Access the below document entitled Individual Case Study Choices.
Read through the case studies and select one (1).
Note: You must answer all questions as posed.
Once you have selected a case study, construct a Word document with a minimum of 1,350 words using Times New Roman 12-point font and 1-inch margins (this comes out to approximately 3.4 pages single-spaced and 5.6 pages double-spaced).
Please include a title page with your final submission with the name of this class and your name. So, it would look something like this:
Group Case Study #6 (Chapter 6)
Business Intelligence and Analytics in Major League Baseball
Early in this century, the Oakland Athletics used readily available traditional player performance
statistics in new ways to decide which players to put on the field, and this change led to better
play and to several division-winning seasons. Their efforts were memorialized in Michael
Lewis’s book Moneyball, and in the 2011 movie of the same name.
Major league teams are now all using data analysis to improve player selection, player
performance, in-game decision making, and player development. The techniques and tools now
in use have moved way beyond what was described in Moneyball. Now, data on every pitch is
captured by a doppler radar system that samples the ball position 2,000 times a second. At the
same time, the batter’s swing is recorded, capturing data about the ball’s speed as it comes off
the bat and the ball’s launch angle. Cameras behind third base record the position of players on
the field 30 times a second. A terabyte of data is captured each game. This is now done at all
major and minor league parks, in most Division 1 college parks, and even at some high schools.
This wealth of performance data is used as input to analytical software for a variety of purposes.
Here are some examples:
In-game decision making: Teams can see where in the field each batter tends to hit the
ball, and they now position fielders accordingly. Therefore, you now often see three
infielders to the right (or left, as the case maybe) of second base, or four fielders in the
outfield. These untraditional defensive configurations – rarely seen in baseball’s 150-year
history–look strange to the average fan, but they are very effective in cutting down on
base hits.
Player selection: Teams can acquire players from other teams, or sign players whose
contracts with teams have run out. Teams have a rough idea of what pitchers they will
face in a year and in what ball parks, which have different dimensions. From the data that
is collected each game, a team can simulate how a batter would do against these pitchers
in those parks during a full season. In this way, a team can project which players would
succeed with them and which might not.
Improved performance: Doppler radar-generated data shows in detail how each pitch was
delivered – the ball’s spin, the way the ball was released by the pitcher, the ball’s
direction and path taken, and other measures. Analysts are now able to show a pitcher
how to change their delivery or motion for certain kinds of pitches. By analyzing data
about his pitching, Justin Verlander revived his career after being traded to the Houston
Astros.
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In 2011 the Houston Astros were one of baseball’s worst teams. They hired Jeff Luhnow away
from the St. Louis Cardinals, one of the early leaders in the use of data analysis, to establish a
program for the Astros. In a two-part McKinsey Quarterly interview, Luhnow described this
work. Initially, many players were resistant to change, for example to new defensive
configurations. But, upper management made it clear to all that the program would continue. A
breakthrough occurred when (1) the club showed players how the data was gathered and used,
and (2) assigned ex-players with software skills as coaches for the minor league teams to explain
the program to players coming up. These moves generated trust and buy-in at all levels. Today,
the Astros’ program is recognized as one of baseball’s best, and the Astros have been one of the
most successful teams on the field. Many of Luhnow’s staffers have been hired away by other
teams.
Luhnow says data analysis in baseball will continue to evolve. In the future, he says, big data and
artificial intelligence will be increasingly important. One area of interest is using biometric data
to predict, and thus prevent, injuries, particularly to pitchers.
Group Case Study #6 Questions – Answer Both
1. Baseball executives typically call their analysis programs “analytics.” Based on this
chapter’s BI and Analytics definitions, would you say that their programs are more
Business Intelligence or more Analytics? Or, some of both?
2. Excel is a popular and powerful program with a good statistical package. Why do you
think baseball teams use tailored software applications for their data analysis, instead of
Excel?
