Maximizing Youth Sports Engagement on Social Media: How Visual Impact and Message Appeal Shape Consumer Responses Online

Authors: Wan S. Jung1, Won Yong Jang2, and Soo Rhee3

¹Department of Professional Communications, Farmingdale State College, New York
²Department of Communication and Journalism, University of Wisconsin, Eau Claire, Wisconsin
³Department of Mass Communication, Towson University, Maryland

Corresponding Author:

Wan S. Jung, Ph.D
Knapp Hall 30
2350 Broadhollow Road, Farmingdale, NY 11735-1021
[email protected]
934-420-2276

Wan S. Jung, PhD is an Associate Professor of Professional Communications at Farmingdale State College, NY. His research interests focus on the credibility assessment process of digital information.

Won Yong Jang, PhD is a Professor at the University of Wisconsin, Eau Claire. He specializes in 1) international communication, 2) news media and society in East Asian countries, 3) climate change policy & communication, 4) public opinion on North Korea’s Nuclear Program, and 5) territorial disputes in the Asia-Pacific Region.

Soo Rhee, PhD is a Professor at Towson University, Maryland. Her research interests include luxury brand advertising, gender portrayals in advertising, dynamics of electronic word-of-mouth, cross-cultural studies in advertising and message strategies in health advertising.

ABSTRACT
An increasing number of people rely on the Internet as their primary information source and use it to share their opinions and thoughts with others. Generally, individuals adopt a systematic approach when processing sports information, evaluating its completeness and accuracy due to the serious consequences of incomplete or inaccurate information, such as monetary loss and negative impacts on child development. However, our study finds that the heuristics of online information, even with subtle changes in design features, generate more positive attitudinal and behavioral changes compared to central cues (i.e., informational posting). Our findings suggest a dissociation between involvement and the effects of heuristics. This study also provides an empirical framework for predicting how people process information in digital media environments. Additional findings and implications are discussed.

Key Words: youth sport communication, visual impact of social media posting, message appeal

INTRODUCTION
The youth sport market is a huge and fast-growing industry, ranging from organized sports leagues to recreational activities. The market for youth sports in the United States stood at 15.3 billion U.S. dollars in 2017 and grew to 19.2 billion U.S. dollars by 2019 (11). With a fast-growing trend (i.e., a growth rate of 25.4% from 2017 to 2019) with various options, parents became more active in searching for information. As social media are pervasive, rapidly evolving, and increasingly influencing parents’ daily life and their sport consumption, parents increasingly turn to the internet as a source of community, which helps them connect, communicate, and share information (18).

The rapid growth of online sports information production and dissemination through social media parenting communities (e.g., Facebook local groups and Nextdoor) raises important research questions about how individuals process online information provided by other consumers (i.e., experienced parents whose child(ren) have participated in your sport programs) in youth sport consumption decision making. Moreover, since sport consumers make decisions about whether or not to adopt online sports information based on their own judgement (e.g., attitudinal formation), how individuals evaluate online information is central to sports communication agendas.

Although the formation of attitudes toward information can be attributed to multiple aspects of that information (e.g., source credibility, information completeness), sport consumers using online resources are more reliant on how the information is presented than on the quality of the argument (10), and subtle graphical adjustments become relevant when online parenting community members share their own experiences with other members on social media platforms. In order to emphasize their own views, web users often create visual prominence using subtle design elements, such as capitalized subject lines, copy-and-paste text art (also called keyboard art, e.g., ≧◡≦), or bullet-point symbols. In addition to subtle design changes, the characteristics of the online posting can be varied based on the degree of informativeness (i.e., emotion-based versus information-based).

The purpose of the current study is twofold. First, it will explore the effect on attitudinal formation and behavioral intentions of the message appeals and subtle graphical adjustments of posts in online parenting communities in the youth sport consumption context. Second, the study will investigate whether the strength of the relationship between attitude and behavioral intentions varies based on message appeals. Overall, the study will seek to advance understanding of digital media by examining how small graphical changes and message appeals impact youth sport consumers’ attitudes and behaviors when searching for consumer-generated information (e.g., testimonials) in online communities.

LITERATURE REVIEW
Parent-to-Parent Online Information in Youth Sport Consumption
“It takes a village to raise a child” is a proverb to explain the role of and community support in parenting. As social aspect is one of the primary factors that drives parents and their children to be involved in sport program (1), the influence of other parents’ opinion and the role of parent community are even more prominent in youth sport consumer’s decision making process. Braunstein-Minkove & Metz (2019) noted in their research on the role of mothers in sport consumption that youth sport consumption might not always about the sport but the experience. Therefore, parents of youth rely on other parents’ opinion to obtain relevant and sufficient information and evaluate various youth sport program options available. In order to provide the best sporting and exercise experience for their children, parents of young children are willing to hear voices of other parents (i.e., testimonial) regarding the type of sports, sports programs, and sporting events their children would participate in.

With the modern technology and the advent of social media, the notion of the village (or supporting community) has been expanded from a physical village to a digital community. Social media platforms support a variety of user generated content to be disseminated to other users and allows users to participate in interactive discussions. Among the various types of social media platforms, Facebook have become the most prevalent web-based service in the world (21) and remaining the most popular site by far (12). Also, Facebook recently provides an option to mark the group type as parenting group, which gives parents new ways to discover and engage with their communities (5). Though the role of online community and the influence of information from other youth sport consumers (i.e., testimonials from other parents in such online community) in youth sport consumer’s decision-making process became more prominent, there is no previous research to explore the effects of the presentation of online information on consumers’ attitudinal and behavioral response in youth sport consumption context.

The Impact of Visual Prominence
Quick and low effort cognitive information processing has been investigated in the field of psychology since the 1970s (e.g., 9, 13), and the research indicates that impression formation is the result of the perceiver’s rapid response to selective or incomplete information. In other words, one’s appraisal of an event occurs without intention or conscious thought. Theories of impression formation in the context of digital communication have been developed by Fogg (2003) and Wathen and Burkell (2002), and their studies suggest that visual prominence—the visual salience that allows people to effortlessly notice the presence of graphic elements (e.g., bold vs. non-bold font)—is a primary driver of attitudinal formation, rather than information quality.

The impact of visual prominence can also be explained by individuals’ reliance, when making decisions, on transactive memory systems, which consist of two key elements: internal memory (e.g., personal experience) and external memory (e.g., another person’s expertise; 14). The presence of an external memory will activate a transactive memory system, and such a dependency on external memory increases efficiency and cognitive labor power (20). Thus, external sources of knowledge can have a significant impact on one’s perception of what to accept as true and how confidently to accept it.

The theoretical and empirical evidence for transactive memory systems is based on offline social interactions (e.g., interactions within family groups). However, recent studies suggest that online sources can also trigger transactive memory systems due to the similarity between the process of outsourcing cognitive tasks to other people and the process of outsourcing cognitive tasks to the Internet (6). This nonhuman transactive memory network is further fueled by the unique features of the Internet (e.g., accessibility, breadth, immediacy of information), but such features may distort one’s ability to calibrate personal knowledge because the boundary between internal and external memory becomes unclear. That is, individuals often mix up information obtained through the Internet with information stored in the brain, and this illusion inflates self-ratings of competence regarding personal knowledge and decision-making (17). Recent research on such illusions also suggests that people tend to believe they can solve problems even in unfamiliar domains and that their decision-making processes are often based on heuristics, such as visual prominence (7, 8); the impact of visual prominence would thus be greater in digital media environments.

Since online parenting community members can establish the visual prominence of their postings on social media platforms only with subtle graphical adjustments, the current study will investigate how subtle changes (e.g., capitalizing subject lines, use of text art) to posts in online youth sport communities influence individuals’ attitude formation and behavioral intentions. Given the exploratory nature of the topic of individual information judgment in digital media environments, the following hypotheses are proposed:
H1: Visually prominent postings in online youth sport communities form stronger attitudes than less prominent postings.
H2: Visually prominent postings in online youth sport communities form stronger behavioral intentions than less prominent postings.

The Impact of Involvement on Message Appeals
The persuasiveness and prevalence of various appeal types (e.g., emotional, informative) have been extensively examined in different contexts, such as brand familiarity (Rhee & Jung, 2019), cultural variability (Han & Shavitt, 1994), and involvement (Flora & Maibach, 1990). However, less is known about the differential effects of appeal types in the context of online youth sport communities, and the current study therefore presents an exploration of the question of which type of message appeal is most persuasive in such communities.
The elaboration likelihood model (ELM; 16) is one of the most prominent theoretical frameworks employed in the message appeal literature and is applied in various contexts, such as public health service announcements (Perse et al., 1996), crisis management (Lee & Atkinson, 2019), and advertising (Stafford & Day, 1995). Studies have also commonly found a moderating effect of involvement on message appeals, and according to the ELM, people tend to rely on argument quality (e.g., information completeness, comprehensiveness) when processing information under high involvement conditions, with persuasion less likely to occur through peripheral cues, such as peers’ emotional experiences. The converse is also true under low involvement conditions.

However, a recent study by Jung et al. (2017) found evidence that contradicts the prevailing literature on the role of involvement in digital media environments; the study claims that individuals often find it hard to motivate themselves to process information thoroughly, regardless of involvement levels, due to the nature of the Internet, which inundates them with massive amounts of non-verifiable information. Individuals therefore tend to compromise the accuracy of their decisions, which can require extensive cognitive effort, by relying on the heuristic aspects of information.

In addition, in the context of online youth sports communities, people tend to seek others’ prior experiences (e.g., a coach’s personality) and emotionally supportive messages because any objective information about a youth sports program (e.g., fees, coach’s experience, facilities) can be easily found through sources such as the program’s website. It can therefore be assumed that the moderating role of involvement in appeal types might be limited by the dominance of social media. Nevertheless, because there is still insufficient evidence for the limited role of involvement in the social media context, we propose the following research question:
RQ1: What effect does involvement have on the appeal types of posts in online youth sport communities?

The Moderating Impact of Involvement on the Attitude–Intention Relationship
Attitudes are among the most significant predictors of behavioral intentions in psychology. According to the theory of planned behavior (TPB), intention functions as an antecedent of behavior and is attributable to individual attitudes, together with subjective norms and perceived behavioral control (Ajzen, 1991). Although a number of studies have provided strong evidence for the relationship between intentions and the three causal variables of the TPB, a meta-analytic study by Cooke and Sheeran (2004) also noted that less than 42% of the variance in intentions can be explained by those variables.

Consequently, there have been numerous attempts to increase the predictive power of the TPB by exploring moderators of the relationship between intention and the TPB variables, such as attitudinal ambivalence (Armitage & Conner, 2000) and certainty (Bassili, 1996). In addition to these moderating variables, Petty et al. (1983) has offered theoretical and empirical evidence that the attitude–intention relationship is more consistent under high involvement conditions, because attitudes established by highly involved people are more stable than those of lowly involved people. Verplanken (1989) also examined whether involvement can explain additional variance in the attitude–intention relationship, although that study was in the context of nuclear energy.

Therefore, the current study will examine the moderating role of involvement in the attitude–intention relationship in the sport communication context.
H3: High involvement will be associated with greater attitude–intention consistency than low involvement.

METHOD
Subjects and Procedure
192 participants who had parenting experiences (male = 64%) from the United States between the ages of 20 and 55 completed the study through Amazon’s Mechanical Turk (MTurk). For participants’ ethnicity, the most common ethnicity was Caucasian (53.6%), followed by Asian (33.9%), African American (5.2%), Hispanic (3.6%), and other racial backgrounds (3.6%). To participate in the study, subjects were requested to provide electronic consent. And subjects were debriefed and compensated upon completion of the study.

Experimental Treatment Conditions
To investigate the effects of visual prominence (high vs. low prominence) and message appeals (emotional vs. informative message) on online youth sport program postings, four versions of online postings were created as stimuli, and the subjects were randomly assigned to one of the four experimental conditions: low prominence and emotional (n = 49), high prominence and emotional (n = 49), low prominence and informative (n = 49), and high prominence and informative (n = 45).

The postings contained an online community member-created message about a local youth soccer program. The community member-created posting consisted of either factual information about the soccer program (informative appeal) (i.e., up to 12 kids in one session with two coaches, all are CPR first aid and AED certified, and having an indoor field) or user experiences (emotional appeal) (i.e., it was such an amazing experience and my son loves his current coach). A youth soccer program was selected as the topic for this study because of popularity of the sport among young parents. The manipulation of visual prominence was carried out by differentiating graphic elements between high prominence and low prominence conditions. Since parent community members on social media platforms can emphasize their posting with subtle graphical alterations, the high prominence version was designed to help the study participants notice the key messages by capitalizing key words, using a bulleted list and line-breaks in order to increase readability, and using a text art. The low prominence version lacks those design features.

Dependent Measures
Attitude toward the online posting
The attitude toward the online youth program posting was measured using
three semantically differential items (i.e., good/bad, favorable/unfavorable, negative/positive) emerged from the literature on the scale (Lee & Hong, 2016). The scale was internally consistent (Cronbach’s  = .91, M = 4.70, SD = 1.81).

Behavioral Intentions
Subjects were also asked to answer their intentions to 1) recommend the youth soccer program on the posting you just read and 2) register for the soccer program in the future on 7-point Likert-type scales ranging from 1 (not at all) 7 (extremely). The items were averaged to create a behavioral intention scale (Cronbach’s  = .83, M = 4.33, SD = 1.73).

Independent Measure
Involvement
Involvement in sports activities may influence the attitudinal formation and behavioral intentions. Thus, this study measured personal involvement with sports activities by using three 7-point (1 = strongly disagree, 7 strongly agree) Likert-type scales, the participants reported on how much they agreed with the following three statements: “I enjoy playing sport,” “Sport plays a central role in my life,” and “Sport says a lot about who I am.” The three items were averaged to measure involvement (Cronbach’s  = .86, M = 5.38, SD = 1.35). This study used a median split to categorize high-involvement (N = 86) and low-involvement conditions (N = 83).

RESULTS
Manipulation Checks
The visual prominence manipulations were examined. Using two seven-point sematic differential items, the participants were asked to rate the extent to which they thought the format of the online posting they just read were “attractive/not attractive” and “likable/not likable” (Cronbach’s  = .83, M = 4.81, SD = 1.75). A t test between the two prominence conditions (low vs. high prominence) showed subjects felt that the youth sport program posting was more visually prominent when it included noticeable graphic elements (M = 5.60, SD = 1.23) than when it lacked the elements (M = 4.05, SD = 1.84), t (190) = 6.82, p < .001.

This study measured the degree of informativeness of online postings (emotional versus informative) by asking participants to rate the extent to which they though the posting they just read was “emotional” and “warmhearted” (Cronbach’s  = .80 M = 4.39, SD = 1.61). A t test between two message appeal conditions showed that the emotional appeal group (M = 4.94, SD = 1.27) perceived the posting to be significantly more emotional than the informative appeal group (M = 3.82, SD = 1.73), t (190) = 5.11, p < .001.
H1 and H2: Visual Prominence Main Effects

Multivariate analysis of variance (MANOVA) was conducted to determine the significant impacts of visual prominence, message appeal, and involvement on attitudes and behavioral intentions. H1 and H2 suggest that participants reading visually prominent postings would form stronger attitudes and behavioral intentions than did participants reading less prominent postings. Follow-up analysis of variance (ANOVA) tests were also performed the examine the effect of visual prominence for each of the dependent variables. Findings revealed that the effect of visual prominence was pronounced in relation to being able to determine consumers’ attitudes (M_High Prominence = 5.30, SD = 2.02 vs. M_Low Prominence = 4.14, SD = 1.38; F (1, 169) = 20.90, p < .001, partial η2 = .12) and behavioral intentions (M_High Prominence = 4.69, SD = 1.64 vs. M_Low Prominence = 4.01, SD = 1.73; F (1, 169) = 7.24, p < .01, partial η2 = .04). Thus, H1 and H2 were supported.

RQ1 and RQ2: Influence of Involvement on Visual Prominence and Message Appeals
The impact of consumers’ involvement on visual prominence and messages appeals were examined by 2 (visual prominence) X 2 (involvement) ANOVAs and 2 (message appeal) X 2 (involvement) ANOVAs with attitudes toward the online posting and behavioral intentions as dependent variables. The ANOVA results showed that that there were not significant interaction effects of the involvement-appeal relation and the involvement-visual prominence relation. The p values of the aforementioned relations were greater than .37. However, the impacts of visual prominence and message appeals were greater under both involvement conditions (see Figure 1 and 2).

H3: Moderating effect of involvement on the attitude-intention relation
This study anticipated that the attitude toward the online posting would form a stronger impact on the formation of behavioral intentions for high involvement conditions. Pearson’s correlation coefficient was used to examine whether involvement modifies the magnitude of the attitude-intention relation. Then, each correlation coefficient values for the high- and low-involvement conditions was converted into z scores by using Fisher’s r to z transformation. In order to compare the z scores for the two conditions, the following formula was implemented to determine the observed z score: Zobserved = (Z1−Z2) ∕ (square root of [1∕N1−3] + (1∕N2−3))

For the high involvement condition (n = 83), the correlation coefficient for the attitude-intention relation was .49 (p < .001). For the low involvement condition (n = 84), the correlation was .25 (p < .05). The test statistics, z = 1.78, p < .001 (one-tailed test), indicate that the correlation in the high involvement condition is significantly higher than it is in the low involvement condition. Therefore, Hypothesis 3 is supported.

DISCUSSION
Our findings suggest a lack of association between involvement and the effects of heuristics. The moderating role of involvement has been well established since the introduction of Petty et al.’s (1983) ELM and Chaiken’s (1987) heuristic-systematic model. According to those theories, involvement is a significant determinant in the selection of an information processing route (peripheral versus central). It is also commonly acknowledged in the sport communication field that individuals generally use a systematic mode (i.e., evaluating completeness/accuracy) when processing online sport information under high-involvement conditions in order to avoid the serious consequences of incomplete or inaccurate information (e.g., monetary loss, negative impacts on child development). However, our study found that the non-systematic mode is often activated for both high-involvement and low-involvement participants, and this finding thus contributes to the literature on individuals’ approaches to online information processing.

According to evidence-accumulation models (2), individuals reach a conclusion once there is enough evidence to support a particular case, but they can also alter the amount of evidence needed for coming to that decision. Although individuals generally want to make accurate decisions, Internet users often compromise the accuracy of their decisions by reducing the amount of evidence required to validate the information they are investigating. This tendency is attributable to online information overload, in which individuals experience difficulties in understanding the nature of a particular topic (Robin & Holmes, 2008). The tendency suggests a new general pattern of the speed–accuracy trade-off (SAT) in social media environments. In line with the SAT, there are two driving forces in the decision-making process (4); one emphasizes faster (or more efficient) decisions, while the other emphasizes higher accuracy. Although there are trade-offs between speed and accuracy, the two can be pursued independently, but they produce a wide spectrum of outcomes, from slower but more accurate decisions to quicker but less accurate decisions. In social media environments, individuals are motivated to engage in less-effortful information processing and are more likely to trade accuracy for speed in the decision-making process.

The current study also found another reason for further examining the role of involvement in social media environments. It has been assumed that persuasion is less likely to occur through emotional messages when an individual is highly involved in an issue because people tend to scrutinize issue-relevant information. However, our findings suggest that emotional messages can be more persuasive than informational messages regardless of the level of involvement, especially in the online youth sport community context, and these findings can be explained by the types of information individuals seek in online communities. Objective information about a youth program (e.g., fees, coaches’ experience, facilities) can be easily found through sources such as the youth program’s website, but people also tend to seek others’ prior experiences and emotionally supportive messages when joining online communities.
It is important to stress that the attitude–intention relationship varies with involvement levels. Our study shows that the attitudes of high-involvement participants are more predictive of the intention to perform a specific act (e.g., signing up a youth sport program) than the attitudes of low-involvement participants. Our findings regarding the attitude–intention relationship suggest that the moderating effect of involvement on that relationship is applicable to not only traditional media environments (e.g., Krosnick, 1988; Verplanken, 1989), but also to social media environments.

In addition to the theoretical implications of this study, understanding parents’ information processing in assessing youth sport program is an integral part of the sport communication landscape. With the growing importance of (local) parenting community groups on social media and the impact of user generated message, this study will help youth sport service providers understand the effective way of crafting online information. This study will shed lights on communication strategies for youth sport providers when they try to utilize a form of testimonial in introducing their services to the market. This study will also lead how social influencer marketing would be employed in delivering and disseminating the promotional messages to the consumers.

This study has some limitations. All its subjects were recruited through Amazon’s Mechanical Turk (MTurk). Although MTurk respondents tend to be more diverse than student samples in terms of demographic, psychographic, and geographic characteristics, some reliability issues (e.g., the work ethic of MTurk respondents) are unavoidable (3). Another limitation is that this study was conducted with samples of people who had parenting experiences because the study used a youth soccer program to develop the experimental stimuli, and the context of parenting might amplify reactions to emotional messages. We therefore recommend that future studies be conducted with more diverse samples and more popular sports topics (e.g., local sports events) in order to exclude the specific study topic and characteristics of the sample as potentially confounding factors.

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George Minoso2024-11-04T18:10:35-06:00November 22nd, 2024|Contemporary Sports Issues, General, Research, Sports Studies, Sports Studies and Sports Psychology|Comments Off

Order of passive and interactive sports consumption and its influences on consumer emotions and sports gambling

Authors:Anthony Palomba¹, Angela Zhang², and David Hedlund³

¹Department of Communication, Darden School of Business, University of Virginia, Charlottesville, VA, USA
²Department of Public Relations, Gaylord College of Journalism and Mass Communication, The University of Oklahoma, Norman, OK, USA
³Department of Sport Management, Collings College of Professional Studies, St. John’s University, Queens, NY, USA

Corresponding Author:

Anthony Palomba

100 Darden Blvd.

Charlottesville, VA, 22903

Anthony Palomba is an assistant professor of business administration at the Darden School of Business at the University of Virginia. He is fascinated by media, entertainment, and advertising firms. First, his research explores how and why audiences consume entertainment, and strives to understand how audience measurement can be enhanced to predict consumption patterns. Second, he studies how technological innovations influence competition among entertainment and media firms. Third, he is interested in incorporating machine learning and artificial intelligence tools to better understand consumer and firm behaviors.

Angela Zhang is an assistant professor in public relations. Her research interests span both corporate crisis communication and disaster risk communication in natural and manmade disasters. Her research primarily aims to understand how people process crisis and risk information and how we can communicate better during crises. For example, her work examines how linguistic cues in crisis messages affect people process crisis information, how and why risk information is propagated on social media, and how users communicate and cope on social media after crises. For corporate crisis communication, her research examines effectiveness of crisis prevention strategies such as CSR and DEI communication, as well as crisis response strategies.

Dr. Hedlund is an Associate Professor and the Chairperson of the Division of Sport Management, and he has more than twenty years of domestic and international experience in sport, esports, coaching, business and education. As an author, Dr. Hedlund is the lead editor of the first textbook ever published on esports titled Esports Business Management, and he has more than 30 additional journal, book chapter and related types of publications, in addition to approximately 50 research presentations. In recent years, Dr. Hedlund has acted as a journal, conference and book reviewer for sport, esports and business organizations from around the world, and he is an award-winning reviewer and editorial board member for the International Journal of Sports Marketing and Sponsorship.

ABSTRACT

This study explores how alternating between video game and television experiences influences consumer emotions and subsequent decision-making. Findings indicate that playing a video game after watching a video clip enhances positive emotions (H1 supported) and affects post-experiment betting scores based on pre-experiment gambling bets (H2 supported). Winning teams in video games and elevated positive emotions also positively influence post-experiment betting scores (H3 and H4 partially supported). The interaction effect shows that the sequence of media consumption (TV to video game) increases betting scores (H5 supported). The study contributes to understanding how appraisal tendency theory and mood management theory explain the impact of media consumption order on sports gambling decisions. Video games, as interactive stimuli, elevate consumer moods and influence betting behavior more than passive viewing. Practically, integrating video game and video clip data aids comprehensive audience measurement and targeted advertising strategies, advancing algorithmic forecasting in enhancing consumer engagement and decision-making.

Key Words: Mood management, Appraisal tendency theory, sports, gambling, video games

INTRODUCTION

The NFL is one of the most powerful media and entertainment brands in the marketplace, routinely curating legions of television and online video viewers for every annual season. In 2019, it averaged about 16.5 million viewers per game, roughly 33% above the 12.43 million viewing average for the top six non-sports programs (Porter, 2021). Additionally, over the last thirty years, the Madden NFL video game franchise has introduced generations to simulated immersive engagement. The legalization of sports gambling (Cason et al., 2020) has expanded how consumers can further engage with the NFL. NFL executives have discussed using mobile cell phones to aid sports fans in stadiums to make live bets throughout the course of a game (Martins, 2020). Audiences can watch the NFL and NFL game day content on the Xbox One, including up to date news and highlights from select NFL teams (Tuttle, 2016). Given these diverse modes of engagement, consumers often switch across a multitude of different activities. This frequent medium switching can significantly impact their moods and, subsequently, how they execute various tasks, including sports gambling. The phenomenon of media multitasking, where consumers engage with multiple forms of media simultaneously, complicates how they regulate their moods and make subsequent decisions (Deloitte, 2018). Younger consumers, in particular, are more inclined to switch between media than older consumers (Beuckels et al., 2021).

The increasingly diverse modes of engagement with the NFL, spanning from live game viewing and video game simulations to real-time betting, have led to a phenomenon of frequent media switching among consumers. This constant toggling between different platforms and activities can significantly impact their emotional states, subsequently influencing their decision-making processes, including those related to sports gambling. While previous research has examined task switching in general contexts (Yeykelis, Cummings, & Reeves, 2014) and the impact of media multitasking on advertising (Garaus, Wagner, & Back, 2017), the specific application of appraisal tendency theory to understand how these rapid emotional shifts induced by media switching affect sports gambling behaviors remains largely unexplored. Moreover, social media use while viewing television, a phenomenon that has grown in the last decade, has reconfigured the commodification of audiences, and has also created different markets to understand how consumers multi-task, and how to measure audience engagement (Kosterich & Napoli, 2016). Uniquely, social media may be used to track propensity to make season ticket purchases (Popp et al., 2023) among other sports consumption activities (Du et al., 2023). Recent studies have implicated the legalization of sports gambling as potentially increasing fandom and engagement among fans, and can further elevate communication across stakeholders involved in a sports event (Stadder & Naraine, 2020).

There is a gap in understanding, however, how consumer judgments and decisions are informed by emotions (Han, Lerner, & Keltner, 2007). Understanding this dynamic is critical for comprehending the evolution of fandom and identifying how sports teams can further engage fans. As consumers navigate between watching games, participating in video game simulations, and placing live bets, their engagement strategies and emotional states may significantly influence their decisions and loyalty. By examining these interactions, sports organizations can develop more effective methods to maintain and enhance fan engagement in an increasingly digital and interconnected world.

The implications of this study are broad and vast for academics along with sports and entertainment managers. The complex nature of media switching in sports consumption furthers our understanding of how affective disposition theory may be applied toward the multi-platform and multi-activity nature of modern sports engagement. It could lead to the development of a more nuanced understanding of how affective dispositions are formed and how they influence decision-making in this context. Microsoft (parent brand of Xbox console series) and the NFL have an agreement in which the NFL can provide fantasy football scores and updates on Xbox One consoles and allow fans to stream certain NFL games from their Xbox One consoles (Chansanchai, 2016). Additionally, Microsoft is able to trace not only what consumers play on Xbox One consoles, but also what TV or SVOD viewing apps fans engage to view content. Together, disparate information on video game play and video viewing can be combined to further identify trends in cross-platform sports consumption behavior and inferred consumer emotional states, which can help illuminate how consumer judgement surrounding sports gambling may be impacted.

NFL INDUSTRY

The National Football league has been a celebrated sports league in the United States and abroad over the last one hundred years. It draws the highest attendance per professional sports game in the United States, at about sixty-six thousand, and during its 2019 season, it hosted nearly sixteen million total viewers per game (Gough, 2021). The total revenue of all NFL teams was slightly over $15 billion in 2019, and average franchise value was just over $3 billion in 2020. Sports betting on Super bowls alone in Nevada accrued nearly $160 million in 2020 (Gough, 2021). While there are no clear figures regarding sports merchandise sales, NFL revenue by team in 2019 was led by the Dallas Cowboys ($980 million), New England Patriots ($630 million), NY Giants ($547 million) and Houston Texans ($530 million) through last place Las Vegas Raiders ($383 million) (Gough, 2020).

Aside from tickets, television revenue, and merchandise, the NFL has produced different avenues to engage fan bases. The league has recently embraced sports partnerships with Caesars Entertainment, Draft Kings and FanDuel. This allows these three external partners to engage in retail and online sports betting and engage with fans as well, using sports content from NFL media, as well as data, to market these experiences to fans (NFL, 2021). In fact, the NFL is expected to earn just over $2 billion annually from the sports gambling marketplace (Chiari, 2018). The NFL’s current TV media deals across CBS, ABC/ESPN, NBC, and Fox earn it just over $10 billion per season (Birnbaum, 2021). Arguably, one of the NFL’s highest profile merchandise revenue streams comes from its partnership with Electronic Arts (EA) to release an annual, updated version of Madden NFL, generating roughly $600 million annually for EA (Reyes, 2021). By embracing diverse engagement avenues, the NFL not only diversifies its revenue streams but also caters to the evolving preferences of modern sports consumers. This multi-faceted approach reflects the league’s recognition of the complex interplay between media consumption, mood, and fan behavior, ultimately enhancing the overall fan experience in an increasingly digital and interconnected world.

NFL FOOTBALL AS A VIDEO GAME EXPERIENCE: MADDEN NFL

There are few video games that possess the dominance and market monopolization as does the Madden NFL franchise. It exists as the only simulated NFL football video game available to consumers (Sarkar, 2020), and it is markedly popular among consumers. In fact, for the last twenty years, every Madden NFL video game installation has debuted as the top selling U.S. game in August each year (Wilson, 2022). The video game franchise itself has blossomed into its own celebrated video game season, as video game play expectedly rises during August in anticipation for the upcoming NFL season (Skiver, 2022). Madden NFL fans have been found to be more devoted and knowledgeable about the NFL. Additionally, they are less likely to miss viewing football games on Sundays, as 42% have stated they never miss a football game due to external activities. They are likely to attend at least one NFL game each annual season (IGN Staff, 2012).

Video gamers’ moods and subsequent judgment may be impacted by their own experiences. Video game play is an immersive experience, as the required technology helps to transport users into a digital world. The level of presence that is achieved can amplify mediated environment perceived quality, user effects, as well as overall experience (Tamborini & Bowman, 2010). Consumer familiarity with video game play may also influence how they experience presence (Lachlan & Krcmar, 2011). Consumers who view NFL games and play NFL video games may experience wins and loss outcomes in both passive and interactive manners. Sports video game play is motivated by possessing deep passion for the sport, gaming interest, entertainment value, competition, and identifying with the team or sport itself (Kim & Ross, 2006). Consumer emotions can be volatile during sports engagement, as winning and losing can impact overall game satisfaction (Yim & Byon, 2018). Emotions are tied to sports engagement in a primal manner, as consumers vicariously live through sports athletes and align themselves with sports teams, invoking a type of tribalism (Meir & Scott, 2007).

MOOD MANAGEMENT THEORY

Mood management theory concerns how consumers may manage their own moods through consumption of different mediums. Zillmann (1988) states that there are several traits that may impact whether a medium may repair or enhance a particular mood. First, there is the excitatory potential, or how exciting a message may be for consumers. Second, there is absorption potential, which examines how well a media message will be absorbed by an individual. Third, there is the semantic affinity, which relates to the connection from the current participant mood to a media message, which can moderate the impact of absorption potential. Finally, there is hedonic valence, in which pleasant messages can interrupt consumers’ bad moods (Zillmann, 1988). This study is focused on exploring how consumers’ gambling decisions are influenced by their experiences, both positive and negative, related to predicting scores between teams, and placing a bet on them. Specifically, it aims to investigate the impact of semantic affinity and the excitatory potential of stimuli involved in the process on consumer decision-making in gambling contexts.

Sports viewing or sports video game play can lead to evaluated states of physiological and psychological arousal, stirring hostile or expressive responses to game outcomes. Arousal has been found to be precipitated by aggressive or hostile states (Zillman, 1983), based on events during the game (Berkowitz, 1989). Hostility can be traced to the dissatisfaction with an outcome, or inability to attain a desired goal. Viewing violent sports competition can also heighten hostility and create greater inclinations toward aggressive behavior. Participants who had high identification with America and viewed an American boxer against a Russian boxer were found to have elevated blood pressure compared to those who had low identification with American (Branscombe & Wann, 1992). Additionally, spectators that have high team identification have higher levels of happiness compared to those with low team identification. The way a message is delivered can impact the effect of a message on consumers, as there are distinct characteristics related to each medium (Dijkstra, Buijtels & van Raaij, 2005).

Mood management is clearly influential as to how participants respond to video and video game play. Participants who may feel frustration may feel further frustration from viewing violent content (Zillmann & Johnson, 1973). One study by Bryant and Zillmann illustrated that participants who view violent sports did not experience mood repair (Donohew, Sypher, & Higgens,1988). Fulfillment of intrinsic needs can influence selection of video games with varying levels of participant demand (Reinecke et al., 2012). Television has been found to reduce boredom and stress among consumers (Bryant & Zillmann, 1984). In managing moods, this can also impact subsequent decision-making, sometimes surreptitiously and without awareness from participants.

APPRAISAL TENDENCY THEORY

Appraisal tendency theory considers how different types of emotions within similar valences (e.g., anger and fear) may impact judgement. There are two types of influences that may impact how consumers make judgments. Integral emotion is based on individual experiences that might preempt but be relevant to a subsequent decision. Differently, incidental emotion is due to conceivably irrelevant though impactful elements that can inform decision-making, which may include being influenced by traffic, watching television, or engaging in other non-relevant actions. These influences can carry over to the decision-making process (Schwarz & Clore, 1983; Bodenhausen, Kramer, & Susser, 1994). Moreover, consumers who are angry tend to perceive less risk from engaging in new situations (Han, Lerner, & Keltner, 2007).

Integral emotion is under examination in this study, as an outcome from a related medium stimulus can impact a subsequent decision that is likely informed by that stimulus. After finding that they have won in a video game, it may be that consumers are less inclined to bet against the team that they just lost against. This subjective pain(joy) based on the first stimulus may be stronger from playing a video game than from viewing a sports clip. Moreover, consumers may seek variety in consumption decisions when they are induced to a negative emotion (Chuang, Kung, & Sun, 2008). Therefore, subsequent decision-making may be informed by the order of passive and interactive media consumed by each individual.

MEDIUM MODALITY

Mediums that engage multiple senses are likely to lead to impactful communication with consumers (Jacoby, Hoyer & Zimmer, 1983). Television offers engagement through visual and auditory senses, while gaming stimulates both but creates an immersive experience, in which consumers are transported into a virtual world (Kuo, Hiler, & Lutz, 2017). Differently, consumers do not have control over passive mediums such as television, as the content is predetermined and is under the yolk of the sender, creating different delivery systems (Van Raaij, 1998). Video game play offers opportunities for players to speed up game play, based on gaming flexibility as well as how quickly a consumer can finish tasks. Video game play is positioned to evoke cognitive responses, through the speed of information dissemination, since the consumer possesses more control over the experience. Conflated with the demanded attention from video game play, consumers will likely have greater affective responses from video game play than from video viewing (Dijkstra, Buijtels, & van Raaij, 2005).

In consideration of this study, it follows that the simulated aspect of video game play can further influence decision-making. Consumers are inclined to experience improved decision-making and risk assessment through video game play (Reynaldo et al., 2020), as well as cognitive tasks (Chisholm & Kingstone, 2015). Video game play may also induce lowered physiological stress (Russoniello, O’Brien, & Parks, 2009), and emotional regulation (Villani et al., 2018). While there is scant research surrounding video game play simulations and making subsequent real-life decisions, it is ostensibly clear that video game play can heighten and sharpen decision-making skills as well as emotion regulation. Consumers who are attentive toward a simulated video game play experience may be influenced by its outcome in making a subsequent decision. This can include perceiving the winning team in the simulated game as likely to beat the same opposing team in a real-life match up.

H1: Consumers who play a video game (view a video clip) first will be more inclined to have lower (higher) positive emotions.

SPORTS GAMBLING

Recently, sports gambling has become legalized or recent legislation has been passed to make it legal in 50% of states in the United States (Rodenberg, 2021). While fans have placed bets on horse-racing and even major league sports, its legalization provides a lawful and safe forum for myriad fans to place bets on teams. However, since many gamblers may not invest time in understanding spreads and other esoteric metrics that gambling managers may use to measure likelihoods of outcomes, playing a Madden NFL game can serve consumers to anticipate potential outcomes in real life match ups. Madden NFL’s algorithms have been harvested in the past to predict Super Bowl outcomes. In fact, EA typically runs one hundred simulations to predict which team will win each year in the Super Bowl (Wiedey, 2020). Additionally, fans are also able to make wagers on major league baseball simulated video games (Cohen, 2020). Younger sports fans may be more inclined to play Madden NFL games as a way to simulate outcomes, and become more familiar with teams to anticipate actual game outcomes. Additionally, sports gamblers are betting on simulated sports, in which Madden NFL video games are simulated through the popular video game streaming site Twitch, and consumers are able to bet on the outcome (Campbell, 2021).

Previous studies have highlighted why consumers engage in sports gambling. One study found that consumers engage in sports gambling to seek out social interaction and relaxation through engagement with betting apps, though their effect on problematic gambling and non-problematic gambling varied across these dimensions (Whelan et al., 2021). Consumers may seek out consumer purchases as a way to blunt negative emotions, or may further satiate their positive mood by pursuing purchases that bring them joy. Video game play can engender excitatory potential, stimulating arousal levels and inspiring consumers in negative moods to make consumer purchases or execute notably different gambling bets. The heightened arousal levels experienced by consumers during video game play can create greater vacillation in subsequent decision-making, including sports gambling bets. Tangentially related to this, if a consumer is in a positive mood, this optimism may impact their inclination to bet more on a sports match up. Additionally, the order of engaging a passive medium versus an interactive medium is critical to analyze. Video game play can heighten immersion in content, and provide further confidence in a team. Consumers may be able to participate in high-scoring video game match ups. Additionally, consumers may be spurred to bet on characters with whom they have virtual relationships (Palomba, 2020). Finally, video game play can lead to experiencing dopamine release, leading to greater felt pleasure (Koepp et al., 1998). Together, these may lead consumers to have greater optimism for post-betting scores.

H2: Consumer pre-experiment bet scores will have an anchoring effect and still inform post-experiment bet scores.

H3: The team that wins in the video game will have a greater positive relationship with post experiment bet scores than the team with the highest score in the video clip.

H4: Consumers who experience strong positive (negative) emotions after viewing a video clip will positively (negatively) influence post-experiment bet scores.

H5: Consumption order and time will have an interaction effect that when consumption order is VG to TV, betting scores will decrease from pre-betting to post-betting (pre-betting will be higher than post-betting); when consumption order is TV to VG, betting scores will increase from pre-betting to post-betting (pre-betting will be lower than post-betting).

METHOD

A 4×2 experiment was conducted here, in which participants were exposed to one of four different video clips, and one of two outcomes in a video game play match up. The New York Giants and Dallas Cowboys were the two teams that were selected for this experiment. Since this experiment took place in the mid-Atlantic region, it was believed that participants were less inclined to like either team. Moreover, these two teams have a storied and high-profile rivalry between them. For the video stimulus, participants were exposed to a randomized video clip highlighting a matchup between the NY Giants and Dallas Cowboys, in which one of four scenarios appeared: a) The NY Giants win by a wide margin (20 points), b) The NY Giants win by a slim margin (3 points), c) The Dallas Cowboys win by a slim margin (3 points), and d) The Dallas Cowboys win by a wide margin (20 points). Each video clip was about five minutes long. The video game stimulus involved playing a Madden NFL video game match up on an Xbox One video game console between the NY Giants and Dallas Cowboys. Participants were able to select which team they desired to play as and in which stadium to play in. The quarters in the Madden NFL game were kept at the default setting of six minutes each, ensuring participants experienced immersion but also maintained the experience to be similar to viewing the video clip.

Participants in the A condition (VG to TV) first played the video game followed by viewing the video clip, and participants in the B condition (TV to VG) first viewed the video clip followed by the video game play. as well as playing a Madden NFL session implicating both teams. After each condition, participants were asked to evaluate their current emotions. After the video clip, participants were asked to state the final score and which team won in the clip to ensure that they were paying attention to the clip itself. Moreover, after the video game condition, participants were asked to state which team they played as, the final score, as well as what sports stadium they played in.

MEASURES

To measure fandom, a scale from (Wann, 2002) was used here. It consisted of statements regarding self-assessment of fandom, including statements such as “I consider myself to be a football fan,” “My friends see me as a football fan,” and “I believe that following football is the most enjoyable form of entertainment.” It was measured on a 1 (strongly disagree) to 5 (strongly agree) Likert scale.

To measure current emotions, a scale from Diener and Emmons (1984) was used here. The scale consisted of emotions statements including “joy,” “pleased,” “enjoyment,” “angry,” and other emotion statements. It was measured on a 1 (not at all) to 7 (extremely much) Likert scale.

It was believed that the current emotions scale, though exhaustive, did not capture extreme aggression that may be felt by sports fans. An ancillary aggression scale (Sinclair 2005; Spielberger, 1999) was used here. The scale consisted of aggression statements including “I feel like yelling at somebody,” “I am mad,” and “I feel like banging on the table.” It was measured on a 1 (not at all) to 5 (extremely) Likert scale.

To measure for team identification, a scale by Naylor, Hedlund, and Dickson (2017) was used here. The scale consisted of statements including “I know a lot of information about my favorite National Football League team,” “I am very knowledgeable about my favorite National Football League team,” and “I am very familiar with my favorite National Football League team.” It was measured on a 1 (not at all) to 5 (extremely) Likert scale.

To measure for commitment to team, a scale by Hedlund, Biscaia, and Leal (2020) was used here. The scale consisted of statements including “I am a true fan of the team,” “I am very committed to the team,” and “I will attend my team’s games in the future.” It was measured on a 1 (not at all) to 5 (definitely) Likert scale.

To measure for brand loyalty toward Madden NFL, a scale by Yoo and Donthu (2001) was used here. The scale consisted of statements including “I consider myself to be loyal to Madden football,” “Madden football would be my first football video game choice,” and “The likely quality of Madden NFL is extremely high.” It was measured on a 1 (strongly disagree) to 5 (strongly agree) Likert scale.

RESULTS

Descriptive analytics were run to break down video clip and video game play exposure to participants. After data-cleaning was executed, one hundred and thirteen participants (n=113) remained for analysis. 63.7% of participants were male. Additionally, across ethnicity, participants were Caucasian (58.4%), Asian-American (16.8%), African-American (8.8%), Hispanic (2.7%) and also identified as other races (13.3%). Among participants’ favorite NFL teams, they included the Washington Commodores (16.8%), New England Patriots (8.0%), and Philadelphia Eagles (8.0%). Less participants were fans of the New York Giants (4.4%) and Dallas Cowboys (1.8%). To gain a sense of faith participants had among each team, participants were asked to imagine making a bet between a pre bet on an imagined match up between the NY Giants and Dallas Cowboys. Participants on average placed the Dallas Cowboys (M=25.77, SD=9.102) past the NY Giants (M=20.67, SD=8.715) and bet roughly $14.37 on average.

Across all video clips, participants viewed the Giants winning by a lot (23.4%), Giants winning by a little (28.7%), Cowboys winning by a lot (25.5%), and Cowboys winning by a little (22.3%). Participants viewed the Giants winning 49.5% of the time and the Cowboys winning 50.5% of the time. In relation to video game difficulty level exposure, 51.3% of participants were exposed to pro-level difficulty (2/4 level of difficulty), and 48.7% were exposed to all-pro level difficulty (3/4 level of difficulty). This was done to ensure that Madden football players felt challenged and greater immersion during video game play (Csikszentmihalyi, 1975; Falstein, 2005; Nacke, 2012; Missura, 2015). 50.9% of participants played as the Dallas Cowboys, and 49.1% played as the NY Giants. In the video game itself, the Dallas Cowboys won 64% of the time, and the NY Giants won 36% of the time. Finally, participants won 74.8% of the time. Moreover, 58% of participants elected to play in NY Giants home stadium, MetLife Stadium, and 42% elected to play in AT&T Stadium, the Dallas Cowboys’ home stadium. Before analyses could be conducted, it was necessary to run factor analyses to reduce the amount of emotion statements necessary for analyses. For all factor analyses across pre-experimental mood, post video mood, and post video game mood, varimax rotations were run.

For post video emotions, the factor analysis had a KMO of .895 and the Bartlett’s Test of Sphericity was statistically significant. The first factor loading had 12.717 eigenvalue and explained 48.913% of variance in the data. The first loading, violent, included I feel like kicking somebody (.919), I feel like hitting someone (.908), I feel like breaking things (.880), I feel like pounding somebody (.880), and I feel like yelling at somebody (.874) and had a Cronbach’s alpha score of .972. The second factor loading had an eigenvalue of 5.022 and explained 19.317% of variance in the data. This scale, entitled irritated, included frustrated (.865), annoyed (.835), angry (.820), depressed (.800), and sad (.768), and had a Cronbach’s alpha score of .928. The third factor loading had an eigenvalue of 2.311 and explained 8.890% of variance in the data. This scale, entitled positive, included pleased (.919), joy (.914), glad (.904), delighted (.900), and fun (.898) and had a Cronbach’s alpha score of .953.

For post video game emotions, a factor analysis was run. The KMO =.879 and the Bartlett’s test of sphericity was statistically significant. The first factor loading had an eigenvalue of 13.119, and it explained 50.458% of variance in the data set. The first factor loading, violent, included I feel like hitting someone (.866), I feel like breaking things (.858), I feel like banging on the table (.853), I feel like pounding somebody (.840) and I feel like kicking somebody (.840) with a Cronbach’s alpha score of .965. The second factor loading had an eigenvalue of 4.640 and explained 17.846% of variance in the data set. This scale, positive, included joy (.915), glad (.910), delighted (.897), pleased (.884), and fun (.860), and possessed a Cronbach’s alpha score of .952. The third factor loading had an eigenvalue of 1.783 and explained 6.858% of variance in the data set. This scale, irritated, included gloomy (.832), depressed (.798), sad (.747), anxious (.628), and angry (.531) and had a Cronbach’s alpha score of .905.

There was emotional variance across mediums (Table 1). Paired T-tests were run across an assortment of feelings here. For most of the emotions that were measured for in this experiment, participants generally felt better after playing the video game against viewing the clip itself across both conditions. For instance, in total, joy (M=4.38, SD=1.928), glad (M=4.45,

SD=1.785), and delighted (M=4.32, SD=1.904) all increased across all conditions after the video game play condition. Hypothesis 1 is supported here.

Table 1

Emotion variance across mediums.

	Total			TV to VG			VG to TV
	Pre stimulus	Post video clip	Post video game	Pre stimulus	Post video clip	Post video game	Pre stimulus	Post video game	Post video clip
Joy	4.04(1.614)	3.75(1.864)*	4.38(1.928)***	4.33(1.492)	4.46(1.691)	4.98(1.742)*	3.73(1.689)	3.79(1.933)	3.04(1.768)**
Pleased	4.28(1.623)	4.63(1.665)	4.66(1.824)***	4.44(1.524)	4.63(1.665)	5.02(1.794)	4.13(1.717)	4.30(1.798)	3.54(1.629)***
Fun	4.48(1.553)	5.09(1.491)	5.46(1.705)***	4.61(1.449)	5.09(1.491)***	5.46(1.705)	4.34(1.654)	4.88(1.585)**	3.37(1.902)***
Glad	4.35(1.535)	3.81(1.827)***	4.45(1.785)***	4.70(1.414)	4.39(1.677)	4.89(1.723)*	4.00(1.584)	4.00(1.748)	3.21(1.796)**
Delighted	3.88(1.700)	3.83(1.827)	4.32(1.904)**	4.11(1.666)	4.26(1.798)	4.71(1.755)*	3.66(1.719)	3.93(1.980)	3.39(1.765)*
Contented	4.97(1.555)	4.39(1.775)***	4.55(1.729)	5.11(1.655)	4.82(1.754)	4.80(1.793)	4.84(1.449)	4.30(1.640)*	3.95(1.699)
Angry	1.45(1.106)	1.37(.771)	1.58(1.333)	1.38(1.001)	1.38(.702)	1.46(1.144)	1.52(1.206)	1.70(1.501)	1.36(.841)
Anxiety	2.33(1.550)	1.67(1.060)***	1.62(1.133)	2.25(1.338)	1.77(1.062)*	1.45(.851)*	2.41(1.745)	1.79(1.345)***	1.57(1.059)
Frustrated	1.88(1.309)	1.69(1.115)	2.10(1.682)**	1.77(1.079)	1.66(1.100)	1.84(1.424)	1.98(1.507)	2.36(1.882)	1.71(1.140)**
Depressed	1.76(1.187)	1.46(.958)***	1.45(.928)	1.71(1.107)	1.43(.892)**	1.38(.822)	1.80(1.271)	1.52(1.027)*	1.48(1.027)
Annoyed	1.86(1.293)	1.76(1.050)	2.13(1.688)*	1.59(.949)	1.66(.920)	1.91(1.621)	2.13(1.526)	2.34(1.740)	1.86(1.167)*
Sad	1.74(1.334)	1.42(.866)**	1.44(.918)	1.80(1.470)	1.38(.676)*	1.39(.908)	1.68(1.193)	1.48(.934)	1.46(1.026)
Gloomy	1.75(1.151)	1.50(.977)**	1.40(.895)	1.77(1.191)	1.41(.781)***	1.32(.741)	1.73(1.120)	1.48(1.027)*	1.59(1.141)
p < .05; p < .01; **p < .001.

To test hypotheses 2-4, multiple linear regressions were running for predicting consumer post experiment score bets in table 2 and table 3. In table 2, Across both conditions, pre bet Giants score (β=.413, p<.001), pre bet Cowboys score (β=-.269, p<.012), and video Giants score (β=.225, p<.021) explained 34.6% of variance toward estimating Giants post experiment bet score. In the TV to VG condition, pre bet Giants score (β=.505, p<.003), pre bet Cowboys score (β=-.442, p<.008) explained 35.5% of variance toward estimating Giants post experiment bet score. In the VG to TV condition, pre bet Giants score (β=.430, p<.018) and Giants winning in VG (β=-.583, p<.024) explained 28.9% of variance toward estimating Giants post experiment bet score.

Table 2

Consumer post bets – Giants.

	NY Giants Total		NY Giants TV to VG		NY Giants VG to TV
	Beta	Sig.	Beta	Sig.	Beta	Sig.
Pre bet Giants score	.413	.001***	.505	.003**	.430	.018*
Pre bet Cowboys score	-.269	.012*	-.442	.008**	-.009	.969
Winning team in VG	-.282	.071	-.213	.442	-.583	.024*
Did player win in VG	.011	.921	-.276	.113	.334	.112
Team played as in VG	.010	.942	.190	.513	-.110	.610
Sports arena played in VG	.083	.462	.073	.695	.261	.296
Video Cowboy score	-.056	.550	-.089	.543	-.039	.801
Video Giants score	.225	.021*	.262	.096	.285	.079
VG Giants score	-.120	.388	.073	.738	-.281	.256
VG Cowboys score	-.115	.376	-.174	.409	-.009	.966
VC Violent	.085	.569	.138	.668	.011	.960
VC Irritated	-.012	.919	.007	.971	-.186	.458
VC Positive	-.079	.550	-.170	.397	-.043	.836
VG Violent Actions	-.164	.276	-.290	.423	.051	.815
VG Positive Actions	-.110	.454	.028	.894	-.207	.399
VG Irritated	-.006	.962	-.101	.608	.023	.937
F	3.814		2.448		2.068
R	.685		.775		.748
R²	.346		.355		.289
Significance	.001		.021		.048

In table 3, across both conditions, pre bet Cowboys score (β=.467, p<.001), Cowboys winning in video game (β= .342, p<.038), and video Cowboy score (β=.226, p<.024) explained 27.4% of variance toward estimating Giants post experiment bet score. In the TV to VG condition, pre bet Cowboys score (β=.394, p<.014), Cowboys winning in video game (β= .613, p<.029), Cowboys video score (β=.352, p<.020), Giants video game score (β=.470, p<.034), and feeling positive after viewing the video clip (β=.476, p<.020), explained 38.8% of variance toward estimating Giants post experiment bet score. In the VG to TV condition, Cowboys winning in the video game (β=.469, p<.035), Cowboys video score (β= .276, p<.047), Giants video game score (β=-.517, p<.021), Cowboys video game score (β=-.450, p<.022), and feeling violent after the video clip (β=-.583, p<.011) explained 46.2% of variance toward estimating Cowboys post experiment bet score. Together, these results supported hypothesis 2 and provided partial support for hypotheses 3 and 4.

Table 3

Consumer post bets – Cowboys.

	Dallas Cowboys Total		Dallas Cowboys TV to VG		Dallas Cowboys VG to TV
	Beta	Sig.	Beta	Sig.	Beta	Sig.
Pre bet Giants score	-.138	.195	.045	.767	-.277	.072
Pre bet Cowboys score	.467	.001***	.394	.014*	.370	.071
Winning team in VG	.342	.038*	.613	.029*	.469	.035*
Did player win in VG	.098	.422	.063	.705	.288	.115
Team played as in VG	-.066	.662	-.294	.300	-.263	.169
Sports arena played in VG	.004	.972	-.221	.231	-.242	.266
Video Cowboy score	.226	.024*	.352	.020*	.276	.047*
Video Giants score	-.120	.236	-.169	.261	.058	.675
VG Giants score	.009	.953	.470	.034*	-.517	.021*
VG Cowboys score	-.128	.349	-.129	.529	-.450	.022*
VC Violent	-.180	.255	.312	.323	-.538	.011*
VC Irritated	.099	.443	-.011	.954	.226	.303
VC Positive	.098	.484	.476	.020*	.088	.629
VG Violent Actions	.090	.569	-.547	.127	.160	.406
VG Positive Actions	-.033	.830	-.098	.634	-.287	.182
VG Irritated	-.019	.895	.383	.054	-.102	.684
F	3.008		2.663		3.251
R	.641		.788		.817
R²	.274		.388		.462
Significance	.001		.013		.004

To answer the fifth hypothesis, a mixed between-within subjects analysis of variance was conducted to understand the effects of consumption order (TV to VG vs. VG to TV) and game results (NY giant wins a lot vs. Cowboy wins a lot) on participants’ sports betting scores on the two teams (NY Giants and Dallas Cowboys, respectively), across two time periods (pre- and post-experiment).

For betting scores on NY Giants, a significant interaction effect was found between time and order (Wilks’ Lambda = .89, F (1, 35) = 4.54, p=.04). Both pre and post-betting scores for those under the order condition TV to VG ( = 15.83, SD=5.79 and = 18.72, SD=8.10) scored lower than those under the VG TO TV conditions ( = 23.57, SD=9.67 and = 20.19, SD=8.54). Betting scores for NY Giant has increased for order TV to VG ( = 15.83, SD=5.79 to = 18.72, SD=8.10) but betting scores for order VG to TV has decreased ( = 23.57, SD=9.67 to = 20.19, SD=8.54). However, the main effects for time were not significant, nor were the interaction effects between time and game results, and between time, game results, and order (Figure 1). For betting scores on Dallas Cowboys, no significant main effects or interaction effects were found on any of the variables.

Figure 1

Pre-betting and post-betting scores.

DISCUSSION

This study worked to demonstrate how toggling between video game and television experiences could influence consumer emotions and inform subsequent decision-making. Consumers who played a video game after viewing a video clip were more inclined to feel positive (H1 supported). Pre-experiment gambling bets informed post experiment bet scores (H2 supported). There was some evidence that suggested winning teams in video games held a positive influence over post experiment bet scores (H3 partially supported) and that high levels of positive emotions also held a positive influence over post experiment bet scores (H4 partially supported). Finally, there was an interaction effect in which consumption order and time, in which betting scores will increase in the TV to VG condition (H5 supported). Together, the evidence illustrates how powerful the order of medium engagement is for consumers, and that these particular sequences can not only impact post-moods, but also decision-making among consumers.

This study contributes to the understanding of how appraisal tendency theory and mood management theory further elucidate the influence of media consumption sequencing on subsequent sports gambling decision-making. Specifically, the sequential order of media engagement was found to affect consumers’ semantic affinities between their recent media exposures (such as watching sports clips or engaging in video game sports simulations) and their subsequent decisions regarding sports wagering, albeit to a limited extent. Additionally, consumers’ moods were elevated by video game play, compared to viewing sports clips, supporting the excitatory potential of interactive stimuli here (Zillmann, 1988; Reinecke et al., 2012). In particular, the winning team in a video game simulation was able to impact post-consumer scores for the Cowboys, and moderately impact post-consumer scores for the Giants. This illustrates that video game simulations can be used to inform subsequent decision-making including estimating a team’s score during a post bet, an advancement of appraisal tendency theory. Previously, this had not been applied to mixed media modality studies, and this illustrates that previous media consumption activities can impact subsequent decision-making. Overall, post-betting scores were elevated in part based on the video game to television media consumption order, illustrating the anchoring effect established from consumers’ first playing video game match ups. Additionally, while pre bets can inform how consumers may produce bets after engaging in media, playing simulated video games can be impactful, whether it is the final score or which team won. It should be stated that the bulk of consumers played as the Cowboys, which may illustrate why the Giants winning in the video game held a negative relationship toward the Giants post bet score. It may be that for some consumers, there is interest in proving a simulation wrong, whereas others are positively informed by this experience.

In regards to mood management theory, in particular semantic affinity and excitation potential, consumer moods were elevated during video game play. From a passive to an interactive activity, this illustrates that this can further intensify emotional valences across positive (e.g. joy, pleased, fun) and negative (gloomy, annoyed) states. This furthers our understanding of how order of media consumption can impact particular moods for consumers. Having agency over an experience, and allowing consumers to co-create their own experiences while playing a simulated matchup further elevates positive feelings. Differently viewing video clips can evoke a range of emotions in consumers, including contentment as well as feelings of anxiety, depression, sadness, or gloominess. A passive entertainment experience that does not include consumers in the co-creation process (especially if their favorite team is not featured in the clip itself), can create dower moods among consumers. Only 6% of participants possessed affinity for either the Cowboys or the Giants, which did not improve mood during video viewing. However, video game play was able to overcome this obstacle and uplift moods.

PRACTICAL IMPLICATIONS

Integrating video game data with video clip data collection facilitates the development of a comprehensive media audience measurement approach. This approach enables practitioners to gauge engagement across both passive and interactive consumption modes. Additionally, it contributes to establishing a new market information framework (Meyer & Rowan, 1977), potentially minimizing analytical redundancies as consumers’ behaviors are tracked seamlessly across various media platforms. The technological disruption of multi-tasking, task-switching, and sequential tasking have created multiple opportunities to measure audiences differently, particularly as 5G becomes widely available in NFL stadiums. Verizon has recently stated that its 5G ultra Wideband service can ensure connectivity for fans during live games (Ashraf, 2023). The ability to engage smart phone devices in a sports stadium allows audiences to gain a sense of how audiences are responding to a game, which may include measuring the amount of bets. For homebound patrons, consolidating data sets in a cohesive and aggregated fashion enables the development of advanced algorithms for forecasting. This helps in deciphering the audience’s mindset based on their past media consumption patterns leading up to watching an NFL game or engaging in Madden NFL gameplay. Currently, Amazon offers X-Ray for Thursday Night Football fans, which is a sophisticated graphical overlay that allows fans to follow statistics in real time along with generated two-minute highlight reels (Forristal, 2023). Therefore, calcified sport consumer profiles and proclivities for communication with each other can be further facilitated through these strategies (Kirkwood, Yap, & Xu, 2018).

This creates a vehicle for programmatic strategy advertising and public relations, by which automated advertisements and public relations addresses can be targeted toward participants after an activity in order to enhance or repair a sports fan experience. More attention from consumers may be given to positive television advertisements that follow engaging programming rather than calm programming (Lee, Potter & Han, 2023). Consumers gain greater joy on spending money on experiential products including sports events (Nicolao, Irwin, & Goodman, 2009) and so consumers may seek out experiences more so than merchandise. Moreover, the ability to track consumer behavior in virtual spaces has implications for how advertisements may be placed and how consumers may engage with them (Ahn, Kim & Kim, 2022). The order of consumption can aid practitioners in elevating video game play. Not only can it impact post betting video game scores, but it can also enhance positive moods for consumers. In particular, consumers who experience their own team or a favored team winning in a video game or simulated match up may feel delighted or joy, which may subsequently encourage them to increase the post experience bet score for one or both teams. This can therefore encourage more risk taking among consumers, and perhaps even more spending for that matter. Furthermore, when fans experience negative emotions after their favorite team loses a live match, the NFL team can strategically encourage them to replay the matchup in Madden NFL. This allows fans to reimagine the live game, thus re-writing the experience itself, and mitigating any temporary damage to brand loyalty or equity.

LIMITATIONS AND FUTURE STUDIES

There were several limitations in this study. First, most participants were inclined to push for in favor of the Cowboys in the pre-bet. Recall that the NY Giants pre bet score was less (M=20.67, SD=8.715) compared to the Dallas Cowboys (M=25.77, SD=9.102). This indicates markedly more confidence in the Dallas Cowboys’ abilities among the participants. However, while the Cowboys won 64% of the time in the video game, participants only played as them for roughly 51% of the time. Moreover, 58% of participants elected to play in the NY Giants arena. Consequently, many participants were surprised by losses to the NY Giants when playing in the Giants’ stadium. Future studies should consider allowing participants to play as their favorite teams or testing various types of advertisements on them. It may also be valuable to examine how participants respond to playing in stadiums that are geographically close to or far from their hometowns. Additionally, investigating how the order of media consumption affects consumer behavior related to memorabilia, tickets, and other sports-related purchases offers a promising area for academic research.

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Qshequilla Parham Mitchell2024-10-11T13:41:23-05:00October 11th, 2024|Research, Sports Studies|Comments Off

Selection and Performance Rationale of Wood vs. Aluminum Baseball Bats

Authors: Vilas G. Pol¹

AUTHORS INSTITUATIONAL AFFILIATION:

¹Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, 47907, United States

Corresponding Author:

Vilas G. Pol

Purdue University

Davidson School of Chemical Engineering

West Lafayette, Indiana 47906

[email protected]

Acknowledgments

We would like to express our sincere gratitude to Sunkalp Vilas Pol for his valuable contribution to this research. His assistance in this article is greatly appreciated and played an important role in the development of this paper. We commend his dedication and enthusiasm for learning.

Selection and Performance Rationale of Wood vs. Aluminum Baseball Bats

ABSTRACT

USA Youth Baseball approves metal/alloy, composite, and wood (or a combination) bats for use in baseball games. However, players, parents, and coaches often face a dilemma when selecting a superior baseball bat, as bat quality depends on material, durability, performance, sensation, player preference, and balance. The purpose of this experimental investigation is to understand the maximum exit velocity of a baseball and overall performance of maple wood vs. aluminum bats. This is accomplished by hitting a stationary ball on a tee as well as with two different pitch speeds (30 and 40 MPH from a roller pitching machine), measured by a speed radar (accuracy ±1 MPH) in a controlled environment. It is hypothesized that when the material of the baseball bat changes, the exit velocity of the ball would change due to the trampoline effect (compression of the solid metal barrel) when hitting with the metal/aluminum bat compared to the solid wood bat. Apart from similar barrel size, length, and weight of the bats, it is observed that the metallic aluminum bat is slightly superior (2-3%) because of the trampoline effect when the balls were hit off the tee and with the machine at 30 MPH speed. Interestingly, for the 40 MPH automatic pitching machine test, the wood bat was 3-4% superior to the aluminum bat, possibly due to high impact speeds with less than 1 ms impact duration and minimum energy losses in the bat, or even due to the strength of the batter. The data were collected by a 12U youth baseball player in three different sessions for better accuracy and reproducibility. In fact, high-quality (hence expensive) wood or aluminum bats could lead to analogous outcomes (±1-2% variations) when used in a controlled environment, not significantly contributing to winning the baseball game.

Keywords: Exit velocity, Trampoline effect, Barrel size and length, Controlled environment, Efficiency

INTRODUCTION

Baseball is a popular American game played between two teams of nine members with a bat, a ball, and gloves on a diamond-shaped field with alternating batters (offense) and fielders (defense). The batter’s goal is to hit the ball hard enough, putting it out of reach of the fielding team to make a complete circuit around the bases to obtain a ‘run,’ with the team scoring the highest number of runs winning the game. This is typically made of either of wood or a lightweight metal such as aluminum. Now the mystery question is which bat (wood or aluminum) should be selected for such an important task.

During the last century, there has been significant research and development in the baseball field including selection of bats, barrel diameter, shape, length, and composition. Naturally, wood and aluminum bats are considered based on their performance, affordability, and safety. Typically, the more expensive bats use higher quality materials, hence better properties such as lightweight materials leading to longer distances and more power while producing minimal vibrations.

Due to lots of discussion in the open literature arguing which material bats are superior, this study particularly focuses on the experimental investigation of the exit velocity of most common aluminum and wood bats. It was hypothesized that using a maple wood bat versus an aluminum baseball bat of the same length, barrel size, weight, and producers used to strike the ball might create a different exit velocity because of varied physical properties of bat materials, including the commonly known trampoline effect (barrel compresses and expands) while using the hollow aluminum bat compared to the solid wood bat.

In 2022, Sherwood et al. studied five aluminum and wood baseball bats and observed that the field performance of these bats strongly correlated with the ball–bat coefficient of restitution COR. They predicted the relationship between wood baseball bat profile and durability based on finite element modelling of 15 profiles used from 15 MLB players (1). Russell described the effect of cylindrical barrel and flexural bending vibrations (2) on softball and baseball bats with respect to their performance including understanding the sweet spot and the origin of the ping (3) sound. Shenoy et al. predicted a model for the performance of solid wood and hollow metal bats with an experimental agreement for the impact speed, ball types, bat models, and impact locations (4).  It is observed that the energy dissipation between the bat and the ball happens through ball deformation, elastic bat vibration and contact friction (4). In 2002, Sherwood et al. investigated the durability of the wooden bat based on the slope of grain impact and impact location, with statistical analysis and finite element modeling. In other study they predicted the relationship between wood baseball bat profile and durability (5). In 2003, Drane and Sherwood described the effect of moisture content on the wooden bat, increasing the velocity by a maximum of 1% (6). In 2002, Penna et al. described that the exit velocity can depend on the skill level of the player or a higher performing bat (7). The systematic literature review created a knowledge gap to investigate the dilemma in selecting the most effective bat that would contribute in winning the baseball game.

This article methodically answers that question with experimental evidence through carefully measuring and comparing the average exit velocities of an aluminum and a wood bat with reproducibility. Though both bats had similar speeds, exit velocity measurements show that the aluminum bat is 2-3% superior because of the hypothesized trampoline effect when the balls were hit off of the tee and against 30 MPH pitches from the ball roller pitching machine. Surprisingly, for the 40 MPH automatic pitching machine test wood bat was 3-4% superior to the aluminum bat possibly due to less than 1 ms impact duration with the minimum energy losses in the bat or even the strength of the batter. This article provides experimental evidence for 12U youth baseball players that high quality wood or aluminum bats could lead to the analogous outcomes with 1-2% variations when used in a controlled environment.

Methods

A standard pitching machine manufactured by Junior Hack Attack was utilized to set up the velocity of the ball being pitched. The speed radar was purchased from Bushnell with an accuracy of ±1 MPH. The velocity gun was calibrated utilizing the set speed of the pitching machine and reading of the radar to a 1 MPH accuracy. The aluminum bat with a length of 31 inches, 23 ounces, and a barrel size of 2 ¾ inches was purchased from Marucci. The maple wood bat with a length of 31 inches, and a barrel size of 2 ¾ inches was purchased from Victus Nox (The brand Marucci owns Victus Nox). A bucket of standard baseballs was purchased from Wilson. A standard batting tee manufactured by Tanners Tees was utilized for the tee tests. An indoor baseball and softball facility (Lifelong Sports, Lafayette, Indiana, USA) was used for these experiments. Figure 1 depicts all used baseball accessories.

Two different velocities of =30 and 40 MPH were set by adjusting left, bottom, and right knobs of a standard pitching machine (Figure 1). The balls were loaded into the pitching machine by a person with approximately 15 second intervals between the pitched balls. The batter wore the requisite safety equipment (helmet, arm guard, leg guard, and batting gloves) while hitting the balls as they were pitched. The speed radar was set up approximately 4 feet behind the batter and the exit velocity was measured after the bat had contacted the ball. Ten balls were set on the batting tee (one at a time) and hit within 15 second intervals. The handheld speed gun was used behind the batter and pointed at approximately where the ball would be headed. Three trial runs were carried out before the final experiment to find errors in the experiment and to correct them. After hitting ten balls with the aluminum bat, the wood bat was used to hit the next ten balls to minimize the error, assuming that the batter’s strength is similar between tests conducted sequentially. Within each set of experimental conditions, the exit velocity of the balls was categorized and reported as the highest (Hi), lowest (Low) and average (Avg) speeds. In some cases, the aluminum bat’s sound frequencies affected the speed gun measurements. These experiments and speed measurements were repeated. Newly purchased baseball balls were used for the measurements to minimize the error. Please note some of the concerns in wood versus aluminum bats are i) the wood bat breaking could happen due to the ball hitting around the handle area or the end, ii) the wood bat could hurt players’ hands due to high impact speeds and vibrations, and iii) the aluminum bat cracking could occur as the metal shrinks in the cold with unsafe storage.

Results

Typically, commercial baseball pitching machines are arm type (stores balls on sides in an arm shape, which automatically dispenses balls) or roller type (person must manually put balls into the machine). Both machines can dispense different pitches (8) such as fastball, curveball, screwball, slider, etc. To carry out the experiments in a controlled air, moisture, and temperature environment for better accuracy, we used roller type dispenser at LifeLong Sports, Lafayette, Indiana, USA.

Fig. 2 depicts the exit velocity data from 10 balls that were hit off of the tee with maple wood and aluminum bats. The highest exit velocity for the balls that were hit by the wood bat ranges from 57 to 62 MPH, while more consistent 61 MPH for the aluminum bat. The lower velocity and average exit velocity data demonstrate that the effect of using either wood or aluminum bat is negligible when the balls were hit off the tee.

In Fig. 3, 10 balls were pitched at 30 MPH and the exit velocity data was collected for maple wood and aluminum bats. The highest exit velocity for the balls that were hit by aluminum bat ranges from 61 to 63 MPH, while being 55 to 61 MPH for the wood bat. The lowest exit velocity for the balls that were hit by wood bat ranges from 40 to 43 MPH, while 50 to 51 MPH for the aluminum bat. Overall, 2-3% superior performance of the metal bat was observed due to hollow vibrating wall of the bat (similar to a drum upon impact), producing a loud ping sound (9).The exit velocity of the balls was almost double the velocity of balls impacting to the bat. In fact, the wall bends slightly in an inner direction retaining some of the vibrational energy and then coils back after impacting on the bat. The low frequency ping sound (1,000 Hz) indicates softer, thinner wall thickness of metal bat while high frequency (2,000 Hz) ping sound indicates bat wall is thicker, hence stiffer (9). The trampoline effect on the metal bat helps gain a little more speed compared to the wooden bat (9).

At high pitch speeds of the incoming balls (40 MPH), the obtained data show a slightly different trend, as seen in Fig. 4. The highest exit velocity for the balls that were hit by the aluminum bat ranges from 51 to 53 MPH, while being 57 to 58 MPH for the wood bat. The low exit velocity for the balls that were hit by the aluminum bat ranges from 40-41 MPH, while being 41-45 MPH for the wood bat. Namely, the wood bat showed a slightly superior exit velocity compared to the metal bat. This could be due to high impact speeds with less than 1 ms impact duration with the minimum energy percolation in the bat (9). As baseballs from the same batch were used for both the 30 MPH and 40 MPH pitch tests, these differences can be attributed to differences in the bat material rather than the baseballs themselves. In these conditions, a solid wood bat could perform better than the thin-walled metal bat because of minimized trampoline effect. The wood bat does not ping as loud as metal meaning that it imparts most of the stored elastic energy to the ball with less energy left in the wall of the bat to vibrate (9). Other possible reasons the wood bat was better with enhanced exit velocity are hitting with the harder grain or the shape of the balls (possibly deformed on the harder wood bat), and differences in manufacturing of the bats. These reasons also support why the wood bat performed superior in the 40 MPH test. When 10 balls were hit on both bats with 30 MPH and 40 MPH pitches, the measured exit velocity ranged from 40-63 MPH at low, medium and highest velocities confirming that most of the stored energy is returned to the ball without significant dissipation.

Discussion

The trampoline effect describes noticeable elasticity in objects impacting at high speeds with applicability to sports such as baseball (the ball and bat), golf (the ball and club), and tennis (the ball and racquet) such that they act like a spring analogous to when we jump on the trampoline and get bounced back. In baseball, the elasticity of a bat upon the impact of baseball is different for wood and aluminum bats. Typically, when the baseball hits a wood bat, the ball compresses losing more than half of its energy, but when using a hollow aluminum bat, the bat compresses rather than the ball.

The fundamental physics understanding of the trampoline effect in baseball and softball bats was documented by Nathan et al. two decades ago (10) who identified that upon the high-speed impact between a bat and baseball, the original center-of-mass kinetic energy is transformed into compressional energy. Certain energy is stowed in vibrational modes (hoop modes), providing this stored energy to the baseball with minimum dissipation of energy with larger ball exit velocity due to the trampoline effect (10). In other words, the elasticity of a bat upon the impact of baseball determines the magnitude of the resultant trampoline effect (Fig. 5). Typically, when the ball impacts on the aluminum bat, because of its hollow nature the bat barrel compresses to lose energy and returns it to the ball soon after. On the wood bat, the ball compresses and loses up to 75% of energy in frictional forces (10). Typically, during the bat-ball collision, the exit velocity of the ball would be dependent on the effective mass/weight of the bat. However, this is a negligible effect in the experiments reported in this work as both bats possess similar masses. The exit velocity is at its peak at the place on the bat where maximum power was applied on the surface of ball, storing more elastic energy, and subsequently imparting it back to the ball (9).

Conclusions

Controlling for the barrel size, length, and weight of the bat, it is experimentally measured and observed that aluminum bat is 2-3% superior when balls were hit off of the tee and against 30 MPH machine-pitched balls because of the trampoline effect. Remarkably, for the 40 MPH automatic pitching machine test, the wood bat was 3-4 % superior to the aluminum bat possibly due to high impact speeds with less than 1 ms impact duration with the minimum energy losses in the wood bat or even the strength of the young batter. Even though both bats had similar speeds, exit velocity measurements were measurably different. Therefore, it can be concluded that high quality wood and aluminum bats could lead to analogous outcomes when used in a controlled environment.

Application in Sport

The outstanding performance of a baseball player can be highly dependent on the selection of a metal or wood baseball bat, its balance, durability and feel in addition to the player’s capabilities. In general, metal bats are known to provide enhanced power, durability, and a broader sweet spot while wood bats provide a traditional feel, tailoring options, and a smaller sweet spot. This article offers insight into the rationale behind selecting a bat with peace of mind for the player, parent, and coach corroborating that high quality (hence expensive) wood or aluminum bats could lead to analogous outcomes with 1-2% variations when used in a controlled environment. Eventually, use of a metal or wood baseball bat is a personal choice, guided by player strength and abilities.

References

Patrick Drane, Joshua Fortin-Smith, James Sherwood, and David Kretschmann, Predict the relationship between wood baseball bat profile and durability, Procedia Engineering, 2016, 147, 425–430.
Alan M. Nathan, J. J. Crisco, R. M. Greenwald, D. A. Russell, Lloyd V. Smith, A Comparative study of baseball bat performance, Sports Engineering, 2011, 13, 153-162.
Daniel A. Russell, Acoustics and vibration of baseball and softball bats, Acoustics Today, 2017, 13(4), 35.
Mahesh M Shenoy, Lloyd V Smith, John T Axtell, Performance assessment of wood, metal and composite baseball bats, Structures, 2001, 397-404.
Blake Campshure, Patrick Drane and James A. Sherwood, An investigation of wood baseball bat durability as a function of bat profile and slope of grain using finite element modeling and statistical analysis, Appl. Sci. 2022, 12, 3494.
P. J. Drane & J.A. Sherwood, The effects of moisture content and work hardening on baseball bat performance, Materials Science, 2003, 1-7 (Corpus ID: 44456022).
J. J. Crisco, R. M. Greenwald, J. D. Blume, and L. H. Penna, Batting performance of wood and metal baseball bats. Med. Sci. Sports Exerc., 2002, 34, 10, 1675–1684.
Nippon Kikai Gakkai Ronbunshu, C Hen, Study on throw accuracy for baseball pitching machine with roller (Study of Seam of Ball and Roller), Transactions of the Japan Society of Mechanical Engineers, Part C, November 2007, 73(735):2962-2967.
R. Cross, Physics of Baseball & Softball, Springer Science Business Media, LLC 2011, Chapter 13, 221- 234.
Nathan, D. A. Russell, L. V. Smith, The physics of the trampoline effect in baseball and softball bats, Physics, 2004, Corpus ID: 6993139.

George Minoso2024-09-26T07:03:33-05:00September 28th, 2024|Sport Training, Sports Studies|Comments Off

The Real Cause of Losing Sports Officials

Authors: Matthew J Williams D.S.M., M.B.A. M.S.

Department of Education, The University of Virginia’s College at Wise, Wise, VA, USA

Corresponding Author:

Dr. Matthew Williams
The University of Virginia’s College at Wise
2001 Greenbriar Drive
Bristol, VA 24202

Matthew J. Williams D.S.M., M.B.A., M.S., is an Associate Professor of Sport Management at The University of Virginia’s College at Wise. His areas of research interest include NASCAR, COVID-19, college athletics, professional sports, and sport management issues..

The Real Cause of Losing Sports Officials

ABSTRACT

Purpose

Recreational Sports, Junior Highschool Sports, and Highschool Sports are witnessing across all types of sports a decline in sports officials. Athletic directors in all three levels have seen a steadily declined in sports officials in the last twenty years. But since the COVID-19 Pandemic, the lack of sports officials has increased so rapidly that it could eventually become a nationwide crisis. The pandemic may have caused the decline of sports officials but it was not the only cause. The age of the sports officials has played a role in the decline of the sport’s officials. But the true main cause of losing sports officials has been the lack of respect for the sport’s officials through the behavior of players, coaches, family members, and sports fans.

Keywords Sports Officials, Players, Coaches, Fans, COVID-19 Pandemic, Respect.

Introduction

Recreational Sports, Junior High School Sports, and High School Sports are all witnessing a lack of sports officials all across the United States. There are so many theories out there on why we are losing sports officials so rapidly. If you have attended a sporting event lately and looked at the sports officials, a constant trend you will witness is the sports officials’ increasing ages and the lack of sports officials that are able to cover the sporting events. The repercussions of the lack of sports officials are already being felt. What is the true reason we are losing sports officials? Did COVID-19 Pandemic play a role in the loss of sports officials, the current age of sports officials, or the constant verbal abuse or threats to sports officials?

Discussion

Even before the 2020 COVID-19 Pandemic Virus, it was apparent to recreational athletic directors, and athletic directors at both junior high and high school that they were already seeing a steady decline in sports officials across the United States over the past decade. The scarcity of officials is a long-running problem in high school sports. (6) From the 2018-19 school year to 2021-22, 32 of 38 states reporting statistics have seen registration numbers of officials drop, according to the National Federation of State High School Associations data. (1) Over the last decade, there has been a steady decline in the amount of referees available. In 2018, the Michigan High School Athletic Association reported that amount of referees available dropped from 12,400 to around 10,000 over the previous decade. (11)

The start of the COVID-19 Pandemic in the spring of 2020 forced a majority of recreational sports, junior high and high school sports across the United States to cease operations and shut down all games until further notice. This action of shutting down all games caused some officials to walk away from officiating. Simply because there were no games for the sports officials to work. As a result of the shutdown, officials had a chance to evaluate if they wanted to return to officiating. So many sports officials did not return to officiate games because of numerous reasons in the fall of 2020 or the spring of 2021. The Alabama High School Athletic Association is working hard to recruit and retain officials in all sports after losing more than 1,000 after the COVID-19 shutdown in the spring of 2020. (2) Washington said the association lost more than 1,100 officials after the COVID-19 shutdown. (2)

In the fall of 2020 and spring of 2021, some of the COVID-19 Pandemic restrictions were lifted and sports returned to somewhat normalcy. However, some officials decided not to return to officiating simply because of their age. There is a concern by some the impact of COVID-19 might hasten the retirement of older officials. (8)

The average age of the sports official was between 45 and 60 and it played a major role in the sports officials’ decision either to continue to be sports officials or not to be a sports official. Officials tend to be near or beyond retirement age the median age for a football referee is 56, according to the National Association of Sports Officials survey. (6) 77% of current officials are over the age of 45, with slightly more than half over the age of 55. (12)

The average age of the sports officials was at least 45 or older during the COVID-19 Pandemic. The COVID-19 Pandemic forced some older sports officials to choose not to return to officiating because simply of the underlying healthcare issues from the COVID-19 Pandemic. Some officials chose not to work during the pandemic because of health/safety concerns, and some of them chose not to return at all. (17) “In talking to some of the state directors, many of these losses are people who were probably on the brink of retirement, and then COVID kind of forced the issue,” explains Dana Pappas, NFHS director of officiating services. (15) The pandemic has also pushed a growing number of referees out, with officials leaving out of fear of getting sick. (16)

During the fall of 2021, some governors across the United States mandated that state employees must be fully vaccinated to prevent and/or limit the spread of the COVID-19 virus. This mandate forced many officials to choose whether to get the COVID-19 vaccination or not get the COVID-19 vaccination. If the sport’s official chose not to take the COVID-19 vaccination due to fears of the side effects of the COVID-19 vaccination or for religious beliefs, they would be banned from officiating junior high school and/or high school games. This mandate forced many officials to stop officiating resulting in a smaller pool of available officials to officiate games. “We already have a shortage of officials, not just in football but other sports,” Weber said”. “That (vaccine requirement) will reduce our numbers, based on what we’re hearing from our officials.” (3) The COVID-19 Pandemic resulted in some officials deciding not to return to officiating, creating an already smaller pool of available officials to officiate games. COVID-19 accelerated the problem, without question. (9)

Today’s parents are more invested financially than ever in their children’s sports careers. Parents are financially supporting their children’s sports careers through travel teams, summer leagues, specialized camps, personal training, and individual lessons. In the hopes that their child will either be drafted into professional sports or earn a college scholarship. Parents being so financially invested has caused an explosion of verbal abuse or threats toward officials from parents. Parents want the best outcomes for their children and are not afraid to voice their opinion to officials either by verbal abuse or threatening officials. Barrett theorized that the rise of travel teams in baseball —not to mention AAU teams in basketball and specialized camps for young football players — has caused parents to feel much more invested in their kids’ athletic careers, both financially and emotionally. (9) The parents feel more emboldened now than ever and are not afraid to voice their opinion verbally toward officials due to the fact they are so financially invested in their children’s sports careers. The parents feel strongly that they deserve the best officials to call the games because they have invested so much financially. “Parents have this sense of entitlement,” Barrett said. “They’re paying so much money, they think they should have better umpires.” (9) “These parents have this mentality of. ‘We pay all this money and travel all this way we expect the best, and referees can’t make mistakes.’ It’s based on society saying it’s okay to yell at people in public if they’re not giving you what they want. It’s asinine.” (13) “The problem is that, as parents spend more time and money on children’s sports, families are “coming to these sporting events with professional-level expectations,” said Jerry Reynolds, a professor of social work at Ball State University who studies the dynamics of youth sports and parent behavior. (7)

Aggressive behavior of abuse toward officials from coaches, players, parents, and fans started well before the COVID-19 Pandemic of 2020. “Before COVID, I felt like this behavior was reaching its peak,” Barlow said. (13) The aggressive behavior toward officials did not stop after the COVID-19 Pandemic was over. But some feel that the abuse of officials has increased resulting in the loss of more officials. Society of today has now become a custom of unruly behavior toward officials, players, and fans. The old saying, I paid my general admission ticket, gives me the right to berate an official, an opposing player, or a coach. This mentality has allowed more aggressiveness toward officials. Parents, coaches, and fans are increasingly aggressive toward officials. (4) People have had seemingly free license to scream, taunt and hurl insults at sporting events — acting out in ways they never would at work, the grocery store, or the dentists office. (14)

Officials have had enough of this type of abusive behavior, which is a major reason why we are losing officials so quickly. No official wants to be verbally abused, harassed, or threatened. Such unruly behavior is the driving force, referees say, behind a nationwide shortage of youth sports officials. (7) We have had the problem of losing officials because of the lack of respect toward officials from parents, family members, and fans well before the COVID-19 Pandemic. The shortfall has persisted for years, as rowdy parents, coaches, and players have created a toxic environment that has driven referees away and hampered the recruitment of new ones, referees say. (7)

The coaches, athletes, parents, family members, and fans of today no longer value or demand sportsmanlike behavior. We now accept unsportsmanlike behavior. Which consists of disrespect or lack of respect for officials through verbal abuse, threats, or harassment. Because we are accepting and allowing this type of behavior from coaches, athletes, parents family members, and fans. This is one of the main reasons why we are losing so many sports officials. “The un-sportsman like conduct of coaches, as well as some parents put people off and they don’t want to come back, they don’t want to return. They get yelled at during their days at work,” added Gittelson. (5) The shortage of officials in high school – and middle school – sports has been a growing concern for several years – in large part due to unsportsmanlike behavior by parents and other adult fans. (10)

Conclusions

The lack of sports officials is becoming a critical situation that recreational athletic directors, junior high school, and high school athletic directors will be facing in the coming years. Some sports officials are deciding to retire because of their age or knowing that their bodies can no longer keep pace with the speed of the game that they are officiating. This is creating a smaller pool of officials from the standpoint that the average age of the sport’s official is at least 45.

The COVID-19 pandemic did play somewhat of a role in reducing of sports officials that we are in right now. The pandemic brought health scares and mandatory COVID-19 vaccinations to some sports officials resulting in these officials making the decision to not return to officiating. But the real cause of the shortage of sports officials is simply the respect that is not given to the sports official by coaches, parents, family members, and fans. The behavior from coaches, parents, family members, and fans of yelling at sports officials, questioning sports officials’ calls, threats of violence towards sports officials, cursing at sports events, and even battery towards sports officials is out of control. No sports official wants to deal with this type of behavior at all nor should this type of behavior be allowed. This is the main reason why we are seeing the pool of sports officials becoming smaller. State legislation, superintendents of schools, principals of schools, and county commissioners need to address this issue of out-of-control behavior toward sports officials. If they do not, we will witness games being canceled, cancellation of seasons, and drastic pay increases that will be demanded by sports officials for the abuse.

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George Minoso2024-02-15T12:01:06-06:00February 16th, 2024|Contemporary Sports Issues, General, Sports Coaching, Sports Management, Sports Studies|Comments Off

Strikes, Pins, Gutter Balls, and…Maps: A Review of the Spatial Geography of NCAA Women’s Bowling

Authors: David F. Zinn

College of Business, Lander University, Greenwood, South Carolina, USA

Corresponding Author:

David F. Zinn
Assistant Professor of Sport Management
Lander University
College of Business
Carnell Learning Center, M54
320 Stanley Ave.
Greenwood, SC 29649
(864) 388-8220
[email protected]

David F. Zinn, EdD, currently serves as an Assistant Professor of Sport Management and the NCAA Faculty Athletic Representative at Lander University. A former NCAA Women’s Basketball Coach and Athletic Director, Zinn’s major research interests include global sport, sport geography, sport leadership, and intercollegiate sport.

Strikes, Pins, Gutter Balls, and…Maps: A Review of the Spatial Geography of NCAA Women’s Bowling

ABSTRACT

Purpose

Spatial geography is important to the understanding of any human activity as this field helps to determine where and why specific activities occur and flourish. As proximity to campus and access to sport opportunity are important determinants in college choice, the spatial relationship between campuses and hometowns are important components in the marketing of programs to potential recruits. The intent of this study is to examine the geography of Women’s Bowling, a relatively unstudied and newer NCAA championship sport, in terms of the locations of institutions sponsoring the sport and the relationship with hometowns of student-athletes on current rosters.

Methods

Rosters for women’s bowlers participating in the 2023 season were downloaded from team athletic websites and distances from reported hometowns and campuses were calculated via Google Maps to provide an approximate distance from a student-athlete’s home to the institution for whom they compete. Distances to hometowns were averaged per team and by NCAA division to determine relative distance to campus and states where bowling recruits tended to originate.

Results

Data from the 2023 season indicated that the sport of Women’s Bowling is highly geographical in nature. While bowlers were willing to attend an institution further away from their hometown at the Division I level as compared to Division II and III institutions, most bowlers tend to commit to programs relatively close to their hometowns. Additionally, data suggests that large percentages of these athletes are from areas located in a relatively small section of the USA.

Conclusions

Spatial geography plays an impactful role in both the sponsoring of women’s bowling and in the recruitment of student-athletes into these programs. Data suggests that, with a few exceptions, the further a school is located from the Great Lakes area, the fewer collegiate programs and the fewer potential student-athletes exist. Additionally, participants in the lower levels of NCAA competition tend to commit to schools much closer to their listed hometown than those who play on an NCAA I team.

Applications in Sport

The findings of this study may prove beneficial to administrators considering adding Women’s Bowling to their offerings and to coaches who are looking for prime recruiting areas to develop their teams. Also, as most of these teams are located at smaller colleges and universities, this data may prove beneficial in considering how limited resources might be best allocated.

Keywords: Bowling, Distance, Geography, Location, Spatial

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Vandy Pacetti-Donelson2024-01-25T11:18:15-06:00January 26th, 2024|Sports Management, Sports Studies|Comments Off