2. ii
ABSTRACT
REVOLUTIONIZING THE GAME: A CASE STUDY ON THE STATISTICAL
ANALYTICS PRACTICES OF THE HOUSTON ASTROS
RYAN ANTHONY
MARCH, 2016
The expansion of statistical analytics has dramatically changed the way sport
organizations conduct business. Statistical analytics can be used to help sport
organizations gain a competitive edge by analyzing data to implement specific analytics
practices based off that data. The purpose of this study was to examine the statistical
analytics practices of the Houston Astros. The Astros were analyzed through a case
study approach developed by the researcher. Data were collected from the Houston
Astros home website, clicking on their roster and front office tabs, and third party
organization analysis. The results demonstrate that the Houston Astros use statistical
analytics to their fullest advantage because they use their analytic data to assist in
decision-making with real game situations, and they have a department staff filled with
highly intellectual analytic professionals. This study recommends that all sport
organizations implement statistical analytics practices within their organization to gain a
competitive advantage over other organizations.
Keywords: Major League Baseball, Houston Astros, analytics, statistics, strategies
3. iii
TABLE OF CONTENTS
PAGE
ABSTRACT ....................................................................................................................... ii
TABLE OF CONTENTS .................................................................................................. iii
Chapter 1 INTRODUCTION AND REVIEW OF LITERATURE ....................................1
Background of Study.......................................................................................................1
Review of Literature........................................................................................................2
Purpose of the Study......................................................................................................10
Research Questions .....................................................................................................100
Chapter 2 METHODS .......................................................................................................11
Description of Organization ..........................................................................................11
Description of Instrument............................................1Error! Bookmark not defined.
Description of Procedures .............................................................................................12
Chapter 3 PRESENTATION OF THE RESULTS ...........................................................14
Hiring Process ...............................................................................................................14
Analysis of Data ............................................................................................................16
Implementation of Data.................................................................................................18
Chapter 4 DISCUSSION AND CONCLUSIONS ............................................................20
Discussion......................................................................................................................20
Conclusions ...................................................................................................................24
Recommendations .........................................................................................................24
REFERENCES..................................................................................................................26
APPENDIXES...................................................................................................................30
4. 1
Chapter 1
INTRODUCTION AND REVIEW OF LITERATURE
Background of Study
In any industry, having an advantage over your competitors is very significant to
achieving success. Statistics are one of the many significant tools that organizations use
to strive for this competitive advantage. One of the biggest industries to utilize statistics
is professional sport. According to Baumer (2015), 112 of 122 (92%) professional sport
organizations state that they are believers in analyzing statistics to gain a competitive
advantage. Over the last twenty years, more than one hundred and fifty years of sports
“knowledge” has been turned on its head by the use of statistical analytics, or
sabermetrics, a term coined by baseball writer/statistician Bill James (Zminda, 2010).
Before the invention of the computer, evaluation of talent was primarily done by scouts
using observation and note-taking techniques. Improvements within the organization
were made through the use of trades with other teams or hiring new coaches and
managers to help the existing personnel play the game better. Today, in addition to
having talent scouts all over the world, organizations have the ability to use statistical
data to evaluate any particular skill they want. By analyzing numerous amounts of
statistics, the computer allows organizations to find value in a player’s game that a scout
may not see with the naked eye.
The emergence of statistical analytics has had a huge impact on sport industries
worldwide. One of the best ways analytics is utilized is through Major League Baseball
(MLB), as there are so many different aspects to the game of baseball to be analyzed like
5. 2
speed, range, velocity, and acceleration. One of the more popular believers of using
analytics in the MLB is the Houston Astros. The purpose of this study was to examine
the statistical analytics practices of the Houston Astros.
Review of Literature
Research for this review of literature was conducted at Robert E. Kennedy
Library on the campus of California Polytechnic State University, San Luis Obispo. In
addition to books and other resources, the following online databases were utilized:
ABI/INFORM Complete, Academic Search Premier, Business Source Premier, Google
Scholar, and SPORTDiscus. This review of literature examined the basics of analytics,
the evolution of statistical analytics into the sports industry, and the implementation of
statistical analytics in Major League Baseball (MLB).
The manner in which businesses try to gain a competitive advantage over their
competitors is constantly evolving. One of the best ways businesses can do this is by
using analytics. According to van Barneveld, Arnold, and Campbell (2012), analytics is
“The process of data assessment and analysis that enable us to measure, improve, and
compare the performance of individuals, programs, groups of organizations, and/or entire
industries” (p. 3).
Analytics can be very helpful in any industry because the data available can be
used to influence important business decisions based on data-driven statistics. Some of
the biggest corporations in the world such as Nike, Apple, and Amazon serve a variety of
products to customers globally and have factories and stores all over the world (Henry &
6. 3
Venkatraman, 2015). It can be very complex and difficult without the assistance of
computer-generated data to find where and why problems occur within a business.
Data are being collected and produced so constantly by humans and machines that
the amount of data available are overwhelmingly large and available compared to any
time in the past (Hardgrave, 2013). Data analytics makes it much easier for
organizations, businesses, and/or entire industries to make quick decisions based on
numbers. Hardgrave (2013) stated:
“Big data is about velocity. Remember the good ol’ days when organizations ran
monthly or quarterly reports as a way to look back at what happened? Today’s
data is instantaneous. Organizations can’t afford to wait until the end of the
month (much less the end of the quarter) to gather and use the data” (para. 4).
From a business standpoint, Henry and Venkatraman (2015) noted that analytics can be
very beneficial because it allows businesses to examine large sets of data, enabling them
to respond to their existing needs in certain areas of operation within the business. Once
these large sets of data are collected, the goal is to use that data to influence decision-
making processes when needing to fix problems found within the business. This explains
the recent trend for businesses to recruit and hire people who know how to handle and
analyze large data sets to make important business decisions. According to SAS (2011),
97% of companies with revenues of more than $100 million are using some form of
business analytics. This is up from 90% just two years ago.
A big part of business analytics is about hiring the right people. Utilizing
analytics is only effective if the findings are understood and interpreted correctly. If a
business or organization has data readily available but doesn’t know how to interpret
7. 4
what any of it means, then the data means nothing, and it cannot be applied to anything.
For example, big Internet companies like Google and Amazon utilize analytics to their
benefit by tracking consumer purchases and online web searches so they can make
adjustments to certain products they have to offer (Mondello & Kamke, 2014). This is
also known as Customer Relationship Management. Many businesses use an
organizational method because it provides valuable data about consumers’ current
purchasing habits in order to potentially predict future buying practices (Mondello &
Kamke). Businesses can then summarize their data, interpret their results, and use their
results found to make necessary adjustments within the company based on certain
patterns and trends analyzed in order to market themselves better to customers.
One type of analytics that is more focused on future events is called predictive
analytics. According to van Barneveld et al. (2012), predictive analytics is “A set of
business intelligence technologies that uncovers relationships and patterns within large
volumes of data that can be used to predict behavior and events, it is forward-looking,
using past events to anticipate the future” (p. 4). Predictive analytics can be vital to an
organization’s success because it increases their business knowledge by applying
complex analysis techniques to interpret large sets of data. For example, organizations
use certain algorithms that can tell them the next best offer to make to each customer
based on past patterns and trends, and the results often lead to improved
recommendations to meet consumer demands and higher rates of sale (Leventhal &
Langdell, 2013). Organizations need strong analytics departments to be able to
understand large data sets and make meaningful decisions.
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One of the biggest global industries in the world today that benefits from analytics
is professional sport. The sports market in North America alone was worth $60.5 billion
in 2014 and is expected to reach $73.5 billion by 2019 (Heitner, 2015). The sports
industry has become a rapidly growing platform for analytics (Fry & Ohlmann, 2012).
There is a tremendous amount of data to be collected and analyzed in sports just like any
other industry. Decisions within sports organizations have become progressively
influenced by mathematics and data analysis (Baker & Kwartler, 2015). Alamar (2013)
states the purpose of sports analytics is “to aid an organization’s decision makers
(personnel executives, coaches, trainers, and so on) in gaining a competitive advantage”
(p. 4). Analytics is very valuable to the sports industry because organizations can utilize
data to find certain values in players that best fit their organization.
For the use of analytics to be utilized successfully within an organization, there
are two main goals (Alamar). First, a strong sports-analytics department will make all of
the information for evaluating players, teams, or prospects systematically and readily
available for upper-management decision-makers. This allows the upper-management
decision-makers within the organization to find the relevant information they are looking
for in a productive and efficient manner, as opposed to having them have to access
multiple sources like websites, unorganized spreadsheets, or other departments within the
organization. The second goal of a strong sports-analytics department is to fully
understand what is being analyzed. As the amount of available data continues to expand,
so does the opportunity of gaining useful information from that data. The challenge then,
is to find and interpret that useful information. Combining statistical analysis with the
insights of talent scouts all over the world leads to a more accurate evaluation of a
9. 6
player’s talent at the professional level (Alamar). Large amounts of numbers alone are
not meaningful; success is achieved by organizations that interpret the data available to
them correctly.
Organizations that need large amounts of statistical data often outsource to other
companies or services in addition to in house statisticians to obtain all the data needed.
The Golden State Warriors of the National Basketball Association (NBA) use outside
companies such as SportVu cameras and Catapult Sports (Berger, 2015).
SportVu cameras have the ability to capture player movement at 25 frames per
second to monitor players’ movement intensity and acceleration in games. These
cameras also obtain information that help to identify fatigue and overuse of a player.
Catapult Sports manufactures wearable technology that has the ability to monitor how an
athlete is moving. A tiny device placed in a sleeve worn on a player’s elbow, features a
location-positioning system, an accelerometer to measure stops and starts, a gyroscope to
measure the body’s movements, and a magnetometer to measure direction. Combining
the in-game data using SportVu cameras and the data from the biomechanical movement
device from Catapult Sports, an organization can lessen the risk of injury to its players
and lead to better management of those players use within the organization.
Use of analytics by a professional sports team is well demonstrated by an example
from a NBA team, the Boston Celtics (Alamar). Through their use of data analytics, the
Celtics drafted future all-star Rajon Rondo in 2006, finding a certain value in him that
other teams either did not see or overlooked. The analytic data used by the Celtics
determined that rebounding guards are an undervalued skill in the NBA. This left Rajon
Rondo available to the Celtics, as he was overlooked by all the other NBA teams until the
10. 7
21st
pick. The Celtics were able to select a player late in the draft who eventually
developed into one of the best point guards in the league because other teams didn’t
understand his potential rebounding value as well as the analytic-thinking Celtics did.
Analytics is so popular in the sport industry because organizations want to gain an
advantage over their competitors on and off the field. Teams that invest in analytic
departments consistently remain on the forefront of collecting new data and using
increasingly improving analytic tools, which will give them that consistent competitive
advantage over other teams in their league (Alamar). According to Maciaszek (2014),
current Oakland Athletics (A’s) general manager (GM) Billy Beane commented:
“The legacy of analytics is that it has allowed really, really smart people to get
into the business. Now it’s a meritocracy where the best and brightest are part of
our business. Ten years from now I won’t be smart enough to apply for my own
job” (para. 10).
There are numerous amounts of statistical data available that are constantly evolving in
the sports industry. Many new job opportunities arose for people who are exceptional
with numbers but not necessarily with sports. As a result, there are now many more Ivy
League-business types in charge of baseball front offices as directors of the sports
analytics department for their organization (Maciaszek).
One of the biggest sport industries to utilize analytics practices is Major League
Baseball (MLB) because there are so many aspects to the game that can be measured
such as speed, range, acceleration, velocity, etc. People have been using statistics to
compare players for more than a century, but now mathematical experts are developing
complex statistical models to improve analyzing and predicting player performance
11. 8
(Wolf, 2015). This new method of statistics was inspired by statistician Bill James and is
referred to as sabermetrics, coming from the acronym SABR (Society for Baseball
Research). Sabermetrics is, unfortunately, often misunderstood because of all the
complex statistics that are involved and not everyone knows how to interpret the results
found. Wolf (2015) claimed, “The development of multifaceted statistical analysis from
the 1860s to the present suggest baseball analytics are nothing new; rather, they have
evolved especially since the 1950s and exploded in the 1970s and beyond” (p. 239).
Analytics have enjoyed a proliferation in the game of baseball, especially in small
market areas. Because the small market teams don’t have the revenue to pay high player
salaries, they must make sure to draft the best players for their organization. The
Oakland A’s (a small market team) GM Billy Beane has used analytics and has managed
to stay relatively competitive over the years doing so (Davenport, 2014). With their
limited budget, the A’s organization instead uses analytics to find value in players that
other teams not using analytics, don’t appreciate. Beane admits, “We are a functioning
business where we make money every year and we don’t spend more than we have.
Metrics are the big thing. We had to turn a subjective process into an objective process”
(Maciaszek, para. 9). In more specific terms, Billy Beane wants the A’s organization to
make decisions objectively, where subjective personal opinions/feelings are not
influenced when making those decisions. For example, the primary focus of the Oakland
A’s when drafting players is to find those players who are skilled in certain analytically
proven performance areas such as getting on base. This has much more value to the A’s
organization than the ability to bunt and steal bases, again, based upon statistical
analytics (Davenport). The Tampa Bay Rays are another example of a small market team
12. 9
using analytics to their advantage (Alamar). They were one of the first teams to use data
from Pitch F/X, which tracks the path of the ball on every pitch in order to better
understand the evaluation of each pitcher.
Selection of players is a key factor within an organization in order to stay
competitive. Although the objective statistical data regarding a player cannot be
overvalued, organizations must also obtain information regarding the player’s mental
skills, personality, clubhouse presence (or lack there of), and character history (Lin, W.,
Tung, I., Chen, M. J., & Chen, M. Y., 2011). While this information is more subjective
in nature and therefore lends itself less readily to statistical analysis, it is no less
important in the ultimate determination as to whether or not a player will be a good fit for
their organization.
It’s not just teams in small market areas, however, that are starting to believe
analytics can contribute to success on and off the field. According to Baumer (2015), 22
of 30 (73%) MLB teams state that they either have already implemented an analytics
department within their organization or are currently making strides in that direction.
Most significantly, in 2003 the Boston Red Sox hired famous baseball writer, historian,
and “sabermetrician”, Bill James, as Senior Advisor of Baseball Operations (Zminda). A
year later in 2004, he was given credit for supporting some of the moves made within the
Red Sox organization that ultimately led to the team’s first World Series Championship
in eighty-six years.
Analytics can help a team determine how best to use the players within their
organization. For example, analytics could show that a specific player gets on base
almost 2.35 times a game when he hits in the #2 spot in the lineup, as opposed to only .85
13. 10
times a game when he hits in the #6 spot. This type of information is always very helpful
for managers to know when putting together their lineup.
Players are often overlooked for a variety of biased reasons and perceived flaws,
whether it’s age, appearance, personality, or some other factor. Bill James and his
sabermetricians have been able to turn more than one hundred and fifty years of baseball
“knowledge” on its head by the using statistical analysis to find value in players that
might otherwise be overlooked. The use of statistical analytics is an art in and of itself.
It can make what was once invisible, visible.
Purpose of the Study
The purpose of this study was to examine the statistical analytics practices of the
Houston Astros.
Research Questions
This study attempted to answer the following research questions:
1. What is the Astros hiring process in their analytics department?
2. How significant of a role do statistical analytics play for the Astros?
3. What is the most significant statistical variable that the Astros utilize?
4. How do the Astros gather and implement their statistical data?
14. 11
Chapter 2
METHODS
The purpose of this study was to examine the statistical analytics practices of the
Houston Astros. This chapter includes the following sections: description of
organization, description of instrument, and description of procedure.
Description of Organization
A case study was conducted on a Major League Baseball franchise’s statistical
analytics strategies. The Houston Astros were evaluated for the case study. Major
League Baseball consists of 30 teams, fifteen in the National League and fifteen in the
American League, and six divisions (three in each league).
The Houston Astros Baseball Club joined the MLB in 1962 as the Colt .45s but
the team was renamed in 1965 to the Astros (Houston Astros, 2016a). The organization
has one major league team, and three minor league teams below them to develop player
talent within their organization. This organization played their first inaugural regular
season game as the Houston Colt 45s in 1962 at the Houston Astrodome and continued to
do so for the next 38 years. They built a new stadium in the year 2000, Minute Maid
Park located on the edge of downtown Houston and continues to play host for the team
today (Houston Astros, 2016b). Minute Maid Park holds 40,963 people and is known for
its replica of a 19th
century locomotive train running along an 800 feet long train track
located high above and behind the left field wall. The train itself is an icon of the
development of the American West and serves as an entertainment feature of the
ballpark. When a player hits a home run, the train will move back and forth along its
15. 12
tracks and make noise to engage fan interaction. Minute Maid Park also has a roof that
retracts completely off the ballpark to reveal the largest open area of any retractable
roofed baseball stadium in existence today. The Astros have never won a World Series
Championship but they continue to be a playoff contender, winning their first National
League title in 2005. Houston businessman Jim Crane bought the Houston Astros
franchise for $615 million in 2011. In 2013, there was a drastic change in the
organization when the Houston Astros made a transition from the National League
Central division to the American League West Division.
Description of Instrument
The instrument utilized in this study was a best practices guide developed by the
researcher (see Appendix A). The purpose of the best practices guide was to address the
specific statistical analytics practices used by the Houston Astros. Examining these
results allowed the researcher to document quantitative and qualitative information for
the Houston Astros franchise.
A pilot study was conducted on the Oakland Athletics to allow adjustments to be
made with regards to the questions being asked by the researcher. Some questions were
altered to provide a better understanding of the statistical practices of the organization.
Description of Procedures
A best practices study was conducted on the Major League Baseball franchise
Houston Astros statistical analytics strategies. The instrument utilized in this study was a
best practices guide developed by the researcher. The researcher began to gather
16. 13
information on this organization during the time period of February 17, 2016 to February
24, 2016. The best practices of the Houston Astros statistical analytics strategies were
analyzed by accessing the Houston Astros home website, clicking on their roster and
front office tabs, third party organization analysis, or other organizations’ websites that
are in Major League Baseball as well. The researcher also searched several statistical
analytics practices pages on the Astros. The results for the Houston Astros statistical
analytics practices will be discussed in Chapter 3.
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Chapter 3
PRESENTATION OF THE RESULTS
The purpose of this study was to examine the statistical analytics practices of the
Houston Astros. A case study approach was utilized to examine the Houston Astros.
This chapter includes the following sections: hiring process, analysis of data, and
implementation of data.
Hiring Process
The Houston Astros have a separate and distinct analytics front office department,
whereas some teams have none at all. The Astros have a “Business Strategy and
Analytics” department with four front office positions. The four main job titles in the
hierarchy of the analytics department within the Astros organization in 2016 are as
follows: Director - Business Strategy and Analytics (Jay Verrill), Manager - CRM (J.
Chase Kanaly), Research Analyst (Whitney Goodman), and Analyst (Justin Wolin).
The Astros hired Jeff Luhnow as general manager (GM) of their organization in
2011, the former general manager of the division rival St. Louis Cardinals from 2003 up
until that point. Luhnow worked as an engineer and technology entrepreneur, and was 37
years old when he left McKinsey and Company, a global management-consulting firm to
go into the baseball business. He had no previous experience in baseball and had not
played the sport since high school. Luhnow was convinced, however, that there was a
place in baseball for some of the same principals that Fortune 500 companies had used.
In his eight years with the Cardinals organization, Luhnow installed a system of
18. 15
analyzing information that he used to put together a great farm system, allowing the
Cardinals to keep their payroll under control while remaining competitive. The Cardinals
also went to the World Series three times, and sixteen of the twenty-five players on the
Cardinal’s 2013 World Series Champion team had been drafted during Luhnow’s tenure.
When Jim Crane, a businessman and self-made millionaire, bought his hometown
baseball team the Houston Astros in 2011 he was looking for someone to run his baseball
operation that valued the traditional baseball fundamentals of scouting and instruction but
also utilized analytics. Crane saw the success that Luhnow had achieved with the St.
Louis Cardinals during his tenure using these similar values and was convinced that
Luhnow was the person who could turn his Astros organization around, which led to
Luhnow’s hiring as general manager.
Luhnow supported the idea of utilizing analytics and used his background in
business to make his first hire with the Astros, an engineer named Sig Mejdal. Mejdal
has two engineering degrees and another in cognitive psychology. Mejdal worked at
Lockheed Martin and NASA before Luhnow hired him in St. Louis and brought him
along with him to Houston in 2011 and is now the Director of Decision Sciences for the
Houston Astros.
Luhnow and Mejdal now lead a nine-man sabermetrics staff that includes a
medical risk manager and analyst, and a mathematical modeler. Not only did the Astros
commit a full-time position to medical analysis, but they also brought in PITCHf/x expert
Mike Fast (a former engineer) to focus on that data source. Since joining the Astros, GM
Jeff Luhnow has hired scouts and instructors at every level of play within the
organization. He has also put together a front office full of people with advanced degrees
19. 16
in economics, law, and business to help with analytics operations within the Astros
organization.
The Astros organization also outsources to a program called Ground Control,
which works by mining advanced statistics and the same kinds of in-house scouting
information and organizes it all together into one large database. Then, in-house
algorithms and analytic models, led by Director of Decision Sciences Sig Mejdal, are
developed that can predict success within the organization.
Analysis of Data
As the amount of available data continues to expand, so does the opportunity of
gaining useful information from that data. The challenge then, is to find and interpret
useful information that furthers the values established by the Houston Astros.
Understanding the values they’ve established is well demonstrated by the Houston Astros
through their utilization of defensive shifts, the practice of moving the shortstop to the
right of second base against left-handed pull hitters. No team in baseball employed
defensive shifts more frequently or effectively in 2014 than the Astros, who moved their
infielders into non-traditional alignments approximately 1,341 times and saved an
estimated 27 runs in the process. This defensive shift strategy goes hand in hand with the
organization’s attractiveness to acquire groundball pitchers, as they had a 51.5% ground
ball rate amongst their pitching staff at the end of 2014, which was the second highest in
Major League Baseball.
To uncover starting pitching talent, baseball’s most expensive commodity, the
Astros’ minor league teams institute a system called a tandem rotation. In a traditional
20. 17
five-man rotation, the starting pitcher goes as deep into the game as he can. In a tandem
system, however, every game features two “starters” who throw four or five innings each.
This increases the opportunity for a number of pitchers to prove they can start and it is
also thought to prevent injury in the long run. Beyond these specific strategies, GM Jeff
Luhnow suggests that the data-driven process of gathering, organizing, and developing
statistical information that the Astros organization has devised is their true achievement.
The way the organization gathers, organizes and develops this statistical information is
by utilizing a program called Ground Control.
Ground Control is a program that takes certain variables into consideration such
as player character, scouts’ input on his potential, academic intelligence, risk of injury,
and number of at bats playing in a wood bat vs. composite bat league and weighs them
according to the values determined by the team’s statisticians, physicists, doctors, scouts,
and coaches. With all of these different variables to consider one can’t simply weight
them all in their head; it is way too much information for someone to grasp. The Astros
have thousands of players on their draft board each year that they are trying to rank in
order based off of these variables, and this is made possible by the use of Ground Control
database. It is essentially the Astros’ brain and knowledge all in one large database.
Ground Control has the ability to display the team’s projected performance for each
player alongside the real statistics. Some players also have green tabs next to their
names, a signal generated by a certain algorithm that the player is ready to be promoted
to the major leagues. Others may have a gray tab that indicates a player should be
demoted, or black that they should be cut.
21. 18
Implementation of Data
Large amounts of numbers alone are not meaningful; success is achieved by
organizations that interpret and implement the data available to them for the optimum
result. Subsequent to the hiring of Jeff Lunhow to set up the department, the Houston
Astros then started hiring personnel that were familiar with and open to the use of
statistical analysis. Jeff Luhnow’s first hire as GM of the Astros demonstrates this as he
hired former NASA analyst Sig Mejdal, who is the mastermind behind writing all the
algorithms and analytic models for their Ground Control database. Luhnow believes that
Mejdal’s talent for compiling and understanding information is a natural fit for baseball
and praises him for his ability to develop programs that help the Astros sort through
thousands of pieces of information such as scouting reports, medical data, and statistics to
assist in evaluating players.
The next step the Astros organization took was firing manager Bo Porter in 2014
less than two years after he was hired. While a lot of front offices in the MLB have
become more modern with their installation of analytics, many teams still feel reluctant to
change and continue to hire old fashioned managers and coaches who are hesitant to fully
buy into new statistical analytics. Bo Porter’s termination was the result of poor
communication between the Astros front office and the coaching staff. Instinctively, the
best way to solve this problem was to hire a manager with experience in both the front
office and coaching. On September 29, 2014 the Astros made progress in the right
direction by hiring a new-age managerial fit in 41-year-old A.J. Hinch. Hinch has
multiple years of experience in the MLB, as he played catcher for six years for four
different teams, served nearly three years in the Arizona Diamondbacks front office as
22. 19
director of player development, and nearly four years as assistant general manager of the
San Diego Padres. In between his two tenures in the front office, Hinch also built up 212
games of managerial experience with the Arizona Diamondbacks. To ease the tension
between the coaching staff and the front office, the Astros wanted to hire someone who
had experience both as an executive and a manager, which is exactly what A.J. Hinch
represented.
A big reason why many current players discredit sabermetrics and analytics is
because most of the analysts have had little playing experience and the players think they
don’t know what they’re talking about. Insert A.J. Hinch, a manager who has played at
the Major League level and is open to sabermetric ideas. Both factors are very important
for getting the players to buy into the ideas generated by the front office using analytic
departments.
Hinch is a manager who will listen to the analytic ideas the Astros front office
have and will implement their ideas into real game situations. Hinch implements the data
generated from the Ground Control database, for example, by shifting and aligning his
players to certain spots in the field based off the data that shows where certain opposing
hitters most commonly put the ball in play.
The Astros organization believes so much in statistical analytics that they are
implementing the data at all levels of their organization such as their A, AA, and AAA
minor league teams. Implementing the organization’s analytic values at the minor league
level will allow the organization to test results before deciding if they should implement
certain ideas on the big league level.
23. 20
Chapter 4
DISCUSSION AND CONCLUSIONS
This study aimed to explore statistical analytics strategies used by the Houston
Astros baseball team. This concluding chapter includes the following: a discussion of the
major findings (including implications), limitations, conclusions (based on research
questions), and recommendations for the organization, industry, and future research.
Discussion
The Houston Astros were in desperate need of rebuilding their organization after
having the worst record in baseball in 2011 and 2012. General manager Jeff Luhnow was
placed in charge of rebuilding the organization in order to achieve the most success.
Results from this study reveal that the Astros organization uses technology and data
analytics to their fullest advantage to help achieve this success.
The Astros organization is innovative in their hiring process by the use of
outsourcing. The Astros hire a department full of highly intelligent individuals with no
previous background working in the baseball industry to help with analytics operations
within the organization. There are now many more Ivy League-business types in charge
of baseball front offices as directors of the sports analytics department for their
organization (Maciaszek). Luhnow continues to implement this idea by putting together
a front office full of people with advanced degrees in economics, law, and business. The
Astros organization can benefit greatly from having all of these intelligent-minded
individuals working in their analytics department, even without previous experience
24. 21
playing or working in the baseball industry. These individuals continue to crunch down
numbers, analyze statistical data, and create new algorithms based off data to find new
statistical values that could possibly be used in real-game situations.
Even though the Astros organization has one of the best analytic programs in the
MLB, they could improve it by adding an “Injury Prevention Analytics” department.
With additional personnel in their Business Strategy and Analytics department, the Astros
organization could create a separate department that solely focuses on the use of analytic
data to help prevent future injuries for players. For example, this department could work
on formulating specific algorithms that have the ability to show which player(s) would
benefit from getting a day off to prevent future injury. This information would then get
handed off to the manager and coaches to be discussed before finalizing a starting lineup
for each game. Having this information available could be very important to achieving
success and keeping a healthy roster through a long, grueling 162 game season.
A significant reason the Astros had a successful 2015 season is because the
manager, coaches, and front office were all on the same page. They all understood what
type of values their organization holds and what type of game plan strategies need to be
implemented to give the team the best chance to win. As the amount of available data
continues to expand, so does the opportunity of gaining useful information from that data.
The challenge then, is to find and interpret that useful information (Alamar). The Astros
demonstrate that they are able to find and interpret useful information through analytics
by their use of defensive shifts. Since the Astros pitching staff is mainly composed of
ground ball pitchers, manager A.J. Hinch uses this to his advantage and has his infielders
shift to different spots in the infield based off of information from Ground Control
25. 22
database that shows the most frequent locations where opposing hitters hit the ball in the
field. It may look odd not having infielders in correct position, but it produces more outs
and lowers the batting average on ground balls by forcing hitters to face three defenders
(shortstop, second baseman, first baseman) instead of the usual two on a particular side of
the infield. The use of defensive shifts has become a popular trend after proven success
by the Astros. Many other MLB teams are starting to adopt the idea as well. So many
teams are starting to use the defensive shift to limit base hits for the opposing team, in
fact, that MLB Commissioner Robert Manfred is reportedly in the process of considering
a rule change to completely ban the use of defensive shifts.
Even if Commissioner Rob Manfred bans the use of defensive shifts, the Astros
could still implement their defensive shift strategy through the use of analytics. Through
the use of spray charts, which shows the most frequent locations opposing hitters hit the
ball in the field, the coaches could use signals from the dugout telling the infielders in
which direction they should anticipate the ball to be hit. This will hopefully give the
infielders a better first step to the ball in order to get the out.
It can be at times very difficult for baseball managers, especially one new to a
team, to lead and motivate 25 male athletes on a team. The players might not like the
new modern-age coaching ideas involved with analytics, or they might not like the
manager in general. Some players do in fact fully embrace the use of analytic statistics,
but others do not. This supports the idea that although the objective statistical data
regarding a player cannot be overvalued, organizations must also obtain information
regarding the player’s mental skills, personality, clubhouse presence (or lack there of),
and character history (Lin et al., 2011).
26. 23
The Houston Astros manager A.J. Hinch is the full package for modern managers.
His front office experience will help communication between the coaching staff and front
office. Both his degree in psychology and background in player development, as well as
him being a former player himself will help him understand player’s psyche and growth.
His willingness to implement analytic practices in real-game situations and his ability to
connect with players and staff in the front office is what makes him the full package and
qualifies him to lead a clubhouse. The Astros statistical analytics program could become
even stronger by signing A.J. Hinch to a long-term contract extension. His continued
involvement with the organization’s advanced statistics and sabermetric ideas will assist
him in his real-game management.
The researcher faced several limitations due to the Internet based research that
was conducted. No data were collected besides information that was published and
available to the public. Most analytic data in the sport of baseball is proprietary
information, meaning whoever owns the information can do with it as they wish.
Because most of this statistical information is meant to be confidential so other teams
can’t access it, the research was harder to find. The research conducted on the Astros
was also limited by a ten-day time constraint given on this study. It must also be noted
that the researcher was enthusiastic and passionate about statistical analytics practices
and although objectivity was the goal, some bias may have impacted the study.
This study examined the statistical analytics practices of the Houston Astros. As
an organization, being able to understand the data being analyzed is very important in
statistical analytics. Large amounts of numbers alone are not meaningful; success is
achieved by organizations that interpret and implement the data available to them for the
27. 24
optimum result. The study shows that the Houston Astros organization utilizes several
analytic practices to gain an edge over their competition.
Conclusions
Based on the findings of this study, the following conclusions are drawn:
1. The Astros hire a front office full of highly intellectual individuals with little to no
background in the baseball industry to form their analytics department.
2. The Astros organization places a highly significant role on statistical analytics.
3. The most significant statistical variable that the Astros use is the utilization of
defensive shifts.
4. The Astros gather and implement their statistical data through the utilization of a
database called Ground Control.
Recommendations
Based on the conclusions of this study, the following recommendations are made:
1. Every team in Major League Baseball should be using statistical analytics
practices.
2. The Astros should implement an “Injury Prevention Analytics” department that
solely focuses on the use of analytic data to help prevent future injuries for
players.
3. If the use of defensive shifts gets banned, the Astros should still utilize their
defensive shift practices by the use of hand signals from the bench.
28. 25
4. The Astros should sign A.J. Hinch to a long-term contract extension and have him
consistently attend meetings to discuss statistical analytics practices.
5. Future research should examine several other MLB teams’ statistical analytics
practices to gain a better understanding of the widespread use of analytics in
baseball.
30. 27
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35. 32
INSTRUMENT
1. How many front office positions does the organization have involved with
analytics?
2. What are the main job titles in the hierarchy of the analytics department within the
organization?
3. Are the statistical data analysts employed by the organization or are they hired
outside vendors?
4. If the organization employs outside vendors as data analysts, do the vendors have
background working in the baseball industry?
5. What statistical data is deemed to be important to the organization?
6. How does the organization collect their statistical data?
7. Does the organization implement this data in all levels of their organization
including their minor league teams, or just the major league team?
8. How does the organization implement all of their data they’ve obtained through
their analytics practices?
Notes:
