Billy Beane: Changing the Game

Purpose

The purpose of this paper is to discuss the case study "Billy Beane: Changing the Game

Introduction

I must preface this paper by saying I know very little about baseball. I started writing with a sense of trepidation. I had to do a great deal of internet surfing to grasp the significance of much of what is being discussed in the case. However, during my surfing a fascinating picture of Billy Beane developed. He is a complex man who did in fact change certain facets of baseball. What I found most interesting is the emotions that he seemed to provoke in some people. Some view him as a brilliant businessman; others curse his name. Before I delve into all the questions however I'd Like to take a quick look at Billy Beane. Michael Lewis authored the book Moneyball, (Lewis, 2003) showcasing Beane.

For his singular, unapologetic iconoclasm in the face of the game's long tradition, Lewis lionized him six years ago in "Moneyball," which became a must-read for both baseball and business aficionados. Beane became the lead evangelist of a new baseball orthodoxy that emphasizes greater statistical analysis in the scouting and development of players. The Moneyball way also diminishes the field manager's organizational influence while it increases the power and profile of the general manager position -- a job that was once largely invisible. In the 140-year history of Major League Baseball, the office of field manager has never held less power than it does now, in the wake of Moneyball. (Bryant, 2009)

"The reason 'Moneyball' became so important was because so many of the owners read [the book]," says Sandy Alderson, himself a seminal figure in the way baseball is run. "For years, the baseball people would tell the owners, 'Leave the baseball to us. You wouldn't understand.' They kept saying they were different. Then the owners realized the dynamics of baseball -- of assessing risk -- were the same as the ones they faced in their outside businesses. (Bryant, 2009)

One of my first thought on starting this assignment was what correlation could there possibly be between baseball and human resource management. In reading and researching the case I found that not only does the case relate to baseball it also touches on cognitive psychology, competitive advantage, decision making, statistical analysis and change management. My research caused me to ask myself several questions. How did Beane go from being a golden boy in 2003 to someone who is rather ridiculed in 2010? How is it that Beane can be seen by baseball savior (to the Major League Baseball (MLB) smaller teams anyways) by some and its arch enemy by others? What the heck are sabermetrics?

One article I read cued in of the Human Resource Management aspects of Beane and Moneyball (the book from which the cases tudy is derived). It states, Moneyball is a book about innovation, resistance to change, competitive advantage, achieving excellence, and, of most relevance here, human resource management. While many would agree that the radical innovation described in Moneyball represents a "new vision of management" in baseball, this article describes how Moneyball lessons might contribute to a "new vision of HRM" in various types of organizations. (Wolfe, 2006) The article goes on to ask four important HR important questions: (1) why did it take so long for the sabermetrics innovation to be adopted? (2) how is it that Beane was successful in having sabermetrics implemented by the A's? (3) does sabermetrics provide a competitive advantage, and if so, how? and (4) is the competitive advantage provided by sabermetrics sustainable? Let's start with a definition of sabermetrics.

Merriam-Webster's defines sabermetrics as the statistical analysis of baseball data (Merriam-Webster, 2010) However, given all the hoopla surrounding the word that seemed a bit too simplistic to me. I delved a little deeper and found a Sabermetric manifesto. It reads (in part) as follows:

Bill James defined sabermetrics as "the search for objective knowledge about baseball." Thus, sabermetrics attempts to answer objective questions about baseball, such as "which player on the Red Sox contributed the most to the team's offense?" or "How many home runs will Ken Griffey hit next year?" It cannot deal with the subjective judgments which are also important to the game, such as "Who is your favorite player?" or "That was a great game."

Since statistics are the best objective record of the game available, sabermetricians often use them. Of course, a statistic is only useful if it is properly understood. Thus, a large part of sabermetrics involves understanding how to use statistics properly, which statistics are useful for what purposes, and similar things. This does not mean that you need to know a lot about mathematics to understand sabermetrics, only that you need to have some idea of how statistics an be used and misused.

The statistics which are available in baseball are a collected record of observations. An individual fan, sportswriter, or even a player or manager will see most teams thirteen or fewer times during the year. His observations may be of some interest, but they are a small (and often biased) sample. In thirteen games, the difference between a great hitter and a poor hitter is just five hits; thus, if the observer happens to see a mediocre player's two best games of the season, he would get an incorrect impression of the player's ability.

In contrast, a player's statistics are a record obtained from all of his games, as observed by the official scorers in the league. This is a much larger collection of observations, and it is converted to a form which can be easily understood; few fans could get a good idea of a player's batting average by watching his 600 plate appearances.

And since sabermetrics is an objective study of the game, it is necessary to use logical reasoning in sabermetric arguments. Thus, a hypothesis can be developed from the information you have, either from statistics or observation; a claim which cannot be directly tested can be evaluated by studying the conclusions which would follow.

A good example is the statement "Pitching is X% of baseball," which has been said with X between 15 and 80. Suppose you want to test the claim "Pitching is 75% of baseball." If this were true, you would conclude that the teams with the best pitching would be much more likely to win the pennant than the teams with the best hitting. However, this isn't the case. The league

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