Using Fan Identification to Assess Rule Change

Modernizing Major League Baseball: Using Fan Identification to Assess Rule Change

Preferences

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Richard Laurence Bailey, M.S.

Graduate Program in Kinesiology

The Ohio State University

2019

Dissertation Committee

Dr. Donna Pastore, Adviser

Dr. Brian Turner

Dr. Leeann Lower

Copyrighted by

Richard Laurence Bailey

2019

Abstract

Evaluating a sport’s fans perceptions and preferences has been an important aspect of sports research for many years. To make these evaluations many theoretical approaches have been used including; fan team identification (e.g., Wann & Branscombe,

1993), commitment (e.g., Mahony, Madrigal, & Howard, 2000), and loyalty (e.g.,

Backman & Crompton, 1991). These theories, and others, have been used to assess a variety of sport related phenomena such as game attendance choices (Wann &

Branscombe, 1993), and expectations of referee performance (Gayton et al., 1998) among others.

Ultimately, fan team identification was chosen as the focus of this study. Wann and Branscombe (1993) showed that a fan’s expectations of a team differ depending on their identification level. Additionally, the differences between individuals with low and high fan identification impact their consumption habits and behaviors. High identification has been found to have the biggest effect on enjoyment (Madrigal, 1995), and directly leads to the purchasing and consumption of more products (Fink, Trail, & Anderson,

2002).

This study assessed fan identification and its influence on preferences for changes to rules in Major League Baseball (MLB) for individuals aged 18-24, an important demographic to capitalize on as baseball fans are aging and youth involvement in the

iii sport has waned in recent years (Paul, 2017). All the rule changes chosen for this study have been either implemented in the minor or major leagues or have been discussed by fans, players, and media.

A survey was designed and administered to undergraduate college students at a large midwestern university. These students were surveyed in their respective classrooms utilizing a convenience sample.

The survey began asking demographic questions. The second section used Wann

& Branscombe’s (1993) Sport Spectator Identification Scale in its entirety to create a mean identification score. Finally, the survey asked a Likert question, scaled one to eight, which said “To what extent would this rule improve MLB?” in regard to each rule changes. A panel of experts and a brief pilot study were utilized to evaluate the survey for content analysis and clarity. After reviewing the feedback and results some format and phrasing changes were made, and the full survey was administered.

Overall, the study demonstrated that in some situations the level of fan identification was a statistically significant predictor of a fan’s preference to a rule change, specifically in regard to mound visits, starting extra-innings with a runner on second base, and the implementation of a pitch clock. Other demographic factors were at times statistically significant predictors as well including how many times an individual watches MLB per month, whether they participated in organized baseball or softball, and gender.

Future research based on this line of inquiry can be used to evaluate other proposed rule changes, in any sport, and could focus on different demographics, such as

iv older age groups, to help paint a more panoramic picture of what people are looking for in gameplay going forward.

Acknowledgments

I am extremely grateful for the many people who helped me to get to this point.

First, I want to thank my father, Richard W. Bailey who inspired me to learn something new every day and instilled in me the love of the law. Equally as important to my development was my mother, Laura J. Bailey, the kindest and most caring person I ever knew. She inspired me to be compassionate and showed me how helping others is the most rewarding type of work. I also want to thank my sisters, Cynthia Bailey Roberts and

Pam Marinko, who I could always count on to be there for me, especially in difficult and trying times. Other important family member were my stepmother, Deborah Bailey, and my stepbrother, Mark Saunders, who looked out for me, cared for me and taught me to be inquisitive and tough. Thank you to all of you for your support.

I also want to thank my friends and colleagues at Ohio State: Mickey Fraina,

Chad Gerber, Sean Dahlin, Jim Evans, Mark Beattie, Daniel Wray, Carter Rockhill, Evan

Davis, Ashley Ryder, Shea Brgoch, Lindsay Bond, and Danielle Daluisio). I also wanted to thank several other individuals who truly helped the process and provided significant contributions to this study (Dr. Mary Hums, and Ben Keller).

Lastly, I want to thank the faculty at Ohio State I had the pleasure of working with. First, Dr. Sue Sutherland, thank you for being a great advisor and mentor and providing me with some terrific opportunities to grow and learn. Dr. Brian Turner also

vi was instrumental in helping me find my passion within Sport Management and helping me to focus on theoretical development. Dr. Turner was the ideal committee member and helped push this project through to completion. I also want to thank Dr. Lee Lower who helped immensely with developing my quantitative skills as well as providing me wonderful opportunities to further my research in other areas. Finally, I want to especially thank Dr. Donna Pastore. She has been a wonderful advisor and mentor to me over the past few years and was instrumental to helping me find my passion in teaching and research. I will always be grateful for her help and guidance.

vii

Vita

2001……………………………………Dublin Coffman High School

2005……………………………………B.A. Political Science, The Ohio State University

2009……………………………………J.D. Barry University School of Law

2009 to 2015…………………………...Attorney at Law, Bailey & Slavin Law

Firm, Bailey Law LLC

2015 to present…………………………Graduate Teaching Associate, The Ohio State

University

2016…………………………………….M.S. Sport Management, The Ohio State

University

Field of Study

Major Field: Kinesiology

viii

Table of Contents

Abstract ...... iii Acknowledgments...... vi Vita ...... viii List of Tables ...... xi Chapter 1. Introduction ...... 1 Introduction ...... 1 Background of the Problem ...... 1 Technology and Creating Rules ...... 3 Fan Identity and The Influence of Preference ...... 5 Statement of the Problem ...... 8 Spectator Perceptions of Technology and Rules ...... 15 Purpose of the Study ...... 21 Research Questions ...... 23 Definition of Terms...... 24 Overview of Remaining Chapters ...... 26 Chapter 2. Review of Literature ...... 27 Overview and history of identity theory ...... 27 How is Identity Theory Applied? ...... 31 How has Identity Theory Been Used in the Sport Context? ...... 33 How Identity is Measured? Critique of Existing Scales and Measures ...... 39 Chapter 3. Methods ...... 48 Population Characteristics/Sampling Method...... 48 Scale Development ...... 50 Validity and Reliability ...... 55 Methods of Entering Predictors ...... 56 Assumptions of Regression Analysis ...... 57 Recommended Data Collection/Analyses ...... 61 Panel of Experts ...... 63 Pilot Test ...... 64 Chapter 4. Results ...... 66

Overview ...... 66 Demographics ...... 66 Data Treatment ...... 69 Validity and Reliability ...... 71 Assumptions of Regression ...... 72 Research Question 1 Factors that Define the most Identified Fans of MLB ...... 76 Research Question 2a Strike Zone ...... 77 Research Question 2b Mound Visit ...... 79 Research Question 2c Ties after 12 innings completed ...... 81 Research Question 2d Starting Extra-Innings with a Runner on Second Base ...... 83 Research Question 2e Pitch Clock ...... 84 Research Question 3a Strike Zone ...... 86 Research Question 3b Mound Visits...... 87 Research Question 3c Ties after 12 innings completed ...... 87 Research Question 3d Starting Extra-innings with a Runner on Second Base ...... 87 Research Question 3e Pitch Clock ...... 88 Chapter 5. Discussion ...... 89 Research Question 1 Factors that Define the Most Identified Fans of MLB ...... 90 Research Questions 2a and 3a - Strike Zone Discussion ...... 93 Research Questions 2b and 2c - Mound Visit Discussion ...... 97 Research Questions 2c and 3c - Ties after 12 innings completed ...... 98 Research Question 2d and 3d - Starting Extra-innings with a Runner on 2nd ...... 100 Research Question 2e and 3e Pitch Clock ...... 103 Implications...... 104 Limitations ...... 111 Future Research ...... 112 Bibliography ...... 114 Appendix A: Panel of Experts Instrument ...... 130 Appendix B: Full Study Survey Instrument...... 137

List of Tables

Table 1 ANOVA RQ1…………………………………………………………………...75 Table 2 Regression Analysis for Research Question 1………………………………….76 Table 3 ANOVA Table for Strike Zone…………………………………………………77 Table 4 Regression Analysis for Automated Strike Zone Block 1………………………77 Table 5 Regression Analysis for Automated Strike Zone Block 2………………………77 Table 6 ANOVA Table for Mound Visit………………………………………………...79 Table 7 Regression Analysis for Mound Visit Block 1………………………………….79 Table 8 Regression Analysis for Mound Visit Block 2………………………………….79 Table 9 ANOVA Table for Ties after 12 innings………………………………………..81 Table 10 Regression Analysis for Ties after 12 innings Block 1...... ……………………81 Table 11 Regression Analysis for Ties after 12 innings Block 2………………………..81 Table 12 ANOVA Table for Extra-innings Runners…..………………………………...83 Table 13 Regression Analysis for Extra-innings Runners Block 1……………………...83 Table 14 Regression Analysis for Extra-innings Runners Block 2……………………...83 Table 15 ANOVA Table for Pitch Clock………………………………………………..84 Table 16 Regression Analysis for Pitch Clock Block 1………….……………………...85 Table 17 Regression Analysis for Pitch Clock Block 2………….……………………...85 Table 18 Descriptive Statistics for Rule Changes……………………………………….88

Chapter 1. Introduction

Introduction

The foregoing study aims to examine the perspective of young adult spectators of baseball, those in their late teens or early twenties, regarding rule changes that may affect the flow and operation of Major League Baseball (MLB). If a rule is to be adopted, administrators should consider the impact on the strategy of the game and how a rule may change the nature and procedures of baseball. When considering how spectators will perceive these changes, it is important to consider how much of their identity is defined by their favorite team, MLB, or baseball in general, and how that identity influences their perception of the rule changes. Other demographic factors will also be considered in an attempt to determine which rule changes would be well received and what types of individuals, specifically within the age group of 18-24, would prefer to happen.

Background of the Problem

Sport is a social practice with its own norms and values (Loland, 2001).

Comparing competitors is a key aspect of competitive sport and to ensure fairness each competitor must be bound to identical rules and have the same restrictions and opportunities. Technology is having a major impact on the development of rules in sports and due to access issues competitors often are not starting on a level playing field

(Balmer, Pleasence, & Nevill, 2012). Dyer (2015) summarized the various influences of

1 sport technology and categorized the as follows: harm or health (to the individual athlete or others), un-naturalness, unfair advantage or consideration of fairness, coercion, safety and spectator appeal, harm to or advantage to the sport itself, deskilling and reskilling, dehumanization, cost, internal goods of a sport, and equal opportunity or access.” (Dyer,

2015, p. 2). All of these aforementioned topics of sport technology influence have been researched in various studies (e.g., Hemphill, 2009; Loland, 2001; Murray, 2010; Miah,

2006 among others), which demonstrates the broad impact of technology in sport.

However, the discourse in this arena has only been researched directly over the past 30 years. As a result, there are still very significant gaps in the literature, and many concepts and ideas that have not been thoroughly considered.

Much of the research in technology in sports to date has focused on how it impacts athlete performance and what the appropriate oversight of performance enhancement should be (e.g., Hemphill, 2009; Loland, 2001). Literature in this realm often focuses on technology and its impact on training and development of athletes, but where is the line between fair and unfair advantages? Is it fair to allow someone to use performance enhancing drugs? What if everyone has the same access and opportunity to use them, should we then rely on general ethics to guide this behavior? What about the ability to train in low-oxygen environments which can enhance the level of hemoglobin in blood, is it fair that some athletes have access to this environment and others do not?

(Loland, 2001, p. 7). A great deal of the literature on sports technology centers around access to facilities, technologies, and medicines that enhance training regimens (Bompa,

1994; Miah, 2001).

Aside from technology in training, there are other concerns that have percolated into common discussion on the use of technology. One area of focus has been on the use of technology to compete in able-bodied sport. Specifically, the case of Oscar Pistorious and the use of special prosthetic legs to compete in track events has been discussed in over 20 articles (Dyer, 2015). Another prominent case looking at the use of technology and how it influenced fairness was the case of Casey Martin, who was a disabled individual suffering from a circulatory disorder. Martin required the use of a golf cart to participate in golf tournaments and the Supreme Court of the United States ultimately ruled that the use of a cart did not fundamentally alter the game and was permissible

(Silvers & Wasserman, 2000). This distinction is critical going forward for rule makers as they contemplate rule changes. It is imperative that any rule change does not fundamentally alter the game, if a rule change were to do this there is a risk of alienating fans.

As time passes, it is highly likely that more and more specific circumstances like the Pistorious and Martin cases, will arise. As a result, the current construction of rules must be evaluated to ensure that equal access is provided to as many participants as possible while also making sure that the fundamental rules and precepts of a game are not substantially altered. This area of research has received, and will continue to, publicity and appraisal.

Technology and Creating Rules

While the issues of eligibility and use of medicines or technologies are vitally important to constantly reevaluate fairness concepts, these are often ethical concerns with

3 widely varying opinions. However, there is another aspect of the advent of new technologies, besides equipment or physiology, that needs in-depth analysis. Specifically, it is important to evaluate the use of technology and how sports marketers and organizations can use it to enhance their on-field product and create new fans. As technology not only provides us with the ability to consume sports in our own ways, through television broadcasts, streaming services on various devices, social media, or in person, it also provides us a great number of alternatives to watching sports. Therefore, sports leagues must contemplate rule changes that can enhance the appeal to spectators, as well as rule changes that may help create a fairer and more exciting product.

While a multitude of articles have been published on how technology influences the performance and behavior of athletes (Balmer et al., 2012; Lippi, Banfi, Favaloro,

Rittweger, & Maffuli, 2008), considerably less research has looked closely at how rules are being augmented to maintain a level playing field and maximize spectator enjoyment.

Similarly, few articles have considered how rule changes will affect sport spectators

(McNamee, 2010; Smith & Clinton, 2016). If athletic performance is peaking and rules are consistently being implemented to maintain a normative structure to sport than it is imperative that rule changes also be evaluated based on the impact it will have on the spectator experience. While rules are often formulated and codified to ensure fairness and a level playing field, in the 21st century rules are also being promulgated to speed up pace of play, streamline officiating, and preserve the health of the participants.

Fan Identity and The Influence of Preference

With the litany of options available to sport practitioners to change games, determining the right approach can be difficult. However, focusing on fan preference is one way this can be accomplished. But how are fans defined and what differentiates them?

A unique facet of athletic competition is that outcomes are unknown, unlike other forms of entertainment such as music, television, or film (Madrigal, 1995). Because of this uncertainty dynamic, actual outcomes provide researchers with myriad ways to assess perceptions about how people viewed the performance in addition to their emotional reactions (Wann & Branscombe, 1992). The type and kind of emotions an individual is likely to experience while watching competitive sports depends upon an individual’s feelings about a team or the athletes involved (Zillman, Bryant, & Sapolsky,

1989).

Wann and Branscombe (1993) showed that a fan’s expectations of a team’s future performance differ depending on their identification level. Those with a high level of team identification exhibited more physiological arousal during competition, were more likely to belittle opposing fans, and expected better performances going forward than those who were less identified (Branscombe and Wann, 1991). Bransombe and Wann

(1991) also found a positive correlation between level of identification with a team and feelings of self-worth and life satisfaction. Similarly, Madrigal (1995) and Wann and

Schrader (1997) found that the level of team identification influenced the enjoyment, or affect, that individuals experienced.

Like the discussion on identity in psychology, identity theory in the sport context has focused on the nature of self-identity and its relationship to social groups, specifically team identification and affiliation. Social scientists have studied the link between an individual’s connection to a sports team and behavioral and emotional outputs over the last 20 years in a variety of ways (Dimmock & Grove, 2006). Several terms have been used to describe this phenomenon, including but not limited to team identification (e.g.,

Coffin, & Hearns, 1998) among others. Moreover, research following these approaches have demonstrated the strong relationship between team identification and spending and consumption habits, which are of vital importance to spectator sport professionals (Fink,

Trail, & Anderson, 2002; Wann & Bransombe, 1993).

Sports researchers have characterized identification as “an orientation of the self in regard to other objects, including a person or group, that results in feelings or sentiments of close attachment” (Trail, Anderson, & Fink, 2000, p. 165-166). This notion is important in explaining consumer behavior in the context of sports and has been proven to be associated with cognition, affect, and behavior.

6 identification relates to the extent of the connection between a fan and their team and how the role of being a fan of a particular team impacts their social identity (Wann &

Branscombe, 1993).

Research has shown that fans with higher team identification are more likely to attend games, pay more for tickets, spend more money on merchandise and stay loyal when team is performing poorly (Fink et. al., 2002). Fans with high levels of identification behave differently than those with lower levels in a variety of ways. Highly identified fans are more likely to have a strong sense of attachment and belonging to a team and view the relationship as familial, intensely personal, and sensitive (Mitrano,

1999). Wann and Branscombe (1993) found that highly identified individuals attend more games, spend more money, more time, and take time to travel to away games, whereas fair weather fans, who demonstrate lower levels of team identification, only show interest when a team is performing well. Furthermore, although not empirically tested, it has been suggested that highly identified fans may bond more with one another and may chose fans of their team to be friends with (Zillman et al., 1989).

Because consumer and spending habits, sense of belonging, and price sensitivity can be extrapolated through looking at fan identification, this is a logical construct to utilize when evaluating fan’s perceptions of rule changes. Highly identified fan preferences should be given credence to ensure that their interest is maintained. Similarly, by evaluating low identity fans feelings on rule changes research can elucidate what types of augmentations would be likely to increase their passion and involvement.

By measuring a spectator’s level of identification to his team and to MLB in general and conducting analyses to see how his level of identification compares with his sensitivity to rule changes there are two distinct benefits. First, this information should allow for different analyses of contemplated rule changes focusing on the spectator and how they want the aesthetics of baseball to look (e.g., faster game, more strategy etc.).

Second, results may point to certain demographics and their preferences; those with higher scores relating to team identification (who may prefer the rules to be unchanging and be unbothered by pace of play issues) versus the casual spectator who have lower identification (who may want a more entertaining option or rules that speed up play).

Another important relationship to evaluate is what the older spectator may value as compared to a younger spectator, however this study does not focus on this relationship and rather centers around the preferences of young adult fans aged 18-24.

Statement of the Problem

As the television market evolves and people continue to consume entertainment through a multitude of platforms it is vital for sports products to continue to evolve. In the fourth quarter of 2017 3.4% of pay TV subscribers dropped their service. This equates to about 500,000 customers (Pressman, 2018). With cable subscription numbers at an all- time low, pundits point to sports as television’s savior as sports are mainly watched live

(“Sports on U.S. TV – Statistics & Facts,” 2018). While tMLB continues to rank #1 on television in many local markets where teams play, the league is still facing major issues in maintaining viewership (Brown, 2017). Because of the multitude of local channels baseball seems to be evolving into more of a regional sport. Given the availability of

8 local broadcasts, national games have begun to struggle with ratings as many spectators prefer regional broadcasts of their team.

Due to the shift in how people are viewing baseball, finding trends in spectator behavior and understanding their opinions is vital. For baseball moving forward, it faces dwindling fan bases and competition from a variety of other entertainment options.

Overall league ratings for MLB were down 6% in 2017 compared to 2016 (Brown,

2017). This is one factor that has led to discussions about how to change baseball to make it more appealing to the consumer. However, if MLB tinkers with the rules of play it may find itself alienating its traditional, highly identified spectators base. Contrarily, if MLB does not change, its popularity may further diminish as other sports perceived to have more action or are shorter in duration siphon off its base.

Many of the viewership issues elucidated above point to a coming problem, and even former players, and hall of famers, like Goose Goosage and Don Sutton, are even finding it difficult to watch MLB because of the lack of action (Miller, 2018). Through late August 2018, the ball was not put in play roughly a third of all plate appearances, these at-bats ended in walks, strikeouts, or hit batters. Since strikeouts were first made a recorded stat in 1913 there have been only six seasons where strikeouts, walks, and home runs made up at least 30% of plate appearances, all of these seasons have occurred since

2012. Furthermore, there were more strikeouts than hits in April of 2018, the first time in

MLB history (Miller, 2018). These changes are also being seen in traditional baseball plays as steals, pitchouts, sacrifice bunts, and hit and runs have all declined in the past decade, which Buster Olney suggests is a sign that the game is “changing quickly, and

9 dramatically” (Olney, 2018). The game is changing on its own due to strategy and emphasis on certain results, which makes it even more important to focus on what the spectator actually wants to see.

In spite of these dramatic evolutions, and a growing concern about the long-term viability of the game, MLB generated $10 billion dollars in 2017 (Berg, 2018). Thus, it is hard to feel the sky is falling when this kind of money is being produced. However, there was a significant dip in attendance in 2018, 4% across the Major Leagues, which has continued into the early part of the 2019 season (Lacques, 2019).

While panic may not have completely set in at this point,there are other issues

MLB must take into consideration as it decides how to move forward. MLB has the oldest viewers of the top major sports, an astounding 50% of its fans are 55 or older, something needs to be done to reach a younger spectator (Paul, 2017). Furthermore, fewer young people between the ages of 7 and 17 are playing baseball, there was a decrease from 9 million participants in 2002 to 5.3 million in 2013 (Paul, 2017). This has led MLB to increase its presence on streaming devices, phones, and tablets in an attempt to reach a younger audience.

As MLB attempts to develop younger fan bases it must realize that the way people consume media and sports has changed dramatically. Susan Jacoby, while compiling research for her book, interviewed 100 ‘semi-fans’ in their late teens and early twenties. Her findings showed that these individuals rarely watched entire games and primarily used technology to watch “snatches” of games. Furthermore, whether a game was only 2 hours or closer to the current 3-hour average wouldn’t affect their viewing

10 habits as they are used to “speedy action being literally one click away” (Jacoby, 2018).

Beyond just pace of play overall, it was been reported by baseball journalist Jayson Stark in 2018 that the average time between “balls hit in play” is three minutes forty-five seconds (Everett, 2018). This is a long time between action, especially when comparing to other sports like basketball, and soccer where the action continues so long as the ball is in play.

Another issue for MLB is the increased popularity of sports that have not historically been as popular in the United States. For instance, Major League Soccer

(MLS) has grown exponentially, from 2007-2012 the league grew 50%, and many other international leagues are now regularly broadcast on American television (Smith &

Clinton, 2016, p. 67). The Euro 2012 tournament had an average viewership 51% higher than the 2008 version with an average of 1.3 million viewers per game, this is especially impressive given that these games were mainly aired live during regular weekday working hours in the United States (Smith & Clinton, 2016, p. 69).

Baseball is an untimed sport and as such, there is no guarantee how long a game may last. It may be only a few hours, but if the contest results in a lot of offense or turns into an extra-inning game it can take a long time to complete. In the 2017 MLB season, games took an average of three hours and five minutes to complete (Brown, 2018).

However, if games go into the 10th inning, the average time added is 29 minutes, and if a game lasts until the 12th inning an average of 72 minutes is added to complete it (Fink,

2017). Between 2012-2017 there were 1,200 extra-inning games, which represented between 7.6% and 10% of the overall games played each year (Fink, 2017). Given this

11 data, it is clear to see why alternatives to the traditional extra-inning game are being suggested, such as beginning extra-innings with a runner on second or calling games ties after a certain inning (SI Wire, 2017b). Interestingly, other sports, like basketball and football, are timed yet new rules like instant replay reviews may actually be lengthening the game itself. The desire to get a call correct is of paramount importance and in real time mistakes can be made, so different sports may be evaluating the pace of play differently predominantly because of the nature of these games themselves.

Changing extra-inning rules is not the only idea being contemplated, MLB

Commissioner, Rob Manfred, has openly considered implementing a 20 second pitch clock, but has been quoted as saying if the players can get the average game time to around two hours and 55 minutes he will forgo unilaterally imposing the rule (Brown,

2018). Many journalists feel the pitch clock, which already exists in the Minor Leagues and even in MLB rule book under rule 5.07(c) (which states a pitcher must throw a pitch

12 seconds from the time he receives it); however it is not enforced. The average pitch in

MLB in 2018 was closer to 24 seconds, so even a 15 or 20 second pitch clock would speed games up (Berg, 2018). However, while spectators, pundits, and journalists may see reason for the implementation of a pitch clock, some pitchers are vehemently against it. Max Scherzer, an all-star pitcher and member of the Major League Baseball Player’s

Association (MLBPA) is “fundamentally against” the pitch clock arguing it would alter the “fabric of the game” which would be changed by the inclusion of a clock, which has never existed in MLB before (Wells, 2019). The tension between the MLBPA and the

12 owners will make the implementation of a pitch clock difficult, but it is definitely one of the most frequently discussed ways to improve the game and speed it up.

Pace of play rule changes are being directly considered and discussed based on how long games last. This all stems from perhaps the most repeated criticism of baseball which is that it is just too slow, which is exacerbated by “a public that is hooked on plot- driven television viewing” and craves action (Paul, 2017, para. 10). Nonetheless, the pitch clock rule, which has already been implemented into minor leagues, is a feasible addition to the major leagues and has been the topic of repeated discussion over the last few years. These discussions have led many journalists to pontificate that a pitch clock would have already been implemented into MLB for the 2018 season, but this prediction was wrong, perhaps due to opposition by pitchers (Axisa & Snyder, 2018).

Pace of play issues, and how to deal with pitch clocks and extra-inning games is only part of the puzzle to making baseball viable for a younger demographic. The discussion on how to automate umpiring has also gained momentum. As discussed before, part of the idea for this is to ensure that calls are made correctly, specifically balls and strikes, as television viewers now have access to graphics on the broadcast that show them where pitches end up in relation to the strike zone. This adds to the pressure of umpiring as every call is scrutinized.

Additionally, technology that is capable of properly calling balls and strikes is more viable now than ever before. MLB Commissioner Rob Manfred has been quoted as saying the technology is improving faster than he thought, although there are legal entanglements about potential patent infringement with the PITCHf/x technology

(McIntosh, 2018). The debate of using an electronic strike zone typically faces opposition from baseball purists, but most people can agree that perfection is unobtainable when a human is judging the location of a 100-mph fastball (Berra, 2015). The PITCHf/x technology uses three cameras, can note the location of a pitch 40 to 50 times before reaching home plate, and follows the ball across the entire strike zone (Berra, 2015). The accuracy has been stated, by Ryan Zander Sportvision’s general manager of baseball products, to be within a half an inch. But is this exact enough? What would the preference of spectators be, to have an electronic strike zone or to have the human interaction between players and umpires and the potential for error? Is there a complete universal agreement on where the strike zone is? These are all problems that need to be assessed before thinking about implementing an electronic strike zone. Nonetheless, it is the subject of ongoing debate.

In spite of the aforementioned issues and limitations, discussion continues to abound regarding the possibility of automating umpiring, specifically behind home plate.

In August of 2018, Ben Zobrist of the Chicago Cubs, was ejected after telling the home plate umpire, Phil Cuzzi, that players would rather there be robot umpires calling balls and strikes (Rogers, 2018).

While many former and current players and a variety of journalists have weighed in on these proposed changes to MLB, how individual baseball spectators will feel about the abovementioned proposals has not been fully evaluated. Therefore, trying to understand how specific types of spectators would feel about rule changes would be extremely valuable information for baseball and could inform their decisions about rule

14 augmentations. It is critical for MLB to face these issues immediately, especially considering their aging base and stagnating growth in youth baseball.

Spectator Perceptions of Technology and Rules

As we evaluate rule changes it is important to consider whether a spectator requires something near perfection in officiating, which is known as transparent justice – the idea that justice can clearly be seen to be done and is innately evident (Collins, 2010)

-, or if there is some benefit to the potential of human error, in so far as it may create and enhance drama. It can be argued that the escalation of drama through human error is a uniquely entertaining aspect of sport spectating to observe. This may be even more entertaining to an individual without a rooting interest in the outcome of a competition.

Future research focusing on the relationship between an individual’s level of identification and how they view the role of umpiring or officiating, and the concept of transparent justice would yield very compelling data regarding how technology should be used in officiating and game operation. However, given the wide range of types of fans in sport, coming up with a consensus about which technologies to use and what rules would enhance a game are extremely difficult.

As different groups of individuals observe sports, their ability to perceive the proper ruling in a specific situation is related directly to their viewpoint and can give credence to different perceptions of how justice was or was not achieved (Collins, 2010).

First, there are the players themselves who have an excellent vantage point and knowledge about the game and the operable rules. However, players are inherently biased and are likely to voice discontent with officials directly, which may make for a more

15 appealing product even if justice is not perfectly meted out. Second, there is the in-person crowd, which is often in a relatively poor location to make a proper determination of a ruling and is also biased. Thus, a crowd’s “berating of referees is more a matter of partisanship than superior judgment---or if it was not, it could be seen to be so” (Collins,

2010, p. 138).

While a crowd is often not in a good position to evaluate the accuracy of a ruling, the identity of belonging to a specific team and the ability to point a finger at someone specific and blame them for shortcomings has a palpable value to the spectator experience. Thus, even if automated officiating becomes prevalent or the scope of what decisions referees can make is limited to produce a product that is more inherently just, it may detract significantly from the potential entertainment value. This balance should always be considered when changing rules of a game that relies on spectators to generate revenue.

Television viewers and commentators also have a unique vantage point and a broad variety of technology to critically assess the accuracy of an officiating decision.

Collins (2010) argues that “television replays destroy the ‘superior view’ advantage of umpires and, in many cases destroy the ‘specialist skill’ advantage since a good part of the specialist skill is to make the right decision in real time” (p. 138). This is a result of access to slow motion replay and the use of multiple angles.

An obvious argument is that technology can produce transparent justice without humans being involved in officiating. Thus, as technology continues to improve at what point does a human official become unnecessary, redundant, or even counterproductive?

In the past year, prominent MLB players, such as Ben Zobrist and Bryce Harper, have publicly blasted officiating, insisting that automated umpiring would provide more equitable and consistent results specifically when calling balls and strikes (Berg, 2019;

Rogers, 2018). Automated home plate umpiring may also remove personality shortcomings of umpires themselves, like umpire Laz Diaz was accused of letting his own emotions impact his performance during a game in 2018. Diaz, the homeplate umpire for a game in September of 2018, yelled at Bryce Harper, from 150 feet away, for some histrionics in center field after Harper expressed displeasure inconsistencies with the strike zone. Diaz continued to argue with Harper in between innings and during

Harper’s next at bat made a strike call on a pitch that was clearly outside (Thompson,

2018). This bizarre set of occurrences were certainly unique, and it could be argued that this was entertaining, however transparent justice was not served and an automated home plate umpire would not have fueled the fire in this way and made calls accurately without personal emotion or bias entering into the decision. Nonetheless, it is difficult to ascertain without research whether fans enjoyed this or not. It is also unclear if arguments between players and umpires which can be entertaining on some levels, that result in star players being ejected, are undesirable from a fan’s perspective as often they come to games, or watch them on television or other formats, to see the star players participate, but may also like the drama of an argument.

Moreover, what is the value of dispute and discord regarding rulings from a spectator’s perspective? Do sport spectators prefer perfection or drama in officiating?

While this is an easy question to answer when a partisan fan feels they were wronged,

17 what about the sport spectator with no rooting interest? Many judgments in sports are marginal in nature so that either of two possible outcomes are reasonable to everyone except the most biased viewers and even with perfect technology. Thus transparent justice, the idea that justice is clearly done and is innately evident, is at times unobtainable (Collins, 2010, p. 139). Therefore, the decision about how to implement technology in officiating should be informed by spectators who have an opinion about the value of human officiating and what role technology should play.

While many people may prefer perfection in rulemaking and officiating. An interesting reality is that even when the newest and best technology is used, there is still the potential for error. However, as opposed to umpires who make a bad decision and are immediately scrutinized for it, when technology fails it often creates ‘false transparency’ where justice seems to be accomplished but, is not really being done (Collins & Evans,

2011). In tennis, ‘Reconstructed Track Devices’ (RTDs) are used to show the position of the ball in line with the playing field. This technology is employed to show whether a ball was in or out. However, there is an average error of 3.6mm (Collins, 2010, p. 140), which is not explained to viewers when they are seeing the technology used. Thus, even though there is a potential that the technology has yielded an inaccurate result, many people are not aware of this as even a possibility. If this technology were transferred and utilized in calling balls and strikes in baseball, an average error of this size, or the half inch margin of error currently existing in PITCHf/x, could yield very different results on close pitches and furthermore may not even be realized by spectators as an issue. But could a human official do any better? Maybe not, therefore why transfer ontological authority – the

18 power of officials to determine what happened (Collins, 2010)- to technology when an error may be made the same way a human would make it. At least with a human official there is the immediate opportunity to confront and discuss the mistake. Technology as we currently perceive it would not be subject to question and could not adapt to correct previous errors with ‘make-up’ calls designed to restore equity in officiating. Although, there is the potential of making automated technology more malleable in the future.

An underlying rationale for many of the potential rule changes being discussed in

MLB is the need to speed up the game (Morris, 2017). These discussions were the impetus for an intentional walk rule that does not require four balls to be thrown to a batter for a walk, instead a signal can be made that an intentional walk is being given.

Another facet of the pace of play discussions is the time-limitations for managers to decide if they wish to challenge a play (SI Wire, 2017a). However, these adjustments appear to be just the tip of the iceberg as many other potential rules are being discussed and tested in various capacities. These pace of play issues are complex, and a variety of factors and perceptions will likely determine the best way to utilize technology while maintaining human interaction. When looking at the role of the umpire it is clear that the job has many different responsibilities. Officials are required to make a number of calls during the course of a game “including balls, strikes, fouls, tips, fair balls, dead balls, home runs, safes, outs, and so on” and “can number several hundred in any contest”

(Russell, 1997, p. 21). Therefore, if an aim going forward for MLB is to speed up the game, one obvious way of doing so would be to automate umpiring decisions which would expedite the decision-making process as well as essentially eliminate on the spot

19 arguments about the accuracy of calls. But it is highly debatable whether this would be good for the game or not. This line of analysis could benefit from substantial research, which at this point is severely lacking.

While MLB has made it a priority to evaluate rule changes that are designed to speed up the pace of play, this is not the case in all high-level spectator sports (“MLB

Attempts to Speed Up Games,” 2018). By comparison, American football and its rule makers have been hesitant to speed up play because of the potential of increased injuries.

In fact, some recent proposed rule changes at the National Collegiate Athletic

Association (NCAA) level have actually centered around slowing the pace of play down

(Schroeder, 2015). One such proposal suggested that offenses be forced to wait 10 seconds before snapping the ball to allow more time for defenses to react. The thought behind this rule change was essentially that the more plays that occur during a game the higher the risk, therefore generating additional action through rule changes may not be advantageous. Although this rule was met with widespread disapproval, it indicates that in the football context rule changes are not about changing the product on the field for spectator enjoyment, rather it is about keeping players safe and maintain fairness. Since this radical idea was suggested and swiftly dismissed in 2014, there has been very little discussion about pace of play in the college game, especially with the rising awareness of concussion trauma and other health related issues that have been the center of rule change discussions.

Conversely, soccer has evaluated the implication of fatigue on scoring and noted that as a game wears on players begin to tire and scoring tends to increase, especially

20 when a player has been ejected after receiving a red card (Ridder, Cramer, & Hopstaken,

1994). Thus, it can be argued that soccer’s pace of play concerns center around creating a more exciting product rather than speeding up the game or changing its length. When looking at potential rule changes in MLB, the focus often tends to be on ending the game sooner in addition to generating additional excitement.

Basketball on the other hand, does appear to be tweaking game operations to speed up the conclusion of a game, but is not centering these changes around the on-court product or operation of the game itself (Rollins, 2017). In 2017, rule changes were implemented in the NBA that reduced total timeouts from 18 to 14 and gave authority to officials to call delay of game fouls violations for free throw shooters who stepped outside the three-point line in between shots and even punishment for teams not ready to begin the second half promptly (Rollins, 2017).

Technology certainly has the potential to change the way many games, including baseball, will be played. However, other rule changes also focus on improving the product by speeding up the game including: limiting mound visits, ending games in ties after 12 innings, starting extra-innings with a runner on second, and implementing a pitch clock. The evolution, proposed function, and effect of these ideas will be thoroughly discussed in this study.

Purpose of the Study

This research aims to determine who are the most identified fans of baseball in the age group of 18-24. By specifically looking at five potential rule changes this study will evaluate how the level of fan identification influences an individual’s response to changes

21 of rules in baseball. Furthermore, the data is aimed at determining what types of rule changes would best maintain spectator interest and conversely what rule changes should not be implemented because they may lessen fan interest.

The rule changes discussed in this study are primarily designed to either speed up the pace of play and/or add to intrigue and excitement. This analysis will evaluate which rules are favorable options for young adults aged 18-24. Furthermore it will be assessed how specific groups of people feel about them by considering fan identification and other demographic factors such as gender, age, race/ethnicity, proximity to stadium, how many times MLB is watched per month, and experience playing organized baseball or softball.

By utilizing a population of college undergraduates aged 18-24, a connection may be drawn to preferences of rule changes based on the level of fan identification determined by the Sport Spectator Identification Scale (Wann & Branscombe, 1993). By knowing the preferences fans of this age have, MLB can work to strengthen its appeal to this demographic, a group MLB needs to focus on to ensure the long-term economic viability of the sport. Armed with this information, rule makers should be able to consider augmentations to the rulebook with the knowledge of how this spectator group will react and adjust rules to appeal to individuals in their late teens or early twenties.

As baseball evolves, it needs to consider changing rules to ensure that the on-field product meets the expectations of its constituency while preserving the historical integrity of the game. Just like in any field, innovation is critical to success. Players, managers, and front office personnel have consistently tried to reimagine the constructs of the game to gain an advantage over other competitors in the spectator sport realm. However, as

22 technological advances occur, strategy diversifies, and the game itself is reimagined in ways that are much different than the historical notion of how the game is played. Rule makers must consider rule changes that either increase fan enjoyment or prevent the widespread adoption of tactics that may produce a less desirable product for consumers.

Research Questions

There are three specific research questions that will be analyzed through this study:

1. What factors help to define the most identified spectators of baseball between the

ages of 18-24?

2. How does the level of identification as a baseball fan influence an individual’s

responses to the changes of rules in baseball?

a. How does the level of identification as a baseball fan influence an

individual’s response to the proposed rule change that would utilize

automated umpires to call balls and strikes?

b. How does the level of identification as a baseball fan influence an

individual’s response to the rule change that limits mound visits to seven

per game?

c. How does the level of identification as a baseball fan influence an

individual’s response to the proposed rule change that would end any

regular season game whose score is tied after 12 innings in a tie?

d. How does the level of identification as a baseball fan influence an

individual’s response to the potential rule change that would begin extra-

innings with a runner on second base?

e. How does the level of identification as a baseball fan influence an

individual’s response to the proposed rule of implementing a pitch clock?

3. What are the overall perceptions of the proposed rule changes discussed in this

study?

a. What are the overall perceptions of the proposed rule change that would

utilize automated umpires to call balls and strikes?

b. What are the overall perceptions of the proposed rule change that would

limit mound visits to seven per game?

c. What are the overall perceptions of the proposed rule change that would

end any regular season game whose score is tied after 12 innings in a tie?

d. What are the overall perceptions of the proposed rule change that would

begin extra-innings with a runner on second base?

e. What are the overall perceptions of the proposed rule change that would

implement a pitch clock?

Definition of Terms

Epistemological Privilege-The concept that an official is the most likely to see what actually happened because of their superior view and their special skills. (Collins, 2010).

Ontological Authority-The power given to officials to determine “…what happened in any particular instance in so far as it affects the subsequent unfolding of the game, the

24 outcome of the game and the way the game is recorded in the statistical archive” (Collins,

2010, p. 136). This gives a finality to umpire decisions.

Optimal Distinctiveness-Social identity is derived from “fundamental tension between human needs for validation and similarity to others” and “a countervailing need for uniqueness and individuation” (Brewer, 1991, p. 477).

Organizational Identification- “A perceived oneness with an organization and the experience of the organizations successes and failures as one’s own which is strengthened by prestige and sentimentality (Mael & Ashforth, 1992, p. 103)

Self-concept-Comprised of a personal identity, defined by a person’s relationship and characteristics, and social identity, the interactions an individual has with social groups

(Tajfel & Turner, 2004, p. 277).

Self-Identity-The “aspects of an individual’s self-image that derive from the social categories to which he perceives himself as belonging” (Tajfel & Turner, 2004, p. 283).

Social identity-Consists of notable group categories based on demographic features, such as gender and race, or organizational membership, like religion and social institutions (Turner, 1982).

Team identification-The extent of the connection between a fan and their team and how the role of being a fan of a particular team impacts their social identity (Wann &

Branscombe, 1993).

Transparent Justice-The idea that justice can clearly be seen to be done and is innately evident. (Collins, 2010).

Overview of Remaining Chapters

The following chapter, chapter two, will focus on the relevant literature related to this study. Specifically, this chapter will take a look at the origin of identification theory, fan identification, and how it is used to measure the perceptions and opinions of fans in regard to rule changes and game operation of sports. Chapter three will focus on the specific methodology that will be used to gather data and assess the perceptions of the individuals studied regarding their level of fan identification in relation to their feelings and the influence that has on the five rule changes that are the subject of this study.

Chapter four will discuss the results of the study and chapter five will focus on the discussion, implications, and future research.

Chapter 2. Review of Literature

How people view themselves has a significant impact on their comportment and emotions. By understanding the way people identify themselves, predictions can be made about a wide variety of behaviors including, but certainly not limited to, their habits, preferences, and reactions. Psychological research has evolved greatly over the last fifty years in the realm of self-identity and how individuals create it. Analysis has focused on personality, upbringing, and interactions with social groups. The focus of this review is to provide an overview of the history of identity theory and its roots in psychology and connect that analysis to sport spectator behaviors. Understanding how people identify themselves, and the strength of those identities, is extremely useful in predicting consumption habits of all kinds.

Overview and history of identity theory

Tajfel and Turner (2004), reprinted from an original article written in 1986 that built on Tajfel’s work in the 1970s regarding social categorization that introduced the idea of social identity theory. Fundamentally, social identity theory stipulates that self- concept is comprised of a personal identity and a social identity. This theory has been the basis for a litany of research in the sport management field focusing on team identification. The authors describe two extremes of social behavior. The first refers to personal identity which is completely defined by an individual’s interpersonal relationships and unique characteristics, and not by belonging to social groups or categories. The second concept refers to social identity and the interactions a person has

27 with social groups (Tajfel & Turner, 2004, p. 277). Social identity consists of notable group categories based on demographic features, such as gender and race, or organizational membership, like religion and social institutions, these features comprise a person’s self-identity, a combination of personal and social identity (Turner, 1982).

Social identity is a major factor of an individual’s identity and sense of self and is strongly connected to group affiliation. Many conflicts stem from an affiliation with a group and struggles with another competing group (Tajfel & Turner, 2004). These conflicts demonstrate one role group affiliation can have in defining self-identity. In situation of intense conflict, it is more likely group members will behave toward each other in accordance with their group membership than their individual beliefs. Social groups provide members with significant ways of identifying themselves. How individuals identify themselves is also known as self-reference which is how an individual defines their place in society. Thus, self-identity is the “aspects of an individual’s self-image that derive from the social categories to which he perceives himself as belonging” (Tajfel & Turner, 2004, p. 283). Tajfel and Turner elaborated that people strive to maintain self-esteem and a positive self-concept through their associations to social groups and these relationships can have a significant impact on one’s social identity either positively or negatively. Furthermore, the value of one’s social group, measured against specific other groups attributes and features, will also frequently influence self-reference.

Tajfel and Turner (2004) additionally expounded that people naturally endeavor to achieve a positive social identity and this identity is largely influenced by their group

28 relationships and comparisons to other similar groups. If an individual’s self-identity is negative in some way, this may be an impetus to leave an existing social group or to make their group more distinct (Tajfel & Turner, 2004, p. 284). These principles illustrate the direct relationship between a person’s individual identity and their affiliation with a group.

Social identity theory clarifies the “conditions under which different social outcomes arise from social categorization” (Grieve & Hogg, 1999, p. 926). Social identity theory is viewed as having two motivation classes. The first facet involves people clarifying their perceptions of the social world and their place in it, in an attempt to derive meaning and create predictability. The second aspect pertains to defining and evaluating self and creating a favorable self-concept which in turn affects self-esteem

(Grieve & Hogg, 1999).

Early research in social-identity theory focused on intergroup behavior and the explanation of intergroup discrimination (Grieve & Hogg, 1999). The next evolution was self-categorization theory which looked at the social self-concept based on comparison with other people and relevant social interactions (Turner, Hogg, Oakes, Reicher, &

Wetherell, 1988, p. 42). Self-categorization theory closely examines the cognitive representations of the self on three levels. First, the social self-concept is based on an individual’s identity as a human being and the commonalities shared with all other human beings. Second, self-concept, at an intermediate level, contemplates social similarities and differences that define a person as a member of a certain social group, but not others (e.g.,, ethnicity, gender, and race). Finally, a person’s self-categorization is

29 based on differences that make an individual unique such as personality (Turner et. al,

1988, p. 45). Consistent with the earlier theory of social identity, self-categorization theory reinforces the concept that social self-perception exists on a continuum and that each individual is unique based on their opinion of themselves and their group affiliations. However, Turner et al. (1988) hypothesized that individuals are depersonalized to a degree by group affiliation.

The depersonalization of individuals because of group membership is explained through subjective uncertainty reduction theory. This theory emerged to demonstrate that people associate with a group identity and adopt the groups beliefs to reduce subjective uncertainty about their own individual identity. (Grieve & Hogg, 1999) Much of the psychological research in this area has focused on two theories of self-esteem; successful discrimination augments social identity and self-esteem, and low or threatened self- esteem encourages individuals to discriminate in an attempt to raise their self-esteem

(Grieve & Hogg, 1999, p. 927). Furthermore, “the process itself of self-categorization that is responsible for social-identification and group-membership-based behaviors reduces subjective uncertainty” (Hogg, 2000, p. 411). The social categorization of an individual is minimized when a person assimilates into a group and attitudes, feelings, and behaviors become governed by the beliefs of the group as a whole and not the individual. This reduces the uncertainty an individual may have about their own identity and beliefs.

Studies have been performed to evaluate the notion of subjective uncertainty and have found that people explicitly defined as group members wanted “consensual

30 validation from ingroup members” (Mullin & Hogg, 1999, pp. 98-99). This finding validated the idea that individuals who are strongly identified with a group will often base decisions on group opinions rather than their own individual beliefs.

The aforementioned theories illustrated that identifying oneself through groups depersonalizes the self-concept. Building off of these ideas, Brewer (1991) hypothesized that the self-concept is “expandable and contractable across different levels of social identity” (p. 476) Her theory of “Optimal Distinctiveness” was derived from the notion that social identity is derived from “fundamental tension between human needs for validation and similarity to others” and “a countervailing need for uniqueness and individuation” (Brewer, 1991, p. 477). Furthermore, she opined that social identity should not only be equated with membership in a group or category. While Brewer viewed self- identity as more complex than some of the preceding theories, she still regarded group membership as a vital component to identity.

While there are a variety of theories that contemplate self-identity, how it is created, and what influences it, all theories reviewed above agree that group membership and identification play a vital role in defining the self. A consistent primary tenet of identity theory is that an individual’s concept of self has multiple role identities that give meaning to past behavior and provide direction for future behavior (Ervin & Stryker,

2001).

How is Identity Theory Applied?

In the context of psychological research, identity theory has been applied in several separate ways. First, researchers, such as Tajfel and Turner (2004), have looked at

31 the “interpersonal-intergroup continuum” and the “attitudes, values, and beliefs that may be plausibly hypothesized to play a causal role in relation to it” (p. 278). This research focused on individual’s belief systems and the role they played in defining social groups and society, and their roles within them.

Another aspect of identity theory research, from the psychological perspective, has focused on the decision making of individuals and whether those decisions are made based on an individual’s traits, their interpersonal relationships, or their group affiliation

(Mullin & Hogg, 1999). An extension of this line of research contemplated the effect of group sizes and structures on the strength of identification and the importance attached to membership with an emphasis on minority-majority contexts (Simon & Brown, 1987).

Identity theory research centered in psychology has also explored “…the interrelationship between social category distinctiveness and strength of group identification” (Brewer, 1991, p. 479). These studies looked at the uniqueness of social groups and their size to evaluate how much they influence an individual’s decision- making. Results demonstrated that the larger the group the more influence it may have on a person’s thought process.

Another evaluation of self-identity looked at organizational identification and how people see a social group as an extension of themselves. Dutton, Dukerich, and

Harquail (1994) pontificated that “Organizational identification is one form of psychological attachment that occurs when members adopt the defining characteristics of the organization as defining characteristics of themselves” (p. 263). Again, it is clear that self-identity theory research has recognized the strong influence that organizational

32 identification has on the way people view themselves and a great deal of research has focused on that relationship.

How has Identity Theory Been Used in the Sport Context?

Watching sports is a popular leisure activity around the world. A unique facet of athletic competition is that outcomes are unknown, unlike other forms of entertainment such as music, television, or film (Madrigal, 1995). Because of the uncertainty dynamic, actual outcomes provide researchers with myriad ways to assess perceptions about how people viewed the performance in addition to their emotional reactions (Wann &

Branscombe, 1992). The type and kind of emotions an individual is likely to experience while watching competitive sports depends upon an individual’s feelings about a team or the athletes involved (Zillman et al., 1989).

Wann and Branscombe (1993) showed that a fan’s expectations of a team’s future performance differ depending on their identification level. Those with a high level of team identification exhibited more physiological arousal during competition, were more likely to belittle opposing fans, and expected better performance going forward than those who were less identified (Branscombe and Wann, 1991). Bransombe and Wann

(1991) also found a positive correlation between level of identification with a team and feelings of self-worth and life satisfaction. Similarly, Madrigal (1995) and Wann and

Schrader (1997) found that the level of team identification influenced the enjoyment, or affect, that individuals experienced.

Like the discussion on identity in psychology, identity theory in the sport context has focused on the nature of self-identity and its relationship to social groups, specifically

33 team identification and affiliation. Social scientists have studied the link between an individual’s connection to a sports team and behavioral and emotional outputs over the last 20 years in a variety of ways (Dimmock & Grove, 2006). Several terms have been used to describe this phenomenon, including but not limited to team identification (e.g.,

Wann & Branscombe, 1993), commitment (e.g., Mahony, Madrigal, & Howard, 2000), and loyalty (e.g., Backman & Crompton, 1991). These studies have looked at various situations such as spectator aggression (Dimmock & Grove, 2005), game attendance choices (Wann & Branscombe, 1993), and expectations of referee performance (Gayton et al., 1998) among others. These studies have demonstrated the strong relationship between team identification and spending and consumption habits, which are of vital importance to spectator sport professionals.

Sports researchers have characterized identification as “an orientation of the self in regard to other objects, including a person or group, that results in feelings or sentiments of close attachment” (Trail et al., 2000, p. 165-166). This notion is important in explaining consumer behavior in the context of sports and has been proven to be associated with cognition, affect, and behavior.

As a result of work done by Wann, and other scholars, a fan’s identification, specifically with a team, has become a major focus in the study of sports spectators’ thoughts and behaviors (Bernache-Assolant et al., 2007). Team identification relates to the extent of the connection between a fan and their team and how the role of being a fan of a particular team impacts their social identity (Wann & Branscombe, 1993).

1999). Wann and Branscombe (1993) found that highly identified individuals attend more games, spend more money, more time, and take time to travel to away games, whereas fair-weather fans, who demonstrate lower levels of team identification, only show interest when a team is performing well. Furthermore, although not empirically tested, it has been suggested that highly identified fans may bond more with one another and may chose fans of their team to be friends with (Zillman et al., 1979).

Identity theory has also shown that team identification played a large role in self- esteem responses. The highly identified fan’s self-esteem is inexorably linked to his favorite team and will likely derive self-esteem through vicarious achievement (Fink,

Trail, and Anderson, 2002). As a result, highly-identified fans more intensely BIRG (bask in the reflected glory) of their teams success. Similarly, they are less likely to CORF (cut off reflected failure) or otherwise disparage their team when it underperforms.

Furthermore, it has been shown that those who BIRG are more likely to attend future games and purchase merchandise. (Trail, Anderson, and Fink, 2005; Cialdini, Borden,

Thorne, Walker, Freeman, & Sloan, 1976).

Another feature of team identification is how highly-identified fans are influenced by how they think outsiders perceive an organization. This “construed external image” of an organization has an impact on members organizational identification, the stronger the external image the greater the degree of organizational identification. (Fink et al., 2002, p. 197).

In another significant study, Madrigal (1995) suggested that perceived quality of opponent, level of team identification, and confirmation or disconfirmation of expectancies (the way expectations are either met or not met through performance outcomes) each had a significant effect on enjoyment. However, the factor that had the greatest impact on an individual’s sentimental state was team identification. Madrigal’s study found that individuals “who view their association with a team as a more important facet of their self-identity tend to experience greater personal joy and seek greater individual association with the team when it experiences successful outcomes”

(Madrigal, 1995, p. 216). These findings reinforce a recurring theme, team identification plays a critical role in how people view themselves and how they define their self-esteem.

In contrast to the avid fan who is highly identified and links their self-esteem to a team’s success, Pooley (1980) suggested that a casual sport spectator forgets the specifics of a sporting event shortly after it is over. In contrast, a committed fan “continues his interest until the intensity of feeling toward the team becomes so great that parts of every day are devoted to either his team, or in some instances, to the broad realm of sports in general” (Pooley, 1980, p. 14). Pooley’s findings show that if a person’s self-identity is largely influenced by team identification, their daily lives may center on sports.

Identifying diehard fans, who are highly identified, is critical to effective marketing as their affiliations will often be lifelong and extremely powerful, making them ideal customers.

While many of the aforementioned studies closely examine the nature of team identification and the behaviors and concepts of self that derive from that affiliation,

Mael and Ashforth (1992) instead looked at the organizational antecedents of identification, specifically in relation to the alumni of a religious college. They defined organizational identification as “a perceived oneness with an organization and the experience of the organizations successes and failures as one’s own (p. 103). It was also determined that organizational identification was strengthened by prestige and sentimentality. Another interesting finding was that organizational identification was not significantly affected by recency of membership (in this situation how recently a person attended the college). This finding suggested that “identification may be relatively robust, withstanding erosion through time” (p. 116). Of course, the relationship between an individual and their alma mater and a person’s affiliation with a professional sports team are very different but nonetheless some corollaries can be drawn. These findings suggest that by manipulating “symbols such as traditions, myths, metaphors, rituals, sagas, heroes, and physical setting, management can make the individual’s membership salient and provide compelling images of what the…organization represents” (Ashforth & Mael,

1989, p. 28). Thus, although this study is focused on college organizational identification, it does prescribe an assortment of factors that can be emphasized to strengthen identification that could be applied to most any situation.

In addition to identity theory, and team identification, there are several related theories that have been used to explain sport spectator behavior. Motivation theory focuses on drama, escape, aesthetics, and physical skill. Motivation can be accounted for by watching any team, thus team identification is not a key factor when utilizing this theory, nonetheless identification motives may be most fully met when it is an individual’s “team” (Fink et al., 2002). However, in spite of not directly focusing on identification, results suggest that as artistic appreciation increases, so does spectator’s level of identification. Fans may be attracted to certain teams or players due to their style of play. Thus, it can be suggested that sport managers should highlight team’s specific aesthetic qualities to take full advantage of motivation forces to increase interest and enhance identification. Another ancillary finding related to motivation concepts is that it is easier to interact with someone when they are more knowledgeable about the team or players, this also strengthens group relations and enhances team identification. (Fink et al., 2002)

Satisfaction theory is another related notion in the sport spectator realm. This concept suggested that profit for a company is contingent on quality, satisfaction, and loyalty (Oliver, 1977). Oliver’s research showed that loyalty increases over time. At low level stages, loyalty is primarily cost based and sensitive to expensiveness. At the next stage, loyalty becomes a combination of liking the service and experiencing satisfaction.

The third stage is conative loyalty, which indicates an intention to purchase a product in the future. The final stage is the action stage, where an individual’s behavior toward a product is a routinized response or habit. The sequence of cost concerns, to the quality of

38 a product, to satisfaction, and finally to loyalty shows the consumption track an individual goes through. Similar in ways to identity theory, Oliver (1977) found satisfaction predicted future intentions of use. It would be interesting to assess how closely satisfaction thoughts and identity concepts parallel each other as they deal with very similar notions.

As evidenced by the litany of articles evaluating organizational identification in the concept of team identification, the emotional bond between an individual and a sport team directly influences their consumption patterns and habits. Furthermore, strong team identification is powerfully linked to unwavering support, whereas low team identification reveals a fan’s indecisiveness and unpredictability. Therefore, the identity theory research is incredibly valuable for predicting fan behavior and, if applied properly, can provide a useful mechanism for organizations to forecast fan responses to a variety of situations.

How Identity is Measured? Critique of Existing Scales and Measures

There have been several significant identity scales created by sport management theorists over the course of the last 35 years. While the underlying concepts are influenced somewhat by predecessor studies in the field of psychology, the scales focusing on team identification are very sport-centric and often only scratch the surface of the underlying scientific principles espoused by psychologists like Tajfel, Turner,

Grieve, Hogg, and Brewer among others. While the sport related scales in identity utilize psychological concepts, they also veer in their own direction focusing on the level of identification a sport spectator has with an organization, typically a team.

The team identification scales vary in form and function, some are unidimensional, some are multidimensional, some focus on identity, others attempt to incorporate measures of motivation. Determining which scale would be most effective for a study requires careful analysis of what type of results you are trying to elicit. Another important consideration when weighing the pros and cons of the subsequent scales is to consider the audience for your study. Some of the following scales focus on nuanced and technical language centered in the discipline of psychology, while others take a more simplistic approach and may be easier to comprehend by laypersons.

Wann and Branscombe (1993) recognized that how sports spectators identify with teams affects their actions. However, at the time of the article there was no reliable measure of team identification. Thus, they created a team identification instrument, called the Sport Spectator Identification Scale (SSIS), to gauge the level of identification and commitment of fans. The authors stated that people “…who are deeply committed to a sports team should differ from the less identified in terms of their investment of time and money, attendance records at performances, and attributional patterns for game outcomes” (Wann & Branscombe, 1993, p. 2). Wann and Branscombe (1993) focused on four key categories to develop their instrument:

In the present research, four general categories of spectator responses were examined: amount of involvement with the team, attributions about the team’s accomplishments, amount of investment of time and money in team activities, and the extent to which other spectators of the team are perceived as special or bonded in some manner. (p. 3)

The seven item scale was found to be a unidimensional measure and had a Cronbach’s alpha score of .91. All the items were significantly inter-correlated which demonstrated 40 internal consistency, and scores were found to be consistent over time through test-retest procedures. There were frequent behavioral, cognitive, and emotional reaction differences observed based on an individual’s degree of identification with a team. This scale has been utilized many times since its creation and has been very effective at gauging team identification.

Gayton, Coffin, & Hearns (1998) utilized the SSIS to assess team identification and spectators’ evaluation of performance of National Basketball Association (NBA) referees. The authors applied the seven item SSIS scale and added a few questions about referee performance including “how many calls (out of 100) the referees should call correctly and how many calls (out of 100) referees actually called correctly” (Gayton et al., p. 1138). The findings showed that fans who scored high on the SSIS expected higher standards for NBA referees than less identified fans. This article showed that utilizing the

SSIS to evaluate spectators’ expectations of officiating is valid and highly-identified fans expect proper procedures to be utilized and fairly apportioned more than less identified fans.

Matsuoka, Chellladurai, Harada (2003) also employed the SSIS to look at

Japanese soccer and the relationship between team identification and a customer’s satisfaction with a game’s outcome and the performance of their favorite team. The authors found that team identification and facets of satisfaction were significantly correlated with the intention to attend a future game. Additionally, team identification explained twice as much variance in intention to attend future games than any other facet of satisfaction contemplated. Another interesting finding was that while both high

41 identity and low identity fans were less likely to attend a future game when they were less satisfied with the performance of their favorite team, the low identity fans were more deterred than high identity fans. As previously stated, the authors modified Wann and

Branscombe’s (1993) SSIS, which consisted of seven items. The authors broke down questions into subparts and partitioned the scale into four items that were labeled as team identification measures, three labeled as behavioral measures, and one as an affective component. They excluded the question about wearing items with team logos because this was not common behavior for Japanese fans in 2003. Their rationale for this division was centered on Mael and Ashforth (1992) which theorized that the construct of identification should not delve into behavioral or affective mechanisms of loyalty.

Matsuoka et al. (2003) showed that to increase attendance, sport marketers should focus on creating and increasing team identification in the Japanese market (p. 251).

There have been numerous other adaptions of the SSIS in many different countries, evidencing its usefulness in explaining team identification and its behavioral byproducts. The SSIS scale has been utilized successfully in the United States (Gayton et al., 1998), Japan (Matsuoka et al., 2003), England (Jones, 2000), Sweden (Antolovic &

Ardby, 2003), Australia (Wann, Dimmock, & Grove, 2003), Norway (Melnick & Wann,

2004), France (Bernache-Assollant et. al., 2007), and Portugal (Theodorakis, Wann,

Carvalho, & Sarmento, 2010).

In 1995 Wann created a scale building off of the SSIS. This time, Wann’s scale incorporated motivation factors to further explain what influences fans and what drives their behaviors while spectating sports and in their everyday lives. This scale is called the

Sport Fan Motivation Scale (SFMS). In addition to the full SSIS, the SFMS contained a section for demographics including, but not limited to, age, gender, race, education level, and household income. It also contained a second section consisting of 38 items designed to look at the dimensions of “eustress, self-esteem, escape, entertainment, economic, aesthetic, group affiliation, and family reasons” (Wann, 1995, p. 380). The SFMS showed that higher scores corresponded positively and significantly with each measure of involvement, demonstrating the validity of the scale, with the exception of the economic subscale. Strong reliability was also shown through test-retest correlations. While the

SFMS focuses on other concepts beyond identity theory, it was conceived as an evolution of the SSIS and is highly effective at explaining fan motivation. Furthermore, Wann

(1995) states that the SFMS can provide information on fan enjoyment.

There are some potential drawbacks to the utilization of the SFMS. This scale seems too broad for a narrow study on team identification and organizational identification as it focuses on motivation factors rather than just the strength of attachment and identification with a team. Another issue with this scale is its length. In total it has 45 items, not including the demographic section, and as a result would require a much larger sample size and may produce results that are outside the scope of what a researcher focusing on identity may hope to elicit. Given the potential complexities of using identity theory, using a shorter measure with narrower definitions may be more useful to practitioners than one that attempts to account for so many variables.

Another scale in the arena of team identification is the Psychological commitment to team scale (PCT), proposed by Mahony, Madrigal, and Howard (2000). This scale

43 followed up on the analysis of how loyalty is developed in service patrons conducted by

Pritchard, Havitz, and Howard (1999) by looking more closely at the strength of an individual’s commitment to a team. While the authors did not specifically use identity in its title, instead focusing on “segmenting sport consumers based on loyalty” (Mahony et al., 1999, p. 15), this scale parallels many of the identification studies in that it assessed the strength of fan’s commitment to a particular team.

The PCT scale was found to have strong predictive validity and could be used for college or professional teams to predict future attendance at sporting events. Another strength of the PCT is its typology, which divided fans into high loyalty, spurious loyalty, latent loyalty, and low loyalty categories. This division of loyalty classifications is a strong improvement on the SSIS as it creates benchmarks of evaluation rather than relying on mean scores that have no meaning apart from the scale. Like the SSIS developed by Wann and Branscombe (1993), this scale is relatively short, containing only

14 items. However, it contemplates loyalty as a two-dimensional construct utilizing behavioral and attitudinal components (Mahony et al., 1999, p. 16). While this scale is more multi-faceted than the SSIS it focuses more on loyalty to a specific team as opposed to identity as a fan, thus its usefulness to evaluate identity outside the context of a specific team is uncertain. However, through assessing this scale, consistency can be seen in the measures and questions used to expound on loyalty and commitment. Furthermore, the PCT reinforces the notion that scales in the realm of team attachment and identification (or in this case focusing on loyalty) are short by design so that they may be easily and quickly given.

In 2003, Wann and Pierce compared the SSIS to the PCT. Ultimately, they found that both scales were effective at predicting fan behavior in spite of operating in slightly different disciplines. Both scales were found to be highly correlated, suggesting they are evaluating similar constructs. One key difference found in this study was the SSIS was slightly stronger than the PCT at measuring general fandom (a key component of measuring organizational attachment, a notion which is in need of more evaluation).

Nonetheless, both scales were concluded to be valid instruments. The major issue found with both of these scales is their narrow focus.

Within sport management, team identification, was originally examined as a unidimensional construct as evidenced by the makeup of the SSIS (Wann & Branscombe,

1993). However, over time, researchers have begun to look at group identity as a multidimensional construct (Dimmock, Grove, and Eklund, 2005).

The next evolution in team identification scales came from Dimmock and Grove

(2006) in which they introduced the Team Identification Scale (TIS). Dimmock and

Grove (2006) utilized the framework of subjective uncertainty reduction theory, which states people strive for clarity and certainty regarding attitudes, behaviors, and perceptions and may conform according to a larger group identification or belief (Grieve

& Hogg, 1999). Furthermore, according to subjective uncertainty theory, identification with groups is sought in more important contexts than situations deemed less important as uncertainty is theorized to wane when bolstered by a larger group belief of an important concept.

The TIS also utilized social identity theory, analyzing how self-concept stems from membership of a social group and the emotional value associated with the affiliation. While previous scales were unidimensional, the TIS has 3 dimensions: cognitive/affective (“degree of self-categorisation and emotional involvement with team”), personal evaluative (“personal value connotation attached to support for team”), and perceived-other evaluative (“subjective impression of the value connotation that others have regarding support for team”) (Dimmock & Grove, 2006, p. 1205). However, these distinctions are complex and would be difficult for a layperson or non-specialist to understand the relationship which limits the value of the scale, especially when being used in a practical real-world situation with time constraints.

Another difference between the TIS and its predecessors is that it focuses more on antecedents of team identification, showing that a person’s desire to reduce subjective uncertainty can lead her to seek an organization that is relatively unambiguous, concentrated, and clearly focused. Because of this antecedent focus, this scale may be less useful than others, like the SSIS, in predicting behavior or responses to change, but that is by design of the authors (Dimmock & Grove, 2006).

While the SSIS, SFMS, PCT, and TIS are all reliable and valid instruments that contemplate team identification, there are significant differences lying beneath the surface. The SFMS goes beyond merely identity theory and team identification and probes into fan motivation thus veering in a different direction than assessing the strength of identification to a team and how that relationship influences people’s perceptions. The

TIS focuses more on antecedent explanations for team identification and utilizes the

46 dimensions of cognitive/affective identification, personal evaluative identification, and perceived other evaluative identification which is likely outside the realm of knowledge for people who lack foundation in psychology. While these multiple dimension scales are interesting and useful in a variety of contexts, they are complicated for a layperson to grasp and may create confusion in regard to results.

Like the SFMS and TIS, the PCT and SSIS have been effective at assessing team identification, but their approach is more simplistic. Because of this straightforwardness, both the PCT and SSIS scales seem capable of being tailored or slightly augmented to branch out into gauging organizational attachment, for instance looking at identification as a fan of MLB as opposed to just a specific team. Consequently, they could be utilized to look at the level of identity as a fan of Major League Baseball (MLB), instead of a specific team, and evaluate that relationship and to rule changes and how an individual’s identity as a MLB fan is influenced by evolutions in the way the game is played.

However, as the PCT is multidimensional, the layers of assessment go beyond the scope of the purpose of this study, thus the SSIS is the best and most viable scale to be incorporated to the survey designed for this dissertation.

Chapter 3. Methods

Population Characteristics/Sampling Method

The population for this study was adult college undergraduates aged 18-24 residing in the United States. It was important to evaluate both baseball fans and non-fans as any decision on a rule change should take into account those who are highly identified fans of specific teams and those that may not have any strong feelings whatsoever. By using this approach any proposed rule can be assessed by its impact on disinterested or non-fans and those who are highly identified. The reality is that baseball can be a very complicated game and while non-fans participating in the study may have difficulty appreciating the nuanced applications of these rule changes, including them will provide a depth of information regarding everyone in the age range much better than limiting the study to those who are a defined fan.

A convenience sample was utilized through targeting adult undergraduate students, aged 18-24, at a large midwestern state run university. The National Center for

Education Statistics estimated that 19.9 million students will attend American colleges and universities in fall 2018 (“Fast Facts: Back to School Statistics,” 2018). Of those 19.9 million students, 12.3 million of them will be under the age of 25, representing 61.8%.

Students aged 18-24 at the university where this study is being conducted represent

89.75% of the undergraduate population (Buss, 2017). The total amount of

48 undergraduates at the specific campus of this university was 59,837, thus 89.75% of this number is 53,704 (“OSU Statistical Summary,” 2017). If a researcher surveying a population that is between 50,000 and 75,000 the appropriate sample size would be between 381-382 (“Sample Size Table,” 2006; Krejcie & Morgan, 1970, p. 608).

Utilizing a sample that relies on convenience is problematic at times because the sample may not be truly representative of the population (Fraenkel, Wallen, & Hyun,

2006, p. 101). However, the results should provide plenty of useful information about the specific target population, individuals aged between 18-24, that MLB should be targeting rule augmentations, and marketing specifically towards. Not all respondents were in this age bracket, but outliers in different age demographics will be omitted from the study.

It is also important to consider sample size to ensure complete and valid results, thus other ways of determining a proper sample need to be considered. Morgado,

Meireles, Neves, Amaral, and Ferreira (2017) suggested that a minimum of ten participants is required for each item in the scale. In the proposed instrument to be utilized in the pilot test there are 33 questions which means the minimum sample should be no less than 330 participants in the final study. However, additional questions were removed after evaluating the range and frequency of answers, the usefulness of the demographic questions, and other issues with the assumptions of regression.

Another way to determine a minimum sample size is to look at the relationship between independent variables (IVs) and dependent variables (DVs). Assuming there is a medium-size relationship between the IVs and the DV, the formula N ≥ 50 + 8m, with

49 m referring to the number of IVs, can be used (Tabachnick & Fidell, 2007). There are 10

IVs in this study thus there needed to be a minimum sample size of 130.

Scale Development

The proposed initial scale, presented in Appendix A, was a composite of several different sources. The first question asked “To what degree do you consider yourself a fan of Major League Baseball” on a scale of one to eight with one being “Not a Fan” and eight being “Very much a fan.” This was meant to be a preliminary indicator of how highly identified an individual is. Ultimately, how the respondent answers this question was unimportant to the overall analysis as anyone, whether a fan of baseball or not, has a very valuable opinion and perspective that was useful to the study.

After the first question, a variety of demographic questions were prepared inquiring about an individual’s sex, age, racial/ethnic group, marital status, whether they have children under the age of 18, their proximity to their favorite team’s home stadium, and if they have played organized baseball or softball. It was unclear prior to the evaluation of data whether all of these demographic questions would produce a wide enough array of responses or whether they may be multicollinear but that will be determined again by analyzing the data. The sex and racial/ethnic group questions were reproduced from two application options for students intending to apply to the university that will be studied, the Common Application (2018) and the Coalition Application

(2018). The remaining demographic questions; proximity to the subject’s favorite team’s home stadium, experience playing organized baseball or softball, current year in college, how many times per week, month, and year an individual watches MLB, and the question

50 inquiring about how strongly the respondent views themselves as a fan of MLB were all created by the author of this study.

After the demographic question section, the Sport Spectator Identification Scale

(SSIS) created by Wann and Branscombe (1993) was given in its entirety to establish the level of an individual’s team identification. This scale first asked an individual to identify their favorite MLB team and was followed by seven specific questions that utilize an eight-point Likert scale. Team identification was measured by aggregating the scores on these seven questions and dividing by seven to create a mean score. In the initial study,

Wann and Branscombe (1993) divided individuals into high, moderate, and low identifiers. The high identifiers had a mean level of identification of 7.15, the moderate identifiers had a mean level of 5.88, and the low identifiers had a mean level of 3.55 (p.

7). While previous authors made the choice to divide scores, divisions of this nature reduce power in similar ways that discarding data would, can create erroneous results, and the cutpoints may characterize individual results differently in spite of the fact that the true results are very close (Altman & Royston, 2006). Furthermore, James, Delia, and

Wann (2019) analyzed the use of the SSIS and found that categorizing fan identification into groups creates difficulty in differentiating between those with low identification and those that have essentially no level of fan identification. This was a distinction that was relevant as MLB should be endeavoring to create new fans as well as make existing fans more identified and involved. Thus, the decision was made to keep the SSIS mean score as a continuous variable and not categorize it.

The final section was comprised of five rule changes that are either in place in the

Minor Leagues, have been discussed by MLB officials, or have been part of broader discussions aimed at making baseball a more entertaining and fairer product for consumers. For each rule there was a description of how it would operate and its current status (whether it has been proposed, adopted, or is a theoretical change as of the 2018

MLB season). The first question asked spectators their feelings on automated umpiring.

Specifically, the idea of utilizing a computer to call balls and strikes, instead of a physical person umpiring. While this is not a rule change that is currently being actively considered, it has been discussed repeatedly by the media, fans, and players. By inquiring about the efficacy of this potential rule change it was hoped that polarizing results will illustrate the dichotomy between the traditional nature of officiating and the trend of increasing the pace of play and utilizing new technology. Furthermore, this question should provide interesting results regarding how spectators value the human interactions between players and umpires and how important it is to them to minimize errors.

The second rule change that was discussed is a new rule that was implemented to start the 2018 MLB season. This rule stipulates that a pitching change must be made if a manager or coach visits any pitcher on the mound for the seventh time in a game, with an exception for injury situations (Adler, 2018). This rule has already been adopted and as a result is ripe for further analysis concerning spectator perceptions. Hopefully, by analyzing this rule change the sensitivity of spectators to rule changes that are designed to speed up the pace of play will be elucidated as the reason for the adoption of this rule change was predominantly based on speeding up the pace of play (Adler, 2018).

The third issue was a theoretical rule change that has been generally discussed which would call regular season games a tie after 12 innings of play. Games that last deep into extra-innings end up lasting many hours and can go late into the night. 29 minutes is added to the average game time if a game goes into the 10th inning, if a game lasts until the 12th inning an average of 72 minutes is added to complete it (Fink, 2017).

Between 2012-2017 there were 1,200 extra-inning games, which represented between

7.6% and 10% of the overall games played each year (Fink, 2017). Additionally, teams are significantly taxed by being forced to overuse their bullpen and pitching staffs in general. Moreover, if games last deep into extra-innings, the likelihood of seeing a position player pitch is raised exponentially. By calling games a tie after 12 innings, the pitching staff’s arms would be preserved, and games would end at a more predictable time. A rule change of this type would ensure a game completes at a predetermined interval. This would be a radical change as nothing like this has ever existed in MLB, outside of exhibitions and Spring Training. Ideally, the results produced by spectators responding to this question would show consumers sensitivity to a significant rule change for the benefit of pace of play and what value spectators place on ensuring a game will end at a more predictable time.

Similarly, the fourth rule change to be discussed was the idea of starting extra- innings with a runner on second base, the baserunner would be the batter prior to the leadoff hitter for that inning (Minor League Baseball, 2018). Much like the notion of calling games ties after the 12th inning, this rule is designed to finish games more promptly, and preserve pitching staffs but is also focused on increasing excitement. This

53 rule is in effect in the minor leagues for the 2018 season and, if implemented at MLB level, could dramatically alter the potential length of a game. However, this would also have a dramatic effect on strategy and tactics in extra-innings. By evaluating this potential rule change collected data should show the sensitivity of consumers to rule changes that change the strategy and tactics of the game in hope of speeding up games.

The final rule to be analyzed was the requirement that pitchers begin their wind- up or motion to come to the set position within 15 seconds when no runners are on base.

If there is a runner on base the pitch timer increases to 20 seconds (Minor League

Baseball, 2018). “The timer shall start when the pitcher has possession of the ball in the dirt circle surrounding the pitcher's rubber, and the catcher is in the catcher's box” (Minor

League Baseball, 2018, para. 19). This rule, in this format, was adopted in the minor leagues for the 2018 season and has been discussed as a potential augmentation in the major leagues as well. Therefore, it is important to consider how consumers would view this rule as it is designed to speed up play but may have an impact on timing strategy and the way pitchers operate.

For all the aforementioned five rules an eight-point Likert scale question was asked. The question for each rule change was “To what extent would this rule change or improve MLB?” with a score of one corresponding to “Not at all” and a score of eight meaning “A great deal.” After completion of the panel of experts and pilot test these questions will be evaluated.

Validity and Reliability

It is paramount to establish validity and reliability for any instrument being used in research. Validity “refers to the appropriateness, meaningfulness, correctness, and usefulness of the inferences a researcher makes” (Fraenkel, Wallen, & Hyun, 2015, p.

148). Several aspects of validity were considered about the study discussed in this paper.

First, content-related validity was assessed, content validity looks at the appropriateness of the content of an instrument and evaluates whether the items of a survey will lead to data designed to explain the intended variables. As components of the survey were newly created, besides the SSIS, and the overall working of the instrument had not been tested, it was important to obtain feedback from a panel of experts and a pilot test. Additionally, construct validity is of vital importance as well. Construct validity looks at the

“characteristics being measured by the instrument” (Fraenkel et al., 2015, p. 149) and how well the construct explains the differences that are being evaluated. This was considered when reviewing information from the panel of experts, pilot test, and full study.

Reliability is evidenced in instruments that perform in “consistent, predictable ways” (DeVellis, 2017, p. 39). Reliability relates to the “consistency of scores obtained- how consistent they are for each individual from one administration of an instrument to another and from one set of items to another” (Fraenkel et al., 2015, p. 155). To demonstrate reliability of this instrument Cronbach’s alphas was used, which is a widely used measure to establish reliability. A Cronbach’s alpha level should be at a minimum of .70, but the higher the better. (Fraenkel et al., 2015, p. 158). Regarding the SSIS, the

55 original study showed that the Cronbach’s alpha level was 0.91 and the scale was internally consistent and unidimensional (Wann & Branscombe, 1993). Similarly, a

French adaptation using the SSIS also had a Cronbach’s alpha of 0.91 with all items being significantly intercorrelated and unidimensional (Bernache-Assollant et al., 2007).

Methods of Entering Predictors

To assess the appropriate style for entering predictors in this study several options were considered. First, backward elimination that focuses on removing variables due to their minimal ability to predict criterion variables was considered. (Lomax & Hahs-

Vaughn, 2012, p. 676). Another option is forward selection, which adds variables based on the level of contribution they add to the prediction of the criterion variable.

Another option is to use stepwise selection. In stepwise selection no potential predictors are included initially in the model (Lomax & Hahs-Vaughn, 2012, p. 677). The first predictor to be added is the one that helps the most to explain the dependent variable, or the variable that has the largest t or F statistic. From there, the next most valuable predictor is added. Finally, when considering predictors that have not been included at an earlier point in the analysis, an evaluation needs to be conducted to determine if the contribution is significant. If a predictor is not significant it is eliminated from the model.

This continues until only predictors that are significant remain.

The final option considered to enter predictors was the hierarchical regression model. As opposed to the earlier models discussed, hierarchical regression requires the researcher to determine the order of entry based on existing theory and research, as opposed to using software to determine the sequence (Lomax & Hahs-Vaughn, 2012, p.

678). For this study, a hierarchical entry was chosen to enter predictors. Given the strength and prevalent use of the SSIS mean score this variable was used in the second block of the hierarchical regression for the five rule changes. The demographic questions that were in the final study’s regression analyses were ones that provide some range and variance in responses and contain enough responses from multiple answer options to ensure a logical analysis and discussion.

Assumptions of Regression Analysis

There are a series of assumptions involved in linear regression: independence, homoscedasticity, normality, linearity, and multicollinearity. The first topic to discuss is independence, which assumes that “…the errors in prediction or the residuals…are assumed to be random and independent. That is, there is no systematic pattern about the errors, and each error is independent of the other errors.” (Lomax & Hahs-Vaughn, 2012, p. 628). The easiest way to assess independence is to examine a scatterplot or residual plot. If the independence assumption has been met, the points on the scatterplot will be randomly configured. On the other hand, if the scatterplot shows some sort of pattern regarding the clustering of positive and negative residuals the assumption may be violated.

If there is a lack of independence the estimated standard errors may be overestimated or underestimated. In the event of a serious violation of independence using generalized or weighted least squares to provide a more accurate method of estimation. (Lomax & Hahs-Vaughn, 2012).

Another regression assumption that must be evaluated is homoscedasticity. This term looks at conditional distributions to determine if there is a constant variance for all the different values of X. (Lomax & Hahs-Vaughn, 2012). Homoscedaasticity is related to normality because when normality is met the variables relationship to each other is homoscedastic (Tabachnick & Fiddell, 2001). To evaluate homoscedasticity, bivariate scatterplots looking at two variables should be roughly as wide throughout the graph and have some bulging in the middle. However, if heteroskedasticity is present it will likely affect standard errors and p-values. If this is the case, heteroskedasticity-consistent standard errors can be used to adjust the inference (Long & Ervin, 2000).

In regard to normality, the third regression assumption to discuss, a researcher must determine if the conditional distributions of Y or the residuals are normal in shape and normally distributed – or for all values of X the Y scores, or prediction errors/residuals- are normally distributed. (Lomax & Hahs-Vaughn, 2012). When a non- normal distribution is encountered this is often the result of outliers, a few extreme observations. Significant outliers may cause non-normality and have a deleterious effect on the results of the regression analysis. Because of the sensitivity of regression estimates, outlying observations can affect the precision of estimates, particularly in regard to the slope.

There are multiple ways to detect outliers. One common way of defining an outlier is an observation that is more than two to three standard errors away from the mean. Once an outlier is found, it must be determined what caused the error. Outliers can be the result of “(a) a simple recording or data entry error, (b) an error in observations, (c)

58 an improperly functioning instrument, (d) inappropriate use of administration instructions, or (e) a true outlier.” (Lomax & Hahs-Vaughn, 2012, p. 629). When an outlier is the result of an error a researcher should attempt to correct the error and redo the regression analysis. If this is not feasible, the observation could be deleted. However, when the outlier reflects an accurate observation, this data may represent important theoretical information and the researcher should be tentative to delete it. When there is only one outlier, a researcher can perform two regression analyses, one including the outlier and one that excludes it. By comparing the results of the regressions there will be evidence of the effect of the outlier.

To determine if there has been a violation of the normality assumption there are two widely used procedures. The first, and simplest, checks for symmetry “…in a histogram, frequency distribution, boxplot, or skewness and kurtosis statistics.” (Lomax

& Hahs-Vaughn, 2012, p. 630). While nonzero kurtosis, that is flat, platykurtic, or leptokurtic is likely to have little impact on regression estimates, nonzero skewness – a distribution that has a non-symmetrical positive or negative skew – may have a significant impact on estimates. Thus, to determine normality special attention needs to be paid to asymmetrical distributions. A researcher should be concerned if skewness values exceed 2.0 in magnitude, either positively or negatively (George & Mallery,

2010).

Another way to detect issues with normality is to use a normal probability plot (a

QQ or PP plot). If data is distributed normally, data points will fall along a straight

59 diagonal line and non-normal data will not. However, using this method is problematic as there is no principle that can differentiate deviation from linearity.

The fourth assumption of regression analysis is linearity, which indicates there is in fact a linear relationship between X and Y, an assumption for most correlations. “If the relationship between X and Y is linear, then the sample slope and intercept will be unbiased estimators of the population slope and intercept, respectively.” (Lomax & Hahs-

Vaughn, 2012, p. 631). Linearity is important because of the expectation that Y will always increase a specific amount for a one-unit increase in X. When a nonlinear relationship exists, and disproportionate increase exists, an increase in Y fails to be predictable by a specific value of X. To detect violations of linearity a researcher should evaluate the scatterplot of Y versus X.

Multicollinearity creates a problem in regression analysis when independent variables are highly correlated (r=0.9 and above) (Pallant, 2016). The standard is not to include two variables with a bivariate correlation of 0.7 or higher in the same analysis. If this is the case, reassessing which variables to include is the appropriate remedy. To determine if multicollinearity exists tolerance and VIF are assessed. If the tolerance value is below .10 it is an indication of multicollinearity. VIF is also used to assess this, if the

VIF is above 10 there is an indication of multicollinearity. Similarly, correlation coefficients over .9, as discussed above, indicate a near certainty of a multicollinearity issue (Dohoo, Ducrot, Fourichon, Donald, & Hurnik, 1997).

Ultimately, when assessing regression assumptions it is easiest to plot residuals. If the “all residuals are within two standard errors of 0,” “… the distribution of residuals is

60 nearly symmetrical, and the normal probability plot looks good” and the scatterplot

“strongly suggests a linear relationship,” then there is good evidence that the data does not violate the assumptions of regression. (Lomax & Hahs-Vaughn, 2012, p. 633).

Recommended Data Collection/Analyses

Prior to the collection of any data, this study was approved by the Institutional

Review Board (IRB). This study focused on adults and did not discuss any provocative topics. Any information that was required or requested to obtain approval was supplied to the IRB and no data was be collected prior to approval. Once IRB approval was obtained the study proceeded to the data collection process.

The survey was be given in person to students at a large midwestern university. It was anticipated that the survey will take approximately ten to fifteen minutes to complete and was not thought to be rigorous. It was be important to consider the timing of this study as people are likely to be less engaged in baseball during the off-season, between

November and March, and this may influence their responses. The study was entirely quantitative with qualitative questions asked in the survey being reserved for later versions of the study.

Data was analyzed through IBM SPSS version 25. Data was input into SPSS and attention was given to the generation of valid descriptive statistics, which was imperative to drawing valid conclusions on the relationships discussed in the study. Means, frequencies, and standard deviations were all calculated and assessed.

The primary analysis that was conducted is a regression analysis. Regression analysis involves the “statistical technique for finding the best-fitting straight line for a

61 set of data” (Gravetter & Wallnau, 2017, p. 533). The purpose of a regression analysis is to determine an outcome for a variable according to a predictor variable (Franekel et al.,

2015). It was hoped that the data will demonstrate a linear relationship, where “one variable is associated with a corresponding increase (or decrease) in another variable”

(Fraenkel et al., 2015, p. 251).

The regression analysis focused on examining the strengths of the aforementioned relationships. It was necessary to conduct linear regression analyses to determine what demographic factors most explain the SSIS mean score and to see what factors were most useful in predicting responses for each of the five rules that are discussed. This was determined by evaluating the data collected and the relationships and symbiosis that was observed between the variables. By doing this, the relationships that exist and what variables are useful in predicting fans’ perceptions of rule changes was determined.

Data screening and treatment procedures also need to be considered when gathering a substantial amount of quantitative data. Tabachnick and Fidell (2007) opined that the issues of missing data relate to the pattern of it. If missing data is random it is less serious of an issue, however nonrandom missing data can create issues when trying to generalize data. Ideally, any missing information will be ‘missing completely at random’

(MCAR), this type of missing information should not bias the analysis, however power may be lost (Kang, 2013, p. 403). Kang, following the logic of Tabachnick and Fidell

(2007), found data ‘missing not at random’ (MNAR) to be most problematic and necessitates more sophisticated corrective measures. Given the nature of this study, the fact that questions are not thought to be inflammatory or controversial and the relatively

62 short nature of the survey, it was hoped that MNAR will not rear its ugly head. However, if it did the question causing the issue may need to be removed or altered in some way.

As for random missing data, and accidental omissions, it is generally thought that

5% or less missing data in a random pattern can be dealt with by a variety of techniques including mean substitution or listwise deletion (Piggot, 2001). For this study, if only a few cases had missing values, 5% or less, then the case will be deleted in its entirety, this is known as listwise deletion (Piggot, 2001). Prior knowledge, where the researcher replaces a value based on their own judgment will not be used in this study.

Panel of Experts

After preparing the cover letter and the initial survey, seven individuals were asked to participate in a panel of experts review of the preliminary survey prior to the administration of the pilot test. The individuals that comprised the panel of experts were one industry expert in professional baseball, two full-time faculty members who teach in sport industry and have significant experience and background in baseball, one full-time faculty member in sport industry with expertise in survey design and formatting, and three sport management doctoral students with quantitative research experience, survey design, statistics, and an understanding of baseball.

Upon receiving feedback from the panel, the survey was modified per suggestions in several ways. First the original screening question inquiring on whether an individual considered themselves a fan of MLB or not (asking only yes or no and excluding those who answered no from completing the survey) was omitted. This decision was made to incorporate fans with little to no interest in baseball in the analysis and to ensure that an

63 adequate sample could be produced and that non-fans perceptions could also be analyzed.

Second, the race/ethnicity question was modified to include all potential race options and the inclusion of the term “Latinx” was made. Next, the marital status of “Partnership” was included to provide an additional option for individuals who didn’t fit into the existing choices. Fourth, additional questions were added to the prompt asking for how many times a year an individual watches MLB. Specifically, how many times per week and per month were added in an attempt to add depth to the data. Additionally, a few semantic changes were made in an attempt to clarify that the focus of the study was on

Major League Baseball and not some other form (college, or minors for instance) and to clarify the scope and details of the rules being investigated.

Overall, the panel of experts felt that the survey was well-conceived, clear, and easy to understand, which indicated good content-validity. Besides the above listed changes, which were more to aid in interpretation and to use proper vernacular, the substance of the survey remained more or less intact.

Pilot Test

After the complete review of the suggestions made by the panel of experts a revised survey was created for the pilot test. The instrument in the pilot test contained 33 questions. There were 12 demographic questions, followed by the complete SSIS comprising of eight questions, and finally 15 questions regarding the rule changes. Five of the rule change questions were Likert questions asking “To what extent would this rule change or improve MLB?” The scale values were from one to eight with one meaning

“Not at all” and eight corresponding to “A great deal.” The other ten questions relating to

64 the rule changes were qualitative inquiring about the effect of the rule change on fans in general and for the individual being surveyed. All qualitative responses were gathered for future research purposes and did not enter into the analysis being discussed in this dissertation.

Research participants were members of a baseball themed class at a large midwestern university (different than the university where the full study was conducted) where a member of the panel of experts taught. The survey was given in person during class time and 36 responses were generated.

Through reviewing the responses and entering the information into SPSS it was obvious that individuals were able to address and answer the demographic questions and the SSIS without any issue, again indicating good content-validity. The major issue that was discovered was the phrasing of the Likert questions for the rule change questions.

The Likert question for each rule change, as it appeared on the pilot test, asked “To what extent would this rule change or improve MLB?” The problem that became apparent in reviewing the responses was that the individuals responding were assigning different weight to the question whether they focused on changing or improving MLB. Thus it was decided to adjust these questions on the final survey to say “To what extent would this rule improve MLB?” in an attempt to standardize the interpretation of the question and obtain consistent results.

Chapter 4. Results

Overview

To test the questions of interest in this current study, multiple linear regression was used. Before conducting the regression on the variables that were the focus of this study the assumptions of regression were analyzed. These statistical analyses are presented within the following sections: demographics, data treatments, validity, reliability, assumptions of regression, and regression analyses. Lastly, the results of the multiple regression are presented in this chapter.

Data was collected from students at a large midwestern university in sport related classrooms. The survey was administered on paper and in person in March and April of

2019 to sixteen separate classrooms. Students were told not to retake the survey if they had done so already.

Demographics

In an attempt to assess who are the most identified spectators of baseball between the ages of 18-24, how their level of identification influences their responses to changes in the rules of baseball, and to gauge the overall perception of rule changes, a target population of 18-24 year old students in large midwestern university were identified.

Through convenience sampling of students in their respective classrooms, during class time, 328 individuals were identified and surveyed. After removing surveys, via listwise

66 deletion, where surveys were prepared by individuals outside the age range, did not state an age, or were substantially incomplete the usable sample was n=304.

The gender and race/ethnicity questions also had 304 responses, 100% of the sample. The breakdown consisted of 70.1% males and 29.9% females. Regarding race/ethnicity, 78.3% of the sample identified as White, 10.2% identified as Black or

African American, 4.6% considered themselves multiracial, 3.9% were Asian, 2.3% were

Hispanic/Latino/Latinx, 0.3% were American Indian or Alaska Native, and 0.3% answered “Race and/or ethnicity unknown.” Given the small amount of populations besides white, race/ethnicity was further broken down to white, 78.3%, and non-white,

21.7%. The category of non-white did include individuals that responded as multiracial, some of which indicated that they were white and part of another category.

All 304 individuals in the final sample reported their age and were college students aged 18-24. The mean age was 20.65, 18 year olds represented 4.9% of the sample, 19 year olds accounted for 13.5%, 20 year olds were 27.6%, 21 year olds were

28%, 22 year olds were 18.4%, 23 year olds were 6.6%, and 24 year olds were 1% of the sample.

Another demographic factor utilized in the regression analyses was the distance peoples permanent zip code was from their favorite team’s home stadium. Of the 304 surveys used in this analysis 293 respondents provided a zip code and favorite team to determine mileage. This information showed a wide range of distance from a favorite team’s home stadium, from 4 miles to 3,116 miles. The median for this statistic was

229.5 miles.

An important question in this survey was whether an individual had played organized baseball or softball, which 301 out of 304 people surveyed responded to. The valid percent for those who answered yes was 75.3%, while those who had never participated in organized baseball or softball represented 24.3% of the 301 individuals who responded to this question.

The final demographic factor considered in the multiple linear regression was how many times per month an individual watched MLB. On the survey three questions regarding how often MLB was watched were asked, how many times per week, month, and year. In some cases, a person used a range to answer these questions (i.e., 5-6 times they watched MLB per month), in these instances the mean of the range was used to create a usable response. Unfortunately, issues of multicollinearity required two of these three questions to be removed. The version of the question that was chosen to remain was how many times do you watch MLB per month. The question asking about how many times per year an individual watched MLB was not chosen due to normality issues, this variable had skewness, 2.066, and kurtosis, 4.752, issues which are indicative of outliers.

Moreover, the outliers in regard to the per year question indicated an error in observation by the respondents as some appeared rely on a calendar year to derive their number while others appeared to rely on a baseball season, which gave the appearance of inconsistency in responses and justified the exclusion of this variable (Lomax & Hahs-Vaughn, 2012, p.

629). The variable examining how many times per week was not chosen because 37.3%,

107/287, of valid answers responded stating they watched zero MLB games per week and

57.1%, 164/287, of valid answers responded by stating they watched 1 or fewer games

68 per week. This resulted in low variance for games per week, 4.075. However, the question inquiring how many times per month an individual watched MLB did not have any skewness or kurtosis issues, with both scores falling between -2 and +2.

Additionally, the per month variable had only 26.9%, 74/275, responses stating the individual watched zero games, and 37.1%, 102/275, responses indicating the individual watched 1 or fewer games per month, which resulted in a variance of 67.389. Thus the per month question blended the best of per week and per year into a more cohesive and useful item.

A few demographic questions were omitted from the survey because of other various issues. Those were degree of MLB fandom, which had multicollinearity problems with the SSIS mean score, current year in college, which was had multicollinearity issues with age and was less universally decipherable, marital status, where 284 of 294 responses were single, were also not used.

Data Treatment

Before conducting any statistical analysis, the survey data was evaluated to determine the scope of missing data. If 5% or less of data points are randomly missing, problems are likely to be less serious and most any procedure for dealing with missing values will yield similar results (Tabachnik & Fidell, 2001). Out of 5,472 questions (or data points) utilized in the formal regression analysis, only 61 values were missing, which represent 1.11%. 11 related to the distance in miles between a fan’s permanent zip code and their favorite team’s home stadium. The distance omissions were not imputed as there was no way to determine the distance without responses to both permanent zip code

69 and favorite team and the missing data was omitted from the analysis via listwise deletion, which resulted in utilizing the smaller sample size of 293 instead of 304. Three missing data points related to whether an individual had played organized baseball or softball, again there was no way to impute or use a mean to determine the answer that would have been given by the responder so again these values were left out via listwise deletion.

There was one missing value within the SSIS. An individual left one of the seven

SSIS questions blank. In this situation, the missing score on the SSIS was determined through mean substitution. The other six responses in the SSIS, in regard to this responder, were totaled and an average score was determined and put in for the missing value. As this mean substitution was used in only one instance of over 2,100 responses in the SSIS, the impact is extremely minimal, and well within the requirement of being below 1% of missing values (Piggot, 2001).

In regard to missing data for the rule change questions, there was one response missing from the strike zone inquiry, five from the mound visit question, two for tie after

12 innings, five regarding starting a baserunner on second base in extra-innings, and five for pitch clock. These 18 missing answers represent the rest of the missing data described above. Given that the missing data was so small and that mean substitution or imputation was impractical given the lack of predictive numbers that could be used to ascertain an accurate imputation, listwise deletion was again used.

Another set of questions, regarding how many times per week, month, and year the surveyed watched MLB was asked on the survey. In some cases, a person used a

70 range to answer these questions (i.e., 5-6 times they watched MLB per month), in these instances the mean was used to create a usable response. As discussed above, issues of multicollinearity required that only one of these questions, how many times per month an individual watches MLB, was used in the regression analysis. This item, as previously discussed, was chosen because it did not violate the assumption of normality, provided a greater variance in answers than per week, and had significantly fewer responses that were “0” or “1.”

Validity and Reliability

In multiple linear regression it is imperative to assess measurement error by looking at both the validity and reliability of the measure. Validity refers to whether a measure accurately represents what it is supposed to, while reliability is the “degree to which the observed variable measures the “true” value and is “error free.” (Hair,

Anderson, Tatham, & Black, 1998).

In regard to validity, the SSIS is a scale that has been used repeatedly for over 20 years and has strong construct and content validity. In regard to the other questions as a result of the panel of experts review and pilot test it was determined that the questions were capable of accurately measuring what they were designed to do and were understood by not only the panel of experts but also the members of the pilot test indicating strong content validity, as discussed in chapter 3.

In regard to reliability, the SSIS has repeatedly had high Cronbach’s alpha scores, see chapter 3. For this study specifically, in looking at the seven items of the SSIS,

Cronbach’s alpha was at .969. As mentioned before, a Cronbach’s alpha level should be

71 at a minimum of .70, but the higher the better. (Fraenkel et al., 2015, p. 158), thus the

SSIS in this study was again highly reliable.

Assumptions of Regression

Residual scatterplots, histograms, and P-P plots were examined for all regression analyses. In assessing independence scatterplots and residual plots were looked at.

Through this analysis it was determined that the independence assumption has been met because the points on the scatterplot and residual plot do not appear randomly configured

(Lomax & Hahs-Vaughn, 2012).

Normality, the determination that data is distributed normally, was measured by looking at a probability plot, also called a P-P plot (Tabachnick & Fidell, 2001). The two components of normality are skewness and kurtosis, if skewness and kurtosis values are zero than the distribution is normal. Skewness looks at the symmetrical qualities of the distribution or responses and kurtosis looks at the “peakedness of a distribution; a distribution is either too peaked (with short, thick tails) or too flat (with long, thin tails)”

(Tabachnick & Fidell, 2001, p. 73). When a reasonably large sample is used

“underestimates of variance associated with positive kurtosis disappear with samples of

100 or more cases; with negative kurtosis, underestimation of variance disappears with samples of 200 or more” (Tabachnick & Fiddell, 2001, p. 74-75). In this study there were

304 cases which would indicate that the variance is not being underestimated based on positive or negative kurtosis. Furthermore, by evaluating PP plots it appeared the normality assumption was sustained.

Homoscedasticity was also measured by observation of the scatterplot.

Homoscedasticity is met when the variance is consistent throughout all values. This is demonstrated by ordinal data on scatterplots being roughly rectangular and concentrated in the center, along the 0 point (Pallant, 2016, p. 160). Unfortunately, this was not always observed in the analyses, therefore using heteroskedasticity-consistent standard errors in the linear regression model was decided upon (Long & Ervin, 2000). By using this treatment observed residuals are used, as opposed to assuming the variance of the residuals is unchanging, which corrects p-values.

Linearity, as was previously discussed, was evaluated by scatterplots as well. If linearity is met, the scatterplot will be oval-shaped and the variables will be normally distributed and linearly related (Tabachnick & Fiddell, 2001). Linearity indicates there is in fact a linear relationship between X and Y, an assumption for most correlations. This was observed in the scatterplots for all six regressions.

Another related aspect of linearity is multicollinearity. As was discussed in chapter 3, if variables have a bivariate correlation of .7 or higher, there should be variables omitted. This is measured in SPSS by looking at collinearity diagnostics

(tolerance and VIF). Tolerance should not be less than .10, and VIF should be below 10 or else there is an indication of multicollinearity. However, these values still allow for correlations between independent variables that are quite high so this is not determinative and should be seen as a warning to check the correlation matrix (Pallant, 2016). In the event that Tolerance and VIF indicate a potential issue, which is the case with several variables in the current study in that the statistics are close to the thresholds, the Pearson

Correlation can be evaluated to determined issues of multicollinearity. If the correlation coefficient exceeds .90 than multicollinearity is almost certain to be a problem (Dohoo et al., 1997). Issues were indicated in this study specifically in analyzing the Pearson

Correlation. This showed that the times MLB was watched per week, month, and year were creating multicollinearity problems. For the variable looking at how many times per week MLB was watched the Pearson Correlation was .975 in relation to times watched per month and .900 for times watched per year. For times watched per month the Pearson

Correlation was .899 to times watched per year. Furthermore, the times watched per week and per year had Tolerances well below .10 and VIF scores in the twenties, indicating significant multicollinear issues. In contrast times watched per year had a tolerance of

.174 and a VIF of 5.736. Even though times per year had fewer VIF and tolerance issues, the outlier issue, as was explained earlier, led to the decision to choose times per month for this study.

Another multicollinearity issue that was examined was the relationship between

MLB fandom and the SSIS mean score. In this relationship the Tolerance was .158 for degree of MLB fandom and .194 for SSIS mean score, the VIF was 6.320 for MLB fandom and 5.164 for the SSIS mean. However, the Pearson Correlation between MLB fandom and SSIS mean was .893, which is extremely close the .9 threshold that indicates a high likelihood of multicollinearity. As a result, MLB fandom, a question created by the author for this survey, was removed from the regression analysis as the SSIS mean is an established scale with more dimensions and high validity and reliability.

Similar issues arose when looking at age and current academic year. In looking at age and current academic year the Pearson Correlation was .860. Given that extrapolating valuable information based on current academic year seemed more difficult to interpret for an average reader who may not immediately know what ages correlate to what academic year and the fact that the relationship is not definite, although correlated, the decision to drop current academic year was also made to ensure no multicollinearity issues between these variables and to use a variable that everyone would immediately recognize.

In summation of the multicollinearity issues, several variables were removed because of the high likelihood they were creating problems. How many times an individual watched MLB per year was chosen over per week or month as it had a much lower Pearson correlation. SSIS mean score was chosen over MLB fandom due to an indication of multicollinearity by the Pearson correlation. Age was chosen over current academic year as the Pearson correlation was very high (although not quite at the .9 level) and age is a more standard and universally comprehendible statistic than year in school.

In aggregate the following variables were used to run the multiple linear regression to determine how much they explain fan identification for 18-24 year-olds and for the five rule changes: Age, race/ethnicity (divided into “white” and “non-white” categories), gender, miles to the responder’s favorite team’s stadium from their permanent zip code, participation in organized baseball or softball, how many times per year an individual watched MLB, and the SSIS mean score.

Research Question 1 Factors that Define the most Identified Fans of MLB

Table 1. ANOVA RQ1 Model F D.F. p-value 1 52.335 6.000 0.000

Table 2. Regression Analysis for Research Question 1 SSIS Mean Score Β SE t-score p-value Constant 3.66 1.529 2.394 0.017 Gender -0.121 0.233 -0.52 0.604 Age -0.046 0.072 -0.638 0.524 Race/Ethnicity -0.068 0.268 -0.255 0.799 Miles from Favorite Team's Stadium 0.000 0.000 -0.858 0.392 Played Organized Baseball or Softball 1.060* 0.265 3.998 0.000 Times MLB Watched Per Month 0.171* 0.014 12.4 0.000 * p < 0.05

For the first research question of the dissertation the variables and demographic information compiled was used to examine how well the SSIS mean score could be predicted by the other independent variables used in the analysis.

The independent variables used to predict the SSIS mean score were gender, age, race/ethnicity, miles from favorite team’s stadium, participation in organized baseball or softball, and times watched MLB per month. The initial model produced the following values, F(6, 256) = 52.34, p < .001. The R2 was 0.55, meaning that the model explained

55% of the variance in the outcome.

In the model, gender, age, race/ethnicity (or white and non-white status), and miles from favorite team’s stadium were not significant predictors of an individual’s

SSIS mean score. However, playing organized baseball or softball, β = 1.060, t(256) =

3.998, p < 0.001, and times watched per month, β = 0.171, t(256) = 12.4, p < 0.001, were both significant. Thus, after controlling for other variables in the model, if an individual played organized baseball or softball his or her predicted SSIS mean score would increase by 1.060 units. Additionally, for each additional time per month that a person watched MLB, his or her outcome was predicted to increase by 0.171 units.

Research Question 2a Strike Zone

Table 3. ANOVA Table for Strike Zone Model F D.F. p-value 1 3.073 6 0.006 2 2.626 7 0.012

Table 4. Regression Analysis for Automated Strike Zone Block 1 β SE t-score p-value Constant 8.209 1.985 4.135 0.000 Gender -0.327 0.264 -1.24 0.216 Age -0.184* 0.092 -2.003 0.046 Race/Ethnicity 0.063 0.315 0.200 0.841 Miles from Favorite Team's Stadium 0.000 0.000 -0.112 0.911 Played Organized Baseball or Softball -0.447 0.312 -1.433 0.153 Times MLB Watched Per Month -0.046* 0.015 12.4 0.003 * p < 0.05

Table 5. Regression Analysis for Automated Strike Zone Block 2 β SE t-score p-value Constant 8.175 2.020 4.047 0.000 Gender -0.326 0.263 -1.237 0.217 Age -0.184* 0.092 -1.991 0.048 Race/Ethnicity 0.064 0.317 0.201 0.841 Miles from Favorite Team's Stadium 0.000 0.000 -0.105 0.916 Played Organized Baseball or Softball -0.457 0.318 -1.439 0.151 Times MLB Watched Per Month -0.047* 0.312 -2.228 0.027 SSIS Mean Score -0.009 0.077 0.121 0.903 * p < 0.05 77

The next set of regressions are designed to determine the relationship between the independent variables of gender, age, race/ethnicity, miles from favorite team’s stadium, participation in organized baseball or softball, and times watched MLB per month, and SSIS mean score and the dependent variable of how much implementing a rule change would improve MLB. The first regression ran focuses on whether using an automated strike zone would improve MLB.

This regression was run in two steps, via hierarchical entry. The first model utilized the above referenced independent variables of age, race/ethnicity, miles from favorite team’s stadium, participation in organized baseball or softball, and times watched MLB per month. The initial model showed F(6, 256) = 3.07, p = .006. The R2 was .067, meaning that the model explained 6.7% of the variance in the outcome.

In the initial model, race/ethnicity, gender, miles from favorite team’s stadium, and participation in organized baseball or softball were not significant predictors of the individual’s perception of how using an automated strike zone would improve MLB.

However, age was statistically significant, β =-0.184, t(256) = -2.003, p = .046 times watched per month was significant, β =-0.046, t(256) = -2.97, p = .003. Thus, after controlling for other variables in the model, for each increase in age (by one year) the outcome was predicted to decrease by 0.184 units, and for each additional time per month that a person watched MLB, his or her outcome was predicted to decrease by 0.046 units.

The second model added SSIS mean score to the above-referenced independent variables from the first model, to determine if model fit and variance explained would improve. Findings showed the following: F (7, 255) = 2.63, p = 0.012, F-change = 0.015,

78 p = 0.903. No additional variance was explained by adding the SSIS mean score to the regression. Besides age and times MLB watched per month, none of the other independent variables were significant predictors of an individual’s belief that using an automated strike zone would improve MLB. Thus, the decision was made to retain the initial model without SSIS mean score for discussion purposes.

Research Question 2b Mound Visit

Table 6. ANOVA Table for Mound Visit Model F D.F. p-value 1 2.726 6 0.014 2 3.093 7 0.004

Table 7. Regression Analysis for Mound Visit Block 1 β SE t-score p-value Constant 2.256 1.931 1.168 0.244 Gender -0.158 0.286 -0.553 0.581 Age 0.104 0.091 1.142 0.254 Race/Ethnicity 0.031 0.324 0.097 0.923 Miles from Favorite Team's Stadium 0.000 0.000 -0.806 0.421 Played Organized Baseball or Softball 0.314 0.305 -1.032 0.303 Times MLB Watched Per Month 0.043* 0.017 2.576 0.011 * p < 0.05

Table 8. Regression Analysis for Mound Visit Block 2 β SE t-score p-value Constant 1.631 1.936 0.842 0.400 Gender -0.136 0.281 -0.484 0.629 Age 0.111 0.091 1.216 0.225 Race/Ethnicity 0.045 0.322 0.141 0.888 Miles from Favorite Team's Stadium 0.000 0.000 -0.706 0.481 Played Organized Baseball or Softball 0.130 0.309 0.419 0.676 Times MLB Watched Per Month 0.012 0.021 0.557 0.578 SSIS Mean Score 0.181* 0.075 2.401 0.017 * p < 0.05 79

The second regression analysis focused on whether the respondents to the survey felt that limiting mound visits to seven per game would improve MLB. Again, this regression was run in two steps, via hierarchical entry. The first model utilized independent variables of gender, age, race/ethnicity, miles from favorite team’s stadium, participation in organized baseball or softball, and times watched MLB per month. The initial model showed: F(6, 254) = 2.73, p = .014. The R2 was .060, meaning that the model explained 6.0% of the variance in the outcome.

In the initial model, race/ethnicity, gender, age, miles from favorite team’s stadium, and participation in organized baseball or softball were not significant predictors of the individual’s perception of how limiting mound visits would improve MLB.

However, times watched per month was significant, β = 0.043, t(254) = 2.58, p = .011.

Thus, after controlling for other variables in the model, for each additional time per month that a person watched MLB, his or her outcome was predicted to increase by 0.043 units.

The second model added SSIS mean score to the above-referenced independent variables from the first model, to determine if model fit and variance explained would improve. The results from the second model were: F = (7, 253) = 3.09, p = 0.004. By adding the SSIS mean score to the regression explained 1.9% more variance in the outcome, F-change = 5.76, p = 0.017, and SSIS mean score was found to be statistically significant, β = 0.181, t(254) = 2.40, p = .017. None of the independent variables were significant predictors of an individual’s belief that limiting cumulative mound visits would improve MLB. By including SSIS mean score, how many times per month an

80 individual watches MLB went from being significant to being statistically insignificant.

Thus, the decision was made to use both the first model and the second model with SSIS mean score for discussion purposes.

Research Question 2c Ties after 12 innings completed

Table 9. ANOVA Table for Ties after 12 innings Model F D.F. p-value 1 7.810 6 0.000 2 6.853 7 0.000

Table 10. Regression Analysis for Ties after 12 innings Block 1 β SE t-score p-value Constant 1.787 2.274 0.786 0.433 Gender 0.796* 0.319 2.496 0.013 Age 0.094 0.108 0.876 0.382 Race/Ethnicity 0.288 0.381 0.758 0.449 Miles from Favorite Team's Stadium 0.000 0.000 -1.197 0.233 Played Organized Baseball or Softball -0.868* 0.387 -2.240 0.026 Times MLB Watched Per Month -0.062* 0.014 -4.473 0.000 * p < 0.05

Table 11. Regression Analysis for Ties after 12 innings Block 2 β SE t-score p-value Constant 1.457 2.345 0.621 0.535 Gender 0.808* 0.319 2.531 0.012 Age 0.098 0.108 0.901 0.368 Race/Ethnicity 0.296 0.379 0.781 0.436 Miles from Favorite Team's Stadium 0.000 0.000 -1.143 0.254 Played Organized Baseball or Softball -0.965* 0.402 -2.403 0.017 Times MLB Watched Per Month 0.079* 0.019 -4.188 0.000 SSIS Mean Score 0.095 0.099 0.962 0.337 * p < 0.05

The third regression analysis looked at whether the respondents to the survey felt that ending games in a tie when the score is even after 12 innings would improve MLB.

Again, this regression was run in two steps, via hierarchical entry. The first model utilized independent variables of gender, age, race/ethnicity, miles from favorite team’s stadium, participation in organized baseball or softball, and times watched MLB per month. The initial model showed: F(6, 254) = 7.81, p = .000. The R2 was 0.156, meaning that the model explained 15.6% of the variance in the outcome.

In the initial model, race/ethnicity, age, and miles from favorite team’s stadium were not significant predictors of the individual’s perception whether ending games in a tie when the score is even after 12 innings would improve MLB. However, gender, β =

0.796, t(254) = 2.496, p = .013, experience playing organized baseball or softball, β = -

0.868, t(254) = -2.240, p = .026, and times watched per month was significant, β = -

0.062, t(254) = -4.473, p = .000. Thus, after controlling for other variables in the model, being female is predicted to increase the preference for allowing ties after 12 innings by

0.796 units. Playing baseball or softball is predicted to decrease an individual’s preference by 0.868 units. Finally, for each additional time per month that a person watched MLB, his or her outcome was predicted to decrease by 0.062 units.

The second model added SSIS mean score to the above-referenced independent variables from the first model, to determine if model fit and variance explained would improve. Model fit remained good, F = (7, 253) = 6.853, p < 0.001. By adding the SSIS mean score to the regression explained 0.3% more variance in the outcome, F-change =

0.925, p = 0.337. Gender, organized baseball/softball, and times watched per month remained significant predictors when SSIS mean score was included, but SSIS mean

82 score was not significant. Thus, the decision was made to retain the initial model without

SSIS mean score for discussion purposes.

Research Question 2d Starting Extra-Innings with a Runner on Second Base

Table 12. ANOVA Table for Extra-Innings Runners Model F D.F. p-value 1 0.964 6 0.450 2 1.655 7 0.121

Table 13. Regression Analysis for Extra-Innings Runners Block 1 β SE t-score p-value Constant 0.236 2.447 0.097 0.923 Gender 0.136 0.332 0.408 0.683 Age 0.168 0.117 1.442 0.151 Race/Ethnicity 0.659 0.378 1.745 0.082 Miles from Favorite Team's Stadium 0.000 0.000 0.329 0.742 Played Organized Baseball or Softball 0.345 0.374 0.924 0.356 Times MLB Watched Per Month 0.008 0.019 0.422 0.674 * p < 0.05

Table 14. Regression Analysis for Extra-Innings Runners Block 2 β SE t-score p-value Constant -0.603 2.458 -0.246 0.806 Gender 0.160 0.318 0.504 0.615 Age 0.178 0.117 1.528 0.128 Race/Ethnicity 0.672 0.376 1.786 0.075 Miles from Favorite Team's Stadium 0.000 0.000 0.472 0.638 Played Organized Baseball or Softball 0.115 0.379 0.303 0.762 Times MLB Watched Per Month -0.032 0.026 -1.240 0.216 SSIS Mean Score 0.231* 0.102 2.258 0.025 * p < 0.05

The fourth regression analysis focused on whether the respondents to the survey believed starting extra-innings with a runner on second base would improve MLB. Again, this regression was run in two steps, via hierarchical entry. The first model utilized

83 independent variables of gender, age, race/ethnicity, miles from favorite team’s stadium, participation in organized baseball or softball, and times watched MLB per month. The initial model showed: F(6, 252) = 0.96, p = 0.45 The R2 was .022, meaning that the model explained 2.2% of the variance in the outcome.

In the initial model, none of the independent variables were significant predictors of an individual’s perception of whether starting extra-innings with a runner on second base would improve MLB.

The second model added SSIS mean score to the above-referenced independent variables from the first model, to determine if model fit and variance explained would improve. Model fit was: F = (7, 251) = 1.66, p = 0.121. By adding the SSIS mean score to the regression explained 2.2% more variance in the outcome. F-change = 5.098, p =

0.121. None of the original independent variables were significant predictors of an individual’s perception of whether starting extra-innings with a runner on second base would improve MLB. However, when SSIS mean score was included it became a significant, β = 0.231, t(251) = 2.258, p = .025. Therefore, after controlling for other variables in the model, for each additional point on the SSIS mean score, his or her outcome was predicted to increase by 0.231 units. Thus, the decision was made to use the second model which included SSIS mean score for discussion purposes.

Research Question 2e Pitch Clock

Table 15. ANOVA Table for Pitch Clock Model F D.F. p-value 1 1.918 6 0.078 2 2.853 7 0.007

Table 16. Regression Analysis for Pitch Clock Block 1 β SE t-score p-value Constant 2.607 2.198 1.186 0.237 Gender -0.486 0.325 -1.495 0.136 Age 0.137 0.103 1.323 0.187 Race/Ethnicity -0.713 0.380 -1.876 0.062 Miles from Favorite Team's Stadium 0.000 0.000 0.203 0.839 Played Organized Baseball or Softball 0.075 0.343 0.219 0.827 Times MLB Watched Per Month 0.036* 0.016 -2.250 0.025 * p < 0.05

Table 17. Regression Analysis for Pitch Clock Block 2 Β SE t-score p-value Constant 1.685 2.258 0.746 0.456 Gender -0.457 0.310 -1.475 0.141 Age 0.148 0.106 1.399 0.163 Race/Ethnicity -0.701 0.376 -1.861 0.064 Miles from Favorite Team's Stadium 0.000 0.000 0.411 0.681 Played Organized Baseball or Softball -0.180 0.333 -0.539 0.590 Times MLB Watched Per Month -0.080* 0.022 -3.718 0.000 SSIS Mean Score 0.253* 0.086 2.952 0.003 * p < 0.05

The fifth regression analysis focused on whether the respondents to the survey felt that utilizing a pitch clock would improve MLB. Once again, this regression was run in two steps, via hierarchical entry. The first model utilized independent variables of gender, age, race/ethnicity, miles from favorite team’s stadium, participation in organized baseball or softball, and times watched MLB per month. The initial model showed: F(6,

253) = 1.92, p = .078. The R2 was .044, meaning that the model explained 4.4% of the variance in the outcome.

In the initial model, race/ethnicity status, gender, age, miles from favorite team’s stadium, and participation in organized baseball or softball were not significant predictors 85 of the individual’s perception of how limiting mound visits would improve MLB.

However, times watched per month was significant, β = -0.036, t(253) = -2.25, p = 0.025.

Thus, after controlling for other variables in the model, for each additional time per month that a person watched MLB, his or her outcome was predicted to decrease by

0.036 units.

The second model added SSIS mean score to the above-referenced independent variables from the first model. By adding SSIS mean score the data showed: F = (7, 252)

= 2.85, p = 0.007. By adding the SSIS mean score to the regression explained 2.9% more variance in the outcome. F-change = 8.71, p = 0.007. Of the independent variables, only times MLB watched per month was statistically significant by including SSIS mean score

(as it was prior to the inclusion of the SSIS mean score), β = -0.080, t(252) = -3.718, p <

0.001. Additionally, the SSIS mean score, β = 0.253, t(252) = 2.952, p = 0.003 was also a significant predictor of an individual’s belief that limiting mound visits would improve

MLB. Thus, the decision was made to use the second model with SSIS mean score for discussion purposes.

Research Question 3a Strike Zone

303 individuals, out of a sample of n=304, responded to the strike zone question.

When looking at the frequencies and descriptive statistics for the question about the implementation of an automated strike zone there were several interesting observations.

First, the most common response, or mode, was “1” or “Not at all” in response to whether or not implementing an automated strike zone would improve baseball (18.8% of people, or 57/304). The least common response was “8” or “A great deal,” with 3.6% (11/304) of

86 people responding this way. Answers at “4” or less accounted for 64.4% of the responses and the mean score was 3.736 and the variance was 4.188. All answers, 1-8, were represented in the data.

Research Question 3b Mound Visits

299 individuals responded to the question about limiting mound visits. The mode for this question was 6 and the median was 4.813 with a variance of 4.099. Specifically, those who responded with a score of 5 or higher represented 59.2% of the responses

(174/299). The least common response was “1” or “Not at all,” 7.4% of those who responded to this question answered this way (22/299). All answers, 1-8, were represented in the data.

Research Question 3c Ties after 12 innings completed

302 individuals answered the question regarding calling games a tie after 12 innings if the score remains deadlocked. There are some very interesting observations to be made when looking at the frequencies and descriptive statistics for this question.

Again, the mode, was “1” or “Not at all” (49.3% of people, or 149/302) and the mean score was 2.8377 with a variance of 5.545. Answers at 2 or less represented 60.9%

(184/302), and scores at 4 or less totaled 76.2% (230/302). 23.8% of responses had a score of 5 or higher, with answers at 7 or higher represented 12.9% (39/302). All answers, 1-8, were represented in the data.

Research Question 3d Starting Extra-innings with a Runner on Second Base

299 people responded to the question about starting extra-innings with a runner on second base. For this question the mode was also “1” or “not at all” (22.4% of

respondents, or 67/299). The mean score was 4.1639, with a variance of 5.393. Answers

between 1-4 represented 53.2% (159/299) of the responses, whereas answers between 5-8

represented 46.8% (140/299). The least common response was “2” with 6.7% (20/299),

the second least common response was “7” with 10.7% responses (32/299). All answers.

1-8, were represented in the data.

Research Question 3e Pitch Clock

299 people responded to the question about implementing a pitch clock. The

mode for this question was “7” (16.7% of those surveyed responded with this answer, or

50/299), the second most common response was “6” (16.4%, 49/299). Scores that were

“5” or higher represented 58.2% (174/299). 9.7% (29/299) responded with “1” or “Not at

all” while 13% (39/299) responded to this question with “8” or “A great deal.” The mean

score for the pitch clock question was 4.9532 and the variance was 4.716.

Table 18. Descriptive Statistics for Rule Changes

Std. Valid n Mean Mode Dev. Variance Skewness Kurtosis

Strike Zone 303 3.736 1 2.047 4.188 0.295 -0.942

Mound Visit 299 4.813 6 2.025 4.099 -0.263 -0.908

Ties after 12 Innings 302 2.838 1 2.355 5.545 1.009 -0.388

Extra-innings Runners 299 4.164 1 2.322 5.393 0.015 -1.24

Pitch Clock 299 4.953 7 2.172 4.716 -0.323 -0.968 88

Chapter 5. Discussion

The current study was designed to examine the factors that influenced an individual’s preference for various rule changes to MLB. Of the five discussed in this paper, one has been implemented at MLB level (limitation on cumulative mound visits)

(Adler, 2018), two have been implemented at the MiLB level (starting extra-innings with runners on second base, and the pitch clock) (Berg, 2018; SI Wire, 2017b), and two are theoretical but discussed by players, pundits and experts with regularity (using an automated umpire to call balls and strikes and implementing ties when a score is deadlocked after the conclusion of the 12th inning) (Thompson, 2018; Fink, 2017).

Given that MLB fans are aging and 50% are aged 55 or older (Paul, 2017) and there are so many entertainment options available to consumers in the modern world it is imperative for MLB to grow its young fan base, but not at the expense of alienating its existing consumers. As a result, this study looked at a demographic, 18-24 year old college students, to try and determine what their thoughts on the above described rule changes would be. To analyze their thoughts, fan identification, specifically team fan identification, was used as an independent variable to see how fans with various identification scores would look at these rule changes. Additional independent variables of gender, age, race/ethnicity, miles from favorite team’s stadium, participation in organized baseball or softball, and how many times an individual watches MLB per

89 month were used to provide additional insight and more points of analysis in the multiple linear regression.

The findings related to the first research question are discussed first. Following that, each rule change that was a point of emphasis in this study will be discussed with both research questions being a part of the same analysis. Implications for relevant stakeholders are then discussed and finally limitations of the current study and directions for future research are identified.

Research Question 1 Factors that Define the Most Identified Fans of MLB

The first research question attempted to determine what relevant factors define highly identified fans of MLB. Essentially, the regression analysis that was conducted aimed to examine how well the SSIS mean score could be predicted by the other independent variables that were used in the analysis.

Gender, age, status as white or non-white, and miles from favorite team’s stadium were not significant predictors of an individual’s SSIS mean score. However, participation in organized baseball and softball and times watched per month were both significant. To recapitulate the findings in chapter 4, if an individual played organized baseball or softball his or her predicted SSIS mean score would increase by 1.060 units.

Additionally, for each additional time per month that a person watched MLB, his or her outcome was predicted to increase by 0.171 units.

Having participated in organized baseball or softball had over a one point impact on the SSIS mean score, which only has eight points. This is a valuable predictor for

SSIS mean.

The other significant finding from the regression analysis for research question 1 was that the more an individual watches MLB the higher their SSIS mean score may be.

For each additional game watched per month the data shows that the SSIS mean score should increase by nearly 0.2 of a point. Given the high volume of games that are played each month during the season, the ability to reach as many individuals as possible via different media platforms is vital. Given that the target population here is between 18-24, utilizing social media, internet, and streaming options to inundate individuals with baseball games and related content could help increase their SSIS mean scores.

Technology, in any form or fashion, that helps MLB games reach viewers could have a profound cumulative impact on SSIS mean scores and fan identification generally. A significant goal going forward for MLB should be to maximize exposure and increase its presence across as many platforms as possible to increase fan identification.

Descriptive statistics for SSIS mean scores were also analyzed and the samples most frequent mean score was “1.” 23 individuals had a score of “1” which means their responses to the SSIS were all at the very lowest point, this represented 7.6% of the sample. Individuals with mean scores of “1” would appear to be non-fans or at best strongly disinterested in MLB. To reach these fans it may be appropriate to market the game differently than MLB would for fans with some familiarity and appreciation for the game. Making an excursion to the ballpark a more entertaining prospect with more of a variety of activities may help. It may also help to find ways to inform these individuals about the exciting aspects of the game and inundate them with dramatic moments in baseball rather than the slower points that may turn them off altogether. Similarly those

91 with scores at “2” or lower represented 20.1% of the sample, while these individuals are more interested in baseball in some ways than those with a flat score of “1” similar methods of teaching the game and showing how exciting it can be would likely be effective to increase their fan identification. Cross-promotions with other sports or entertainment mediums may help create a narrative that baseball is a vital part of the experience of being young and is engaged in popular culture. The perception that baseball is a game for older individuals should be fought tooth and nail to reach these demographics.

For casual fans, like those who scored more than “1” on the mean score but are not in the upper ranges of the distribution, it is important to stay in the forefront of their minds. Research has shown that casual sports fans forget the specifics of games they attended very quickly so reminders of the experience are very important (Pooley, 1980).

In contrast, committed fans are the types that become involved in the sport on a daily basis, and getting individuals to check a team’s website or message board, or at least check scores and tune in for an inning would likely have a positive effect on their identification according to this regression that says simply the more games an individual watches the more invested they become. Similarly, getting individuals involved in organized baseball or softball, at any level, would be another great option for improving team fan identification scores. Once fan identification increases individuals become less cost sensitive, show conative loyalty and the desire to purchase products in the future, and if these individuals become highly identified they will consider MLB fandom as part of their daily habits (Oliver, 1977). When this high level of identification occurs, people

92 will spend more on tickets and merchandise and stay loyal when a team is performing poorly, which would be extremely beneficial to maintained interest in MLB product

(Fink et al., 2002).

In contrast those with high team fan identification, probably do not need to be as aggressively marketed towards as they are already engaged. In the sample 33.2% had a score of “6” or higher and are already engaged in baseball. To continue to reach these groups, a similar process of inundating social media, the internet, and television to maximize exposure to MLB would be a tactic that is supported by this data. However, when making rule changes, it is important to consider what impact this would have on these fans with high identification scores. MLB must be careful not to discourage highly identified fans in their quest to reach new fanbases because those with high team fan identification are more likely to purchase merchandise and attend future games (Trail et al., 2005; Cialdini et al., 1976). Moreover higher fan identification had the biggest effect on enjoyment when engaging in the sport (Madrigal, 1995).

Research Questions 2a and 3a - Strike Zone Discussion

The multiple linear regression analyzing the sample’s feelings on automating the call of balls and strikes and removing this responsibility from the home plate umpire produced a wide range of opinions. The level of fan identification, measured by the SSIS mean score, was not a significant predictor of an individual’s preference regarding this rule. However, when SSIS mean score was not considered, age and how many times an individual watched MLB games per month were both statistically significant. These relationships, which showed that for each increase in age (by one year) the outcome was

93 predicted to decrease by 0.184 units and each additional viewing per month decreased an individual’s preference for the implementation of a strike zone by 0.046 units, demonstrated that the older a fan is and the more they watch MLB per month, the less they like the idea of automated strike zone generally. Thus, it can be gleaned that the more someone watches MLB the more they prefer a home plate umpire having the responsibility of calling balls and strikes. This again supports the general idea of increasing exposure to the game to this age range. Also, the age significance may also show that the older an individual is the more averse they are to an automated strike zone.

This could be because of the tradition of the game, or increased exposure to MLB.

It is possible that the aspects of drama and aesthetics, not measured by the SSIS but considered in motivation theory, may be a possible explanation for decrease in approval of an automated strike zone the more a person watches MLB. If the interplay between a home plate umpire and the batter, pitcher, or catcher can be construed as dramatic and aesthetically pleasing it could be argued that this rule should not be put in place because artistic appreciation increases a spectator’s level of appreciation of a game

(Fink et al., 2002). Given that baseball has an average of three minutes and forty-five seconds between balls put in play, something that happens in between those events that spectators would enjoy should not be removed from the equation without more research on people’s true feelings on this radical change (Everett, 2018). Therefore, even though there was little consensus about the efficacy of an automated strike zone, a strong argument could be made that by implementing automated strike zones, aesthetics and

94 drama could be minimized and these factors have been proven to motivate individuals to watch sports generally (Fink et al., 2002)

The data also demonstrates that individuals who watch little to no MLB may prefer an automated umpire. Several potential explanations exist for this relationship.

First, it can be hypothesized (though not researched) that individuals who do not watch baseball may be of the belief that implementing automated umpires will speed the game up and simply just want the game over with so they can do something else. Finding ways for individuals to participate in organized baseball or softball may be a way to increase their interest and understanding of the game and its predilection towards being time consuming. Also, simple exposure to more MLB games would be an approach that the data corroborates. However, other explanations for the preference of implementing an automated strike zone may center around the pace of play initiatives which have been discussed frequently over the past few years (Goldman, 2019). The idea that speeding up the game would appeal more to the younger demographics has gained support because many baseball officials believe quick play for younger fans is more appealing to them as they are tethered to their devices and want immediate action (Goldman, 2019). However, it is also supported by the data in this regression that the older the individual (at least within the range of 18-24 years old) the less they like the idea of an automated strike zone.

Beyond the scope of the multiple linear regression, some descriptive statistics support the idea that leaving the responsibility of calling balls and strikes with home plate umpires is still preferred. 18.8% of the sample responded that using automation to call

95 balls and strikes would not improve MLB at all, and only 3.6% felt this would improve

MLB a great deal. The rest of the sample was scattered between 2-7 however, 64.4% answered with “4” or below and the mean of 3.736 indicates that many individuals are skeptical about the positive impact that this rule change would have.

In reality, if an automated home plate umpire were to be utilized many problems would have to be solved including how to adjust strike zones for batters of different sizes, how to deal with potential errors or breakdowns in the technology making the calls, and merely clarifying specific specifications of the strike zone which up until now has been largely at the discretion of the home plate umpire (Berra, 2015). Another result of implementing this rule change would be the removal of human interaction between the umpire, batter, catcher, and pitcher. Often times, especially when there is disagreement, arguments between an umpire and an aggrieved player make for dramatic incidents that may result in heated exchanges and ejections. This element of the game would be eliminated by implementing this rule and it is possible that older fans or those that value tradition would find this rule change unsavory. In contrast, implementing this rule may shorten games and speed up the action generally in baseball.

Overall, this rule change could be appealing to younger fans who have shorter attention spans and do not want to spend in excess of three hours watching baseball, either on television or in person. However, it is not clear how much time a rule change like this would actually save. Furthermore, because the data in this study shows that the more an individual watches MLB the less they want automated umpiring, the potential of alienating fans who invest a great deal of time in baseball seems high. It is clear that if a

96 rule change like this were to be considered, the decision would benefit from significantly more analysis.

Research Questions 2b and 2c - Mound Visit Discussion

The analysis of the sample’s response to the rule limiting cumulative mound visits in a game also had some interesting statistics that generated a variety of talking points and thoughts. First, the only significant predictor in the initial regression was how many times per month an individual watches MLB. This significance was eliminated when

SSIS mean score was added to the regression, which (like the strike zone rule) suggests that frequency of views and the SSIS mean score may be measuring similar things.

Nonetheless, this finding showed that the more an individual watches MLB the more in favor of limiting mound visits they are. Essentially, for every additional watch per month of MLB the belief that mound visits would improve MLB increased by 0.043 units. This could be explained by the idea that watching a great amount of MLB games exposes viewers to a large quantity of pitching changes, more frequent stops in the action, and additional commercials, which could all cause longer games.

By including SSIS mean score, the statistics were changed. Times MLB watched per month was no longer significant, but SSIS mean score was. The analysis showed that for every additional point on the SSIS mean score the belief that mound visits would improve MLB increased by 0.181 units.

When analyzing the descriptive statistics some additional information can be ascertained. First, this question had a mean of 4.813 and only 7.4% of individuals felt that this rule would not improve baseball. Similarly, the mode was “6” for this question and

59.2% of individuals responded with a score of “5” or higher. This suggests over 90% of people feel this would improve MLB at least somewhat.

Another interpretation of this information could suggest that few people are diametrically opposed to the adoption of this rule. This is substantiated by the reality that limiting mound visits wouldn’t significantly impact the way the game is played between the lines, instead its impact would be most apparent in strategic decisions. Because of this there would be little, if any, effect on drama or aesthetics (Fink et al., 2002). Another speculation that can be made from the data suggests that as so few people feel the rule will not improve MLB, most feel that there is little negative impact and as it would rarely apply, wouldn’t change the game too much (except in regard to pace of play) it was a great choice for adoption (Adler, 2017). This rule has now been in place since 2018 in

MLB and there have been no significant issues or public outcry arising from its codification. Moreover, given that it only applies in situations where there are seven cumulative mound visits many individuals may be unaware this rule change has even happened.

Research Questions 2c and 3c - Ties after 12 innings completed

The idea of calling games ties after the completion of the 12th inning when scores remain deadlocked was one of the most divisive proposals in this study based on the statistics. There were three significant predictors that existed regarding this rule change, and all three remained statistically significant with the inclusion of the SSIS mean score.

As the SSIS mean score was not a significant predictor, the original model and statistics were used for this discussion.

The first significant independent variable was gender, the data showed that being female increased the preference for allowing ties after 12 innings by 0.796 units. This is a pretty large shift which could suggest that females may feel MLB games take too long.

Another possible explanation for this relationship is that females, generally, may be less emotionally invested in a game’s outcome than a male would be, although there are certainly women who are very highly identified fans. This hypothesis is not completely evidenced by this study and a larger sample size comprised of more females would be necessary to decipher this relationship.

Another significant variable in regard to the preference for calling games a tie was participation in organized baseball or softball. This binary variable demonstrated that within this sample participating in organized baseball or softball decreased an individual’s desire for ties after 12 innings by 0.868 units. Again, this is a large shift given that the entire scale is from one to eight. It is possible that the experience of playing organized baseball or softball instills a desire in those individuals to see a defined winner or loser. This relationship may also be explained by the idea that participating in competitive sports creates an aversion to results that do not determine a winner. A tie lacks a game-winning moment or something of dramatic or aesthetic quality which may lessen the level of appreciation a fan has for the game (Fink et al., 2002).

Finally, again the data demonstrated that how many times an individual watches

MLB games per month is a significant predictor of a person’s preference for ties after 12 innings. However, this relationship is less predictive than gender or participation in organized baseball or softball. For each additional game viewed per month a person’s

99 preference for ties decreased by 0.062 units. Given that how many times an individual views games in a month is indicative to their level of fandom it can be proffered that fans that invest more of their time to watch games are more likely to be opposed to ties.

People may be just naturally be opposed to ties in general, and the data from this regression analysis suggests that the more of a fan of baseball you are the more likely you will find a tie unsatisfying.

Beyond the statistics that were derived from the multiple regression analysis, multiple descriptive statistics provide additional illumination regarding the sample’s thoughts on ties in baseball. For this rule the Likert question again asked if this rule would improve MLB. The mean answer for this question was 2.8377 and the mode was

“1” or “Not at all,” which was given 49.3% of the time (149/302). The second most common response was “2” (11.6%, 35/302). Furthermore, only 23.8% of responses answered “5” or higher.

These descriptive statistics show that regardless of any independent variable or analysis there seems to be a strong dislike for this notion. This could be explained by a general preference for a decisive outcome and a dislike of ties generally.

Whatever the exact rationale or reasoning the individuals in the sample had for their responses the strength of the findings demonstrates that this rule change would be a bad idea as most younger fans do not want to see it.

Research Question 2d and 3d - Starting Extra-innings with a Runner on 2nd

In MiLB starting extra-innings with a runner on second base has already been implemented for the 2018 season (SI Wire, 2017b). The idea of implementing this rule in

100

MLB has been discussed as a measure to preserve bullpens and end games at a more predictable time.

When the regression analysis was run for this rule the only independent variable that was significant was the SSIS mean score. It was shown through analysis that an increase of one point on the SSIS mean score predicted an increase in the preference for starting extra-innings with a baserunner on second by 0.231 units. Therefore, the data shows more highly identified a fan is, the more they like the idea of this rule. A possible explanation is for the increase in drama as starting with runners on base in extra-innings would create highly volatile situations immediately. This may also increase the amount of scoring in extra-innings which may improve the aesthetic falling in line with the idea that drama and aesthetics increase appreciation (Fink et al., 2002).

In contrast to the proposed rule change that would call games a tie after twelve innings, this is clearly a preferred option among the sample. The mean score for this proposed rule change was 4.1639 (which was over a point higher than the mean for ties).

Even though the mean in this case was over 4, indicating that most respondents felt this rule would improve MLB, the mode for this question was “1” or “Not at all.” In fact

22.4% (66/304) of individuals who responded to this question responded “1” indicating that they felt this rule change would not improve MLB at all. Furthermore, as 53.2% of respondents responded between 1-4 and 46.8% of respondents answered between 5-8, there is a lack of consensus about how positive a change this would be and over a fifth of the respondents responded with the lowest possible answer. Those that responded with

“1” may look at this change as too fundamental of a change to the game and the ingrained

101 strategy in extra-innings. However, because a higher SSIS mean score corresponds to an increase in preference for this rule, a highly identified younger fan demographic may be more amenable to this than highly identified older fans. Further research would be required to determine how this relationship changes among older demographics.

All in all, the data still indicates that there is a lack of consensus about the benefits of this rule. With so many individuals feeling this rule would not at all improve

MLB rule makers should further analyze this population, and older populations, and their sensitivities towards this change. If this rule were to be implemented it would mark a significant change in the structure and gameplay of baseball and essentially would make extra-innings a completely different situation than the rest of the game.

One possible explanation for the spread of responses and the amount of “Not at all” responses is that this rule change would have too significant of an impact on the product and gameplay itself. Other issues with the rule may relate to the idea that for a runner to get on base they must have earned it in some way, not just had it given to them.

This rule change would alter gameplay and is not only related to pace of play concerns so perhaps there are less invasive rule changes available for consideration (certainly the mound visit rule would not have such a profound impact, but the time savings are likely much more minimal). Starting runners on second in extra-innings is an augmentation that would impact strategy, feel, and outcome. However, given that a game that goes into a tenth inning on average takes 29 minutes longer, and games that last until the twelfth inning take an extra 72 minutes to complete and as many as 10% of games each year go into extra-innings, this is certainly an area where major time savings could be had (Fink,

102

2017). However, this study cannot state, with any certainty whatsoever, what the impact on fans over age 24 would be. Future research for other age groups is a necessity before applying this rule.

Research Question 2e and 3e Pitch Clock

The final rule change analyzed in this study was the implementation of a pitch clock. This rule is clearly focused on speeding up the pace of play in MLB (Brown,

2018). In the initial model of this regression only times MLB watched per month was significant (p = .025). When SSIS mean score was added to the regression, times per month increased in significance (p < .001) and the SSIS mean score was also found to be significant. Therefore, the statistics from the second model were used. The results showed that for each additional time per month and individual watched MLB the outcome decreased by 0.080 units. This indicates that the more an individual watches

MLB the less they like the idea of a pitch clock. However, when evaluating the impact of the SSIS mean score (which in many ways is a similar gauge of fandom as times watched) an increase in the outcome of 0.253 units was predicted for a one unit increase in the SSIS mean score. Thus, the two significant variables, while somewhat similar measures, show very different relationships. This dichotomy makes analysis dubious except that it shows a lack of consensus among passionate fans about this rule change.

In spite of the contrary statistics evident in the regression analysis, some descriptive statistics can still illuminate the sample’s preferences regarding the implementation of a pitch clock. The mode for this question was “7” (16.7% or 50/299) and the second most common response was “6” (16.4% or 49/299). Furthermore 58.2%

103 responded with a “5” or higher indicating that the majority of the sample feels that the implementation of a pitch clock would improve MLB. In contrast, only 9.7% (29/299) said this would not at all improve the game.

While it is difficult to determine how highly identified fans precisely feel about this rule change because of the contrary statistics, what is evident is that this rule is looked at pretty favorably overall by the sample. The descriptive statistics suggest that fans 18-24 in general would be in favor of adopting a pitch clock. By adopting this rule, and enforcing it (which is a necessity to ensure any positive effect on shortening the game), MLB may be able to attract some new fans and this study further suggests adoption of this rule wouldn’t definitively alienate highly identified fans as there is a positive relationship and correlation between SSIS mean scores and higher scores regarding the adoption of a pitch clock.

In spite of these statistics there are several issues worth mentioning again. The first is that some very prominent pitchers, like Max Scherzer, have openly stated their strong dislike for this rule change (Wells, 2019). This rule change would also necessitate the use of a clock, which has never been a part of MLB in its entire history in spite of a rule being on the books for some time regarding this (Berg, 2018).

Implications

High team identification has major impact on behavior, highly identified fans are more likely to have a strong sense of attachment to their team and view the relationship as intensely personal and sensitive (Mitrano, 1999). Moreover, high identification leads to spending more money on a product and stay loyal during poor performance so it is

104 logical for MLB to do whatever it can to increase fan team identification (Fink et al.,

2002). So what can be done to increase fan identification?

MLB and other organizations who focus on spreading and developing the game of baseball should be aggressive in exposing people to the game and its nuances.

Community outreach programs, youth baseball and softball leagues, and targeted promotions and events could help get young individuals playing baseball at an earlier age and combat the decrease in participation that has been observed in recent years (Paul,

2017).

Similarly, by analyzing the findings in research question one it is evident that exposure to baseball and simply watching baseball should increase SSIS mean scores that measure the strength of fan identification. If MLB wishes to attract these younger demographics it needs to utilize the media platforms that are most popular among those individuals. Young adults aged 18-24 do not consume traditional media the same as their older counterparts. Instead, they utilize social media platforms like Twitter, Instagram, and Snapchat and use streaming services to watch television. In fact, 71% of Generation

Z’s, those born between 1996-2011, entertainment consumption is via streaming platforms, and one-third is on a mobile device (Velasco, 2017). Thus, MLB should work to increase its social media presence and attempt to cross-promote with individuals and entities that have voluminous followers in younger age brackets. Promotions should be conducted through social media and other streaming platforms in an attempt to reach the most individuals possible in younger demographics. The focus of these campaigns should

105 center around the top five social platforms for Gen Z: YouTube, Facebook, Instagram,

Snapchat, and Twitter (Velasco, 2017).

As for the specific rule changes discussed in this study, there has been a wide range of opinions that have elucidated what should be done going forward. To start, MLB executives and rule makers should be hesitant to implement an automated strike zone based on the results of this study. There is no clear indication that the 18-24 demographic that was the focus of the study thinks an automated strike zone would improve MLB and given the relationship between times watched per week and a decrease in approval of the strike zone an argument can be made that the more involved fans will dislike this idea.

While the instant study only focused on 18-24 year old individuals, maintaining and developing fans outside of this age range is still very important and preferences for tradition and history and more years following baseball may make the notion of an automated strike zone even less appealing for older fans. Implementation of this rule would significantly alter the aesthetic of baseball. Furthermore, as this rule change would limit human interaction between players and umpires an entertaining and dramatic aspect of the game, in many people’s eyes, would be eliminated. Finally, a ball or strike call is typically made very quickly, even when an appeal is made to an umpire on first or third base the time between the ball being delivered and a ball or strike call being made is only a couple seconds, thus the impact on pace of play may not be that substantial with this rule change. If speeding up the game is the priority, there are likely less significant elements that could be changed that wouldn’t alter the feel of the game so substantially.

Also, there is no guarantee that automated balls and strikes will be perfect, the technology

106 still has a margin for error and may fail to work properly in situations, the notion that perfection in calls is obtainable seems overstated (McIntosh, 2018; Berra, 2015)

In regard to the limit on mound visits, this rule had broader approval than others in the study. The only statistically significant independent variables were times MLB watched per week and SSIS mean score. Which demonstrated that the more an individual watched the more and the higher their fan identification, the more they felt this rule change would improve MLB. This indicates that invested fans like this idea. Given that this rule change is designed to speed up the game and does not have any impact on the way the game is actually played between the lines it was a logical choice for implementation. However, given that the rule is only triggered upon a seventh mound visit, it is quite possible that many fans, especially casual ones, are unlikely to see it applied or even know it exists. But, when it is applied it is likely to arise in games where there is a great deal of offense and the game is progressing slowly. In those types of situations, this rule may be quite effective in speeding up the game.

One idea that should not be implemented is the rule that would call a game a tie.

While this is a limited convenience sample, the convictions evidenced by the response to the Likert question on ties were very strongly tilted towards the belief that the rule change would not improve MLB. Given the availability of less polarizing options regarding extra-innings, such as starting innings with a runner on second or simply maintaining the status quo, adding ties to baseball would likely alienate existing fans and be ineffective at creating new ones.

107

While some people thought ties were a good idea, for instance females tended to view this rule more favorable, many did not. Those who participated in organized baseball or softball were more likely to dislike this rule and the more MLB games an individual watched per month also predicted a lower score response on the Likert scale.

Maybe most telling is that almost half of all those surveyed (49.3%) gave this rule change the lowest score possible indicating that it would not improve MLB at all.

In addition to the evidence from this study that suggest people do not want, and would not like, ties in baseball there are other practical implications that make this rule problematic. First, if a game would be called a tie there needs to be a decision made about how to treat that tie in the standings and it could significantly impact pennant races and make it more difficult for fans to understand the standings and implications of outcomes. Additionally, if a tie rule were put in place, strategies would be altered as far as bench and bullpen utilization and teams may adopt tactics that are designed to not lose, as opposed to aggressively trying to win a game. For instance, a pinch-hitter for an away team may not be used in the top of the twelfth inning in a tie game because the pitcher that would be replaced is the best available option to retire the side in the bottom of the twelfth and the game will end after the inning. All in all, while this may have some impact on pace of play and would keep games from being extremely long, the respondents disliked this rule the most and it would seem the idea of putting ties into baseball could irritate quite a few people.

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Compared to the idea of ties, starting extra-innings with baserunners on second is a more generally acceptable idea in the eyes of the sample of this study. The SSIS mean score was a statistically significant predictor of an individual’s perception of this rule.

The regression showed that the preference for starting extra-innings with a baserunner on second increased by 0.201 units for each point in the SSIS mean score. Thus, highly identified fans, according to the SSIS mean score, generally favored this rule change.

However, the most common response was that this rule change would not improve MLB so there are indications of a lack of consensus to this idea.

If the extra-inning baserunner rule were put in place, multiple issues and concerns could arise. Starting extra-innings with a runner on second would be awarding a base to a player who didn’t earn it. This is fundamentally different than the standard precepts of the game (one could argue intentional walks are similar, but these are the result of a decision by the fielding team and not automatic). Furthermore, the strategy in an inning could change in significant ways if this rule were implemented. Sacrifice bunts and flies could become standard in extra-innings if this rule were implemented, especially in the bottom half of innings of tie games when the runner on second represents a walk-off winning run. The strategy of the first batter laying down a sacrifice bunt to get the runner to third followed by the second batter merely trying to hit a fly ball to the outfield for a sacrifice fly seems like a very logical strategy to be employed and this, in time, could become stale and boring in its own way. In spite of the above discussed issues, this rule could end games faster and create more plays in the field and in that way could be well received. If MLB decides they must implement some rule to limit extra-innings, or speed

109 them up in some way, this is a better alternative than ties. As this rule has been in the

MiLB since 2018 it would be useful to evaluate the statistics and outcomes in the minors before implementing it in MLB (Thompson, 2018; Fink, 2017). Even if the MiLB statistics support this rule change, analysis of its impact on older fans and traditionalists would be important as it would deeply alter a major aspect of the game.

The idea of a pitch clock is another interesting rule change. The results of this study were unique in that times MLB watched per month resulted in a decrease in preference for implementation of the pitch clock, but SSIS mean score indicated an increase in preference for the pitch clock. Making a generalizable statement about invested fans preference in this case difficult. But what is telling is less than 10% (9.7%) felt this rule would not at all improve MLB. What this indicates is that most people think there would be some benefit to adding a pitch clock, but highly identified fans and those heavily invested in baseball are not in agreement. Evaluating the preferences of fans that are older than 24 would be a wise next step for MLB as traditionalists and older fans may have a more definite outlook on this rule. However, without that information and with the understanding that MLB is focused on speeding up the game, this rule change seems like a viable option.

While many types of fans may be in favor of the pitch clock rule not all are, and some players may take umbrage with this idea. One group that has individuals who are vehemently opposed to this idea is pitchers (Wells, 2019). Because many pitchers dislike this notion it will be difficult to implement it because the Major League Baseball Players

Association (MLBPA) may not want it adopted and a rule change of this type would need

110 to be collectively bargained. Additionally, there would be strategic implications of this rule. For instance, baserunners may be able to time pitch deliveries better and as a result have an easier time stealing bases and getting larger leads. Moreover, batters will be able to recognize when a pitch may be delivered and predict its arrival better. Another issue would be that the pitch decision making may become rushed and pitchers may be rushed to deliver a different pitch than they would have. Finally, changing the pace of pitch delivery may lead to an increase in arm injuries in pitchers. Many of these issues could create advantages for offenses though and given that fewer runs are being scored and less balls are being put in play in the modern game maybe this could have a positive impact on viewership (Everett, 2018). But if MLB’s goal is to increase offense, other rule changes would seemingly be more effective like eliminating defensive shifts or lowering the pitching mound.

Limitations

The study detailed above provides multiple interesting analytical results but has several limitations. First, while this survey aimed to assess the rule change preferences of

18-24 year olds a representative sample of all those within that age range was not obtained. Due to the use of convenience sampling at a large midwestern university those who are not in college are unrepresented in this analysis. Additionally, it is possible that the individuals surveyed in this study have different levels of fandom than the average college aged student as the surveys were collected in specific classes within a sport related discipline.

111

Furthermore, this study does not provide insight into the preferences of any individual outside of the age range, so while some inferences can be made about what 18-

24 year olds prefer, anyone outside of that age range was excluded from the study. Thus, it cannot be said that a rule change that is supported by this sample would be supported by the general population of MLB fans.

Also, while multiple demographic factors were considered and used in this analysis it is entirely possible that a specific notion that may have had a significant impact on the results was not utilized.

Another reality of changing rules in any context is that prior to their implementation the effect of the changes is speculative. It is completely feasible that once a rule change is put into place unforeseen issues may arise or the desired effect may not be produced so the only real way to gauge some of these changes would be to enact them.

From a statistical standpoint, there was a limitation worth noting. For populations between 50,000 and 75,000 a sample size of 381-382 was recommended, however this sample was only n=304.

Future Research

Concurrent with the aggregation of the quantitative data from this study qualitative responses were given in response to the rule changes. Future research will look closely at those responses and try to explain more of the reasoning behind the surveyed individual’s preferences for these rule changes.

When it comes to rule changes, especially in this era, proposals and ideas are being discussed most off-seasons by MLB executives. The rules chosen in this study have

112 been the topic of frequent debate, but others have arisen that deserve evaluation. One such rule proposes that any relief pitcher must face a minimum of three batters before a pitching change could occur. This rule would be interesting to evaluate as it also may improve pace of play, but if implemented would alter the game strategically.

As for demographics, further evaluation through the gathering of larger and more diverse samples, may help provide more clarity to the relationship between being race/ethnicity and gender and the relationship to SSIS mean score. Whether these demographics indicate a preference for faster games, rules that promote pace of play, or merely just a general indifference or dislike of MLB is unclear and larger and more robust samples could help add depth to that discussion.

Technology could also serve as the basis for further discussion on this issue.

People’s sensitivities to the use of technology in sport and the time issues it creates could be further evaluated. Age may be a relevant factor in determining who prefers what types of rule changes especially when the use of new and novel technology is a lynchpin to the proposed augmentation. Future research utilizing a broader age range would be necessary to examine this relationship and could provide some clarity about what rule changes would bring in new fans without alienating existing ones.

113

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Appendix A: Panel of Experts Instrument

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Dear Potential Participant:

We request your participation in a study that compares adult baseball spectator’s level of identification with their favorite baseball team to their sensitivity regarding rule changes that have been adopted or are being discussed as possible ways to improve the game. Because of the many different entertainment options available to the average American it is important for Major League Baseball (MLB) to assess the impact of rule changes on spectators.

Specifically, we are interested in gauging the relationship between identity, demographic characteristics, and people’s perceptions of potential rule changes.

This survey includes a series of questions pertaining to the level of your fandom of baseball and an additional series of questions that seek to gauge your perceptions on potential rule changes. Any legal or technical terminology will be explained if you are unfamiliar with it. We ask that you provide written answers to questions which relate to your feelings about these situations when called for.

You may refuse to participate in this study without penalty or loss of benefits to which you are otherwise entitled. If you choose to participate in the study, you may discontinue participation at any time without penalty or loss of benefits.

We do not anticipate any unforeseen risks or benefits regarding participation. There will be no experimentation of any kind. You will not be audio or videotaped. Your participation will be required on only one occasion. Participation is entirely voluntary. Complete confidentiality will be honored in analyzing all data and no personal identifiers will be collected besides general demographic information. Any discussion of results will be based on group data. It is estimated that this survey will take between 10-15 minutes to complete.

For questions, concerns, or complaints about the study, or you feel you have been harmed as a result of study participation, you may contact Richard Bailey at [email protected] or 614-519-2899 or Dr. Donna Pastore at [email protected] or 614-940-2058.

For questions about your rights as a participant in this study or to discuss other study- related concerns or complaints with someone who is not part of the research team, you may contact Ms. Sandra Meadows in the Office of Responsible Research Practices at 1- 800-678-6251.

Sincerely,

Richard L. Bailey, M.S. J.D. Donna L. Pastore, Ph.D. Instructor of Sport Management Professor of Sport Management

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SCREENING QUESTION:

Do you consider yourself a fan of Major League Baseball (MLB)?

⧠ Yes ⧠ No (If the answer is “No” the individual will not continue with the rest of the survey.)

SECTION 1:

Personal Demographics

1. Gender: ⧠ Male ⧠ Female ⧠ Non-Binary / third gender

⧠ Prefer not to say

If you would like the opportunity, we invite you to share more about your gender

identity below:

2. Age: ______years old

3. Are you Hispanic or Latino?

⧠ Yes ⧠ No

4. Racial / Ethnic Group:

⧠ American Indian or Alaska Native

⧠ Asian

⧠ Black of African American

⧠ Native Hawaiian or other Pacific Islander

⧠ White

⧠ Race and/or ethnicity unknown

5. Marital status: ⧠ Single ⧠ Married ⧠ Divorced ⧠ Widowed

6. What is the zip code of your permanent address?

______132

7. What is the zip code of your current residential address?

______

8. Have you ever played organized baseball or softball at any level?

⧠ Yes ⧠ No

9. How many times a year do you watch baseball?

______

10. How strongly do YOU see YOURSELF as a fan of Major League Baseball?

Not at all a fan 1 2 3 4 5 6 7 8 Very much a fan

SECTION 2:

Sport Spectator Identification Scale (Wann & Branscombe, 1993)

Instructions: Please list your favorite baseball team on the line below. Please be very descriptive in your response (e.g., the Atlanta Braves Major League Baseball team).

______

Now, please answer the following questions based on your feelings for the team listed above. There are no "right" or "wrong" answers, simply be honest in your responses.

10. How important to YOU is it that the team listed above wins?

Not important 1 2 3 4 5 6 7 8 Very important

11. How strongly do YOU see YOURSELF as a fan of the team listed above?

Not at all a fan 1 2 3 4 5 6 7 8 Very much a fan

12. How strongly do your FRIENDS see YOU as a fan of the team listed above?

Not at all a fan 1 2 3 4 5 6 7 8 Very much a fan

13. During the season, how closely do you follow the team listed above via ANY of the following: a) in person or on television, b) on the radio, c) television news or a newspaper, or d) the Internet?

Never 1 2 3 4 5 6 7 8 Almost everyday

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14. How important is being a fan of the team listed above to YOU?

Not important 1 2 3 4 5 6 7 8 Very important

15. How much do YOU dislike the greatest rivals of the team listed above?

Do not dislike 1 2 3 4 5 6 7 8 Dislike very much

16. How often do YOU display the team's name or insignia at your place of work, where you live, or on your clothing?

Never 1 2 3 4 5 6 7 8 Always

SECTION 3:

For this question please consider the potential rule changes to Major League Baseball (MLB) and evaluate each of the following statements by the specific response options provided:

Rule 1: Utilizing an electronic strike zone to call balls and strikes instead of an umpire making these calls in real time.

17. To what extent would this rule change improve the game of baseball?

Not at all 1 2 3 4 5 6 7 8 A great deal

18. What effect do you think this rule change have on the game (please feel free to comment on the impact of the pace of the game, the building of drama, the change in game strategy, your overall enjoyment, or any other pertinent factor)?

Rule 2: Limiting visits to the mound by coaches or managers. Managers or coaches must make a pitching change if they visit the mound for the seventh time in a game. The previous rule had no cumulative limit on visits but dictated that a second visit by a coach or manager in the same inning mandated a pitching change. This rule has been implemented in MLB as of the 2018 season.

19. To what extent would this rule change or improve the game of baseball?

Not at all 1 2 3 4 5 6 7 8 A great deal 134

20. What effect do you think this rule change have on the game (please feel free to comment on the impact of the pace of the game, the building of drama, the change in game strategy, your overall enjoyment, or any other pertinent factor)?

Rule 3: Calling a regular season game a tie after 12 innings

21. To what extent would this rule change or improve the game of baseball?

Not at all 1 2 3 4 5 6 7 8 A great deal

22. What effect do you think this rule change have on the game (please feel free to comment on the impact of the pace of the game, the building of drama, the change in game strategy, your overall enjoyment, or any other pertinent factor)?

Rule 4: Beginning extra-innings with a runner on second base (the previous batter prior to the leadoff hitter for that inning would be the baserunner).

23. To what extent would this rule change or improve the game of baseball?

Not at all 1 2 3 4 5 6 7 8 A great deal

24. What effect do you think this rule change have on the game (please feel free to comment on the impact of the pace of the game, the building of drama, the change in game strategy, your overall enjoyment, or any other pertinent factor)?

135

25. To what extent would this rule change or improve the game of baseball?

Not at all 1 2 3 4 5 6 7 8 A great deal

26. What effect do you think this rule change have on the game (please feel free to comment on the impact of the pace of the game, the building of drama, the change in game strategy, your overall enjoyment, or any other pertinent factor)?

136

Appendix B: Full Study Survey Instrument

137

1. To what degree do you consider yourself a fan of Major League Baseball (MLB):

Not a Fan Very Much a Fan 1 2 3 4 5 6 7 8

SECTION 1:

Personal Demographics

2. What is your gender:

⧠ Male

⧠ Female

⧠ Non-Binary / third gender

⧠ Prefer not to say

If you would like the opportunity, we invite you to share more about your gender

identity below:

3. Age: ______years old

4. What is your current year in college?

⧠ Year 1

⧠ Year 2

⧠ Year 3

⧠ Year 4

⧠ Year 5+

5. Racial / Ethnic Group:

⧠ American Indian or Alaska Native

⧠ Asian

138

⧠ Black or African American

⧠ Hispanic/Latino/Latinx

⧠ Native Hawaiian or other Pacific Islander

⧠ White

⧠ Race and/or ethnicity unknown

⧠ Prefer not to say

6. Marital status:

⧠ Single

⧠ Married

⧠ Divorced

⧠ Widowed

⧠ Partnership

7. What is the zip code of your permanent address?

______

8. What is the zip code of your current residential address?

______

9. Have you ever played organized baseball or softball at any level?

⧠ Yes

⧠ No

10. Approximately, how many times do you watch Major League Baseball? Please

use whole numbers, for example 7 (not ranges).

______per week

139

______per month

______per year

SECTION 2:

Sport Spectator Identification Scale (Wann & Branscombe, 1993)

Instructions: Please list your favorite baseball team on the line below. Please be very descriptive in your response (e.g., the Atlanta Braves Major League Baseball team).

______

Now, please answer the following questions based on your feelings for the team listed above. There are no "right" or "wrong" answers, simply be honest in your responses.

10. How important to YOU is it that the team listed above wins?

Not important 1 2 3 4 5 6 7 8 Very important

11. How strongly do YOU see YOURSELF as a fan of the team listed above?

Not at all a fan 1 2 3 4 5 6 7 8 Very much a fan

12. How strongly do your FRIENDS see YOU as a fan of the team listed above?

Not at all a fan 1 2 3 4 5 6 7 8 Very much a fan

13. During the season, how closely do you follow the team listed above via ANY of the following: a) in person or on television, b) on the radio, c) television news or a newspaper, or d) the Internet?

Never 1 2 3 4 5 6 7 8 Almost everyday

14. How important is being a fan of the team listed above to YOU?

Not important 1 2 3 4 5 6 7 8 Very important

15. How much do YOU dislike the greatest rivals of the team listed above?

Do not dislike 1 2 3 4 5 6 7 8 Dislike very much

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16. How often do YOU display the team's name or insignia at your place of work, where you live, or on your clothing?

Never 1 2 3 4 5 6 7 8 Always

SECTION 3:

For these questions please consider these potential Major League Baseball (MLB) rule changes and evaluate each of the following statements by the specific response options provided:

Rule 1: Utilizing an electronic strike zone to call balls and strikes instead of an umpire making these calls in real time.

17. To what extent would this rule improve MLB?

Not at all A great deal 1 2 3 4 5 6 7 8

18. What effect do you think this rule change will have on Major League Baseball (you may comment on the impact on the pace of the game, building of drama, changes in game strategy, your overall enjoyment, or any other factor)?

19. How would this rule change influence you as a fan?

Rule 2: Limiting visits to the mound by coaches or managers. Managers or coaches must make a pitching change if they visit the mound for the seventh time in a game. The previous rule had no cumulative limit on visits but dictated that a second visit by a coach or manager in the same inning mandated a pitching change (this rule still exists in addition to the cumulative mound visit). The rule mandating a pitching change be made after the 7th visit was implemented in MLB as of the 2018 season.

20. To what extent would this rule improve MLB?

Not at all A great deal 1 2 3 4 5 6 7 8

21. What effect do you think this rule change will have on Major League Baseball (you may comment on the impact on the pace of the game, building of drama, changes in game strategy, your overall enjoyment, or any other factor)?

141

22. How would this rule change influence you as a fan?

Rule 3: Any regular season game whose score is tied after 12 innings is ruled a “tie”

23. To what extent would this rule improve MLB?

Not at all A great deal 1 2 3 4 5 6 7 8

24. What effect do you think this rule change will have on Major League Baseball (you may comment on the impact on the pace of the game, building of drama, changes in game strategy, your overall enjoyment, or any other factor)?

25. How would this rule change influence you as a fan?

Rule 4: Beginning extra-innings with a runner on second base (the previous batter in the lineup prior to the leadoff hitter for that inning would be the baserunner).

26. To what extent would this rule improve MLB?

Not at all A great deal 1 2 3 4 5 6 7 8

27. What effect do you think this rule change will have on Major League Baseball (you may comment on the impact on the pace of the game, building of drama, changes in game strategy, your overall enjoyment, or any other factor)?

28. How would this rule change influence you as a fan?

142

Rule 5: Requiring pitchers to begin their wind-up or the motion to come to the set position when no runners are on base within 15 seconds. If there is a runner on base the pitch timer increases to 20 seconds. “The clock begins once the pitcher receives the ball in the dirt circle surrounding the pitcher's rubber, the catcher is in the catcher's box and the batter is in the dirt circle surrounding home plate” (per MiLB rules).

29. To what extent would this rule improve MLB?

Not at all A great deal 1 2 3 4 5 6 7 8

30. What effect do you think this rule change will have on Major League Baseball (you may comment on the impact on the pace of the game, building of drama, changes in game strategy, your overall enjoyment, or any other factor)?

31. How would this rule change influence you as a fan?

143