Module 1, Assignment 3: Moneyball
Wednesday January 8, 2014
B6025 Management Decision Models
Adhering to the belief that the success of a major league baseball player is dependent not on their ability to score runs and win games but on a subjective collection of characteristics and misleading statistics seems preposterous. But as illustrated by Michael Lewis’ Moneyball, this seems to be how most major league baseball players have been scouted for decades. In Richard Thaler’s review of the bestselling novel, he explores how the common mistakes made by most scouts when making decisions are not unique to the sport of baseball.
An overarching belief in baseball is that experts accurately predict the success of a player. It seems traditional baseball scouting has developed specific heuristics to decide which players to draft. But this scouting method can be subject to biases when not used properly as each expert has his/her own biases which, if not carefully checked, can color their decisions in subtle ways. As Thaler notes, many player were simply ignored because “they did not match up with the scouts' mental prototype of a successful ballplayer”. What followers of sabermetrics – the practice of using empirical analysis to measure in-game activity- proposed was that “reliable statistical evidence will outperform … heuristic(s) every time” and is a far better indicator of a player’s success. (Thaler, 2003)
When Billy Beane, the main focus of Moneyball, “relied on objective evidence, explicitly ignoring anything that could be dismissed as ‘subjective’ he was going against a social norm in baseball scouting. (Thaler, 2003) Many scouts were using “baseball's conventional wisdom” to scout major league players. Granted, for the scouts portrayed in Moneyball, this scouting method had been, to some degree, successful, otherwise it would have not become the status quo. But in baseball, use of sabermetrics is a more efficient and objective way to select the best possible players in the draft.
Although this method has been proven as more effective, Beane faced the resistance of the other scouts for the Oakland A’s, because it had never been done in their organization. This is the reason change makers are often challenged when attempting to augment keystones of a culture, adhering to “the way things are done” is a large hurdle for decision makers to overcome. The unconscious group think of the majority of scouts prevented them from seeing the value of Beane’s methods.
This reaction seems to be human nature, for people to adhere to the social norms of their given environment. These tendencies have been ingrained in our makeup for millennia, from the time when straying from the tribe meant certain death. This is most graphically illustrated when the social norm is later deemed unacceptable, e.g. chattel slavery in North America or the German Holocaust of the 1930’s and 40’s. In both cases, the people involved reacted to the societal proof heuristic; since most in their environment deemed this behavior acceptable, it was not questioned. In fact both examples illustrate how challenging it is to “change the mind” of a group of people, as both incidents resulted in long and costly wars.
A specific exchange that is recounted by Thaler between Beane and the other scouts shows how something as simple as not reviewing all options, further affects effective decision making. In Moneyball, Beane has an advantage over other scouts because he looks at the players as statistics on a spreadsheet- this objective approach is one that eliminates an obvious drawback to the traditional scouting method. Traditionally, scouts only see a select group of players. Since these scouts have only observed the ‘best’ players in the nation, not every possible baseball player eligible for the majors, they fall victim to a potential pitfall of the representativeness heuristic- that small samples are less representative than large samples are when making decisions. (Garns, 1997) Operating under the assumption that whoever the majority of scouts deemed the best players were, the scouts only observed those select players, never taking a step back and looking at each players performance compared to all other baseball players in the nation. Because of the small group of players that are scouted, the talent of the few did not represent the abilities of the whole. Because Beane uses compiled data from almost every player in the draft, he is able to get a much more accurate view of the players without becoming “biased by what [he] saw, or thought [he] saw, with [his] own eyes” something traditional scouts are vulnerable to. (Thaler, 2003) Beane was able to recognize and exploit this new method of scouting, making his team more successful than organizations with twice his salary budget.
Because of the circumstances Beane faced, the third lowest salary budget in baseball in 2002, he had to be more strategic in his scouting choices and had less opportunities to be wrong, something the other scouts didn’t face. It was this necessity, I believe, that prompted Beane to hire Harvard grad, Paul DePodestas and to rely on statistics instead of the generally accepted rules of baseball scouting. His ability to see that numbers are concrete and a far better indicator of success than the intuition of a scout prompted him to seek out, and get, “young players that other teams simply [did] not want”. (Thaler, 2003) This method of decision making likely saved the Oakland Athletics, a struggling franchise at the time, millions of dollars. Beane’s ability to re-frame the problem of scouting talent in major league baseball set him apart from his peers.
What Thaler also addresses in his review is the far reaching implications Moneyball could have on businesses. If the status quo of an organization is actually costing millions to the company’s bottom line, is it not logical to change it? While for someone outside of the company the answer would be a resounding and fast ‘yes’, the CEO may face many hurdles to get this accomplished. (S)he has to overcome the company culture to make drastic changes.
While working for my former company, Nordstrom, I observed many inefficiencies that were accepted, because they contributed to or reinforced the company’s culture. The most glaring example that comes to mind is the way technology is funded and utilized. The company culture is to not ‘follow the crowd’ and to make conservative decisions - something, if taken at face value, would be considered admirable. This tactic had worked in the past, and may have caused the leadership team of the organization to overestimate the value of this method when the circumstances change.
“Although considered innovative in many areas, Nordstrom had stayed away from large investments in systems technology” but in the mid 1990’s, the internet and all its possibilities became available to everyone. (Pederson, 2005) The rapid nature of this new technology was in exact opposition of a key component of Nordstrom’s culture. So while other retailers were creating websites to reach their customers on this new medium, Nordstrom did not. Instead, they waited to explore all the pros and cons, deciding not to jump on the tech bandwagon just yet, and continue on “one customer at a time”. It wasn’t until October 1998, that Nordstrom launched its website, www.nordstrom.com, two years after Macy’s, its largest competitor. Nordstrom’s slow reaction to this new technology cost the company unknown millions and illustrates how some decision makers can fall victim to the predilection of letting past successes color their ability to predict outcomes with different factors and circumstances.
It would seem that the Nordstrom leadership team in the mid 1990’s saw “patterns where none exist(ed)”. (Hayashi, 2001) They relied on a pattern’s past successes to dictate a decision in a very different environment. Nordstrom’s tendency to weigh all options and outcomes before coming to a decision, in this instance, had became a liability.
Some may argue that Nordstrom’s hesitancy to invest in an untested medium like the internet was a good business decision, given the information at the time. Because Nordstrom had remained profitable overall, like the traditional baseball scouts they could afford to be wrong, at least initially. Unfortunately, few can justify their continued inaction in this area of their business. With a large group of competitors in a global market, Nordstrom’s conservative approach to technology investment is costing them innumerably.
To date information system technology in the company has not had an overhaul since late 2002; the prior system revamp was in 1995. (Pederson, 2005) Since then, the capability of merchandising software has grown exponentially, and one has to wonder how much more revenue Nordstrom could be generating with an aggressive investment in technology and the systems that rely on it. After working and observing the many ways managers make decisions, I have adapted my style to include my past experiences, observed patterns and the use of self awareness, to check my thought processes before a decision is made. I, also, attempt to attempt to answer the root question and not get bogged down by how this decision was made in the past. “If you challenge the conventional wisdom, you will find ways to do things much better than they are currently done." (Lewis, 2003)
References * Lewis, M. (2003). 4. Moneyball: the art of winning an unfair game (p. 98). New York: W.W. Norton.
* James R. Evans. (2012). Statistics, Data Analysis, and Decision Modeling, 5th Edition Retrieved from http://digitalbookshelf.argosy.edu/books/9781269148719/id/pg280
* Jim Sluzewski (2013) Macy’s, Inc. Fact Book, pg 60. Retrieved from : http://www.macysinc.com/Assets/docs/for-investors/annual-report/2013_fact_book.pdf
* Jay P. Pederson. (2005). International Directory of Company Histories, Vol.67. “Nordstrom, Inc. History”. Retrieved from: http://www.fundinguniverse.com/company-histories/nordstrom-inc-history/
* Hayashi, Alden M. (2001). When to TRUST Your GUT. Harvard Business Review, 79(2), 59–65. Retrieved from: http://web.ebscohost.com.libproxy.edmc.edu/ehost/detail?sid=2972ee8c-60ba-43b6-8948-77392a2e6c9a%40sessionmgr4001&vid=1&hid=4206&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=bth&AN=4039074
