A Case Study of the Actor-Network Effects of Moneyball on Major League Baseball

A Research Paper submitted to the Department of Engineering and Society

Presented to the Faculty of the School of Engineering and Applied Science

University of Virginia • Charlottesville, Virginia

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Science, School of Engineering

Corey Nolan

Spring 2020

On my honor as a University Student, I have neither given nor received unauthorized aid on this

assignment as defined by the Honor Guidelines for Thesis-Related Assignments

Advisor

Sean M. Ferguson, Department of Engineering and Society

1

Introduction

Entering the 2002 Major League Baseball (MLB) season, the Oakland Athletics found

themselves in a tough spot. As one of the poorest organizations in the MLB, they were unable to

re-sign three star players Jason Giambi, Johnny Damon, and Jason Isringhausen as the New

York Yankees, Boston Red Sox, and St. Louis Cardinals, three large-market, powerhouse

organizations, signed the free agents to deals the Athletics could not match. Desperate to keep

his team competitive but unable to afford the exorbitant price tag of high-profile athletes, Billy

-minded assistant Paul DePodesta

to find a way their team could compete with larger market teams with only a third of their salary

cap.

DePodesta was long a student

to find a way the Athletics could compete (A Guide to Sabermetric Research, 2020). Sharply

departing from tradition scouting theory, the theory DePodesta and Beane discovered did not

emphasize how an athlete looks or their physical tools; instead, it asked if a player could get on

base or hit, meaning players with higher on-base (OBP) and slugging percentages (SLG).

many runs a team scores, therefore maximizing their chance to win games.

With about $40 million to assemble a competitive team, Beane took a gamble and trusted

his assistant, having faith that the objective analytics would help them field a more competitive

team than Athletics clinching

their division in 2002 and 2003 with a team consisting of many players

-competitive and at best Double-A or Triple-A talent (Sickels,

2

2011). inated the league

in wins per dollar spent, tying the Yankees for most wins while spending less than one-third on

their payroll (Figure 1).

Figure 1

shorthand for anything related to analytics in sports can be seen

in entertainment (how fans watch the game and what statistics they pay attention to), sports

betting, and even in other sports like soccer, where managers and statisticians replicated the

Moneyball

soccer labor market undervalues players with higher total distances run per game (Weimar &

Wicker, 2017). This massive network effect stemming from Moneyball is very intriguing and

complex, but it is important to narrow the focus of my research to understand the strongest actor-

network effects that occurred. Therefore, this research paper will serve as a case study on the

Actor-Network theoretical framework applied to the effects of Moneyball on the MLB,

specifically the actor-network of the players, management of organizations, and analytics.

3

Theoretical Framework of the Actor-Network Theory

The Actor-Network Theory (ANT) is a theoretical framework used to consider the

shifting networks between society and technological achievements that assigns agency to both

human and non-human actors. It treats these actors or actants as active participants in a

dynamically changing network of relationships, holding that no actor acts alone. ANT considers

every factor in the social and natural worlds as actors in a continuously woven web of relations

within which they are located (Law, 2009). Darnell, Giulianotti, Howe, and Collison (2018) hold

hen new ideas or movements are formed

research paper.

To understand how ANT is an appropriate framework for my research, I reviewed

Reassembling the Social: an Introduction to Actor-Network-Theory and the first

three sources of uncertainties of ANT he explores in this book. The first source of uncertainty

holds that there are no groups, only group formations that are constantly formed and re-formed

et al., 2018, p. 91). The third source of uncertainty

holds that if society cannot explain power, which requires explanation in ANT, then the notion of

agency extends past humans and human action.

Essential to my research, the first source of uncertainty holds with the Moneyball

movement, as play ly changing,

shaping each other, and being re-formed. I support this claim later in this paper with clear

evidence regarding how each actor drastically changed after the spread of Moneyball.

4

In the second source of uncertainty, actions and agencies lead to the previously

mentioned social action and organization. Applied to my topic, Michael Lewis solely possessed

the agency of publishing Moneyball, which had a profound effect on players,

management, and analytics. One of the greatest network effects of Moneyball was a shift in

. From a talent acquisition standpoint, every baseball team has always had

its power to oppose the power of other teams in signing free agents. In the team-player dynamic,

teams generally held all of the power, as they were the ones cutting the checks.

After the Moneyball movement spread across the League, the previously undervalued

players gained power. These players understood what was occurring and used their newfound

power to leverage their salaries to pit teams against each other in their own power struggle, just

like superstars have always done. Over time, these players began to lose their leveraging power

as they were now appropriately compensated and the player market had adjusted (Brown, Link,

& Rubin, 2017) and are

experiencing the endgame of Moneyball (Sheinin, 2018).

With the third source of uncertainty, analytics are non-human factors with notable

agency. In regards to my research, neither a player's skill nor changes in the management of

organizations can directly explain the previously mentioned power as the shift in power was

fueled by analytics. Therefore, because these newfound analytics "modify a state of affairs by

making a difference" it is reasonable to consider agency beyond humans only to non-human

actants (Latour, 2005). For this reason, I hold that analytics are appropriately considered an actor

through the ANT framework.

In baseball, we can apply Darnell (2018), who uses ANT to paint a more specific picture

about how agency led to action and organization in sports development in Jamaica, to better

5

understand the network-effects of the Moneyball revolution. Harnessing analytics allowed

organizations and players to better understand the value of a player. Teams changed their

assessment schemes from what a player looks like on the field to what the data informs them,

which is how teams still operate today. The analytics related to Moneyball were black-boxed and

As seen, the agentic forces of analytics led to organizational changes, but they also empowered

players to train to become what organizations now view as more valuable. Additionally, in the

complicated network of organizational decision-making, owners do not micromanage those

below them. Therefore, the relationships between coaches, hiring managers, and scouts were

impacted most significantly as they, in the complicated nexus of a baseball team, had to

completely reassess what players are now more valuable, what players are available, and then go

fill their talent gap with these players. This is important to have in your mind going forward as I

am black-boxing the actual analytics related to Moneyball and focusing more on using ANT to

look at the relationships between players, organizational management, and analytics as a whole.

Evidence and Data Collection

Using ANT, I draw upon the following set of evidence: the propagation of Moneyball

-game strategy, player off-

nce to better

understand the actor-network effects of Moneyball and the mutually shaping relationships among

players, organizational management, and analytics.

Propagation of Moneyball across the MLB

6

While the change Beane and DePodesta brought to the Athletics in 2002 was an

incredible turning point for their organization, the Moneyball effect led to a paradigm shift that

changed the entire game of b

following their shift to statistics-based drafting and player acquisition was the focus of Michael

(2003) book Moneyball: The Art of Winning an Unfair Game, and the sabermetrics

approach to drafting and scouting quickly gained traction throughout the league. In 2004, the

Boston Red Sox u

advice to hire Theo Epstein, who shared the same analytical approach to the game as both

, p. 184). The following year, the Red Sox won the

World Series. By this time, the Moneyball approach to baseball proved itself with a small market

team clinching their division twice and a large market team winning the World Series.

With ANT, I discovered the connection between James and DePodesta to be a leader-

follower relationship. Bill James developed his analysis of baseball over two decades and

became a network builder in the baseball analytics community. DePodesta followed his

teachings as an acolyte. These are not two random people out of the woodworks rather two

baseball fanatics with a passion to discover the objective truths about baseball. They both had

specific training in analytics and took their social capital with them, as James was hired by the

Red Sox and DePodesta became the chief strategy officer for the Cleveland Browns. There is a

very clear power in their association with sabermetrics.

Before diving into more analytics, it is important to remember why the Athletics decided

to do something absolutely different

existing relationship with Beane, who was in a position where he would completely change his

7

operation because of their dismal prospect on the future season with small-cap space and the

departure of three stars. Hired as the GM and Assistant GM, Beane and DePodesta were two of

the most prominent actors in the clubhouse and had the power to do something completely

different, which is exactly what they did. With this story in mind, I want to talk about players,

organizations, and the evolving story of analytics. Known as the Moneyball effect, theory,

movement, revolution, or philosophy, the Beane and

DePodesta

Player Salaries Shift due to Moneyball Analytics

Many scholars have focused on the economics of Moneyball and how the player market

eventually corrected itself through players who truly had offensive success receiving salaries that

matched their value to an organization. Specifically, Brown, Link, and Rubin (BLR) (2015)

wrote Moneyball After 10 Years: How Have Major League Baseball Salaries Adjusted?, which

took a deeper dive into changed given important

analytics from Moneyball. They separated baseball players into three groups based on their

contract type Reserve Clause, Arbitration Eligible, and Free Agents to determine how

performance and salary changed in the post-Moneyball period. Because Reserve Clause players

give their rights to their team and Arbitration Eligible players can only increase salary through

arbitration upon expiration of their contracts, I will only focus on Free Agents, who retain all of

their rights when their contracts expire. BLR viewed their market as competitive and expected

(Brown

et al., 2017, p. 1). Consistent with their hypotheses, BLR found that as an effect of the rapid

8

spread of Moneyball theory across the League, Free Agents with higher OBP saw a statistically

significant increase in their salary in the Post-Moneyball period.

With analytics increasingly informing salary, players can harness analytics to prove they

have merit or value more powerfully. Knowing that getting on-base makes a hitter valuable,

players can transform themselves to fit new expectations through training. Players can quantify

the most lucrative way to train to make themselves more valuable to a team and receive a higher

salary. This is possible because owners, coaches, and managers all use and trust these same

analytical tools and the findings that came out of Moneyball.

Players Approach In-Game Strategy Differently

While Moneyball focused on maximizing the value of position players, it led to a

revolution of adopting analytics that identified not only new hitting techniques and technologies

bolstering a chers and defensive players could

be more efficient.

A player in the Athletics organization once said:

(Rosner & Shropshire, 2010, p. 359). While this seems like a serious

concern for a player worried about making the 25-man roster, this quote came somewhat

facetiously from the 2002 American League MVP, Miguel Tejada, and highlights the importance

Moneyball places on players getting on-base to create the best probability score and therefore

win. Consequentially, one of the primary effects of Moneyball philosophy was the way offensive

players, even reigning MVPs, approached at-bats. This new approach to at-bats led to the rise of

(TTO) a walk, strikeout, or homerun as seen in Figure 2 (Major

League Baseball Batting Year-by-Year Averages, 2019)

9

Figure 2

As batter patience proved to be valuable, the league average of Pitches Per Plate

Appearance (Pit/PA) rose from 3.73 in 2003 to 3.93 in 2019 (MLB Stats, Scores, History, &

Records). Interestingly enough, the Athletics averaged the most Pit/PA between 2002 and 2004,

highest Pit/PA when they won the World Series in 2004. The most recent MLB playoffs is also a

-bats; 3 out of 4 teams in division

playoffs sat above the league average in Pit/PA, with the Houston Astros, who were exposed and

punished for tipping off their batters what pitch was being thrown, being the only outlier.

To the displeasure of many critics, the sh -bats

caused pitchers to throw more pitches and more bullpen substitutions, which has substantially

Additionally, Sabermetri Firstman (2018) mentioned that the advanced analytic Launch

10

Angle, a product of the Moneyball analytical revolution, shows more successful players

launching the ball higher and contributing to the increasing homerun or strikeout count of the

TTO. This reveals

game through many complex relationships. -game

strategy, in turn, convinced more organizations to adopt this approach to the game, influencing

more players around the league to take more pitches at-bat and further propagating the TTO.

Analytics have also inspired experimenting with new physical technology, namely the

Axe handle. Used by stars like 2017 World Series Champion and MVP George Springer and

2018 AL MVP and World Series Champion Mookie Betts, the Axe Bat is a new development in

baseball that allows a hitter to grip the bat with less force and experience a greater range of

motion through the swing (Sarris, 2018). With data from Axe Bats, a study across 15 batters

shows that Axe Bat users gain about 74 on-base plus slugging percentage points, which

correlates to about 7.4 more bases per 100 at-bats (Long & Cole, 2016). Another study from

Adam Foster (2012) at Baseball Prospectus revealed that using the Axe Bat increased bat speed

by 0.5 mph, added 5.7 feet in distance, and increased hard-hit ball velocity by 3 mph.

As such micro-innovations in technology and the approach to in-game strategy are

produced, they can be rapidly evaluated with the increasing volume and availability of data and

analytics. The baseball community can quickly gauge the effects of innovations that might have

failed or remained minor because of the sheer volume of data sets and analytics available in the

world of baseball. An increase in player technology, as analyzed in the next section, helped the

baseball community gain insight into how such a fundamental part of baseball the 150-year-old

baseball bat is a candidate to be improved through advanced analytics (Gupta, 2014).

Moneyball Revolution Influenced Off-Field Approach to Baseball

11

Since the Moneyball movement occurred, a spike in technology aimed at tracking and

analyzing player performance occurred in baseball. In an attempt to find a competitive edge at a

player-level, position players and pitchers have had their every movement in practice and

training tracked through advanced throwing, hitting, and wearable technology and analytics. In-

stadium advanced analytics provide a plethora of actionable information for pitchers, hitters,

organizations, broadcasters, and umpires. Consistent with the focus of this paper, I primarily

looked into the ways this offspring of the Moneyball movement affects players and

organizations.

Introduced in 2006, the PITCHf/x Tool tracked and categorized the speed, location,

break, and result of every single pitch thrown in the MLB through a sophisticated system of

cameras (Brooks, 2020). Following the Moneyball movement, many analysts desired more

-makers.

In effect, TrackMan, the leader in golf swing analytics since 2003, rolled out TrackMan Baseball

over the PITCHf/x technology in every single Major and Minor League ballpark in 2017 to

provide more plentiful and accurate data on every pitch and swing as seen in Figure 3.

Figure 3

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With the data provided by TrackMan, pitchers and batters alike can identify their

strengths and weaknesses. This allows pitchers to train or avoid their bad pitches while

exploiting their effective ones and batters to train it hit or avoid the pitches they struggle against

while more aggressively seeking the pitches they hit well. This technology also shapes how

organizations manage pitchers with information about durability (e.g. decreasing speed or break

of a pitch during a game or between two games) to make decisions about who should start, rest,

or is experiencing decreasing effectiveness. Additionally, organizations utilize this information

when opposing teams.

.

[s] of

game (Zakarin, 2017). The symbolic power of these artifacts completely changed the off-field

approach to baseball. They take on social capital in the sense that players trust the associated

analytics to help them improve their game and organizations trust these analytics to inform better

roster and talent acquisition decisions.

13

Another set of technologies changing the way players and organizations prepare off the

field are new

technology in baseball, he covers motion-capture, wearable, batting technologies (K-Vest and

SwingTracker) and camera pitching technologies (Edgertronic and Rapsodo) used by more than

half and every team in the League, respectively. The batting technologies use sensors to capture

body and bat movement to analyze and predict swing efficiency and ball flight, and the pitching

camera technologies use cameras to analyze

as quantitative statistics about how the ball crosses the plate. The effect of Moneyball pushing

technology forward to provide more data has given more agency to analytics as it constantly re-

forms the relationships between players and organizations. Players and coaches can now

figuratively speak through new technologies like TrackMan, showing how a person can become

an entirely different player through the use of analytics, as discussed later in the Trevor Bauer

story. These advanced, cutting edge analytics inform organizations about the offensive and

defensive performance of their players while allowing players to take new actionable information

to improve their standing within their organization and improve their craft to demand greater

salaries from these clubs.

Organizational Management Before and After Moneyball

Organizational management within the league changed as teams adopted the philosophies

of Moneyball, which can be seen through how organizations quickly began to compensate

players identified as more successful through the practices of Moneyball. Beane personally

remarked that the market corrected itself and his experience of the endgame of Moneyball now

that every team is employing his practices and valuing the same players (Sheinin, 2018). Articles

in the few years following the publishing of Moneyball als

14

analytical approach to assembling a competitive team changed the organizational management of

many other teams specifically the Red Sox. The agency of analytics to re-shape the

relationships between organizations and players follows under ANT theory. Baseball

organizations now focus much more intensely on developing in-house analytics teams to gauge

the performance of not only their own team but also that of their opponents

in UVAToday, he explores how many UVA students have

been placed in the ranks of the -growing sports analytics field that was brought to

prominence by the best- Moneyball Their work with the Dodgers, Marlins, and

Athletics sports analytics teams completely changed the way organizations incorporate statistics

into scouting prospects and opponents to find a competitive edge.

Summary of Evidence and Data Analysis

Through my analysis of player salaries, changes to the in-game and off-field approach to

baseball, and transformation of organizational management, I identified the effect of the

Moneyball movement on the relationships among players, organizations, and analytics within the

continually changing network of the MLB. The new analytics and practices of Moneyball shed

light on an unadjusted market where teams could cheaply acquire players worth more than their

salary and gained previously unknown-to-be-valuable players more power in their relationships

with organizations. I contributed to the current literature by tying together the unique Free Agent

market of baseball and the shift in the power dynamic between these players and organizations.

Unpacking this after the Moneyball revolution, teams view a player as worth more if they fit into

the outputs and expected outcomes discovered by the Athletics. While tangentially related,

15

questions still exist about how regulations in the MLB that prevent the Reserved Clause and

Arbitration Eligible player markets from adjusting to competitive markets.

Informed by analytics, the in-game and off-field approaches to the game were

Moneyball and the desire of players and organizations to harness this power helped identify the

effect analytics had on players and organizations with the ANT framework. Identifying that

players and organizations alike utilize the information provided by wearables and advanced

analytics to improve their training and game preparation show how Moneyball increased

technology and analytics around the League while balancing the power between players and

A still

existing question here is the limited population sample of the efficacy of new batting technology.

While the small sample reduced the ability to make statistically significant findings, the general

findings of the research do merit looking further into the effects batting technologies may have

on offensive performance.

Finally, while the baseball cohort has long been renown as the leader in sports of

collecting data with records going back to the 1870s, the Moneyball revolution poured fuel on

the fire of organizations developing their own data analytics teams, adopting statistical-based

strategies, and investment into new technology to gain a competitive edge in the League.

cultivation of full-fledged analytics teams in every MLB club painted a picture of the continual

short- and long-term network-effects of Moneyball on MLB organizations.

Discussion

16

If Moneyball did one thing better than anything in the history of baseball, it would be

bringing the baseball and data communities together. To understand how these groups mesh

together, I document the negotiation between these different parties to come to a common

ground and then analyze how Trevor Bauer, an MLB Pitcher, harnessed analytics to transform

his pitch design and ultimately become an All-Star.

Retraining the Baseball and Data Communities

Moneyball is a symbolic representation of a whole host of relationships and tensions that

created a massive demand for more analytics. Players, fans, organizational management,

analytics, emerging technologies, umpires, and broadcasters are all involved. To become

involved in the analytics revolution, the baseball community needed to become knowledgeable

about data, and the data community needed to become knowledgeable about baseball.

Looking at the roots of the Moneyball movement, Beane, as a part of both the baseball

and data communities, had to establish his expertise and skill in the data domain as authoritative

originally scoffed at the idea of

decis

community to be more open to the notion of incorporating data into their decisions.

As GMs and front offices advocated for more data, the data community needed to

understand more about baseball. In 2006, the world-leading MIT Sloan Sports Analytics

Conference began. This conference came during a time when GMs and front offices in the

League advocated for more data to better inform their decisions. As the foremost conference for

all sports analytics, this became a breeding ground for negotiation between the baseball and data

community about their desires and abilities to produce and incorporate more data. This year, Bill

17

regarding recent developments in the

game of baseball and continuing the dialogue bridging the baseball and data communities (MIT

Sloan Sports Analytics Conference, 2020).

As the demand for sports analytics increase, the development of university-level data

science schools is becoming popularized through the need to retrain the data community to have

more mobility and be able to address a plethora of problems in many different fields. Among

$120M School of Data

Science (Hester, 2019). The craze for data analytics has driven major changes in the academic

community that promise to ease the process of retraining the data community to tackle problems

in a variety of fields including baseball.

While data scientists and analysts may be able to produce results, Dr. Lashbrook of

e analytics to a

Coach, Scout, or GM so they can incorporate it in their strategy. A knowledge of scouting in a

His

testimony to the importance of the data community understanding a sport like baseball is

mirrored in the words of many coaches in baseball.

e ball are doing on

both players and coaches to be open to embodying data-driven analytics that they need

stically-driven approach to baseball. Additionally,

18

being able to man The negotiation

these actors have been retrained to understand not only the importance of data but also that it can

produce massive benefits if one learns to manipulate it properly.

The retraining of these groups would not be complete without players understanding the

expertise and skill the rest of the baseball community has to be authoritative, so player retraining

is seen coming from the top of an organization because they are the ones in the baseball

community with the power to harness more data and develop data analytics teams. In an ESPN

article, Crasnick (2018) identifies the unique dynamic that the bridge between the statistical and

baseball worlds will more likely resonate when delivered by a member of the baseball

-office official with a statistics degree

-

Information Coordinator, previous baseball player Sam Fuld, states that it is tough for players to

the right to know exactly what matters when it comes down to decision-

2018). This negotiation between the data and baseball communities is extremely important as

players not only have the peace of mind knowing what metrics they are judged on but also

possess the power to harness the data and focus on skills in areas they originally are not aware

they can improve. Following participants within the baseball community establish their skill in

the data domain as authoritative to baseball players paints a picture of participants in the baseball

world helping the baseball and data communities find common ground.

19

In baseball today, Trevor Bauer is a living example of how the baseball and data

communities came together and how players can become completely different based on the

technology they use. latest technology:

Edgertronic high-speed video, TrackMan radars to Pitch F/X optical tracking cameras to

Rapsodo, a radar- (Lemire, 2017). All four of these tools, as previously

discussed as the most advanced pitching analytics, were crucial to helping the

scientific method to improve [himself] as a baseball player seen as

immutable mobiles in the ANT lens that produce meaning in the black-box and power in the

system of baseball player training. Bauer is able to speak through these technologies to become

an entirely different person, as he would not be considered great on face value. He highlights the

importance of analytically-based trainin

would have had any chance of getting [to the MLB] just purely based on athleticism if [he]

The datafication of players and their abilities

empowers players. This translation of agency in baseball from analytics to the players is a

testimony to how analytics empowered players to prove they have more merit and allowed

players that would not have been considered valuable to form themselves to expectations and

beco

speaks toward the paradigm shift in player evaluation in the MLB from what a player looks like

to what the numbers prove.

Overall Conclusion

As the incorporation of analytics continues to grow, the relationships between players,

organizations, and analytics will continue to change. The Moneyball revolution completely

changed the face of baseball. Player salaries, in-game and off-field approach to baseball, and

20

organizational management experienced drastic transformations as an effect of the representation

of relationships and tensions known as the Moneyball movement. While these changes

continually occur, negotiation between the baseball and data communities continues as they

retrain and reshape one another. Building on this work, unboxing Moneyball analytics and the

translation of these tools from one team to another could help propagate the notion that analytics

is not just helpful, but completely viable given both t

data and the ease of adoption of the analytical tools. This future work could help identify the

unknowns of why the Moneyball movement was the revolution that made sports analytics

universal in the game of baseball.

21

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