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Chapter 1 • An Overview of Business Intelligence, Analytics, and Decision Support 53

use association mining techniques to estimate relationships between different purchasing
behaviors. That is, if a customer buys one product, what else is the customer likely to pur-
chase? Such analysis can assist a retailer in recommending or promoting related products.
For example, any product search on Amazon.com results in the retailer also suggesting
other similar products that may interest a customer. We will study these techniques and
their applications in Chapters 6 through 9. Application Cases 1.4 and 1.5 highlight some
similar applications. Application Case 1.4 introduces a movie you may have heard of:
Moneyball. It is perhaps one of the best examples of applications of predictive analysis
in sports.

Application Case 1.4

Moneyball: Analytics in Sports and Movies
Moneyball, a biographical, sports, drama film, was model to help the Oakland Athletics select play-
released in 2011 and directed by Bennett Miller. The ers based on their “on-base percentage” (OBP), a
film was based on Michael Lewis’s book, Moneyball. statistic that measured how often a batter reached
The movie gave a detailed account of the Oakland base for any reason other than fielding error, field-
Athletics baseball team during the 2002 season and er’s choice, dropped/uncaught third strike, fielder’s
the Oakland general manager’s efforts to assemble a obstruction, or catcher’s interference. Rather than
competitive team. relying on the scout’s experience and intuition, the
The Oakland Athletics suffered a big loss to the assistant general manager selected players based
New York Yankees in 2001 postseason. As a result, almost exclusively on OBP.
Oakland lost many of its star players to free agency Spoiler Alert: The new team beat all odds, won
and ended up with a weak team with unfavorable 20 consecutive games, and set an American League
financial prospects. The general manager’s efforts to record.
reassemble a competitive team were denied because
Oakland had limited payroll. The scouts for the Questions for Discussion
Oakland Athletics followed the old baseball custom 1. How is predictive analytics applied in Moneyball?
of making subjective decisions when selecting the 2. What is the difference between objective and
team members. The general manager then met a subjective approaches in decision making?
young, computer whiz with an economics degree
from Yale. The general manager decided to appoint What We can Learn from this application
him as the new assistant general manager. case
The assistant general manager had a deep pas-
sion for baseball and had the expertise to crunch Analytics finds its use in a variety of industries. It
the numbers for the game. His love for the game helps organizations rethink their traditional prob-
made him develop a radical way of understanding lem-solving abilities, which are most often subjec-
baseball statistics. He was a disciple of Bill James, a tive, relying on the same old processes to find a
marginal figure who offered rationalized techniques solution. Analytics takes the radical approach of
to analyze baseball. James looked at baseball statis- using historical data to find fact-based solutions
tics in a different way, crunching the numbers purely that will remain appropriate for making even future
on facts and eliminating subjectivity. James pio- decisions.
neered the nontraditional analysis method called the
Sabermetric approach, which derived from SABR— Source: Wikipedia, “On-Base Percentage,” en.wikipedia.org/
Society for American Baseball Research. wiki/On_base_percentage (accessed January 2013); Wikipedia,
The assistant general manager followed the “Sabermetricsm,” wikipedia.org/wiki/sabermetrics (accessed
Sabermetric approach by building a prediction January 2013).

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