certainly helped popularize replacement level and did much to increase the emphasis on the replacement baseline in the sabermetric community. It is possible to recognize that while also recognizing the contributions of those who used replacement level pre-VORP.
In his first nationally-published edition (1982), James used a system based on Offensive and Defensive Winning Percentage to rank players. If a player was considered to have played full-time (90 games in the field and 10 games at bat—based on outs made—for the strike-shortened 1981 season), then his ranking was just the Winning Percentage based on the sum of his offensive and defensive wins and losses. However, for part-time players, the missing games were filled in by a .333 W%—a de facto replacement level.
In 1983, James made it more explicit. He first found the player’s total win-loss record, then calculated the chance that a .400 player would compile that record.
Read the full article here: http://www.baseballprospectus.com/article.php?articleid=18790
Originally published: October 30, 2012. Last Updated: October 30, 2012.