X-Rays Vision: Finding Success Through Sabermetrics in Major League Baseball

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Honors Theses - Providence Campus College of Arts & Sciences

2023

X-Rays Vision: Finding Success Through Sabermetrics in Major X-Rays Vision: Finding Success Through Sabermetrics in Major

League Baseball League Baseball

Madelyn Dickinson

Johnson & Wales University - Providence

, [email protected]

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Dickinson 1

X-Rays Vision:

Finding Success Through

Sabermetrics in Major League

Baseball

By Madelyn Dickinson

Advisor: Scott Palmieri

Date: May 2023

Submitted in partial fulfillment of

the requirements for the University Honors Scholar designation

at Johnson & Wales University

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Abstract

This study aims to evaluate the correlation between a Major League Baseball team’s

success and the team’s use of sabermetrics, advanced baseball analytics. The timeline of the

sabermetric revolution and its implementation into each MLB team are cross examined with

that team’s payroll, attendance, win/loss record, playoff success, and fan opinions. This study is

conducted by cross-analyzing raw data regarding team records, attendance, and payroll data as

well as background internet research on the sabermetric revolution as well as fan reactions to

its use from social media or team-specific online forums. This research finds that there is a

positive correlation between a team’s success and their use of sabermetrics.

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Table of Contents

Introduction…………………………………………………………………………………………………………………………….6

Methodology……………………………………………………………………………………………….………………………….7

Research Overview………………………………………………………………………………………………………………….9

History of Sabermetrics………………………………………………………………………………………………………….11

Results……………………………………………………………………………………………………………………………………18

The Sabermetric Revolution and MLB Attendance Trends………………………………………….18

Payroll Disparity in Major League Baseball…………………………………………………………………20

Moneyball and the Oakland Athletics………………………………………………………………………..21

Evaluating Players Using Sabermetrics: ERA vs. FIP…………………………….………………………29

Sabermetrics and Attendance……………………………………………………………………………………32

Sabermetrics and Success: The Red Sox and Bill James………………………………………………37

Sabermetrics and Popularity: The Rays vs. The Marlins………………………………………………40

Discussion………………………………………………………………………………………………………………………………44

Implications……………………………………………………………………………………………………………….44

Limitations…………………………………………………………………………………………………………………44

Future Possible Research…………………………………………………………………………………………..45

Conclusion…………………………………………………………………………………………………………………45

Works Cited………………………………………………………………………………………………………………………….47

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Table of Figures

Figure 1: Average MLB Attendance 1980-2009: American League Average vs National League

Average……………………………………………………………………………………………………………………………………18

Figure 2: Baltimore Orioles Attendance, Win/Loss Record, Postseason Appearance, Payroll,

2016-2019……………………………………………………………………………………………………………………………….22

Figure 3: Oakland Athletics Attendance, Win/Loss Record, Postseason Appearance, Payroll,

2000-2002………………………………………………………………………………………………………………………………25

Figure 4: New York Yankees Attendance, Win/Loss Record, Postseason Appearance, Payroll,

2000-2002……………………………………………………………………………………………………………………………….25

Figure 5: Oakland Athletics Payroll vs. New York Yankees Payroll vs. MLB Average Payroll 2000-

2002……………………………………………………………………………………………………………………………..………...26

Figure 6: Cleveland Guardians Attendance, Win/Loss Record, Postseason Appearance, Payroll,

1996-2007………………………………………………………………………………………………………………………….……33

Figure 7: Cleveland Guardians Total Attendance vs. AL Average Attendance 1996-2007………..33

Figure 8: Oakland Athletics Total Attendance 1998-2003…………………………………….……….…………34

Figure 9: Oakland Athletics Total Attendance vs. AL Average Attendance 1998-2003……………..34

Figure 10: Tampa Bay Rays Payroll vs. MLB Average Payroll……………………………………………………36

Figure 11: Tampa Bay Rays Attendance, Win/Loss Record, Postseason Appearance, Payroll,

2016-2019……………………………………………………………………………………………………………………………….37

Figure 12: Boston Red Sox Attendance, Win/Loss Record, Postseason Appearance, Payroll,

2000-2007………………………………………………………………………………………………………………………………38

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Figure 13: Miami Marlins Attendance, Win/Loss Record, Postseason Appearance, Payroll, 2010-

2019………………………………………………………………………………………………………………………………………42

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Introduction

Although it is among the oldest of the professional American sports leagues, baseball

has changed arguably the most – not only in fundamental rule changes, but in the way the

game is managed, watched, and marketed. As the sport became more popular with the rise of

radio and, eventually, television, the use of statistical analysis to measure players’ and teams’

success became more popular as well. Originally used solely for record-keeping and basic

evaluation, baseball statistics quickly evolved into something much more. In the 1980’s, avid

fan and baseball analyst Bill James began what is now known as the Sabermetric Revolution,

using nuanced statistics and theories to evaluate the game in a much more complex fashion,

shedding a light on baseball that had never quite been seen before. Rather than glance at a few

simple statistics to evaluate a player, coaches and fans alike began cross utilizing these new

analytics to paint a bigger picture of success (and failure) on the baseball diamond.

The rise of sabermetrics has helped in painting a more accurate picture of a player’s

worth based on numbers alone, has helped small-market teams able to compete even without

the massive salaries of bigger teams, and has made the sport more enjoyable to watch for

many fans. On the contrary, some argue that sabermetrics have made baseball too numbers-

focused, and are killing America’s most favorite pastime. This thesis analyzes the rise of

sabermetrics over the late 20

and early 21

centuries and is impact on the overall popularity

of Major League Baseball. I will consider the timeline of the implementation of sabermetrics in

relation to team attendance, payroll, and success in the regular and post-season to evaluate the

positive effects of statistics on baseball.

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Methodology

The driving research question for this project was “how do sabermetrics positively affect

fan experience in Major League Baseball?” In searching for the answer to this question, the

main method of data collection consisted of accumulating a definitive timeline of the creation

and use of sabermetrics in the MLB and cross analyzing this timeline with the success of all 30

teams in terms of division placement, postseason success, total attendance, and payroll per

season. Data was collected using raw data from The Baseball Almanac and entered into a

comprehensive spreadsheet to visualize trends and patterns. Qualitative data on fan

experiences with certain teams was also collected using online forums, social media, and

opinionated team-specific articles. Studying and cross analyzing these results allowed for a

tangible sense of “success” and “popularity” to be determined in answering the research

question.

All attendance and payroll data were taken from The Baseball Almanac. Payroll numbers

used are based on the total 40-man roster including all players on the active, injured, or reserve

lists per season. Attendance records are compared to league-specific average rather than of the

entire league due to historical rule differences between the two leagues that may sway

popularity and attendance. Data ends in 2019 due to the shortened 2020 season, which had

zero attendance all season due to COVID-19 protocols. 2021 is also not shown due to different

states’ legislation on attendance protocols leading to ballparks having differing attendance

allowances for most of the season. Payroll records begin in 1984 due to salary data before this

time being less detailed. Because the Sabermetric Revolution did not become mainstream until

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after this time, and major payroll disparity did not become an issue until the 90s, payroll data

from before this time is largely inconsequential to this thesis.

Unless otherwise stated, all charts and graphs are from the spreadsheet, with data

taken from The Baseball Almanac. Any records of payroll, win-loss record, and postseason

success from 2020-2022 stated is also taken from the Baseball Almanac though it is not

expressed in the spreadsheet.

Link to spreadsheet: https://jwuwildcats-

my.sharepoint.com/:x:/g/personal/j02219223_jwu_edu/ERyAXylkiWVPnn79-

myBpmQB573azLccMtF9bI8Et2CIbA?e=Hn2YxC

Citation: “MLB History Year-by-Year (1876-2023).” Baseball Almanac, https://www.baseball-

almanac.com/yearmenu.shtml.

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Research Overview

Sabermetrics themselves have been studied from a mathematical standpoint for

decades. Researchers have worked to measure the value of how well certain formulas can

evaluate a player, and many have even worked to formulate their own equations they believe

are more effective. There has also been research done on how attendance affects a team’s

performance or vise versa. There has been much discussion on how sabermetrics have changed

baseball from a standpoint of managing, but there is little to no research on how sabermetrics

have positively affected baseball in terms of team attendance, fan opinion, and win/loss

records per season in relation to both payroll and a given team’s use of analytics. Additionally,

most of the conversation surrounding the effect of sabermetrics on baseball is purely opinion-

based rather than research-based. Most of the published research relates solely to the Oakland

Athletics and their use of sabermetrics during the early 2002 rather than looking at the matter

from a broad standpoint spanning several decades and the league as a whole.

A thesis written by University of Tennessee at Chattanooga student Jacob Moorefield

examined the use of analytics, specifically by the Oakland Athletics, and how many big

businesses use a similar strategy to improve overall performance (Moorefield 1). This research

concluded that the use of sabermetrics was able to help the Oakland A’s construct a

competitive roster that could compete with larger-market teams such as The Boston Red Sox

and The New York Yankees, citing Oakland general manager Billy Beane’s use of analytics to

sign players as well as Oakland’s ability to reach the playoffs (Moorfield 2-4).

Another study by Washington University in St Louis students Jason Chang & Joshua

Zenilman investigates the impact that scouting players using the Moneyball strategy has had on

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the length and price of free agent contracts. This study looked at certain statistics used to scout

players as well as the qualities that determine the value of a player when it comes to offering

them a Major League contract. Overall, this study found that using sabermetrics to evaluate

players has had a lasting impact on how MLB players are assigned value in the free agency

market and has resulted in value being less related to observable traits (Chang, 29-30).

While both studies contain research that will be touched on and covered in this thesis,

neither take the extra step to find any possible correlation to sabermetric methods and success

and popularity of Major League Baseball teams.

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History of Sabermetrics

In the world of sports, numbers are used to quantify everything. Whether it be to

measure success, failure, or anything in between both on and off the field, statistics can be

used to size up just about anything regarding sports, and perhaps no other sport has a heavier

reliance on numbers than America’s favorite pastime – baseball. Baseball statistics serve not

only to benefit and entertain fans, but they can also help front offices and managers to make

big decisions both on and off the field at all levels of the game. Baseball statistics, or

Sabermetrics (named after the Society for American Baseball Research, or SABR) have evolved

into something so complex that even most die-hard fans or players themselves do not fully

understand the extent to which they can be used, but it took many years for this revolution of

sorts to truly take off.

In 1859, the still young world of baseball would be forever changed by the publication of

the first official box score in the New York Herald. While statistics had been sporadically

documented and published over the previous years, Henry Chadwick is credited as the first

person to put together a comprehensive box score that detailed several statistics from a

Brooklyn Excelsiors vs Brooklyn Stars game (Pesca). The box score that Chadwick put together is

much different from the ones we see today, but this box score is responsible for allowing

baseball fans, for the first time, to get a bigger insight into games they were not attending

beyond a simple statement of which team won. It was Chadwick who devised the term

“strikeout,” a phrase that is obviously still widely used today (Pesca). Chadwick went on to

become a leader in the world of baseball rules, working on many of the game’s earliest

rulebooks and even serving on the committees that devised said rules. Throughout this time, he

Dickinson 12

continued using his box score model, which continued to gain popularity and became the

mainstream framework for keeping score of baseball games (Henry).

In 1906, Chicago sportswriter Hugh Fullerton, who would later play an instrumental role

in uncovering the 1919 Chicago Black Sox scandal, boldly claimed that the Chicago White Sox

would win the World Series over the heavily favored Chicago Cubs. Fullerton was among one of

the only prominent baseball figures to make this prediction, which was based on his own

independent application of baseball analytics (Neyer). While this feat did not garner much

attention at the time, it was one of the first prominent instances of analytics being used to

correctly make a prediction about a future event in the sport. In 2010, Fullerton also published

an article in The American Magazine that detailed some of his research, which was primarily

conducted manually using a stopwatch (Neyer). Around the same time, another baseball writer

by the name of F.C. Lane boldly clamed in Baseball Magazine that batting average was a useless

measure of success. He argued that it was misleading to count singles and home runs as equals,

and even attempted to develop his own formula that contained different values for singles,

doubles, triples, and home runs that would eventually serve as the framework for many

modern baseball statistics, most notably Bill James’s famous Runs Created statistic (Baumer 13-

14).

In 1944, Brooklyn Dodgers’ general manager Branch Rickey made a front office move

the likes of which had never been seen before; he hired Allan Roth as baseball’s first official

statistician (Neyer). Much of Roth’s work involved a heavier focus on on-base percentage,

which he considered to be much more valuable than a player’s batting average. His analytics,

which were mainly based on this foundation, led to the Brooklyn Dodgers making several

Dickinson 13

instrumental moves, the most notable of which being moving Jackie Robinson to bat cleanup.

Roth saw that Robinson’s 1948 RBI percentage was higher than anyone else’s on the team, but

his position in the lineup did not give him many opportunities to do so. After he was moved to

the cleanup spot in 1949, Robinson drove 124 Brooklyn Dodgers over home plate and won

National League MVP (McCue).

In 1954, Rickey published a ten-page article in Life magazine, where he argued in favor

of Roth’s analytical approach and denounced many traditional methods of managing and

running an MLB team. At the time, most people paid this approach no mind; however, this was

one of the first times that sabermetric research was published in such a popular mainstream

magazine (McCue). What stood out most in the Life article was a formula that Rickey and Roth

had developed known simply as “The Equation,” which contained eight different terms that

attempted to completely sum up a player’s performance. While this equation had a long way to

go, several pieces of it would be later used as the framework of some of the most widely used

sabermetrics today, such as the Offensive Performance Statistic (on-base percentage + slugging

percentage), which were essentially represented in the first two terms of The Equation

(Baumer, 14).

The next significant instance of analytics reaching a widespread audience came in 1964,

with the publication of Percentage Baseball. Written by analytic enthusiast Earnshaw Cook, the

contents of this book were able to reach a large national audience through a Sports Illustrated

profile (Neyer). This was the first real book dedicated entirely to baseball statistics and the

application of formulas and probability theory to the sport, but most of the information being

so mathematically complex unfortunately made most of the book extremely hard to

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understand or be taken too seriously (Deford). Several people in the baseball world, however,

did take some of Baseball Percentage to heart, notably Davey Johnson, who went on to be an

extremely successful manager (Baumer, 14). Johnson, and several other managers and GMs in

baseball at the time, took particular interest in Cook’s claims that on base percentage was

wildly more important than batting average, a sentiment that is the foundation of many

modern-day sabermetrics and theories.

Along with the 1970s came the rise of the information age. People were beginning to

have access to endless information, and this would only become truer in coming decades as

technology such as computers and phones became common in households. The rise of

advanced analytic use in baseball grew exponentially along with this new age of technology and

information. In 1969, the first comprehensive collection of MLB statistics, The Baseball

Encyclopedia, was published, and while this book did not contain any sabermetrics itself, it

would go on to inspire many statisticians, notably Bill James, to extrapolate its data to develop

advanced analytics (Neyer). In 1971, the Society for American Baseball Research, or SABR, was

founded (Schell). Since then, the members of SABR have worked to create a comprehensive

database of all things baseball, from history to records to statistic, the latter of which became

more prominent as the use of sabermetrics became more commonplace. It is from the acronym

SABR that Bill James would coin the term “sabermetrics” in 1980, when the sabermetric

revolution really took off (Neyer).

In 1977, Bill James, a writer and avid baseball fan, began putting out his self-published

Baseball Abstracts, which ran annually until 1988 (Schnell). In these books, James took

information from box scores and The Baseball Encyclopedia to evaluate players to analyze

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players using sabermetric methods he created. The 1977 edition sold just 75 copies, but by

1978 James had 250 fans eager to purchase his latest edition. A very favorable Sports Illustrated

article praising James’ work during the 1981 season, and by 1982 his Baseball Abstract was

being mass-marketed by a publishing company that picked up the works, and even sold in

popular bookstores (Neyer). While this was far from the first publication on sabermetric

research, this was the first work that was able to reach such a wide audience and truly begin to

grab the attention of the world of baseball. This was partially due to how effortlessly James was

able to explain his work, making it relatively simple to understand even to an average baseball

fan, and due to the precision of much of his work (Brocklebank). By the early 1980s the age of

analytics had started to truly take off, and it finally found a name in “Sabermetrics” thanks to

Bill James and SABR (Schnell).

Another extremely influential (and popular) book on analytics came in 1984, when

sports historian John Thorn and mathematician slash baseball fan-turned- sabermetrician Pete

Palmer co-authored The Hidden Game of Baseball. This book expanded on many of James’s

views (as well as the work of Earnshaw Cook), arguing that most of the popular statistics

commonly used were essentially useless in predicting wins and losses (Neyer). One of the most

notable advancements made by Percentage Baseball was the popularization of the use of linear

weights, which was essentially Palmer’s refinement of F.C. Lane’s early sabermetric work.

Linear weights, essentially a way to assign value to certain events (such as a double being worth

more than a single, but not as much as a home run), serve as the framework for many common

sabermetrics today, such as FIP and wOBA (Slowinski). The pair went on to publish the first

edition of Total Baseball in 1989, which worked to correct several mistakes they felt existed in

Dickinson 16

The Baseball Encyclopedia, as well as more accurate sabermetrics that were becoming more

and more common. This work gained popularity very quickly, and by its fourth edition, Major

League Baseball began endorsing it as its official encyclopedia (Neyer).

In 2002, the Oakland Athletics famously won the AL West with the third-lowest payroll

in baseball, and after the loss of three extremely key players to the post-2001 season free

agency (2002). In 2003, Michael Lewis’s famous Moneyball: The Art of Winning an Unfair Game

detailed how Billy Beane had used sabermetrics to scout and sign several very undervalued free

agents who helped bring the A’s success at a very low price (Lewis). While the Moneyball story

has been dramatized over the years (especially after the 2011 Hollywood film adaptation), the

Moneyball theory holds a lot of truth to it and helps small-market teams, notably the Tampa

Bay Rays, to this day (Schnell).

Also in 2002, Bill James published Win Shares, a whopping 729 pages of sabermetric

analysis on how to evaluate every player in major league baseball history by a single number

each per season. While much of this work was slightly flawed in its analysis, it was essentially a

more accurate representation of Pete Palmer’s Total Player Rating formula and would go on to

inspire the modern day Wins Above Replacement (WAR) formulas that have become extremely

popular and widely used in modern day baseball (Neyer). 2002 was also the year Bill James

gained the ultimate respect of the world of baseball when he was hired by the Boston Red Sox,

not just as a sabermetrician but as a direct senior consultant to co-owner John Henry and GM

Theo Epstein, who had been fascinated by James’s work for many years. With James in this role,

the Red Sox would go on to break an 86-year drought by winning the World Series just two

Dickinson 17

years later in 2004 (Neyer). With James retaining this role until 2019, the Red Sox won four

titles in just fifteen years, winning again in 2007, 2013, and 2018 (Vareldzis).

While James’s hiring by a Major League Baseball team made the most national headlines

due to his fame in the rapidly growing analytics community, he was far from the first

sabermetrician to accomplish this feat. For example, Eddie Epstein worked as a statistician with

the Baltimore Orioles and then San Diego Padres from the late 80s to late 90s, around the same

time Craig Wright was working for the Texas Rangers under the official “Sabermetrician” title,

and of course there was Allan Roth back in the 40s and 50s (Neyer). By 2012, all 30 Major

League Baseball teams had not only a sabermetrician working for them in some capacity, but an

entire department dedicated to analytics (Schnell).

In a way, sabermetrics have become a household name in baseball perhaps even

without the casual fan noticing. Advanced statistics such as OPS, WAR, WHIP, and many more

have become so common in baseball that the average television broadcast shows a batter’s

slugging percentage - a manifestation of the work of several early statisticians who attempted

to assign value to different events - right there with his batting average in a slash line. Many

statistics that just a few decades ago were widely criticized for their complexity are now

considered common knowledge in the sport.

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Results

The Sabermetric Revolution and MLB Attendance Trends

Now that we have a general idea of the timeline of the implementation of Sabermetrics

in Major League Baseball, we can take a closer look at how some of these events directly

affected the success of certain teams, both in win percentage and total attendance at home

games.

Figure 1

As seen in Figure 1, average attendance has generally increased at a gradual rate. Note

that in 1981, as well as in 1994-1995, there were strikes that affected total games played, and

total attendance due to there being less games to attend. We can see a steep increase in

attendance, especially in the AL beginning in 1970, when SABR was founded, dropping back

down in 1981 due to the strike (Weyrich).

We can also see a very steady increase in attendance in both leagues, especially the

American League beginning in the mid 1990’s as Sabermetrics were becoming more and more

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

Average MLB Attendance 1960-2009

NL Average Attendance AL Average Attendance

Dickinson 19

popular, and it became more common for teams to employ sabermetricians. The AL gaining

more attendance overall during this time can be explained by several key generally unpopular

AL teams who were able to find success through sabermetric-based tactics, which will be

explored more later.

It is important to note several factors that also likely had an influence on the rise in

attendance. For starters, in 1960 there were just 16 clubs. As more and more clubs have been

added over time, attendance obviously has risen due to there simply being more games to

attend as new teams and new ballparks were added. The large jump in attendance that

occurred around the late 1960s is largely attributed to the fact that by the 1970 season, there

were 24 ball clubs in total. By 1995, the Arizona Diamondbacks and the Tampa Bay Devil Rays

became the 29

and 30

MLB teams, and the number of teams has remained at 30 ever since.

Because of this, we can infer that the steady increase in attendance from the mid 1990s to 2009

is independent of this factor (Lindholm).

Another factor that could potentially influence total attendance is stadium size. Many

clubs have revamped or completely rebuilt their ballparks, especially during the Sabermetric

age of around 1980 to present day. If, for example, a team’s stadium capacity increased by

10,000 seats, their total attendance possibility for their 81 home games will increase by

810,000. However, this factor is largely inconsequential when looking at the data. While some

teams have made their new stadiums bigger, or simply expanded the existing stadium to add

more seats, many have actually decreased in capacity, usually in favor of saving in construction

costs or increasing the size of the field itself. For example, The St Louis Cardinals’ new Busch

Stadium, opened in 2006, holds 46,000 fans, while the old Busch Stadium held much more at

Dickinson 20

60,000 fans per game (Cardinals). Teams like The Chicago Cubs and The Boston Red Sox also still

play in their original stadiums; Wrigley Field was opened in 1914 and holds 41,600 fans, while

Fenway Park was opened in 1912 and holds just 37,700 fans. When we look at all of these new

stadiums, the theoretical maximum capacity actually has decreased in the last two decades,

which makes it all the more remarkable that total attendance has only risen (Traub).

With these factors aside, it is only logical to assume that a fundamental change in the

game itself was a big reason as to why attendance rose so much during the 1995-2009 period.

This fundamental change was largely rooted in the rise in popularity and use of Sabermetrics in

Major League Baseball. While it is not a change that cannot always be explicitly seen on the

baseball diamond, it has ultimately made teams more watchable by making statistics-based

tweaks to rosters and lineups, such as how Branch Rickey used Allan Roth’s Sabermetric

analysis of Jackie Robinson to move him to the cleanup spot, where he was able to produce at a

much higher level (McCue).

Payroll Disparity in Major League Baseball

Among the professional American sports leagues, Major League Baseball is the only

sport to not have a salary cap, meaning teams can spend as much – or as little – money as they

wish (Boylan). While this can benefit many teams whose ownership wants to push all the chips

in and pay several big stars without worrying about a spending limit, this has also proven time

and time again to prevent many teams from really being able to succeed in the post-season.

Some clubs simply do not wish (or cannot afford) to spend as much money as their competitors.

For decades, payrolls across the board in Major League Baseball did not differ too drastically,

Dickinson 21

even with the lack of salary cap. Somewhere around the mid 1990s, however, this began to

change. Suddenly there were teams spending as much as double what the average team was,

instead of just a few thousand more dollars, leading to a massive disparity in competitive

balance between the now 30 MLB clubs. (Calandra).

With many teams still opting to spend league average or less, the payroll gap between

high spending and low spending teams began to grow exponentially, making it seemingly

impossible for low-spending teams to have any hope of competing with high spending teams

like the New York Yankees. In the simplest light, the contract a baseball player receives is

directly proportional to his performance on the team. If a team wants to include Mike Trout, for

example, the price tag is going to be extremely high. However, if there is one thing that

sabermetrics has taught us, it is that there is often much more to a player than meets the eye,

and the analytics can give us a much more detailed evaluation of a player’s performance – or

more importantly, his potential. Michael Lewis’s 2003 book Moneyball: The Art of Winning an

Unfair Game, details a team that did just this. (Boylan).

Moneyball and the 2002 Oakland Athletics

Not only have sabermetrics improved the game of baseball by making it more

interesting to watch, but they have also enabled clubs with lower payrolls to still be able to

compete at the same level as big-market teams. Many teams have varied drastically in the

relative amount they spend depending on their chances of finding real success in a given

season. With prospect development being such a big part of the sport, it is commonplace for

teams to take a step back on the Major League stage while they wait for their top prospects to

Dickinson 22

develop in the Minors, hoping to spend money building a team around them when they make it

to the big leagues. The most notable example of this in recent years was when the Baltimore

Orioles cut their payroll by $127,000,000 from 2018 to 2019 (Ginsburg).

Figure 2

As seen in figure 2, after making the playoffs in 2016, the Orioles suffered back-to-back

last place finishes in 2017 and 2018. Instead of continuing to spend money only to be

unsuccessful, they decided to take a massive payroll cut, offloading the majority of the money

on the books by trading away their best players (along with their contracts) to more

competitive teams in exchange for prospects they hoped would help them a few years down

the line (Ginsburg).

It is commonplace for MLB teams to fluctuate between spending high and spending low,

depending on the states of their roster and farm system at a given time. However, there are a

few teams that have gone decades without spending any real money and have almost always

had payrolls much lower than league average, one of the most notable being the Oakland

Athletics.

Dickinson 23

From 1988-1990, the Athletics made it to the World Series each year, an achievable feat

even by spending, at most, just $5,000,000 more than the average team due to the relatively

small payroll gap that still existed. As the rest of the league continued to spend more and more

each year, the Athletics were left near the bottom of the pack. For example, the Athletics’

payroll increased by roughly $10,000,000 from 1990-2000, while league-average payroll

jumped up by roughly $40,000,000. Some teams were spending almost triple what Oakland

was, and these numbers would only continue to grow. To put this in even greater perspective,

Oakland’s 1991 $40,000,000 payroll was roughly the same as their 2022 payroll was – in 1991

they had the highest payroll in all of baseball, in 2022 they had the second lowest (Douglas).

In 2000 and 2001, the Athletics were able to reach the playoffs without a large payroll,

mostly due to several players on the team who were paid very little but were the stars of the

team: Johnny Damon (who was there only for 2001), Jason Giambi, and Jason Isringhausen (Au).

Thanks to these players (and some others), plus a little bit of luck, the A’s played in the

American League Division Series for the second year in a row in 2001. After that season, all

three of those aforementioned top players left in free agency, signing with richer teams: the

Red Sox, the Cardinals, and the Yankees, respectively (Lewis, 22-23).

Heading into the 2002 season, no one expected Oakland to find any sort of success.

They had just lost three key players that were seen as one of the only reasons they were able to

reach the playoffs in 2000 and 2001, and their payroll was almost half the size of the league

average $65,000,000 and a whopping $80,000,000 below the league-high New York Yankees’

(Au). To top it off, general manager Billy Beane did not make any notable big signings in the

offseason – at least not at first glance – to fill in the large gaps that were left by Giambi, Damon,

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and Isringhausen. Oakland was not expected to come anywhere close to the 102 wins they had

pulled off the season before, and most ESPN writers projected that they would have a mediocre

second or third place finish in the division. Some did predict a first-place finish due to their

surprising dominance the season before, but the overall consensus was that without replacing

their three stars, especially Giambi, they simply would not be successful (Look). In a shocking

turn of events, however, the A’s won the division with 103 games and the third lowest payroll

in baseball, a measly $40,000,000. Moneyball details how Billy Beane and his team were able to

achieve this feat using sabermetrics.

The 2002 Oakland Athletics captivated baseball fans across the nation. Not only was one

of the poorest teams in baseball succeeding beyond what anyone could have expected, they

seemingly could not lose. From August 13 to September 4, 2002 – arguably one of the most

difficult and important stretches in a baseball season, when games begin to matter more and

more as summer and the season draw to a close – they won 20 games in a row which, at the

time, was an American League record (2002). Journalist and author Michael Lewis became so

fascinated with this team that was managing to win so many games with such a low payroll and

so few stars that he decided to find out just how Billy Beane pulled off such an incredible feat,

and thus, Moneyball was born (Lewis, XI).

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Figure 3

Figure 4

Lewis, like many others, was fascinated by the prospect that the Oakland Athletics, a

team with consistently far below average home game attendance and payroll, were able to

compete in the postseason (see figure 3) alongside powerhouses like the New York Yankees,

who had far above average attendance and payroll (see figure 4).

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Figure 5

As seen in figure 5, the lack of salary cap in the MLB allows richer teams like the

Yankees, who have no problem spending money, to grow at an exponential rate, while clubs

like Oakland’s tend to grow at a slower, more linear rate. However, as seen in Figures 3 and 4,

both teams were able to reach the playoffs all three of these years, something that is to be

expected for a team with the highest payroll in baseball, but certainly not from a bottom of the

pack club like the A’s.

While Oakland’s success during this period seemed to many like a fluke, or just dumb

luck, it was, in fact, largely due to Beane’s use of sabermetrics to draft and sign players as well

as construct his 40-man roster. In losing Giambi, Damon, and Isringhausen, Oakland lost several

key pieces in both their lineup and their bullpen, holes that fans knew were not going to be

filled by ownership spending high on top free agents such as Barry Bonds or previous Oakland A

Giambi (Schwarz). However, Beane and his scouts were able to find buried treasure in the

apparent pile of scraps that were left after the wealthy teams had taken their pick of the free

$20,000,000

$40,000,000

$60,000,000

$80,000,000

$100,000,000

$120,000,000

$140,000,000

2000 2001 2002

OAK Payroll vs NYY Payroll

OAK Payroll Average MLB Payroll NYY Payroll

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agent class. Beane saw potential in players that other teams overlooked or simply thought were

no longer as effective as they once had been.

Before the age of statistics, players were scouted by what is known as “the eye test,”

where scouts would simply watch several games and judge a player based on how they

physically appeared to perform on the field. Billy Beane, and many others like him, preferred to

heavily supplement this evaluation with advanced analytics. The Moneyball strategy relies on

the theory that many players are undervalued by the market, meaning that the basic statistics

most scouts would look at, such as batting average and amount of home runs for batters or ERA

and win/loss record for pitchers, do not show nearly the full picture (Boylan). Players that are

better than what the basic numbers show are theoretically going to perform much better than

their price in free agency would suggest.

Using this strategy, Billy Beane signed former Red Sox Scott Hatteberg, prior to the 2002

season, perhaps one of his most famous deals of all time. The rest of the league thought

Hatteberg’s career was over after he suffered an injury to his dominant arm right before hitting

free agency. Because he had never been a flashy hitter, most teams viewed him simply as a

decent catcher who was now extremely limited to play his position at all due to his injury.

Beane, however, saw Hatteberg in a different light – “an efficient device for creating runs who

could also catch” (Lewis 160). Beane signed Hatteberg to a deal worth less than one million

dollars for the 2002 to play first base, a deal that would soon prove to be an extreme steal, as

Hatteberg became an above average defensive first baseman, and finished third in the league in

pitches seen per plate appearance (Lewis 171).

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Many argue that the Moneyball strategy is overrated, and that it cannot bring teams any

real success (Rego). Some also argue that the 2011 film Moneyball largely overdramatized

reality. For instance, the movie does emphasize on the fact that Oakland had the 2002 Cy Young

winner, Barry Zito, and AL MVP Miguel Tejada on their roster, two players that obviously had a

large impact on the success of the A’s in the 2002 season (Au). However, though Zito was on

the 2002 roster, Oakland drafted him back in 1999, meaning that his first three years with the

team, 2000, 2001, and 2002, they were able to pay him an extremely low salary because of

arbitration rules and team control (Lewis, 22). 2002 being Tejada’s breakout season was also, in

part, credited to the Athletics’ heavy emphasis on the sabermetric approach. The front office

wanted to breed patient hitters, who would focus more on simply getting on base – a

philosophy that was heavily emphasized by Bill James - by showing discipline at the plate rather

than swinging for the fences with every pitch; Tejada even remarked “If I don’t take twenty

walks, Billy Beane send me to Mexico” (Lewis, 151). While this obviously was not the driving

reason Tejada won MVP, it certainly played a part, as it did with the rest of the team.

Additionally, a team having Cy Young or MVP winners does not necessarily mean that they are,

as a whole, a good team. For example, the Los Angeles Angels, as previously mentioned,

currently have two of the game’s biggest stars in Mike Trout and Shohei Ohtani. Since 2016,

Trout has won MVP twice and Ohtani once (Ohtani also finished in the top 5 in Cy Young voting

in 2022), yet the team has not seen a winning season, let alone made the playoffs, since 2015

(Blengino).

While this strategy is certainly not guaranteed to bring a team to the postseason, it does

help in finding somewhat of a solution to the payroll gap, which most experts can agree is one

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of baseball’s most pressing issues. The use of sabermetrics to build a roster that can compete

despite a team’s small budget can drastically raise the floor of that team’s potential

performance in a season; for example, this strategy allowed the 2002 Oakland A’s to finish in

first place despite being projected to finish in second or third (Look).

The use of sabermetrics in baseball has not only improved the success and attendance

records of small market teams, it has also made big market teams arguably more exciting to

watch as well. If you are a fan of a wealthy, successful team, you are likely going to look forward

to series against worthy opponents in your division rather than against bad teams that will

hardly put up a fight. If those bad team can get even marginally more talented, it overall makes

games generally more watchable and entertaining for both sides.

Evaluating Players Using Sabermetrics: ERA vs. FIP

Much of the Moneyball strategy, and the sabermetric approach in general, revolves

around the theory that “traditional” statistics are far too simple to completely measure the

abilities and success of a player. When signing Scott Hatteberg and others like him, Billy Beane

was far less concerned with his batting average and home run count and was much more

interested in his on base percentage and ratio of walks to strikeouts.

For example, when evaluating a pitcher, the traditional stats that are usually looked at

are win/loss record, and earned run average, or ERA. Win/loss record for a starting pitcher is

dependent on a pitcher getting the win for his team; in order to do this, he must pitch a

minimum of five innings, and his team must gain the lead while he is pitching and then maintain

this lead (Bravo). Earned run average, or ERA, is a slightly more complicated formula invented

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by early statistician Henry Chadwick to essentially determine how many runs, on average, a

pitcher will give up over the span of nine innings. The formula for this is: (Earned runs/innings

pitched) x 9, and it is expressed as a number rounded to the nearest hundredth (Bailey). For

example, a pitcher could have a 3.40 ERA, meaning that if he were to pitch the full nine innings

of a game, we can expect him to give up 3.40 runs.

While these stats may seem like a decent way to determine the value of a pitcher, many

sabermetricians find them to be very outdated and inaccurate in assessing the abilities of a

pitcher. Win/loss record especially has been found to be very unreliable in isolating the ability

of a pitcher. A pitcher could give up no runs over the span of eight innings, but if his team also

does not score any runs and ends up losing once the relief pitcher comes in in the ninth, the

starter could record a loss. Contrastingly, a starter could go out and give up seven runs in five

innings, but if his team scores eight and keeps the lead, he will get a win despite a less than

satisfactory performance (Gray). ERA has also been found to have issues as it does not consider

factors that pitchers have no control over, such as the defense behind them or the unique

dimensions of a given ballpark – a home run to the Yankee Stadium short porch or around

Fenway Park’s Pesky Pole would be easy flyouts in most stadiums (Zerillo).

This is where the sabermetrics come in:

FIP = ((13*HR) + (3*(BB+HBP)) - (2*K)) / IP + cFIP

In which HR is home runs, BB is bases on balls (walks), HBP is hit by pitch, K is strikeout,

IP is inning pitched, and cFIP is league FIP constant, which is adjusted each year to resize FIP to

a scale on-par with league average ERA, so the two can be compared (Grosnick). A common

misconception by those who oppose the heavy use of sabermetrics is that we should not

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replace the tried-and-true statistics that have existed for so long. The correct method, however,

is to use advanced analytics to supplement the already existing ones to paint a full picture. In

the case or ERA and FIP, FIP is used not as a replacement for ERA, but as more of a sequel, to be

looked at and assessed after ERA (Grosnick). Ideally, FIP is used in tandem with ERA to

determine how unlucky a pitcher is, and how accurate his ERA is to showcasing his true abilities.

FIP essentially assigns value to events that the pitcher has direct control over, the obvious ones

being walks, hit by pitch, and strikeouts. Home runs are included because if a ball is hit out of

the park, there is no chance for a defender to make a play on it. Other hits are not included

because once the ball is put in play, the pitcher has no control over if the players are able to

properly field it (Weinberg).

Using ERA and FIP together can tell us a lot more about the raw skill of a pitcher when

we remove factors that are beyond his control. If a scout were to just look at a pitcher’s ERA

and see that it is high, it could be assumed that the pitcher is not very good. However, if he had

a FIP much lower than his ERA, it would show that much of his high ERA can be attributted to

bad luck, or a below average defense behind him. Contrastingly, a pitcher could have a low ERA

and a high FIP, which might indicate that much of the reason he is not letting up lots of runs is

because he has an elite defense behind him that is able to bail him out on balls put in play

(Weinberg). If we looked at just ERA or just FIP, we would be missing half the picture, as is the

case with the use of all sabermetrics. When used correctly, however, we can much more

effectively determine the success potential of a given player, which has been shown to help

many GMs find hidden gems when constructing a roster. If Billy Beane simply looked at Scott

Hatteberg’s batting average, for example, he likely would not have deemed him worthy of a

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contract. It was because he took the step further to look beyond the surface that he was able to

do something special.

Sabermetrics and Attendance

One of the most remarkable feats of the sabermetrics revolution has been its ability to

increase attendance at MLB games, especially at the home ballparks of teams that have been

known to rely heavily on sabermetrics.

For example, we can look at the Oakland Athletics’ attendance records from 1998 to

2003. When looking at attendance for a given MLB team, one of the biggest factors that is going

to affect these numbers is the ballpark itself. Major league ballparks vary drastically in their

sizes, which affects maximum capacity per game, and, subsequently, total attendance per

season. Oakland Coliseum (currently branded as RingCentral Coliseum), where the A’s have

played since 1966, currently boasts the largest capacity in the MLB with 56,782 seats available

per purchase for each home game, comparatively the historical Fenway Park in Boston holds

just 37,755 seats (“Biggest”). With each team playing a total of 81 games at home per season,

theoretically Oakland could see a maximum yearly attendance of 4,599,342, while Boston could

only reach a maximum of 3,058,155. Of course, neither team is generally going to come even

close to this, but there are several factors that affect how close they do come.

Popularity of a team, of course, is a big factor, some teams are inherently more beloved

than others. For example, teams like the Chicago Cubs and the Boston Red Sox have such long,

rich histories of playoff runs and MLB legends that fans did not dwindle too much even when

each team had World Series droughts so long that many of those fans never saw one in their

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entire lifetime (Miller). Star power on the team is also a huge factor; for example the Los

Angeles Angels can, again, credit a large portion of their attendance nowadays to the fact that

their team includes two of the biggest stars in baseball in Mike Trout and Shohei Ohtani

(Miller).

Generally, however, we can usually see an increase in attendance during period of

success and a decrease during the more unsuccessful periods, all aforementioned factors aside

for the most part.

Figure 6

Figure 7

For example, we can see in figures 6 and 7 that from 1996-2001, the Cleveland

Guardians had five postseason appearances and five first-place finishes in six years. During this

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

CLE Total Attendance vs AL Average Attendance 1996-

2007

CLE Total Attendance AL Average Attendance

Dickinson 34

time, their attendance was well above league average. Beginning in 2002, the team went

through a significant lull, sporting losing records four out of five seasons from 2002-2006.

During this time, we see a significant decrease in attendance, dipping below league average and

reducing by half from the 1999 season to the 2003 season. We can see the attendance

beginning to gradually rise again following 2003, due to the team slowly but surely performing

better. In 2007, when the Guardians made the postseason again, we can see the attendance

start to rise at more rapid rate.

Figure 8

Figure 9

When we look at Oakland’s total attendance from 1998-2003 (figures 8 and 9), we can

see that their attendance increased steadily with each year, even to the point where their

500,000

1,000,000

1,500,000

2,000,000

2,500,000

1998 1999 2000 2001 2002

OAK Total Attendance vs AL Average Attendance

OAK Total Attendance AL Average Attendance

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attendance was above American League average in 2003, an uncommon sight due to the

relative unpopularity of the Athletics and Oakland Coliseum, which is consistently rated the

second-worst ballpark in all of Major League Baseball (Peterson).

Contextually, 1999, while it was not a year the team reached the playoffs, was Oakland’s

first winning season since 1992. This explains the 210,000 people increase in attendance from

1998-1999. As the team continued to succeed in 2000 and 2001, the attendance was also given

a boost due to stars on the team such as Giambi, Damon, and Isringhausen. However, in 2002,

when those three stars left, the attendance continued to rise. Typically, when an organization

lets their three best (and most popular) players walk in free agency because they cannot afford

to pay them and replaces them with what is essentially the leftovers that no other team

wanted, fans are going to be upset. However, we can see here that their attendance grew from

the season before. This is directly related to Oakland’s use of sabermetrics to build their roster,

giving fans something unconventional to be excited about.

During this period, the majority of people present at Oakland Coliseum were not

attending A’s games at the rates they were because they loved the stadium or were diehard

fans. The Athletics also did not necessarily have any players who were considered “stars”

throughout the league. These attendance records were a direct result of the fact that Oakland

could not stop winning, and their winning ability was due to their use of Sabermetrics to

construct a team that was able to compete despite an extremely low payroll.

Another example of a team that has seen an increase in attendance related to their use

of sabermetrics in roster construction is the Tampa Bay Rays. Like the Athletics, the Rays are a

generally unpopular team known for serial underspending. In fact, since they were founded in

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1998, there has been just one season where their payroll was above league average – 2000,

when their payroll was just $6,000,000 above the league average $57,000,000. The Rays’ home

ballpark, Tropicana Field, is also the only ballpark in the MLB that is typically rated lower than

Oakland Coliseum, making the Rays a generally unfavorable candidate for high attendance

records (Peterson). The Rays have only seen one season, their inaugural one, with an

attendance record better than American League average (Augustyn). However, the Rays have

also been known as one of the more sabermetric reliant teams in all of baseball over the last

decade, a strategy that has allowed them to find much more success than expected, much like

the early 2000s Athletics (Arthus).

Figure 10

$20,000,000

$40,000,000

$60,000,000

$80,000,000

$100,000,000

$120,000,000

$140,000,000

$160,000,000

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

TB Payroll vs Average MLB Payroll

TB Payroll Average MLB Payroll

Dickinson 37

Figure 11

Though The Rays’ payroll from 2017-2019 was consistently only about half of MLB

average, they were able to see an increase in attendance as well as performance in the league

and postseason (figure 10, 11). Though not pictured due to the shortened season and zero fan

policy during the 2020 season, the Rays were able to make it all the way to the World Series in

2020, despite having a payroll that was only about 22% of their opponent’s: a measly $28

million compared to the Los Angeles Dodgers’ $125 (Thompson). While many critics of the Rays’

heavy reliance on sabermetrics rather than spending argue that this approach is unsustainable

and will never provide the team with real, consistent success, there is no denying that the

strategy has brought Tampa Bay a lot of good throughout the last decade.

Sabermetrics and Success: The Red Sox and Bill James

Sabermetrics have also been shown to help teams that do not necessarily have a low

payroll. Before Bill James was brought on as senior advisor and sabermetrician for Baseball

Operations, the Boston Red sox had gone 86 long years without a championship season, despite

having an above average payroll more often than not since the 80s and being an extremely

Dickinson 38

popular team with consistent high attendance and one of the most popular ballparks in

baseball (Vareldzis).

Figure 12

As seen in figure 12, while the Boston Red Sox consistently ended the season with

winning records, attracted high attendance, and had an above average payroll, it was after Bill

James’s addition to the staff that they began finding success in the playoffs, even winning two

championships in four years.

Working under newly appointed general manager Theo Epstein, another sabermetrics

fanatic, these key front office moves were made in an attempt to stray from traditional

methods and use a more analytical approach to win (McGrath). In a New Yorker article

published in the midst of the 2003 season, James and Epstein’s first season working together,

Ben McGrath wrote that in taking the statistical approach and hiring James, Boston “have not

merely sided with the brainiacs; they’ve enlisted the help of the founding nerd” (McGrath).

Bill James has often referred to baseball statistics as so complex that they resemble an

extremely thick fog that contains an entire army. “The sentry returns and reports that there is

no army out there, but the problem is, he has underestimated the density of the fog” (James).

In other words, many people are quick to discredit analytics due to their surface level value.

Many times, statisticians who notice patterns have a hard time differentiating between which

Dickinson 39

ones truly are just random and which ones appear persistently enough to mean something, and

out of all the duos who truly understood these patterns, James and Epstein were among the

best. One of their most famous, or rather, infamous, strategies was to eliminate the closer role.

James argued that the role of closer, a team’s best relief pitcher who is brought in in the ninth

inning to protect the lead and nail down a victory, was unnecessary. Citing a computer

simulation, James explained that, for example, putting your best relief pitcher in a bases loaded

in the seventh inning situation with the lead on the line is much more effective than saving him

for the protection of a three-run lead in the ninth (McGrath). Unfortunately, James was wrong.

According to James, much of this decision actually came from ownership wanting to

save money - even sabermetric-savvy Bill James knows the value of a closer - and he cites the

failure of the “Closer by Committee” debacle as one of the reasons he gained the respect and

trust of the Red Sox organization, stating “Buddy, if you’re going to play in Boston, you’ve got to

take the heat” (James). James brushed this failure off, and helped Boston win a championship

just the next year, largely in part to his (and Epstein’s) philosophy that drawing walks and

simply getting on base are wildly important, as they eliminate the possibility of what James

often refers to as baseball’s “only finite resource” – outs (McGrath). Rather than focus on home

runs, batting average, and the rest of the stats that show a player at face value, James focused

on getting on base by any means necessary, racking up the opposing team’s pitch count while

keeping his own team’s down, and focusing on homegrown talent and veterans who come

cheap but know the game like the back of their hand rather than expensive, young stars who hit

the free agent market after a successful season (St. John).

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Much of the issue with sabermetrics “not working” is not the numbers themselves, but

the wrongful application of them. Clearly, when people like Bill James and Theo Epstein put

them to good use, they can work wonders. James’ work in particular was fundamental in

pushing Boston over the threshold to finally start winning championships. Another notable feat

that undoubtedly played a role in the 2004 championship was James’s role in recruiting star

pitcher Curt Schilling to sign with Boston. James wrote a letter to Schilling offering his own

statistical research as to why, contrary to Schilling’s original belief, Fenway Park favors right-

handed pitchers who produce a lot of fly balls (Cipriano). James also helped the front office

recognize the importance of defense, largely influencing Epstein to trade Nomar Garciaparra at

the deadline ahead of his impending free agency, receiving two defensively talented players in

Doug Mientkiewicz and Orlando Cabrera in a four-way trade with the Chicago Cubs, the

Minnesota Twins, and the Montreal Expos. After the trade deadline, the Red Sox finished the

second half of the season with the best record in baseball (Cipriano).

Sabermetrics and Team Popularity: The Rays vs. The Marlins

Many argue that Sabermetrics are simply not effective enough to truly help small-

market teams, as there has yet to be a small market team in the Sabermetric age that has used

analytics to win a World Series (Brocklebank). While the sabermetric approach may not be

enough to win a team a World Series without spending more, there is no question that the

strategy has greatly raised the floor of these teams’ potential performance. For teams in

“rebuilding” phases or teams like the Rays that simply do not spend a considerable amount of

money, sometimes all fans can ask for is a team that is watchable. Though a team may not be

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winning it all very often (or at all), there is a drastic difference between a team that is already

crawling to the finish line on pace for 100 losses in July, and a plucky team that at least has a

dog in the fight come October. For some fans, the puzzle-like approach of seeing how

ownership will go about constructing a roster each year is exciting as well. Fans on DRaysBay, a

large online community for Rays fans, spoke in an article as to their reasoning for being diehard

fans of a team that is often laughed at by the rest of the league. One fan stated that she

became a fan by proximity when she moved to Tampa Bay, but it was the “creative thinking

about how to deconstruct and then reconstruct baseball with all its moving parts” (Strom) that

turned her into the obsessive fan she is today. Another fan stated that the Ray’s approach to

the game “makes me have to grapple with what I thought I knew about the sport, and ways

that maybe I can learn more and change with the times” (Strom).

Contrastingly, the Miami Marlins, another team that has consistently been low

spending, tend to leave fans much more frustrated. The Marlins have seen success before,

winning a World Series in 1997 and again in 2003, but they have not had a winning record since

the 2009 season, and had five last-place finishes from 2011-2019 (Fernandez). Before the 2015

season, ESPN published a definitive master ranking of all 122 teams in the MLB, NBA, NHL, and

NFL, and the Marlins came in at 155

. ESPN criticizied the Marlins for relying too heavily on the

now old-school strategy of scouting to identify prospects base on how they look to the naked

eye (Great). An opinion piece published on popular online Marlins community Fish Stripes

stated fans’ frustration with the Marlins for this exact reason – with such a large majority of

baseball embracing the age of analytics, and the front office not keen to spend big, the Marlins

have been left in the dust (Mullin). More complaints came during the 2015 season, when the

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team’s record dropped to the worst in all of baseball even after ownership had attempted to

make moves in the offseason to propel the team to the postseason. However, fans felt that, as

per usual, ownership was too cheap to acquire any big names in free agency, and “of course,

when it did spend, it spent incorrectly” (Jong). By failing to adopt the sabermetric approach that

many other small-market MLB teams have, the Marlins not only failed to find any success

following their 2003 World Series win, but they also frustrated fans and saw major drops in

attendance.

Figure 13

As seen in figure 13, The Marlins had ten straight losing seasons from 2010-2019, only

had one year, 2012, with a payroll higher than league average, and generally saw several

instances of substantial drops in attendance from year to year. Most notably, attendance

decreased by almost 850,000 people from 2017 to 2018, remaining about the same in 2019. In

these two years Miami saw attendance totals over 1.6 million people lower than NL average.

When we compare the W/L records and attendance to a team that we know has heavily utilized

sabermetrics, such as Tampa Bay, we can see that while the payroll numbers may be similar,

the other numbers and trends are not. For example, in 2010, Tampa Bay actually had a lower

payroll than Miami, but Miami finished last and saw a significant attendance deficit from NL

Dickinson 43

average. Tampa Bay finished with a winning record, made the postseason, and while their

attendance was still lower than AL average, it increased from the year before and was much

closer to average than Miami’s. This is an example of how the use of sabermetrics may not be

enough to single-handedly win a team a championship, but it can certainly improve team

performance, and increase attendance by exciting fans rather than frustrating them.

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Discussion

Implications

The results of this study indicate that the use of sabermetrics by an MLB team has the

potential to not only raise the success level of that team, but also to increase attendance,

popularity, and overall fan experience of that team.

The research shows that among teams with lower payrolls, there is a clear difference in

win/loss records, attendance, and general popularity between a team that uses the sabermetric

approach, and a team that does not, with the team that does take this approach finding greater

success in all three areas. The research also shows an overall increase of attendance following

the onset of the sabermetric revolution in the 1980s, showing that there is a tangible, positive

effect to the use of advanced analytics.

This study supplements pre-existing research showing that, specifically, the 2002

Oakland Athletics found success using the Moneyball strategy, by showing that this strategy

and the general application of sabermetrics in general have also caused an increase in success

and team popularity among several MLB teams.

Limitations

The main limitation of this study is that, when assessing success and popularity of a

baseball team, there are virtually infinite factors that could have a possible effect on the

matter. While the main factors thought to be driving influences were explained and, the

influence of sabermetrics was isolated to the best possible ability, there is a possibility that

there are other factors that could have had an influence in team popularity and average

Dickinson 45

attendance each year, especially when evaluating 30 teams over the course of more than 40

years.

Another limitation to this study is that prior to 21

century, payroll and attendance data

was not kept as hyper-accurately as they are today. While the data used in this study were the

most accurate available and were all used from the same source for consistency’s sake, there is

always the possibility that data predating the 1990s are not completely accurate.

Areas For Future Possible Research

As sabermetrics continue to become more mainstream and the payroll disparity gap

continues to widen, there is immense opportunity to examine if the trends examined in this

study continue to be seen in the coming years of Major League Baseball. Focusing on one team

throughout an extended period may also bring about more specific results, as it was shown in

this study that some teams rely much more heavily on sabermetrics than others do.

A similar study that closely follows a sabermetrician throughout the course of a Major

League Baseball season and the decisions they make based on analytics would also paint an

extremely clear picture of use of sabermetrics in baseball.

Dickinson 46

Conclusion

With the ever-growing issue of the payroll gap in a sport not regulated by a team salary cap,

sabermetrics have entirely changed the game of baseball for the better. Whether it be for the

player, the manager, or the spectator, the experience of everyone involved has improved in

some way. The usage of advanced analytics has allowed undervalued players to have a chance

to shine, whether it be by someone taking a chance on them to be an everyday player or being

moved into a slot in the lineup that better suits their abilities. The Moneyball strategy has

significantly helped owners get more bang for their buck and has allowed managers of these

teams the chance to work with a more creative roster that can help the team find unexpected

success. Sabermetrics helping to level the playing field have made the game more exciting for

fans of all teams to watch and have given many of the analytic fanatics a new reason to be

passionate about the sport.

When we look at the timeline of sabermetrics use in the MLB in relation to attendance,

payroll, and general team success both in win/loss record and postseason performance, there is

a clear correlation between the use of advanced analytics by a team, and both success and

popularity of that team.

Dickinson 47

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