Examining Work-Family Conflict and Family-Work Conflict among Collegiate Coaches at the NCAA Division III Level 

Authors: Rachel Berkowsky1, MS, Stephanie Singe1, PhD

Corresponding Author:

Rachel Berkowsky, University of Connecticut Department of Kinesiology, Gampel Pavilion

2095 Hillside Rd U-1110, Storrs, CT 06269

Email: [email protected], Tel: (860) 486-1121


1University of Connecticut Department of Kinesiology, Storrs, CT

Examining Work-Family Conflict and Family-Work Conflict among Collegiate Coaches at the NCAA Division III Level

ABSTRACT

Athletic coaching within the National Collegiate Athletic Association (NCAA) setting is known to be a stressful profession. Time commitments for coaches can extend beyond normal hours, limiting time for coaches to be at home supporting family and household chores. This imbalance between work and home can lead to increased stress and create role conflict. Work-family conflict (WFC) and family-work conflict (FWC), the result of the imbalance, can impact overall satisfaction among work and family domains. Purpose: Investigate WFC and FWC among NCAA Division III full-time collegiate coaches by using the validated 10-item WFC scale. Methods: This cross-sectional survey study examined 746 responses using the WFC scale (α=0.911) and used descriptive statistics and Mann Whitney U tests to identify differences between gender, marital status, parental status, and years of experience. Results: Coaches were middle-aged (41±12 years) with an average of 16±11 years of experience. Majority of the sample were men (61.5%), married (61.1%), and just over half (52.8%) had children. Married coaches reported significantly higher levels of WFC (U=56837.0, p=0.001) and FWC (U=54737.5, p<0.001) compared to unmarried coaches. Coaches with children reported significantly higher levels of WFC (U=61080.5, p=0.007) and FWC (U=51543.5, p<0.001) compared to their counterparts without children. Coaches with less than three years of experience reported significantly lower levels of WFC (U=13220.5, p=0.027) compared to those with more than three years of experience. Conclusions: Gender alone may not be a strong predictor of WFC and FWC in coaching. Marriage adds to the complexity of balancing coaching demands, and parenting responsibilities are a major source of conflict. As coaches gain experience, their responsibilities and expectations grow, increasing conflict. Application in Sport: WFC appears to be influenced by life circumstances (i.e., marriage, children) more than gender. Sport organizations may want to have targeted support, especially for those coaches with families, and who are in the mid-to-late career stages. Coaches may need to be proactive in their planning but also take advantage of organizational policies that could help them manage coaching and family responsibilities.

Key Words: work-life balance, family strain, job issues

INTRODUCTION

Work-family conflict (WFC) and family-work conflict (FWC) are conflicts that occur because work and family responsibilities are not compatible with one another and can cause stress in the opposite domain (1, 2). These two conflicts have been studied extensively in the athletic training population (3-9), however there is a gap in the literature for studying it within the collegiate coaching population, and in particular, at the National Collegiate Athletic Association (NCAA) Division III level. To the best of our knowledge, there is only one older study that evaluated WFC and FWC among a random sample of collegiate assistant coaches for four women’s team sports across the three NCAA divisions (10). Most research within the realm of WFC and FWC has been done at the NCAA Division I level among head coaches (11-13). The Division I level is often described as non-stop with long working hours and constant travel (12, 13), and more recently now has the added pressures of the transfer portal and Name, Image and Likeness (NIL) deals.

Division III is the largest of the three NCAA divisions, with 429 member schools and over 200,000 athletes (14). Most of the head and assistant coaches at this level are men, as they represent 74% (n=6,183) of the total number of head coaches and 69% (n=12,875) of the total number of assistant coaches (15). Because one of the philosophies of Division III is to help student-athletes focus on their academics and earn a four-year degree (14), rather than having a greater focus on athletic and sport performance, there may be unique stressors that Division III coaches experience and should be explored further.

The Division III level does not offer athletic scholarships and the time commitment for both coaches and athletes varies drastically between Division I and Division III levels. Additionally, Division III only receives about 3% of all NCAA revenue annually (16), indicating that the athletic budgets coaches utilize at this level are much smaller compared to Division I. Another unique stressor that Division III coaches face include fewer support staff or coaches that are only contracted part-time and work another job on top of being a collegiate coach. This would plausibly lead to greater workloads, as coaches would have to take on more administrative tasks. For example, many Division I programs have an academic advisor assigned to work with specific sports teams, and because Division III programs operate on a smaller budget, the coaching staff may be the ones providing academic counseling support for their athletes (15). There are over 2,200 athletic academic advisors at the Division I level and only 282 at the Division III level (15). These unique stressors that Division III coaches encounter could contribute to the level of WFC and FWC they experience.

WFC and FWC

Work-family conflict and FWC are two distinct forms of conflict but are interrelated to one another, implying that contribution to the work (or family) role is made more difficult and challenging by participation in the family (or work) role (1). The main components of these two conflicts include the general demands, the time devoted, and the strain produced by a given role (17). The demands of a role involve the necessary tasks, responsibilities, and expectations that are linked to that role. Time-based conflict stems from when the time spent on work (or family) hinders the ability to execute responsibilities at home (or at work). Lastly, strain-based conflict arises when strain and stress from one domain (work or family) negatively impacts the other domain (17). It has been previously stated that most individuals self-report their family is more important than their job, implying that WFC levels would be greater than FWC levels (18-20).

NCAA Coaches and Mental Health

The NCAA recently completed a survey among over 6,000 coaches at all three Divisions to get a better understanding of how coaches support their own mental health (21). About one-third of coaches that participated in the survey cited feeling overwhelmed and mentally exhausted on most days of the week. Some contributing factors to these feelings include concerns about their athletic department budgets and managing personal situations like challenges with childcare (21). If coaches are feeling stressed, overwhelmed, and mentally exhausted on the job, they could bring these emotions home with them to their families, causing conflict, leading to FWC. Moreover, if coaches are experiencing conflict with their families at home, this could affect how they interact with their coaching staff and athletes, leading to WFC.

Previous Research on WFC and FWC in the Sport Setting

It has been shown that head coaches at the Division I level report experiencing moderate to high levels of WFC and FWC across all stages of their career regardless of gender (13). Furthermore, WFC and FWC were affected by parental status and having children in the home, as coaches with children self-reported higher levels of conflict (13). In an older study completed across the three NCAA divisions, it was found that men and women assistant coaches for four women’s team sports experienced low to moderate levels of WFC and FWC (10). Among collegiate athletic trainers, men have experienced higher levels of WFC than women, and those who were married or had children reported more WFC than those who were not married or did not have children (6). As stated previously, there is a gap in the literature for studying WFC and FWC in collegiate coaches at the Division III level.

Purpose and Hypotheses

Depending on the stage of career that the coach is currently in, they may have families or be in long-term relationships, which could add to the complexity of conflict they experience both at home and on the job. Gender may also play a role in the amount of conflict that occurs depending on the responsibilities they encounter at home. To the best of our knowledge, there is no study that has evaluated WFC and FWC among NCAA Division III coaches. Therefore, the purpose of the current research study was to investigate the WFC and FWC experiences among full-time Division III collegiate coaches. We hypothesized the following:

1A: Men athletic coaches will have lower levels of WFC compared to their women counterparts.

1B: Men athletic coaches will have lower levels of FWC compared to their women counterparts.

2A: Married coaches will have higher levels of WFC compared to unmarried coaches.

2B: Married coaches will have higher levels of FWC compared to unmarried coaches.

3A: Coaches with children will have higher levels of WFC compared to those without children.

3B: Coaches with children will have higher levels of FWC compared to those without children.

4A: Athletic coaches with less than three years of experience will report lower levels of WFC compared to those with more than three years of experience.

4B: Athletic coaches with less than three years of experience will report lower levels of FWC compared to those with more than three years of experience.

METHODS

Research Design

An online cross-sectional survey (Qualtrics, Provo UT) was used to collect data on WFC and FWC among full-time collegiate coaches in the NCAA Division III setting. Prior to data collection, institutional review board approval was obtained. The scale used has been reported as a valid and reliable instrument to collect data on WFC and FWC (17). This WFC scale has been used within sport previously among athletic trainers in the secondary school (4, 5, 8) and collegiate (6, 7, 9) settings, and among head coaches in the NCAA Division I setting (11-13), but not yet with coaches in the NCAA Division III setting.

Respondents

To participate in this study, participants were full-time coaches working in the NCAA Division III level. This excluded any volunteer, part-time, or graduate assistant coaches. A research team accessed the publicly available 429 NCAA Division III athletic programs schools’ websites to create a database of the coaches’ emails. An email with the survey was sent out to all the coaches listed in January 2025. Following the initial invitation, a reminder email was sent three weeks later. From there, data was collected based on how many coaches accessed the email and completed the survey. Prior to completing the survey, participants were given an information sheet about the study which provided their consent by accessing the survey.

Procedures

Quantitative analysis through a cross-sectional survey was utilized. Coaches at the Division III level responded to a survey administered through the Qualtrics platform. The survey was expected to take 15-20 minutes to complete and contained questions that have been previously reviewed by three experts in work-life balance for clarity and content as they relate to the aims of the study. Prior to the survey, participants were informed that they may withdraw from the study at any point. Furthermore, participants were informed that there were no identifying markers to be collected, and the responses were completely anonymous and could not be connected to the participant in any way. There were three screening questions asked at the start of the survey to confirm eligibility, which confirmed that they work full-time in the NCAA Division III setting, the title of the coaching position they hold, and confirmed the level of sport they coach is varsity (rather than junior varsity). If the participants answered “no” or “other” to any of these questions, they were directed to the end of the survey, excluded from the study, and thanked for their time. For those that were eligible, they were able to begin the survey. The survey began with demographic questions asking about age, gender, number of children, marital status, and employment status.  The final part of the survey included a validated scale (17) to measure conflict both in the work and family setting, that has been previously used in studies including the coaching population (13).

Instrumentation

The WFC Scale is a reliable (α = 0.89) 10-item scale (17) that measures various components of conflict, including time, strain, and behavior-based conflict. A 7-point Likert scale was used where 1 = strongly disagree, 2 = disagree, 3 = somewhat disagree, 4 = neutral, 5 = somewhat agree, 6 = agree, and 7 = strongly agree. Results of the scale were analyzed as instructed in the validation of the scale and were calculated by summing the Likert scale numbers to give a score ranging from 5 to 35. This scale is bidirectional in nature, where the first five questions are regarding WFC and the last five questions are about FWC. Although true cut-off scores are not available, researchers have suggested scores between 5 to 15 are considered low conflict, 15.1 to 25 is moderate conflict, and 25.1 to 35 would indicate high conflict (22).

Data Analysis

Data from the survey responses were exported from Qualtrics into Excel (Microsoft Corporation). The data was cleaned and filtered through, and responses were excluded if critical responses were incomplete, questions were left unanswered, or the entirety of the scales were incomplete per scale instructions. The responses that remained were analyzed using SPSS (version 30.0; IBM Corporation). Descriptive statistics were performed to calculate means for demographic information. Mann-Whitney U tests examined differences between men and women coaches within the WFC and FWC scales, as well as to compare those who were married and unmarried, with and without children, and who had less than or greater than three years of coaching experience. In all cases p<0.05 was established as the level of statistical significance.

RESULTS

A total of 16,483 emails were sent in January 2025. There were 1,279 subjects that began the survey (7.7% response rate), and 1,228 surveys were completed (96% completion rate). After reviewing the responses and removing those that were not eligible or did not complete the entirety of the scales per scale instructions, 746 responses remained and were analyzed further. The Cronbach α was calculated for the WFC to determine the internal consistency within our population and yielded a value of 0.911.

Participant Demographics

On average, the participants’ age was 41 ± 12 years, and at the time of completing the survey, had 16 ± 11 years of experience coaching and were working an average of 50 ± 15 hours per week. Of the 746 coaches, there were 61.5% men (n=457), 38.1% women (n=283), and 0.4% (n=3) other. Of note, three coaches chose not to report their gender and left the survey question blank. Participant marital status resulted in 61.1% married (n=455), 26.3% single (n=196), 6.3% cohabitating (n=47), 2.4% divorced (n=18), and the final 4.0% (n=29) were comprised of those who are separated, engaged, in a committed relationship but non-cohabitating, widowed, or newly dating. There was one participant who left the martial status question blank. A little over half of the coaches had children (n=393, 52.8%; n=351, 47.2% no children). Of note, two coaches chose not to report whether they have or do not have children and left that question blank.

WFC and FWC

Participants reported a mean score of 21.9 ± 7.7 on the WFC scale, indicating a moderate level of WFC. Participants reported a mean score of 15.1 ± 7.2 on the FWC scale, also indicating a moderate level of FWC. Table 1 displays WFC and FWC scale questions and the means and standard deviations for each question.

Gender and WFC

Men coaches (n=457) reported lower levels of WFC compared to women coaches (n=283), which supports hypothesis 1A, however a Mann Whitney U test revealed it was not a significant difference (U=63358.0, p=0.643). Men reported a mean WFC score of 21.7 ± 7.9, while women reported a score of 22.1 ± 7.3. The Cohen’s D was 0.05, which indicates a very small effect size.

Gender and FWC

Men coaches reported higher levels of FWC compared to women coaches, which did not support hypothesis 1B, however a Mann Whitney U test revealed it was not statistically significant (U=62209.0, p=0.384). Men reported a mean FWC score of 15.3 ± 7.3, while women coaches reported a score of 14.8 ± 7.1. Table 2 presents WFC and FWC means and standard deviation results by men, women, and overall. The value of Cohen’s D was 0.06, which is a very small effect size.

Marital Status and WFC

Married coaches (n=455) reported significantly higher levels of WFC (p=0.001) compared to unmarried coaches (n=290), which supports hypothesis 2A. Married coaches reported a mean WFC score of 22.5 ± 7.7, while unmarried coaches reported a score of 20.9 ± 7.6. A Mann Whitney U test revealed this was statistically significant (U=56837.0, p=0.001). The Cohen’s D was calculated to be 0.209, which is a small effect size.

Marital Status and FWC

Married coaches also reported significantly higher levels of FWC (p<0.001) compared to unmarried coaches, which supports hypothesis 2B. Married coaches reported a mean FWC score of 16.0 ± 7.5, while unmarried coaches reported a score of 13.7 ± 6.6, which was a significant difference identified by a Mann Whitney U test (U=54737.5, p<0.001). Table 3 presents WFC and FWC means and standard deviation results by married coaches, unmarried coaches, and overall. The Cohen’s D was reported as 0.321, indicating a small effect size.

Parental Status and WFC

Coaches with children (n=393) reported significantly higher levels of WFC (p=0.007) compared to those without children (n=351), which supports hypothesis 3A. Coaches with children reported a mean WFC score of 22.4 ± 7.7, while coaches without children reported a score of 21.2 ± 7.6, which was a significant difference identified by a Mann Whitney U test (U=61080.5, p=0.007). The Cohen’s D was 0.157, which is a small effect size.

Parental Status and FWC

Coaches with children also reported significantly higher levels of FWC (p<0.001) compared to those without children, which supports hypothesis 3B. Coaches with children reported a mean FWC score of 16.7 ± 7.7, while those without children reported a score of 13.3 ± 6.2. A Mann Whitney U test revealed this was statistically significant (U=51543.5, p<0.001). Table 4 presents WFC and FWC means and standard deviation results by those with children, those without children, and overall. We calculated Cohen’s D to be 0.483, which is a moderate effect size.

Years of Experience and WFC

Coaches with less than three years of experience (n=47) reported significantly lower levels of WFC (p=0.027) compared to coaches with more than three years of experience (n=697), which supports hypothesis 4A. Those with less than three years of coaching experience reported a mean WFC score of 20.0 ± 6.5, while coaches with more than three years of experience reported a score of 22.0 ± 7.8. A Mann Whitney U test revealed this was statistically significant (U=13220.5, p=0.027). The Cohen’s D was 0.259, which is a small effect size.

Years of Experience and FWC

Coaches with less than three years of experience also reported lower levels of FWC compared to coaches with more than three years of experience, which supports hypothesis 4B, however a Mann Whitney U test revealed that it was not statistically significant (U=15049.5, p=0.350). Those with less than three years of coaching experience reported a mean FWC score of 14.0 ± 6.5, while coaches with more than three years of experience reported a score of 15.2 ± 7.3. Table 5 presents WFC and FWC means and standard deviation results by those with less than three years of coaching experience, more than three years of experience, and overall. The Cohen’s D value was 0.165, indicating a small effect size.

Discussion

Coaching is known to be a stressful and demanding profession (10, 23, 24), regardless of the NCAA Division the coach is employed with. The stress and time commitments that coaches endure can lead to conflict both within their profession and their family. In the current literature, more is known about WFC and FWC among Division I coaches and less is known within Division III coaches. Therefore, the purpose of the current study was to investigate the WFC and FWC experiences among full-time NCAA Division III collegiate coaches.

We found women coaches reported slightly higher WFC and lower FWC than men, but the differences were not statistically significant and had very small effect sizes. These results allude to the idea that gender alone may not be a strong predictor of WFC in coaching. Support systems for coaches should be inclusive and flexible, focusing on broad social networks and support (25), rather than gender specific support programs. Our results indicated that married coaches reported significantly higher levels of WFC and FWC compared to unmarried coaches, with small to moderate effect sizes. This implies that marriage adds to the complexity of balancing coaching demands, and organizations should consider family-inclusive policies, such as flexible scheduling or family support programs. We found coaches with children experienced significantly higher levels of WFC and FWC compared to those without children, with small to moderate effect sizes. Some implications that should be taken into consideration from these results are that parenting responsibilities are a major source of conflict; therefore, coaches should consider childcare support, family leave, or reduced travel demands for coaches with children. Collegiate coaches with less than three years of experience reported lower levels of WFC and FWC compared to coaches with more than three years of experience, though only WFC was statistically significant. This implies that as coaches gain experience, their responsibilities and expectations grow, increasing conflict. Mentorship programs and workload management may help retain experienced coaches.

As previously mentioned, WFC and FWC occur when the responsibilities and demands of both work and family are mutually incompatible with each other, making it more difficult to participate in both roles (1). Our findings are consistent with previous research (18-20), that indicated the overall levels of WFC would be higher than FWC levels. Many employees express that their family is more important than their work, which implies that they would report more WFC rather than FWC (20). Guteck et al. found in two separate sample populations that both men and women self-reported higher levels of WFC than FWC, a similar finding to the sample in the present study of collegiate coaches (19).

Work-Family Conflict

Singe et al. investigated WFC in a sample of almost 600 collegiate athletic trainers across all three NCAA divisions and found that those who were married or had children reported higher levels of WFC compared to athletic trainers who were not married or did not have children (6). These findings align with our present study among Division III athletic coaches. Furthermore, Dabbs et al. found in 840 NCAA Division I head coaches that they self-reported moderate levels of WFC, regardless of gender, and the overall conflict level was impacted by the presence of children at home (13), which corroborates the results that we found among NCAA Division III coaches. Pitney and colleagues also found moderate levels of WFC were self-reported among 415 secondary school athletic trainers, regardless of gender, family situation, or number of children (8). Sagas et al. found their sample of 115 collegiate assistant coaches reported low to moderate levels of WFC (10), however it is important to note that a different WFC scale was used than the one we used in the present study.

Family-Work Conflict

The study completed by Dabbs et al. (13) also found their sample of Division I head coaches reported moderate levels of FWC, which is consistent with the sample in the present research study. Also in line with our findings, Dabbs et al. noted the presence of children affected FWC levels and found there was no statistical difference in FWC levels between the men and women coaches in their sample. Contrary to our findings, Eason et al. found their sample of 226 athletic trainers self-reported low levels of FWC (9). One plausible reason for this contrast is the difference in participant demographics. A majority of our sample of athletic coaches were married and had children, whereas a majority of the sample in Eason et al.’s study identified as being single with no children, which would imply less incidence of there being FWC (9). The study completed by Sagas et al. also found the collegiate assistant coaches self-reported low to moderate levels of FWC (10).

Limitations and Future Research

One limitation of this study is the cross-sectional nature of the survey design. This limits the understanding in being able to track longitudinally the WFC and FWC over the course of a season or full academic year. Additionally, the data collected was self-reported through the survey, so there is a potential for the coaches to under or overreport the levels of conflict they are experiencing in their job and family domains. The time of year that the survey was emailed to coaches (January 2025) is another limitation and could have affected the responses and levels of conflict as it was winter break/holiday season. Future research should analyze the levels of conflict over the course of a full season and off-season, to understand how WFC and FWC fluctuates at different time points throughout the year.

CONCLUSIONS

This study presents valuable information into the conflict that NCAA Division III coaches experience within their family and profession. The findings did support hypothesis 1A that men coaches would report lower levels of WFC, although it was not a statistically significant finding. Our findings did not support hypothesis 1B as men reported slightly higher levels of WFC. This suggests that in the coaching population, gender alone may not be a strong enough predictor of WFC and FWC. Our results supported hypotheses 2A and 2B that married coaches would report higher levels of WFC and FWC compared to unmarried coaches, indicating that marriage adds another layer of responsibility with balancing family and work demands. The results also confirmed hypotheses 3A and 3B that coaches with children reported higher levels of WFC and FWC compared to those without children, adding to the notion that parenting can be a significant source of conflict. Lastly, our results supported hypotheses 4A and 4B that coaches with less than three of experience would report lower levels of WFC and FWC, however the differences in FWC reported were not statistically significant. As coaches gain experience throughout their career, conflict can increase as they take on greater responsibilities within their profession. Future research should investigate WFC and FWC in a longitudinal manner among coaches, as our cross-sectional study design limits the ability to track conflict throughout the duration of a full season.

APPLICATION IN SPORT

For collegiate coaches, at the Division III level, our present findings underline the importance of prioritizing family-friendly policies to support coaches and their families. Coaching is known to be a demanding profession, so prioritizing support systems can help improve the well-being both in the coach’s personal and professional lives, as well as the success of their team (8, 25, 26). Married coaches and coaches with children may be at a higher risk for WFC and FWC, so offering coaches with policies directed at family life can help greatly. Furthermore, those with less than three years of coaching experience reported lower levels of WFC. Prioritizing work-life balance training into coaching certification programs or creating mentorship programs may assist in navigating the challenges of working in the NCAA Division III setting. Setting boundaries in the workplace by specifying and limiting when co-coaches and athletes can communicate with the coach can help balance the demands of work and family (27, 28). Leaning on social support networks and recognizing when you as the coach are feeling overwhelmed and need additional assistance can also benefit the overall well-being of the coach (27, 28). A mentorship program could pair a more experienced coach with a younger coach to share work-life balance strategies and create an open line of communication (27). Supervisors should promote a culture of balance and help identify appropriate work-life integration strategies for their coaches.

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2025-07-15T09:23:26-05:00November 25th, 2025|General, Research, Sport Education, Sports Coaching, Sports Studies|Comments Off on Examining Work-Family Conflict and Family-Work Conflict among Collegiate Coaches at the NCAA Division III Level 

The Evolving Role of Technology and Analytics in Coaching: Transforming Practices and Enhancing the Impact on the Profession

Authors: Lawrence W. Judge1, Matt Moore2

1College of Health, Ball State University

2 College of Social Work, University of Kentucky 

 

 

Corresponding Author: 

Dr. Matt Moore

Associate Dean of Academic and Student Affairs

College of Social Work

University of Kentucky

601 Patterson Office Tower

Lexington, KY 40506

[email protected] 

ABSTRACT 

This commentary examines the evolving landscape of coaching, focusing on the transformative integration of artificial intelligence, advanced analytics, and real-time performance tracking. These technologies enhance athlete monitoring, optimize decision-making, and redefine coaching pedagogy. However, the rapid adoption of data-driven methodologies presents challenges, including resistance among experienced coaches and ethical considerations regarding athlete privacy. This commentary explores strategies for effectively integrating coaching tools into coaching while preserving the critical human elements of mentorship and decision-making. As the digital age reshapes sports, embracing innovative technologies is essential for meeting athletes’ complex, evolving needs and achieving performance goals. This integration ensures a balance between innovation and the enduring human elements of coaching, elevating the profession to unprecedented levels of effectiveness and impact.

Keywords: Leadership, Development, Strategy, Mentoring, Performance, Education

Introduction

In the evolution of coaching, technology has transitioned from rudimentary tools to sophisticated systems that have transformed the way athletes are trained and developed (Zhang et al., 2023). Early coaching methodologies heavily relied on basic instruments such as stopwatches, tape measures, and handwritten training logs to assess performance metrics and track progress (Sohail et al., 2022). These tools, while limited, served as the foundation for the integration of technology into coaching practices. Video analysis, introduced in its nascent stages, provided groundbreaking insights into athletes’ movements, enabling coaches to refine techniques with unprecedented precision (Cronin et al., 2019). Similarly, the advent of heart rate monitors and early biomechanical sensors marked the initial shift toward data-driven decision-making in athletic training (Goudsmit et al., 2022).

As technology evolved, so did its application in sports. The introduction of analytics into coaching practices in the 1970s marked a significant turning point (Passmore & Woodward, 2023). One notable example is the Oakland Athletics’ pioneering use of statistical analysis under General Manager Billy Beane, a methodology that revolutionized talent evaluation and team composition in professional baseball (Abisaid & Cassidy, 2017). Popularized as the “Moneyball” approach, this strategy demonstrated the potential of empirical data to transcend traditional methods and optimize performance, sparking a broader analytics revolution across various sports (Gin, 2018). Building on this foundation, modern coaching now incorporates advanced technologies such as wearable devices, artificial intelligence (AI), virtual and augmented reality, and machine learning algorithms to deliver real-time performance analytics, injury prevention insights, and skill acquisition strategies (Catapult, 2023; Müller et al., 2022; Wang et al., 2024).

Despite these advancements, the adoption of technology in coaching presents challenges, particularly among seasoned professionals accustomed to traditional practices (Judge et al., 2024). Resistance to change underscores the importance of balancing innovative tools with the human elements of coaching, including mentorship, trust, and the nuanced understanding of individual athletes’ needs (Passmore & Woodward, 2023). Effective integration of technology requires not only familiarity with innovative tools but also an appreciation of how these tools can complement established coaching principles, rather than supplant them. Additionally, data analytics plays a crucial role in helping athletes evaluate their performance by providing insights into key metrics, enabling personalized training strategies and enhancing decision-making to improve outcomes (Bennett & Szedlak, 2023).

This commentary explores the historical evolution, current applications, and future potential of technology in coaching, offering a comprehensive framework for understanding its transformative role in improving athlete performance and competitive outcomes. By examining how technology integrates with and enhances traditional coaching practices, the work aims to provide actionable insights for leveraging innovation while preserving the foundational principles that define the profession and the commitment to maximizing athlete potential. This dual focus ensures that coaches can navigate the rapidly advancing digital landscape without compromising the interpersonal connections essential to athlete development (Bishop et al., 2023).

Current Roles of Technology in Coaching

The integration of advanced technologies, particularly analytics and AI, has significantly transformed the landscape of sports coaching, enabling precise, evidence-based approaches to athlete development (Catapult, 2023; Zhang et al., 2023). These tools allow coaches to analyze extensive datasets, offering actionable insights for decision-making, personalized training design, and effective athlete monitoring (Baraniuk, 2015; Zhang et al., 2023). Historically, coaching was driven by intuition, anecdotal evidence, and experiential knowledge (Sohail et al., 2022). The advent of AI and advanced analytics has augmented these traditional methods, introducing unparalleled precision and efficiency into coaching practices (Judge et al., 2024). Furthermore, these advances in technology empower athletes to self-reflect on their performance by providing real-time, data-driven insights that foster deeper understanding and targeted improvements (Bennett & Szedlak, 2023).

Modern performance analytics tools provide granular assessments of key metrics, including speed, distance, exertion levels, and tactical patterns (Judge et al., 2021). These insights enable tailored interventions that optimize training regimens and improve competitive tactical strategies that engage coaches and athletes in a collaborative process (Talha & Sohail, 2023). Wearable technologies, such as GPS trackers and heart rate monitors, deliver real-time data on physiological responses and recovery profiles, enhancing injury prevention and facilitating optimal workload management (Catapult, 2023; Müller et al., 2022). Additionally, cloud-based platforms streamline communication between coaching teams and athletes by enabling seamless sharing of playbooks, video analyses, and tactical adjustments (Cronin et al., 2019).

Innovations in skill acquisition and cognitive training have further elevated coaching methodologies. Virtual reality (VR) and augmented reality (AR) create immersive simulations of competitive environments, fostering improved decision-making and technical precision under realistic conditions for both coaches and athletes (Müller et al., 2022). Technologies such as PlaySight© and TrackMan© provide sport-specific feedback on mechanics and strategy, offering coaches and athletes valuable data to refine performance (Bishop et al., 2023; Stanescu, 2018). Emerging innovations, including Catapult’s Vector S7/T7 wearable GPS-tracking systems, deliver detailed insights into athlete movement, speed, and workload, facilitating personalized training and injury prevention strategies (Catapult, 2023). Similarly, Omega’s AI-powered systems analyze historical and real-time performance data, generating comprehensive feedback to enhance race preparation with data related to split times, stride frequency, pacing, and race strategies (Wired, 2023).

These innovative technologies bridge the gap between practice and competition by enabling targeted skill development, data-driven decision-making, and tailored performance optimization (Catapult, 2023; Stanescu, 2018). Data metrics and AI in sport go beyond what a coach can see by providing in-depth, quantifiable insights into an athlete’s biomechanics, performance trends, and recovery patterns, enabling a more comprehensive understanding of strengths and areas for improvement that might otherwise be overlooked (Bishop et al., 2023).

Despite these advancements, it is critical to maintain a balance between technology and traditional coaching practices. Over-reliance on automated systems can undermine essential human elements such as emotional intelligence, trust, and mentorship, which are fundamental to effective coaching (Goudsmit et al., 2022). Coaches must critically assess and integrate tools that align with their methodologies and philosophies while preserving the interpersonal dynamics that underpin athlete development (Judge et al., 2024). By synthesizing advanced technologies with traditional coaching principles, practitioners can create comprehensive training environments that address the physical, cognitive, and emotional dimensions of athletic performance (Passmore & Woodward, 2023).

This section underscores the importance of blending coaching tools with evidence-based practices to maximize their impact while safeguarding the human-centric essence of coaching. Integrating wearable sensors, cognitive training platforms, and collaborative digital tools into coaching workflows ensures an integrated approach that meets the multifaceted demands of modern sports (Catapult, 2023). This integration is essential for meeting modern athletes’ expectations in highly competitive environments. Furthermore, the data-centric revolution is complemented by the potential for greater customization and enhanced feedback mechanisms, which together can pave the way for more effective coaching interventions and superior athletic performance (Zhang et al., 2023) (See Table 1).

Future Roles of Technology in Coaching

Advanced technologies form the backbone of evidence-based coaching strategies, facilitating a personalized approach tailored to each athlete’s physiological and psychological needs (Cronin et al., 2019; Rajasinghe et al., 2022). As such, the integration of experimental technologies not only enhances performance optimization but also reshapes the future role of the coach as a data-driven strategist and mentor. Athletes are also increasingly becoming consumers of data, using detailed performance metrics to engage in self-reflection, identify areas for improvement, and make informed decisions to enhance their training and competitive outcomes (Bishop et al., 2023). Among these innovations, technologies like TrackMan© stand out by offering real-time data on critical metrics, such as release angles, velocity, and distance in track and field events. Such precise measurements empower coaches to refine techniques with unprecedented accuracy (Judge et al., 2021). Similarly, in golf, TrackMan© enhances swing mechanics and ball trajectory analysis, enabling targeted adjustments that optimize performance outcomes (Bishop et al., 2023). Reflexion’s touchscreen lightboards and mixed reality headsets, enhance athletes’ focus, decision-making skills, and mental resilience by strengthening cognitive abilities critical for competitive performance (Reflexion, 2023). PlaySight© empowers tennis players and coaches by providing instant video feedback and detailed data points, such as stroke speed, ball placement, rally length, serve percentages, and location of unforced errors, allowing athletes to analyze their technique, adjust strategies, and track progress with precision (Stanescu, 2018). PlaySight and other advanced software systems save coaches and athletes valuable time by automating video analysis and providing instant feedback, allowing coaches and athletes to focus more on strategy and individualized development rather than manual data collection and review (Judge et al., 2021; Stanescu, 2018).

The National Basketball Association (NBA) initiated the Launchpad program, selecting companies to develop basketball technologies. For instance, SkillCorner utilizes computer vision and machine learning to generate player tracking data from existing video feeds, enabling detailed analysis of player movements and strategies. Similarly, Springbok Analytics employs AI-based technology to transform MRI data into 3D digital twins, quantifying an athlete’s musculature for precision health and performance optimization (NBA, 2023).

Moreover, the NBA has partnered with Sony’s Hawk-Eye Innovations to deploy 3D optical tracking technology, capturing real-time movements of players and the ball in three dimensions with sub-second latency. This system enhances officiating accuracy and provides detailed performance data (Hawk-Eye Innovations, 2023). These technological advancements serve as a bridge to previously elusive performance metrics, enabling granular analysis of biomechanical efficiency, tactical awareness, and psychosocial factors. Such insights not only inform but also revolutionize training regimens, allowing coaches to create hyper-personalized programs tailored to the physiological and psychological profiles of individual athletes (Catapult, 2023).

Beyond sport-specific tools, technology has made significant strides with AI in enhancing athletes’ mental performance. For example, AI-driven applications such asNeuroTrainer andMentalEdge provide personalized cognitive training programs to improve focus, decision-making, and mental resilience, while tracking vital internal metrics such as confidence and concentration (MentalEdge, 2023; NeuroTrainer, 2023). These platforms deliver tailored mental health and performance support, complementing physical preparation with robust psychological strategies (Talha & Sohail, 2023). Monitoring the physical and psychological attributes of athletes provides coaches with a holistic understanding of how best to prepare practice and training opportunities that simulate competitive settings (Passmore & Woodward, 2023).

Similarly, predictive modeling through AI enables coaches to anticipate performance trends and design hyper-personalized training regimens. Tools such as IBM Watson’s Sports Performance Analytics analyze vast datasets to identify patterns, forecast outcomes, and provide initiative-taking adjustments to maximize developmental trajectories (IBM, 2023). Platforms like Megalabs AI further demonstrate the potential of AI in sports training by using advanced algorithms to assess athlete performance and suggest data-driven interventions (Megalabs, 2023). By leveraging historical data and advanced algorithms to forecast future performance trends, injury risks, and game outcomes, coaches and athletes can strategically prepare for competition with greater precision and foresight (Megalabs, 2023).

Balancing Technology with Traditional Coaching Practices

As technology advances, maintaining a balance between its application and the humanistic core of effective coaching is paramount (Judge et al., 2024). While technological tools offer unprecedented data-driven insights into athlete performance, they remain insufficient substitutes for the interpersonal connections, mentorship, and empathy that underpin successful coaching relationships (Carson & Collins, 2016; Driska et al., 2017). These humanistic elements are indispensable in cultivating trust, resilience, and holistic growth in athletes, outcomes that technology alone cannot achieve. The integration of technology must enhance, not replace, the relational dynamics essential to coaching (Rajasinghe et al., 2022). Research underscores that the mentorship and emotional intelligence of coaches are critical in navigating the psychological and emotional challenges faced by athletes, fostering a foundation for long-term development and achievement (Carson & Collins, 2016). Thus, while technology can serve as a powerful adjunct in optimizing training and performance, it must be grounded in and guided by the human-centered principles of the coaching process (Driska et al., 2017). This balance not only ensures effective athlete development but also reinforces the irreplaceable role of coaches as mentors and leaders in the evolving landscape of sports.

Coaches must adopt a strategic approach to technology, utilizing it to complement their expertise rather than overshadowing it. For instance, wearable devices provide critical performance metrics, but their true value lies in a coach’s ability to interpret these data points and translate them into actionable insights (Catapult, 2023; Goudsmit et al., 2022). This equilibrium ensures that the art of coaching, characterized by intuition, adaptability, and emotional intelligence, remains integral to athlete development. Over-reliance on technology risks diluting these people skills, potentially leading to standardized approaches that overlook individual athlete needs (Sperlich et al., 2023). Coaches must critically evaluate the relevance and utility of each technological tool to ensure it aligns with their objectives and enhances the natural flow of training sessions. Coaches must also help athletes make sense of the data in a way that supports their technical, tactical, mental, and physical growth (Judge et al., 2024).

Table 2 illustrates the critical balance between integrating new coaching technologies and preserving traditional practices, emphasizing the importance of maintaining personal connections, leveraging intuitive experience, and fostering holistic athlete development alongside the adoption of innovative tools.

The Role of Relationships in Coaching

At its core, coaching is built on trust, empathy, and mentorship. These human-centric attributes enable coaches to inspire athletes, navigate challenges, and provide a sense of purpose that transcends physical performance (Judge et al., 2024). Unlike technology, which focuses on quantifiable metrics, the human aspects of coaching address intrinsic motivation, emotional intelligence, and adaptive problem-solving (Rajasinghe et al., 2022). Studies have shown that a strong coach-athlete relationship significantly influences athlete satisfaction, engagement, and performance (Passmore & Woodward, 2023). Consequently, even as technology becomes increasingly integrated into coaching, preserving the integrity of these interpersonal dynamics is essential.

Integrating Human-Centered and Data-Driven Approaches

The most effective coaching strategies blend human intuition with technological precision. While data can provide valuable performance insights, its utility is contingent on the coach’s ability to interpret and apply it within the broader context of athlete development. For example, injury prevention algorithms may flag overreaching and or overtraining risks, but the coach’s awareness of an athlete’s mental state and external stressors can provide critical context for tailoring interventions (Goudsmit et al., 2022). By combining the quantitative power of technology with the qualitative insights derived from interpersonal relationships, coaches can address athletes’ holistic needs and support the growth and nurturing of the athlete-coach relationship (Passmore & Woodward, 2023).

Challenges in Balancing Innovation with Tradition and the Road Ahead

Despite its transformative potential, over-reliance on technology can undermine essential coaching principles. Automated systems and analytics platforms, while efficient, risk depersonalizing the coaching experience (Driska et al., 2017). Algorithms often lack the flexibility to accommodate the unique, context-dependent variables that human coaches intuitively recognize (Sperlich et al., 2023). Furthermore, the adoption of technology poses a learning curve for seasoned coaches accustomed to traditional methods, highlighting the need for ongoing education and training in technological applications (Passmore & Woodward, 2023). Addressing these challenges requires fostering a culture of collaboration between coaches, sports scientists, and data analysts, ensuring that technological integration enhances rather than detracts from the human aspects of coaching.

The future of coaching is set to be fundamentally transformed by advancements in technologies such as AI and advanced analytics, which offer unparalleled opportunities to revolutionize strategic planning, optimize athlete performance, and redefine the landscape of sports development. The successful integration of these tools requires maintaining the balance between leveraging technological innovation and preserving the coach’s pivotal role as a mentor, strategist, and leader. Coaches who master the art of blending traditional practices with support from innovative technology will not only thrive but also redefine the coaching profession by offering their athletes a multidimensional support system.

Concurrently, the sports industry is increasingly incorporating technology through the strategic employment of data scientists and analysts within collegiate and professional teams. Roles such as Performance Science Analysts and Data Scientists are becoming essential, as teams leverage these professionals to collect and analyze performance data. This analysis translates complex metrics into actionable insights, informing strategic decisions and personalized training interventions (Indeed, 2023).

The convergence of AI-driven cognitive training tools and the integration of data science technology into coaching methodologies signifies a change in thinking in the sports industry. By leveraging these advancements, coaches can cultivate athletes who are not only physically adept but also possess the cognitive agility required for high-level competition. This integrated approach to athlete development is redefining performance optimization in modern sports.

Applications in Sport

The integration of technology into coaching represents a transformative frontier, providing tools that enhance precision in performance analysis and training methodologies. Yet, the heart of coaching remains deeply rooted in its human elements—empathy, trust, adaptability, and connection. By combining technological advancements with time-honored practices, coaches can create a dynamic, holistic, and sustainable approach to athlete development. This balance not only elevates athletic performance but also ensures that coaching continues to be a profoundly human-centered profession.

The rise of the Sport Scientist as a key position within collegiate and professional teams exemplifies this evolution. Sport Scientists collect and analyze vast amounts of data, ranging from biomechanical efficiency to cognitive performance metrics, translating these insights into actionable strategies for coaches. Their role bridges the gap between data-driven innovation and the human-centric principles of coaching, creating a collaborative environment where technology enhances, rather than replaces, the core values of mentorship and personal connection.While advancements in technology offer unprecedented opportunities to optimize athlete performance, successful coaches understand that these tools are only as effective as the human insight guiding their use. The essence of coaching lies in forming meaningful relationships, delivering individualized motivational strategies, and fostering resilience, qualities that remain inherently human. By integrating traditional coaching expertise with advanced technological tools, coaches can unlock their athletes’ full potential, cultivating a harmonious environment where data and human-centered guidance coalesce to achieve excellence. The future of coaching lies in this symbiotic relationship, ensuring that innovation complements, rather than competes with, the enduring principles of mentorship and connection.

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2025-06-09T14:04:33-05:00November 14th, 2025|Commentary, Research, Sport Education, Sport Training, Sports Coaching, Sports Studies|Comments Off on The Evolving Role of Technology and Analytics in Coaching: Transforming Practices and Enhancing the Impact on the Profession

A Comparison of Perfectionism and Time of Sport Specialization of Division-1 Athletes 

Authors: Jason N. Hughes1, Colby B. Jubenville2, Mitchell T. Woltring3, and Helen J. Gray 

1Department of Business, Accounting and Sport Management, Elizabeth City State University, Elizabeth City, NC, USA 

2Department of Health and Human Performance, Middle Tennessee State University, Murfreesboro, TN, USA 

3Department of Health, Kinesiology, and Sport, University of South Alabama, Mobile, AL, USA 

4Associate Dean of Academic Affairs, North Carolina Agricultural and Technical State University, Greensboro, NC, USA 

Corresponding Author: 

Jason Hughes, Ph.D., M.S.,  

1704 Weeksville Rd.  

Elizabeth City, NC 27909 

[email protected] 

252-335-3488 

Jason N. Hughes, Ph.D., is an Assistant Professor of Sport Management at Elizabeth City State University in Elizabeth City, NC. His research interests include sport specialization, perfectionism, and athletic burnout. 

Colby B. Jubenville, PhD., is a Professor of Sport Management at Middle Tennessee State University. His research interests include student success, leadership, and emotional intelligence in business. 

Mitchell T. Woltring, Ph.D., is an Associate Professor at the University of South Alabama. His research interests include student-athlete success and service learning. 

Helen J. Gray, Ph.D., is the Associate Dean of Academic Affairs at North Carolina Agricultural and Technical State University. Her research interests include sport management, youth sport, and pedagogy in sport, leisure, and tourism.

ABSTRACT 

Sport specialization has become increasingly popular among athletes aiming to gain a competitive edge. Despite its prevalence, there is a notable lack of research exploring the psychological impacts of sport specialization. One area that remains insufficiently studied in relation to sport specialization is perfectionism—a psychological trait known to influence both positive and negative outcomes in sports. The primary purpose of this study was to examine the previously unexplored relationship between the time in which an athlete specializes in sport with perfectionism concerns and strivings. A series of one-way ANOVAs were conducted to investigate the relationship between time of sport specialization based on the Developmental Model of Sport Participation and perfectionistic strivings and concerns.  The results of the analyses showed that there was not a relationship between sport diversification and perfectionism. However, participants did score high on perfectionistic concerns despite adhering to proper diversification, participants showed higher scores in perfectionistic concerns than strivings. This suggests that athletes, parents, and coaches need to be aware that sport diversification may not be a buffer against negative psychological consequences. The results suggest that sport specialization’s psychological repercussions are confined to whether the athlete is concurrently engaged in sport specialization 

Key Words: perfectionistic concerns, perfectionistic strivings, athletes, sport diversification, athletic development 

INTRODUCTION 

Early sport specialization among young athletes has surged, drawing increased scholarly attention. Research suggests that youth athletes are engaging in sport specialization at rates from 17% to as high as 41% (4, 30). In response, researchers have emphasized the need to examine both motives and the consequences of. Sport specialization refers to rigorous, year-round training focused on a single sport to the exclusion of others (21).  Motivations for why athletes choose to specialize include improving specific skills, securing financial reward, and aiming for professional success (37). Ironically, researchers argue that this approach might hinder rather than help these goals. The consensus among experts is that well-rounded athletic development is better achieved through sport diversification, which involves engaging in multiple sports (37).  

Advocates of sport specialization assert it plays a vital role in developing elite-level skills through deliberate practice. They argue that athletes who concentrate on one sport can attain greater proficiency than those who play multiple sports (37). Supporting this claim, one study found that both current and former elite soccer players dedicated more time to deliberate, soccer-specific training than non-elite athletes who were sport-diversified (14). This study suggested that deliberate practice during sport specialization significantly contributed to elite athlete status (14). Moreover, research on elite soccer players suggests that specialization enhances motivation, dedication, and enjoyment, leading to increased focus and commitment to improvement (36). 

Critics of early sport specialization challenge its effectiveness, arguing that intense skill development at a young age may yield ambiguous results. A study on Russian swimmers found no performance advantage for early specializers compared to those who specialized later; in fact, those who specialized later showed greater progress (2). This suggests that early specialization may not be universally beneficial. Instead, it might be more appropriate in certain sports such as women’s gymnastics, diving, women’s basketball, figure skating, and dance, where early peak performance occurs before full body maturation (22). Furthermore, a 2023 meta-analysis found that world-class athletes engaged in multi-sport diversification, started their main sport later, and accumulated less main sport deliberate practice (19). 

The pursuit of athletic scholarships and professional contracts remains a major motivator for sport specialization among young athletes. (24). Yet, the actual probability of attaining such rewards is notably low. Studies show that only 2% of high school athletes received a college scholarship, with an even lower percentage (1.2 % for females and 1.1% for males) obtaining full scholarships. The prospect of reaching professional levels is even less likely. The NCAA reports that only 0.9% – 5.1% of collegiate athletes make the professional ranks, depending on the sport. In high-profile sports like college football and basketball, only 1.34% of athletes advance to play professionally (29). Despite these sobering statistics, many athletes continue to specialize with the hope of achieving collegiate and professional success. 

Another key criticism of sport specialization revolves around the potential harmful and unintended consequences, particularly of physical and psychological health. The most cited concern of sport specialization is the prevalence of injuries. Sport specialization may expose athletes to increased risk of overuse injuries due to the frequency of repetitive motions, higher training volumes, and voluminous competitions (26, 31, 22, 12, 11). While physical injuries are often the focus, there is limited comprehensive epidemiological data on the emotional and psychological impacts of sport specialization (32). Previous research suggests that specialization can contribute to an increase in social isolation, overdependence, athletic burnout, reduced enjoyment, heightened dropout rates, and a decline in motivation (25, 27, 33, 28). 

A compelling psychological construct within the context of sport specialization is perfectionism. Perfectionism is defined as having “a commitment to exceedingly high standards combined with a tendency to critically appraise performance accomplishments” (15, 20). It is conceived as a multidimensional personality disposition construct capturing an individual’s pursuit of flawlessness in achievement and their concerns about failing to meet these high standards (13). Contemporary researchers posit that perfectionism overlaps a wide domain of ranges that fall in line with two higher-order dimensions: perfectionistic concerns and perfectionistic strivings (33). Perfectionistic concerns reflect the extent to which individuals are concerned about failing to achieve the standards that are placed on them by themselves or others, leading them to engage in harsh self-evaluation, which can negatively affect athletic performance (25). Moreover, perfectionistic concerns were positively correlated with burnout, rumination, fear of failure, amotivation, and performance-avoidance (21). The higher order of perfectionistic strivings is linked with self-oriented striving, where one places high goals on oneself intrinsically, and the setting of very high personal performance standards (18).   

Overall, research suggests that athletes who engaged in diversification were more likely to achieve sporting success. One survey of 376 Division-1 intercollegiate athletes revealed that, apart from the sport of swimming, 83% of college athletes reported participating in various sports, and many had different initial sporting experiences from their current sport (26). Diversification offers opportunities to cultivate a more versatile skill set essential for athletic success. Among elite athletes, those who participated in multiple sports during their formative years (ages 0-12) required less specialized training to acquire high-level skills in their chosen sport (1). Experts opine that early diversification, followed by specialization in later adolescence, leads to increased enjoyment, fewer injuries, and prolonged participation (2, 16, 35), which ultimately contributes to overall sport success (2). 

A framework for understanding sport involvement can be found in the Developmental Model of Sport Participation (DMSP). The DMSP is a framework that outlines pathways for youth sport involvement, emphasizing how participation can lead to different outcomes such as lifelong engagement, elite performance, or dropout. It integrates developmental, psychological, and social factors to guide sport programming and coaching practices. By outlining various pathways of sport participation, the DMSP provides insights into how individuals’ involvement in sports can potentially unfold over time. Young athletes enter the model in one of two ways: the sampling pathway or the early specialization pathway. In the early sport specialization pathway, athletes starting from age six to adulthood specialize in one sport characterized by a high deliberate amount of practice, a low deliberate amount of play, and focus on one sport. The other pathway, the sampling pathway, involves a high amount of deliberate play, a low amount of deliberate practice, and involvement in multiple sports in the initial stage (7). 

According to the DMSP, athletes who enter the sampling pathway, there are four main stages of development that align with specific ages and developmental needs. In the first stage, called the “sampling years”, there is an emphasis on deliberate play and sport diversification by participating in the sampling of multiple sports. The goal of the sampling years is that during this stage, youth athletes can either participate in sport sampling, meaning they play multiple sports, or they intensively participate in only one sport. This occurs approximately at the ages of six to twelve years old.  Proceeding this stage, at approximately age thirteen, serious athletes transition into the “specializing years”. The second stage of progression is called the “specializing years”, which happens around adolescence, during the ages of thirteen to fifteen years old, when youth athletes begin to focus on a smaller number of sports. While fun and enjoyment are still crucial features of their participation, sport-specific specialization starts in this phase, characterized by deliberate play, balanced practice, and a reduction in the involvement of other sports. During this stage, youth athletes can take three routes: continue participating in sport as a recreational activity, they can progress to the investment stage or opt to discontinue altogether (7). The final stage, known as the” investment phase”, occurs at 16+ years of age.  This stage is characterized by a high amount of deliberate practice, a low amount of deliberate play, and an increased focus on one sport (7). During this stage, the athlete becomes committed to high-performance goals in a specific sport where strategic, competitive, and skill development are the primary focus (22).  

To date, there has been insufficient research that has investigated the effects that specializing in sport might have on perfectionism. Thus, this study sought to investigate if there was a difference between athletes who specialized early or later in their athletic careers using the DMSP as a framework to construct our study (7, 8, 9). For this study, two research questions are being assessed. Research question I hypothesized that there is a significant difference between the time in which an athlete specialized in a sport during the sampling years (ages 6-11), specializing years (ages 12-14), investment years (ages 15-17), or post-investment years (ages 18+) with perfectionistic concerns. Research question II hypothesized that there is a significant difference between the time in which an athlete specialized in a sport during the sampling years, specializing years, investment years, and post-investment years. A series of one-way ANOVAs were conducted, one for each research question.  

METHODS 

Participants 

A total of 416 student-athletes (156 males, 260 females) from Division-1 colleges and universities participated in this study. Participants ranged in age of 18-25 years (M = 20.24, SD = 1.36), and competed in 15 overall sports. Participants were recruited following approval from the primary researcher’s institutional review board. Recruitment was conducted through an online survey administered via SurveyMonkey.com. Inclusion criteria stipulated that respondents must concurrently compete or be a member of an intercollegiate athletics team at a Division-1 NCAA institution.  Participants were recruited from various Division-1 NCAA schools representing all the Power Five and Group of Five conferences. Data collection from participants took place over a period of years beginning in 2018 and ending in 2024. 

Measures 

Participants completed a demographic questionnaire, a self-perceived sport specialization questionnaire, a questionnaire of subscales of perfectionistic concerns and strivings, and a questionnaire asking when athletes specialized in sports.  

Perfectionism 

Multiple measures were employed to assess the higher-order constructs of perfectionistic striving and perfectionistic concerns, following recommendations from previous studies (33, 34). The foundation for this study was provided by Hewitt and Flett’s Multidimensional Perfectionism Scale (H-MPS) (20) and Gotwals and Dunn’s Sport Multidimensional Perfectionism Scale (Sport-MPS-2) (17). Components from both inventories were amalgamated to form a 7-point Likert scale. The combined measures exhibited strong reliability (α = .892), consistent with previous findings (20, 17). 

Perfectionistic Concerns. To assess perfectionistic concerns accurately, three subscales were employed in the study. Two subscales from the Sport Multidimensional Perfectionism Scale-2 (Sport-MPS-2) (17) were utilized. The first subscale, titled “concerns over mistakes,” comprised eight items and assessed participants’ reactions to failure in competition, such as feeling like a failure as a person. The second subscale, “doubts about actions,” consisted of six items aimed at capturing participants’ uncertainties about the adequacy of their pre-competition practices. Additionally, a segment of Hewitt and Flett’s Multidimensional Perfectionism Scale (H-MPS) (20) was integrated to gauge fear of negative social evaluations. This segment, extracted from the “socially prescribed” perfectionism subscale, encompassed 15 items probing participants’ perceptions of others’ expectations of perfectionism from them, such as “People expect nothing less than perfectionism from me.” 

Perfectionistic Strivings: Perfectionistic strivings encompass self-oriented striving and the establishment of high personal performance standards. To assess this higher-order construct, two subscales were employed from both the Sport Multidimensional Perfectionism Scale (Sport-MPS-2) (17) and the Hewitt & Flett Multidimensional Perfectionism Scale (H-MPS) (20). To measure self-oriented perfectionism, the five-item self-oriented perfectionism subscale from the H-MPS was utilized. This subscale includes items such as “One of my goals is to be perfect in everything I do.” For the assessment of high personal performance standards, the seven-item personal standards subscale from the Sport-MPS-2 was employed. Example items from this subscale include “I hate being less than the best at things in my sport.” (17). Evidence supporting the internal consistency of these subscales has been provided, with reliability coefficients (α) exceeding .74 for both the H-MPS and the Sport-MPS-2 (10, 17) 

Sport Specialization 

In line with established methodologies (4, 22), a self-perceived questionnaire was utilized for this study. The questionnaire consisted of a three-point scale classification method, whereby respondents classified themselves as high, moderate, or low in terms of sport specialization. The questionnaire’s questions included: “Have you quit other sports to focus on one sport?”, “Do you train more than eight months out of the year in one sport?”, and “Do you consider your primary sport more important than others?” Respondents indicated their responses to these questions using a categorical classification system, where “yes” responses were assigned a value of 1 and “no” responses were assigned a value of 0. Based on the cumulative score from these questions, individuals were classified into different levels of specialization: a score of 3 denoted high specialization, a score of 2 indicated moderate specialization, and a score of 0 or 1 signified low specialization. 

Time of Sport Specialization 

To align with the Developmental Model of Sport Specialization, participants were asked three questions aimed at determining when they specialized in their current sport. Specifically, athletes were asked if they engaged in any other sport besides their current primary sport during their sampling years (ages 6-11), specializing years (ages 12-15), investment years (ages 15-17), and post-investment years (ages 18+). 

Data Analysis 

All data were assessed with IBM SPSS Statistics. A series of one-way ANOVAs were employed for this study.  

RESULTS 

Results for Perfectionistic Concerns 

For research question I, the research sought to investigate the hypothesis that there is a significant difference between the time in which an athlete specializes in a sport during elementary/primary school, middle school, high school, or college with perfectionistic concerns. Descriptive results from the participants for perfectionistic concerns and time of sport specialization can be found in Table 1. 

 

A one-way between-subjects ANOVA was conducted to compare the effect of when an athlete specializes in sport on perfectionistic concerns in elementary/primary school, middle school, high school, or college as conditions. There was not a significant effect on perfectionistic concerns for the four specialization time frames [F (3, 413) = .996], p > .05. Therefore, concerning the first research question, it was determined that the timing of specialization in sport did not exhibit any association with perfectionistic concerns among the participants. Regardless of whether athletes specialized during their sampling years, specializing years, investment years, or post-investment years, there was no discernible correlation with perfectionistic concerns, despite the athletes exhibiting high scores on this measure. 

 

Results for Perfectionistic Strivings 

For research question II, the research sought to investigate the hypothesis that there is a significant difference between the time in which an athlete specializes in a sport during sampling years, specializing years, investment years, and post-investment years with perfectionistic strivings. Descriptive results from the participants for perfectionistic strivings and the time of sport specialization can be found in Table 3. 

A one-way between-subjects ANOVA was conducted to compare the effect of when an athlete specializes in sport on perfectionistic strivings in the sampling years, specializing years, investment years, post-investment years. There was not a significant effect on perfectionistic strivings for the four specialization time frames [F (3, 413) = .805], p > .05. As it pertains to research question II, it was found that the time in which the participants specialized in sport was not a significant predictor of perfectionistic strivings. The analysis revealed that regardless of whether participants specialized in their primary sport during sampling years, specializing years, investment years, and post-investment years, there was no observable association with perfectionistic strivings. 

DISCUSSION 

The primary aim of these analyses was to investigate the relationship between the timing of sport specialization and perfectionism. Contrary to our hypotheses, the results indicated that regardless of the stage of sport specialization, there was no significant association observed with either perfectionistic concerns or perfectionistic strivings. Although this was not the primary focus, participants in the study displayed elevated scores on perfectionistic concerns overall. 

One potential explanation for the lack of differentiation between groups, despite athletes scoring high on perfectionistic concerns, could be attributed to the similarity in experiences among athletes. It is hypothesized that athletes may have had comparable sporting experiences, particularly since a significant portion of participants specialized during college (N = 235, ≈ 56%). This similarity in experiences might have led to the development of perfectionistic concerns in a uniform manner across the sample. 

Another potential reason for the absence of variation is due to the smaller number of participants who experienced early specialization in sampling and specialization years (N= 85, ≈ 20%) as compared to the high number of athletes who specialized later in investment and post-investment stages (N= 331, ≈ 80%). Our sample, however, parallels previous studies about when athletes tend to specialize, suggesting that sport diversification might not be a buffer or contributor to psychological constructs, either negative or positive ones. For example, a study found that athletes who engaged in sport diversification had no discernible difference in the measurement of mental toughness (5). It might be that psychological constructs develop over time and have a myriad of factors that contribute to their development, and that sport specialization and diversification play a small role, if any. 

The athletes in our study exhibited elevated levels of perfectionistic concerns but not perfectionistic strivings. According to the Development Model of Sport Participation, the ages of 13-15, yet even athletes who engaged in sport diversification prior to this stage still reported elevated perfectionistic concerns. These findings may contradict arguments that support sport diversification as a safeguard against negative psychological outcomes. However, it is important to consider that the participants in our study were current Division-1 NCAA athletes who were actively specializing in sport and no longer engaged in diversification. This suggests that concurrent sport specialization is more important than the stage of specialization. 

Given these findings, further longitudinal research on sport specialization and the timing of specialization is warranted. Understanding how specialization impacts athletes’ psychological well-being over time, particularly in comparison to those who engage in sport diversification, could provide valuable insights into the potential risks and benefits associated with different approaches to sport participation.  

These findings collectively suggest that the timing of sport specialization may not be a critical factor in determining psychological outcomes such as mental toughness or perfectionism among athletes. Instead, other variables such as individual personality traits, coaching styles, and environmental influences may play a more substantial role in shaping these psychological characteristics. 

Since our sample was limited to Division-1 college athletes and contained few individuals who specialized early, future research should examine athletes in sports where early specialization is the norm, such as gymnastics and figure skating, to explore differences between early and later specializers. Additionally, our findings imply that sport diversification may not act as a preventive measure against future psychological issues. Any psychological effects of sport specialization appear more closely tied to the current intensity and environment of specialization than to the specific age at which specialization began. 

LIMITATIONS 

While the present study contributes to the overall knowledge regarding athletes’ perceptions regarding sport specialization and perfectionism, this study is not without limitations. The sample included only Division-1 NCAA college athletes, a population considered “elite” due to their high level of athletic achievement. This homogeneity may have limited the variability of responses and reduced generalizability to broader athletic populations, such as youth, high school, or recreational athletes. Given their success, these athletes may also be more resilient to the negative effects of sport specialization and perfectionism, which may not be the case in less experienced or less accomplished athlete groups. 

Secondly, the classification of athletes into low, medium, or high levels of specialization relied on the widely used Jayanthi scale, which includes only three items. While this scale is prominent in the literature, its brevity may limit the depth and accuracy with which an athlete’s specialization history is captured. It may overlook key dimensions such as training intensity, emotional investment, or motivational drivers behind specialization, potentially leading to overly simplistic classifications. 

Third, the study utilized a cross-sectional and retrospective design based on self-report surveys. Participants were asked to recall past experiences and report on them at a single point in time, introducing potential recall bias and limiting the ability to draw causal inferences. A longitudinal design, tracking athletes’ specialization and perfectionism over time, would likely yield more robust and temporally sensitive data. 

Finally, purposive-homogeneous sampling was used, selecting participants from a distinct and specific subpopulation. While this method allows for targeted recruitment and can yield insights from a well-defined group, it may introduce researcher selection bias and limit generalizability. That said, this study was not designed to generalize to the broader population but rather to provide insight into a specific group of athletes who have achieved a high level of competitive success. 

CONCLUSION 

While the results of the study were contrary to our research hypothesis, the results of this study are not without merit. Findings from the current study add to the literature but also provide areas to be further studied. Athletes are continuing to specialize in sport at an increasing rate, despite current research showing that sport specialization is a non-adaptive behavior that yields very little benefit while carrying many potential negative consequences. Sport management professionals, coaches, parents, and athletes should be fully aware of the consequences of sport specialization, both physically and psychologically, before having athletes become specialized. The results of the present study indicate that even if an athlete follows the Development Model of Sport Participation by practicing proper sport diversification by the recommended age, it might not be enough to blunt the effects of maladaptive perfectionism, even if they reach the highest levels of competition, such as Division-1 athletics. Our results suggested that there was no difference between the athletes who specialized early or later in their athletic career.   

APPLICATIONS IN SPORT AND FUTURE RESEARCH 

Sport specialization continues to provoke debate among scholars, coaches, and parents, particularly regarding its efficacy and developmental impact. Similarly, perfectionism remains a focal point in sport psychology research, with ongoing research surrounding its adaptive and maladaptive dimensions. The current study aimed to add to the current body of knowledge for the sport community regarding both perfectionism and sport specialization.  

The Development Model of Sport Participation Model serves as a guiding framework for  

for coaches, athletes, and researchers to examine the implications of sport specialization and diversification. This study aimed to enhance understanding of how DMSP related to perfectionism in sport. The results of the analysis indicated that there was not a significant relationship between when an athlete specializes in sport, whether in their sampling, specialization, investment or post-investment years with perfectionistic strivings and perfectionistic concerns. While the null hypothesis was accepted, the finding still offer valuable insight for scholars, coaches and parents. Notably, even among elite Division-1 athletes are prone to maladaptive perfectionism, despite engaging in sport diversification properly. The lack of differentiation based on specializing timing raises concerns, given perfectionism association with negative psychological outcomes. Although these athletes achieved the highest levels of success, suggesting resilience, it remains uncertain whether similar patterns, or more severe psychological consequences, would manifest in less accomplished or younger athletes lacking the same resilience or comparable coping mechanisms. The need to further investigate this issue is clear. 

The physical consequences of sport specialization remain well documented, but its psychological ramifications warrant more research. Our findings support earlier research that the timing of sport specialization may be less impactful than concurrent sport specialization. Coaches and parents may benefit from using this information to better support athletes’ mental health, particularly while engaging in sport diversification. Despite an overwhelming percentage of participants adhering to DMSP principles, nearly all were engaged in specialization at the time of data collection and still reported elevated perfectionistic concerns. In a similar study also involving college athletes, there was no discernible difference found in mental toughness between early sport specializers and those who diversified (5). Similarly, our current study indicates that the stage of sport specialization, whether early or late in an athlete’s career, does not predict perfectionism tendencies. 

Athletes are continuing to specialize in sport at an increasing rate, despite current research showing that sport specialization is a non-adaptive behavior that yields very little benefit while carrying many potential negative consequences. Furthermore, one can surmise that Name, Image, and Likeness in college athletics, with increased financial incentives and opportunities, may exacerbate the rate of sport specialization in the future, since athletes no longer need to reach the professional levels to reap financial reward.  Sport management professionals, coaches, parents, and athletes should be fully aware of the consequences of sport specialization, both physically and psychologically, before having athletes become specialized.  

The study sets a foundation for future research on sport specialization, albeit with limitations. Participants retrospectively reflected on past experiences, and the study’s cross-sectional design may have drawbacks. A longitudinal approach, tracking athletes during active participation, could yield more precise insights. Additionally, the exclusive focus on Division-1 NCAA athletes may limit generalizability; exploring athletes across various levels and ages is imperative. Furthermore, investigating specialization dynamics in different sports, particularly those requiring early specialization like gymnastics, versus those promoting diversification, is crucial. Moreover, exploring how team sports compare to individual sports regarding specialization and perfectionism would add depth to understanding these phenomena. This study sought to explore an emerging area of research in sport specialization. Overall, this study provides a basis for further research as well as provides future suggestions by offering additional opportunities to further investigate the effects of sport specialization on perfectionism. 

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2025-05-22T15:03:47-05:00October 31st, 2025|Research, Sport Education, Sport Training, Sports Coaching, Sports Exercise Science|Comments Off on A Comparison of Perfectionism and Time of Sport Specialization of Division-1 Athletes 

Managerial practices and coach satisfaction: A summer camp recreation and athletics case study 

Author: Jimmy Smith1

1Department of Kinesiology and Sport Management, Gonzaga University, Spokane, WA, USA

 

Editor’s Note: This article uses the pseudonym Camp Mid-East. While the dates of the study and camp name are withheld, The Sport Journal has verified the identity of the author and confirmed the camp’s existence through a virtual meeting. This note serves to assure readers that reasonable steps have been taken to confirm the legitimacy of the content presented.

Corresponding Author: 

Jimmy Smith, Ph.D.

Gonzaga University

502 E. Boone Ave

Spokane, WA 99258

[email protected]

509-313-3483

Jimmy Smith, Ph. D., is an Associate Professor of Sport Management at Gonzaga University in Spokane, WA. His research interests include organizational behavior.

ABSTRACT 

This case study examines how specific managerial practices influenced coaching staff satisfaction at Camp Mid-East, a residential summer camp in the United States. In response to persistent challenges related to staff retention and satisfaction, the camp implemented a mission statement, operational guidelines, and structured communication strategies within its athletic and recreation department. Using a pre- and post-camp survey design, the study measured changes in coach perceptions across four domains: communication, operational clarity, mission alignment, and overall satisfaction. Descriptive statistics and Wilcoxon Matched-Pairs Signed-Rank Tests were used to analyze the data. Results indicated improvements in communication practices, with more variable outcomes related to mission clarity and satisfaction. These findings contribute to the growing body of research on organizational support in recreational settings and offer practical insights for camp administrators seeking to improve staff engagement, reduce burnout, and enhance the overall staff experience through intentional leadership practices.

KEYWORDS: coach satisfaction, managerial practices, outdoor recreation, staff retention, summer camp

INTRODUCTION 

Organized camping has been a notable facet of American culture since its inception in 1861, gaining widespread appeal among diverse demographics (2, 49). The American Camp Association (ACA) reports significant growth in the camping industry, characterized by increased attendance and revenues, with millions of children, parents, and adults participating in various camping experiences (5). From 2017 to 2019, ACA reported a 30% increase in attendance at accredited camps, rising from 7.3 million to 10.3 million campers (2, 5). The ACA is currently partnering with the University of Michigan Economic Growth Institute, and the ACA revealed that the youth camp sector generates an annual economic impact of approximately $70 billion, underscoring the industry’s substantial influence across the United States (5).

Previous research on camping has explored various aspects of participation, including the benefits it provides, especially its ability to promote well-being through time spent in nature. Research has highlighted the psychological advantages of spending time in natural environments, including stress relief and a mental break from daily routines (13, 29). Additional scholarship has further emphasized the mental health benefits of outdoor environments, particularly as safe spaces that foster emotional resilience among youth and adults (27, 41). Additional studies have explored the satisfaction derived from activities such as cooking, teamwork, and forming bonds through shared experiences with family and peers (9, 26).

There are numerous types of camping, from day camps to residential camps, tenting, and RVing. Residential camps, or sleep-away camps and the setting for the current research, provide immersive experiences where children and adolescents, typically aged 6 to 16, reside in camp settings for extended periods during the summer, engaging in various activities (6). The success of these camps relies heavily on the efforts of camp professionals (e.g., counselors, coaches, and staff) who are committed to delivering memorable camper experiences. Each summer, thousands of dedicated staffers, counselors, and coaches work to provide the best experience possible for millions of youth campers (4). Research exploring camp staff experiences has primarily focused on factors such as job motivation (43), retention rates (45), and emotional challenges (58, 59). Some studies address the social-emotional behaviors of counselors, their interactions with campers, and the high rates of burnout and job dissatisfaction within this sector. Findings suggest that organizational support and communication are essential in mitigating burnout among seasonal camp staff (12, 20, 63). Additionally, the role of camp counselors in promoting positive youth development through sports and leadership has been emphasized (32, 35, 54, 57).

The camping industry faces current staff retention and well-being challenges, especially as camps adjust to operational shifts and staffing shortages following the COVID-19 pandemic (30, 33). A 2021 ACA report highlighted these post-pandemic challenges, noting that camps must now balance staff shortages with the increasing needs of campers in a more complex emotional and operational environment (4, 30). Despite a considerable body of research on camp experiences, there remains a gap in understanding the organizational and operational strategies that support camp counselors and coaches, particularly in how structured communication, mission statements, and operational guidelines can enhance staff satisfaction.

The current research explored implementing managerial practices to improve coach satisfaction at Camp Mid-East, a residential summer camp in the United States. By analyzing the impacts of a clear mission statement, defined operational guidelines, and strategic communication practices, the study seeks to illustrate how these elements contribute to job satisfaction among camp coaches. Literature on organizational clarity and communication strategies indicates that these interventions may positively influence employee satisfaction and retention (60). Therefore, this study posed the following broad research question: Will implementing a mission statement, operational guidelines, and structured communication within the athletic department at Camp Mid-East enhance coach satisfaction?

The structure of the manuscript is designed to clearly convey the study’s context, findings, and implications. The manuscript begins with a description of the empirical setting at Camp Mid-East to establish the study’s context. This is followed by a review of literature related to outdoor recreation, challenges faced by camp staff, and the influence of leadership and organizational practices on staff satisfaction. The methods section outlines the study design, participants, data collection, and analysis procedures. Next, the results of the pre- and post-camp surveys are presented, highlighting key findings related to communication, operational guidelines, mission alignment, and satisfaction. The discussion interprets these findings in relation to prior research and practical implications for camp leadership. Finally, the conclusion addresses limitations and offers recommendations for future research on staff satisfaction and organizational practices in residential camp settings.

EMPIRICAL SETTING

According to the ACA (2024b), there are 3,904 camps available, from day camps to overnight camps for youth, adults, and families. Overnight summer camps in the United States vary widely in size, typically hosting between 100 to over 1,000 campers. Many camps are separated by gender and operate for durations ranging from one to eight weeks, with tuition costs reaching the thousands. For example, Camp Neshoba in Maine has charged as much as $10,500 for an eight-week session, accommodating 190 campers with nearly 100 staff members. Summer overnight camps primarily offer recreational activities, including a range of sports, arts and crafts, and wilderness training.

In a youth residential camp setting, an Activity Director often oversees various programming areas, and the coaches manage activities for the children. The staff that watches over the youth at these camps are hired for dual roles as counselors and coaches based on previous experience in a sport or activity. For example, a counselor may be hired because they have experience with baseball as a collegiate player or are a fine arts major in college focusing on ceramics.

Camp management faces ongoing challenges related to communication and staff organization. Henderson et al. (2007) noted that recruiting competent and caring staff, counselors, and coaches is among the greatest challenges for camp directors. Employee retention is critical for organizational cohesion: a 2011 survey by a regional camping association found staff retention rates ranging from 25% to 75%, with an average return rate of 50% (1 as cited in 45). A 2018 ACA study further reported that 60% of camp staff intended to return for the following summer (3). Understanding the motivational tendencies of staff can aid directors in interpreting and predicting employee behaviors and overall job performance (42).

Camp Mid-East, the location for this case study, is a co-ed camp founded in 1953. At the time of data collection, this camp hosted more than 400 youth campers and offered a variety of activities with a focus on recreational programming, over an 8-week period during the summer. Campers participated in sports such as baseball, basketball, gymnastics, sailing, and soccer and non-sport activities like ceramics, robotics, cooking, and other crafts. Camp Mid-East operated under the core values of gratitude, attitude, and courage, which are defined through thankfulness, attitude as a daily choice, and courage through everyday actions. Staff, counselors, and coaches, primarily college students, complete a multi-day training program covering safety, camper profiles, and team-building.

LITERATURE REVIEW

Outdoor recreation, such as camping, has many benefits. Bultena and Klessig (1969) identified significant psychological relief from participating in recreational camping, a theme reinforced by later studies (c.f. 29). These works highlight how immersion in nature reduces stress, improves mood, and enhances well-being, which aligns with more recent research on the mental health benefits of outdoor environments (17, 52). Beyond psychological relief, camping fosters independence and resilience by requiring participants to complete tasks like cooking and cleaning while promoting social bonding and community-building, particularly in youth settings (28, 26, 48, 59). One popular form of camping, residential or sleep-away camping, offers an immersive environment where participants live together for extended periods, facilitating unique social and developmental opportunities. Camps employ staff, counselors, and coaches who play a critical role in facilitating meaningful experiences for youth participants and ensuring the successful operation of residential camps (48).

Challenges Faced by Camp Staff

Burnout of camp staff has become a critical concern for camp administration, mirroring challenges faced in coaching and other high-stress professions. Kelley (1994) explored burnout in coaches, identifying it as the result of prolonged exposure to stress, role conflicts, and emotional exhaustion. This research continues to expand to include summer camp coaches, who often face similar stressors. Camp coaches work long hours, manage the behaviors of young campers, and navigate interpersonal conflicts, all of which contribute to emotional fatigue, stress, burnout, and turnover (45, 58, 63).

As McCole et al. (2012) noted, key factors contributing to burnout are seen as important topics by the ACA. Amonett (2021) underscores the importance of creating mentally healthy environments through strategies like regular check-ins, fostering open communication about mental health, and offering proactive support to staff. For instance, recognizing early signs of burnout, such as behavioral changes or social withdrawal, allows camp administrators to intervene before these issues escalate. Moreover, Amonett (2021) advocates for a culture in which leaders share their own mental health experiences, helping to foster a supportive atmosphere where staff feel comfortable seeking assistance. This proactive approach reduces burnout, enhances staff performance, and improves the camper experience. Wahl-Alexander, Richards, and Washburn (2017) found that the physical and emotional demands placed on camp staff and inadequate organizational support significantly increased the likelihood of staff not returning after just one season.

Recent studies have highlighted ongoing challenges related to staff burnout and retention, particularly during periods of increased operational and societal stress. Camps have faced difficulties retaining experienced staff members, resulting in a greater reliance on less experienced counselors and coaches (10, 14). Edwards et al. (2013) emphasized the importance of implementing comprehensive support structures to help staff navigate these intensified demands, including effective communication systems and emotional support resources. These efforts are essential in promoting staff wellness, as fostering a healthy work environment reduces burnout and improves staff retention. Camps prioritizing their staff’s mental and emotional well-being may be better positioned to provide high-quality experiences for campers, resulting in more positive outcomes for both staff and participants.

Leadership and Managerial Practices in Camps

One of the most effective tools for aligning staff with the goals and values of an organization is the use of a mission statement. A well-crafted mission statement provides a clear sense of purpose and guides decision-making and conflict resolution (36, 53). Mission-driven leadership fosters a sense of belonging and purpose among staff, enhancing job satisfaction and performance (36, 46, 53). Braun et al. (2012) highlight that the rationales behind mission statement development, such as motivating employees and promoting shared values, are positively associated with various organizational outcomes, including staff engagement and performance. Clear communication of a mission statement enhances job satisfaction and reduces turnover rates.

Additionally, aligning mission statements with organizational structures and involving stakeholders in their development contributes to their overall effectiveness. This alignment fosters clarity of purpose among staff, thereby enhancing job satisfaction and alleviating confusion regarding roles and expectations. Furthermore, effective mission statements can serve as motivational tools, significantly influencing employee behavior and organizational commitment.

While the personal and emotional experiences of campers and staff are well-documented, fewer studies have examined the impact of managerial practices on camp operations and staff satisfaction. However, research consistently emphasizes that leadership plays a critical role in shaping the camp experience for both campers and staff. Strong leadership, effective communication, and clear operational guidelines are essential for creating a positive work environment, directly influencing staff satisfaction and retention. Leaders who engage in transparent communication foster a supportive organizational culture, improving team dynamics and encouraging staff to feel valued and motivated to stay longer (21, 31, 47). Additionally, well-structured leadership frameworks that provide autonomy, competence, and relatedness further enhance employee engagement and increase staff retention rates (43).

Camp counselors and coaches can thrive in environments where expectations are clearly defined and where they feel supported by administrative leadership. Halsall and Forneris (2018) found that organizational support is critical in reducing burnout among camp counselors. Their study revealed that when staff have access to necessary resources and open communication channels, they experience lower levels of burnout and are more likely to return for multiple camp seasons. This idea aligns with broader research, consistently highlighting the importance of leadership clarity and effective managerial practices in maintaining employee satisfaction and well-being. Tian et al. (2020) emphasized that transformational leadership, characterized by clear communication, goal setting, and a supportive environment, significantly improves employee retention by reducing burnout and enhancing job satisfaction. Similarly, Bailey et al. (2012) focused on predictors of burnout in camp staff, finding that leadership clarity and feelings of being valued and having well-defined expectations are critical factors in reducing burnout and improving staff well-being and retention.

While previous research has examined leadership, communication, and organizational support in various contexts, a gap exists in understanding how specific managerial practices affect camp staff satisfaction, particularly coaches. This study seeks to address this gap by exploring how implementing a mission statement, operational guidelines, and structured communication systems at Camp Mid-East impacts coach satisfaction. In an era of increasing challenges in retaining qualified staff, understanding the role of management practices in fostering job satisfaction is crucial. Camps that invest in clear communication, mission alignment, and operational support their position to retain staff and deliver high-quality programming to campers.

By investigating the link between managerial practices and staff satisfaction, this study contributes to the growing body of research on camp operations, offering practical insights for administrators aiming to refine their leadership strategies. Moreover, it underscores the need for camps to prioritize staff well-being and professional development as essential to operational success.

METHODS 

This current research study used a quantitative case design to explore the impact of managerial practices—specifically, the implementation of a mission statement, operational guidelines, and communication strategies—on coaching satisfaction at Camp Mid-East. Pre- and post-camp surveys assessed the effectiveness of these interventions, an approach well-suited for investigating complex, context-specific phenomena in real-life settings (62).

Research Design

A quantitative case study approach was selected to analyze how mission-driven interventions influenced coaching satisfaction. By focusing on a single camp, this design allowed for a detailed examination of the effects of the camp’s mission, guidelines, and communication on coaching satisfaction. Pre- and post-camp surveys enabled a comparative analysis, capturing changes in satisfaction over time and providing insight into the impact of these managerial strategies (19). The survey data gathered before and after the camp facilitated a matched analysis using inferential and descriptive statistics.

Data Collection

All counselors and coaches had the opportunity to participate in the study. Participants included male and female coaches aged 18–40 who could opt into or decline to participate in the survey. The study aimed to quantitatively assess coaching satisfaction across various experience levels. Given the limited sample size, the findings were intended to be context-specific to Camp Mid-East, aligning with the case study approach’s emphasis on in-depth, contextual insights (62).

A survey was developed to measure the impact of the camp’s mission, operational guidelines, and communication strategies on coaching satisfaction. The survey’s content validity was confirmed through a review by five residential camp athletic administration professionals at other camps (23, 24). Both pre-and post-camp surveys contained 16 Likert-scale questions (1 – strongly disagree to 4 – strongly agree), covering perceptions of the mission statement, operational guidelines, communication strategies, and overall satisfaction factors, such as salary (37). Participants were assigned unique identification numbers to maintain confidentiality, and only complete pre/post-camp surveys were included in the analysis.

An orientation session over two days introduced coaches to the camp’s mission, guidelines, and communication protocols. Additional weekly small group meetings throughout the camp reinforced these practices. Observations were conducted to ensure adherence to safety protocols and effective interactions between coaches and campers (50). Post-camp surveys were administered at the camp’s conclusion. All data was securely stored to ensure confidentiality (55).

Data Analysis

Descriptive statistics summarized overall trends in coaching satisfaction, focusing on items related to mission alignment, communication, and policy implementation. This analysis provided a comprehensive understanding of the changes in satisfaction and the effectiveness of the managerial interventions (39). A Wilcoxon Matched-Pairs Signed-Rank Test was used to compare pre- and post-camp survey responses, as this nonparametric test is appropriate for ordinal data from paired samples in small sample studies (22). The Wilcoxon Matched-Pairs Signed-Rank Test was chosen because it is well-suited for analyzing paired ordinal data, such as Likert-scale survey responses, without assuming a normal distribution. Given the small sample size and the use of pre- and post-surveys from the same participants, this nonparametric method provided a robust approach to detecting meaningful changes in coaching satisfaction over time.

RESULTS 

Statistical analyses evaluated coaches’ perceptions of mission statements, policies/procedures, effective communication, and compensation and administrative support satisfaction. Surveys were distributed to all 68 counselors and coaches in the study population. Of these, 65 surveys were usable for analysis, resulting in a response rate of approximately 95%. The survey assessed coaches’ and counselors’ perceptions of organizational goals, communication, policies, compensation, and overall satisfaction within the camp setting.

The survey descriptive results and statistical analyses presented in Tables 1 and 2 provide participant responses before and after camp across four core areas: Communication, Guidelines, Mission, and Satisfaction. Table 3 provides a closer look at the data that resulted in statistical significance. These findings shed light on both stable and variable aspects of participant perceptions.

Communication

As shown in Table 1, Communication items maintained high scores from pre- to post-camp. For instance, item 5 (communication) reflects the highest levels of satisfaction with minimal variability, with a pre-camp mean of 3.89 (SD = 0.31) and a post-camp mean of 3.92 (SD = 0.32). This stability suggests a broadly positive perception of camp communication practices.

In contrast, items 11 and 12 experienced declines in satisfaction, as depicted in Table 1. For item 11, the mean decreased from 2.61 to 2.25, and item 12, from 2.25 to 1.95, indicating areas where communication may not have fully met participant expectations. The increase in standard deviations for these items highlights more significant response variability, which may point to inconsistent communication experiences among participants.

Guidelines

Responses related to the camp’s guidelines displayed variability, with some items improving slightly and others showing minor declines (see Table 1), suggesting mixed responses. For example, item 2 saw a slight decrease in mean from 3.62 to 3.49, while item 4 showed an increase from 3.57 to 3.63, with a reduced standard deviation. This mixed response may suggest varying interpretations or clarity regarding guidelines among participants.

Mission

As outlined in Table 1, responses regarding the camp’s mission remained consistent, though slight declines were noted in items 3 and 7. Item 3 decreased from a mean of 3.67 to 3.45, while item 7 showed a minimal drop from 3.05 to 3.02. Although these differences were not statistically significant, the results indicate that reinforcing the camp’s mission throughout the experience may improve participant alignment with camp goals.

Satisfaction

The satisfaction category, summarized in Table 1, showed the most pronounced declines, particularly in items 6, 14, and 16. Item 6, for example, dropped from a pre-camp mean of 2.62 to a post-camp mean of 2.25. The increased standard deviations in these items suggest diverse individual experiences, indicating that some participants may have felt less satisfied with aspects of the camp as it progressed.

Statistical Analysis

A Wilcoxon Signed Ranks Test was conducted to assess changes between pre- and post-camp responses, with results presented in Table 2. This nonparametric test, suitable for paired samples with non-normally distributed data, identified significant and non-significant changes. Table 3 represents statistical significance related to the pre/post survey with a summary of this below.

Significant Differences

Items pre/post Q6: As indicated in Table 2, this item demonstrated a statistically significant change, with a Z-score of -3.138 and a p-value of .002. This reflects a notable decline in satisfaction, consistent with findings in Table 1.

Items pre/post Q11: Table 2 shows that this item also experienced a significant change (Z = -2.800, p = .005), suggesting a meaningful decrease in participants’ perceptions of communication quality.

Items pre/post Q14: This item, with a Z-score of -2.318 and a p-value of .020, reflects another statistically significant drop in satisfaction.

Non-Significant Differences

Other items not displayed in Table 2 did not exhibit statistically significant changes, with p-values above 0.05. For example, items 1.1 – 2.1 (Z = -0.352, p = .725) and 1.7 – 2.7 (Z = -0.354, p = .724) indicate stable perceptions, suggesting that responses for these items remained consistent from pre- to post-camp.

Summary of Findings

This case study examined the effects of targeted managerial interventions—including a mission statement, operational guidelines, and structured communication strategies—on coach satisfaction at Camp Mid-East. Sixteen survey items were used to measure pre- and post-camp perceptions across four key domains: communication, guidelines, mission alignment, and satisfaction.

Analysis revealed that three of the sixteen items (19%) showed statistically significant declines from pre- to post-camp, while the remaining thirteen items (81%) showed no significant change, indicating generally stable perceptions across most areas. The three items that did significantly decline were:

Item 6 – Satisfaction with compensation: declined from a mean of 2.62 to 2.25 (p = .002),

Item 11 – Clarity of communication from supervisors: dropped from 2.61 to 2.25 (p = .005),

Item 14 – Perceived administrative support: decreased from 2.62 to 2.30 (p = .020).

While these declines highlight areas for improvement, other items remained stable or even slightly improved. For instance, Item 5 (general satisfaction with communication) retained high ratings from pre- to post-camp (3.89 to 3.92), and Item 4 (clarity of camp guidelines) showed a modest increase (3.57 to 3.63), albeit not statistically significant. Items tied to the camp’s mission—such as Item 3 (understanding of the mission) and Item 7 (alignment with camp values)—remained relatively consistent but saw slight, non-significant declines (3.67 to 3.45 and 3.05 to 3.02, respectively).

Further, while communication was a consistent strength across most items, variability emerged in responses to Items 11 and 12, indicating that not all staff experienced communication equally. This points to an opportunity to refine communication systems to ensure consistent clarity and access to information for all team members.

The results in the guidelines and mission domains suggest mixed interpretations or engagement, with no statistically significant changes but some variability in mean scores. These findings imply that while the structural interventions were clearly introduced, their reinforcement throughout the camp may have been uneven or insufficient to shift perceptions meaningfully.

The most notable shifts occurred in the satisfaction domain, where items related to compensation, administrative support, and overall experience revealed declines. These results suggest a potential disconnect between staff expectations and their lived experiences, especially as the camp progressed.

While the interventions did not produce widespread statistically significant changes, the findings reflect the complexity of staff satisfaction in seasonal camp environments. Importantly, this case study is not intended to produce generalizable outcomes but rather to offer context-specific insights that contribute to the broader conversation on leadership, organizational practices, and staff well-being in recreational settings. These exploratory results underscore the need for continued, multi-site research that investigates the long-term and cumulative effects of managerial strategies on staff engagement and satisfaction in youth camps and similar settings.

DISCUSSION 

This study aimed to bridge the gap in the literature by examining the effects of managerial practices—specifically the implementation of a mission statement, operational guidelines, and structured communication—on coach satisfaction in a summer camp setting. While previous research has focused on the benefits of camping for participants and the psychological effects of outdoor experiences (29, 61), less attention has been given to the experiences of camp staff, particularly coaches. Even fewer studies have explored how leadership and organizational strategies within camps impact the satisfaction, retention, and overall effectiveness of these staff members.

Key Findings

The results of this study indicate that implementing a mission statement, operational guidelines, and structured communication strategies led to slight improvements in coach satisfaction at Camp Mid-East in some areas, while other areas showed statistical significance. These finding aligns with existing research that emphasizes the importance of organizational clarity in enhancing job satisfaction and reducing burnout in recreational and educational settings (8, 58). Coaches at Camp Mid-East reported higher levels of satisfaction with their roles and responsibilities following the introduction of these managerial tools, supporting previous studies suggesting that clear communication and aligned organizational goals can significantly improve staff morale (32, 56).

The most notable improvement was observed in communication, with coaches reporting increased satisfaction regarding their ability to receive timely updates and feedback from camp leadership. This finding echoes the work of McCole et al. (2012), who found that open and consistent communication is a key factor in employee satisfaction. Furthermore, the structured weekly meetings and open-door policy implemented at Camp Mid-East allowed coaches to feel more connected to the camp’s leadership, thereby reducing misunderstandings and fostering a more collaborative work environment. This also aligns with Edwards et al. (2013), which highlighted that camps with robust communication strategies were more successful in retaining staff year after year.

The findings of this study are consistent with a growing body of literature that underscores the importance of organizational support and clarity in maintaining staff satisfaction. For example, Wahl-Alexander et al. (2017) found that camp counselors who received clear organizational support experienced lower burnout and higher job satisfaction levels. Similarly, research on youth sports coaching has highlighted the role of communication and mission alignment in improving the performance and retention of coaches (32, 56).

However, this study builds on existing research by focusing on the managerial practices of a summer camp’s athletic department. While past studies have examined the role of leadership in outdoor recreation settings broadly, few have investigated how specific managerial tools, like mission statements and operational guidelines, directly influence the job satisfaction of camp coaches. By implementing these tools at Camp Mid-East, this research provides evidence that aligning staff with a clear mission and operational structure can improve their satisfaction and effectiveness. Additionally, literature has underscored the importance of organizational clarity in the context of post-pandemic challenges. Amonett (2021) highlighted the growing need for camps to support their staff through improved communication and operational guidelines, especially as camps face new challenges related to staff shortages and increased emotional demands.

Bridging the Gap in Existing Research

This study addresses a significant gap in the literature by examining the relationship between managerial practices and coach satisfaction within residential camps. Previous research has focused on campers’ experiences or the broader benefits of camping, while camp life’s operational and managerial aspects have yet to receive much attention. Although studies on burnout and staff retention highlight the need for better support systems, few have investigated managerial tools that can prevent burnout and enhance job satisfaction (8, 58).

The findings suggest that implementing a clear mission statement, operational guidelines, and structured communication systems improves coach satisfaction and addresses staff retention and performance challenges. High turnover rates disrupt camper experiences and create operational difficulties. This research demonstrates that these managerial tools can effectively enhance coach satisfaction, providing practical solutions for camp administrators to improve staff retention and performance.

Furthermore, this study builds on prior findings by illustrating how mission-driven leadership aligns staff with the camp’s broader goals. Previous research, such as Braun et al. (2012), has emphasized the significance of mission statements in organizational contexts. This study extends that work by providing empirical evidence that effectively communicated and reinforced mission statements positively impact staff satisfaction in summer camps.

CONCLUSION 

This study contributes to the growing body of research on organizational leadership in residential camps by providing empirical evidence that managerial practices—specifically, the use of a mission statement, operational guidelines, and structured communication—can positively impact coach satisfaction. While the observed improvements were modest in some areas, the findings underscore the value of clear organizational strategies in fostering a supportive and effective work environment for seasonal staff. As camps continue to face post-pandemic staffing challenges, these results offer actionable insights for camp administrators seeking to enhance staff morale, retention, and overall program quality.

APPLICATIONS IN SPORT

The findings of this case study offer practical insights for those working in sport-based summer camps and similar youth sport environments. While the managerial interventions at Camp Mid-East—implementation of a mission statement, operational guidelines, and structured communication—did not produce widespread statistical changes, they did yield important lessons for camp leaders, coaches, and administrators. Specifically, three areas—compensation satisfaction, clarity of communication from supervisors, and perceived administrative support—emerged as key concerns, with significant declines observed from pre- to post-camp.

For coaches and activity leaders, these results highlight the importance of consistent communication and feeling supported by leadership. Structured communication systems (such as weekly check-ins, feedback loops, and open-door policies) were well received in some areas, but inconsistencies noted in supervisor communication suggest a need for clearer messaging across all levels of staff. Coaches benefit from knowing what is expected of them, how their performance is evaluated, and where to seek help or guidance during high-stress moments in the camp season.

For camp directors and sport program administrators, the study underscores that even well-intentioned managerial tools must be implemented thoughtfully and reinforced consistently. Simply introducing a mission or set of guidelines at orientation may not be sufficient. Ongoing reinforcement throughout the season—through meetings, signage, and leadership modeling—is likely needed to help staff internalize and act upon those values. Additionally, the findings on declining satisfaction around administrative support and compensation suggest that camp leaders should consider how recognition, feedback, and fair treatment can impact staff morale, especially in high-demand roles like coaching.

For parents and guardians, this study provides assurance that some camps are working toward building stronger support structures for the individuals entrusted with leading and mentoring their children. Staff who feel supported and valued are more likely to provide positive, consistent experiences for campers—both on and off the field.

Finally, for researchers and sport management professionals, the results support the need for continued study into seasonal staff satisfaction and retention in sport-specific contexts. Although the findings of this single case are not generalizable, they open the door for further exploration of how mission-driven leadership and communication frameworks can influence staff outcomes in youth sport and recreation.

By grounding conclusions in the actual data and acknowledging where changes did and did not occur, this study contributes to a growing dialogue about staff well-being in sport settings. It invites practitioners to ask not just what policies are in place, but how they are implemented, communicated, and experienced by staff in real time.

LIMITATIONS AND FUTURE DIRECTIONS

While this study provides valuable insights into the impact of managerial practices on coach satisfaction, several limitations must be acknowledged. The small sample size restricts the generalizability of the findings to larger camps or recreational settings. Future research could investigate the applicability of these findings to diverse types of camps and examine the long-term effects of these managerial practices on staff retention and performance.

Engaging leadership, which fosters autonomy, competence, and relatedness, has increased staff engagement and satisfaction (44). By focusing on inspiring, strengthening, and connecting employees, such leadership styles enhance team effectiveness, improve retention, and increase commitment to the camp’s mission and values. This alignment of leadership behavior with critical psychological needs creates an environment where staff feel supported and valued, leading to sustained engagement over time.

Additional limitations were the way in which methods and mediums of communication guidelines and mission messaging were delivered to counselors and coaches. Lines of communication were offered but may have yet to be shown to be the best ways of communication during a summer camp setting. Feedback during camp on the best communication mediums should have been offered to counselors and coaches.

These findings are especially relevant for Camp Mid-East, as staff often navigate multifaceted roles while working with youth from diverse backgrounds. Aligning leadership with engaging principles—such as fostering connection and inspiration—can significantly enhance staff morale and retention (44, 16). Reduced staff turnover strengthens the relationships between staff and campers, improving overall program quality. By investing in leadership and operational strategies prioritizing staff well-being, camps can continue delivering high-quality programming and cultivating an enriching environment for campers and staff.

It should be noted here that while the findings offer useful insights into how managerial practices may influence coach satisfaction, it is important to note that only a small number of statistically significant changes emerged. Specifically, three of the sixteen survey items showed meaningful differences from pre- to post-camp, suggesting that the interventions—while thoughtfully implemented—had limited measurable impact over the short camp session. Most responses remained stable, indicating that while communication, guidelines, and mission alignment were introduced, they may not have been reinforced consistently enough to shift perceptions across the board. These results should limit expectations about the immediate effectiveness of such practices and reinforce the need for ongoing support, sustained implementation, and further research across multiple settings to better understand how managerial strategies contribute to staff satisfaction in seasonal camp environments.

Additionally, while this study focuses on coach satisfaction, future research should explore the effects of managerial practices on other aspects of camp staff performance, such as leadership development and camper outcomes. Investigating how these managerial tools influence staff performance across various domains could yield a more comprehensive understanding of the factors contributing to successful camp operations.

This study contributes to the growing body of literature on camp management by highlighting the often-overlooked role of managerial practices in shaping staff satisfaction, particularly in summer camp athletics. The research demonstrates that implementing a mission statement, operational guidelines, and structured communication systems enhances coach satisfaction at Camp Mid-East. These findings align with previous studies emphasizing the importance of organizational clarity, communication, and leadership in reducing burnout and improving job satisfaction among camp staff (8, 32, 58).

By addressing existing research gaps, this study underscores the practical significance of mission-driven leadership and clear operational structures in maintaining high staff satisfaction. As camps face increasing staffing challenges and operational demands—particularly in the post-pandemic landscape—this research offers actionable insights for camp administrators seeking to enhance management strategies. Camps that prioritize staff well-being through effective communication and organizational support are better equipped to retain experienced personnel, improving the overall camp experience for campers and staff.

While the study’s findings are valuable, limitations such as the small sample size and focus on a single camp indicate the need for further research to explore how these managerial practices impact staff in diverse camp settings. Future studies could examine the long-term effects of these interventions on both staff retention and camper outcomes, enhancing our understanding of how leadership strategies influence the success of camp programs. This study emphasizes the importance of effective leadership and organizational practices in enhancing job satisfaction among camp staff, providing a framework for camp administrators to create supportive, mission-driven environments that foster staff well-being and camp success.

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2025-09-25T15:13:42-05:00October 24th, 2025|Research, Sport Education, Sports Coaching, Sports Facilities, Sports Health & Fitness|Comments Off on Managerial practices and coach satisfaction: A summer camp recreation and athletics case study 

The Novelty of New Stadiums: Evidence from 40 Years in Major League Baseball 

Authors: Richard Flight1 and Mark Mitchell2

Mark Mitchell, DBA

Professor of Marketing

Associate Dean, Wall College of Business

NCAA Faculty Athletics Representative (FAR)

Coastal Carolina University

P. O. Box 261954

Conway, SC 29528

[email protected]

(843) 349-2392

Richard Flight, PhD is Associate Professor of Marketing at Coastal Carolina University in Conway, SC. He previously worked in minor league baseball with the Memphis Redbirds and Birmingham Barons as well as Division I college athletics at Samford University.

Mark Mitchell, DBA is Professor of Marketing at Coastal Carolina University in Conway, SC. He has served for 10 years as the NCAA Faculty Athletics Representative (FAR). He has conducted much research on minor league sports.

The Novelty of New Stadiums: Evidence from 40 Years in Major League Baseball

ABSTRACT

Purpose: The purpose of this study is to advance a new model to estimate the stadium novelty effect for newly-built Major League Baseball (MLB) stadiums over the last 40 years. Unlike prior studies that use nominal annual attendance data, this study uses marginal attendance change to capture the impact new stadiums have on attendance when mitigating (or controlling for) the impact that team performance has on attendance.

Methods: The incidence of the construction of new MLB stadiums is identified over a 40+ year period. Using a difference-in-differences (DiD) method, a base attendance model is estimated. Then, the new stadium construction observations are added to capture the effect they have on predicted attendance. Unique to this study, marginal change in attendance is used rather than change in (absolute) nominal attendance. Year-over-year percentage change in attendance helps nullify key deficiencies in prior studies such as stadium size disparities and variations in market size. Additionally, this research combines the effects of extensive team performance variables and player salaries to control for non-stadium externalities which also impact attendance.

Results: There have been 23 new MLB stadiums built from 1980-2023. Stadiums for expansion teams or team relocations are not included in this study. Collectively, the MLB teams that built new stadiums see, on average, a 29.6% increase in attendance during the first year in the new stadium with effects lasting up to 21 years. When controlling for other factors (player salaries, winning percentage and other team statistics) the novelty effect is significant (b = .216) in multiple regression analysis.

Conclusion: Teams that build new baseball stadiums can expect an increase in attendance when controlling for team performance and player salaries. This effect holds even while some new stadiums were purposefully built to have fewer fans and offer a ‘closer-to-game’ fan experience. In other cases, the addition of luxury boxes reduced the number of available seats but added a class of seats that demand a premium price from consumers. This strategy allowed teams to cultivate new fans and new revenue streams for their teams.

Application in Sport: A baseball stadium is a fixed asset with an anticipated lifespan. No stadium lasts forever in its original form. At some point, a stadium must be remodeled or replaced to meet the needs of current consumers or fans may shy away from attending games. New stadiums can help grow attendance, diversify the fan base, and develop new revenue streams to help teams compete financially in Major League Baseball.

Key Words: stadium novelty effect; Major League Baseball; attendance; new stadium construction; franchise expansion

The Novelty of New Stadiums: Evidence from 40 Years in Major League Baseball

INTRODUCTION

Ballpark managers, team owners, and city officials often cite lagging attendance as the prime reason to build new sport facilities and stadiums. They argue an out-of-date stadium discourages fan attendance and recommend the investment in new-and-improved stadiums. A key goal associated with building a new facility is revenue growth by increasing fan attendance with the promise of an enhanced fan experience, often with an expanded premium ticket and entertainment options. These new facilities often offer operating efficiencies with the use of new technology to lower operating costs and boost profit margins for stadium operators (28).

Historically, when a team builds a new stadium their observed attendance goes up (35). Anecdotally, a new facility brings greater enthusiasm from not only the fan base but also from media partners, advertisers, and players that see grandeur in the new stadium. For example, the Atlanta Braves moved from Turner Field (located in downtown Atlanta) to then-named SunTrust Park (located in the northern suburbs) in 2017. Total attendance for the first season at Sun Trust Park increased approximately 24% over the final season at Turner Field. The new stadium offered a comprehensive gameday experience including dining and shopping that went beyond a traditional baseball game. Further, the suburban location was more accessible to many fans, including expanded parking facilities (32). Though fan attendance can sometimes decline after the opening year (38), the average attendance per game in Atlanta’s SunTrust Park actually increased in year two and year three (3).

The purpose of this study is to advance a new method to estimate the Stadium Novelty Effect in Major League Baseball by examining newly-built MLB stadiums and the associated attendance figures over a 40+ year period. First, a brief description of relevant literature is provided. Next, the study methods are presented as well as the data analysis plan. Finally, the findings are presented and the implications for baseball team owners and communities are advanced.

THE IMPACT OF NEW STADIUMS IN SPORT

Fan Attendance and the Fan Experience

The phenomenon of attributing increased fan attendance to the introduction of a new stadium is known as the Stadium Novelty Effect (2, 7, 8, 14, 18, 27). This effect, also referred to as the Honeymoon Effect (4), has been observed in numerous applications including: European soccer (10, 35); baseball (6); basketball (5); and hockey (18).

There is broad agreement that attendance tends to increase with the introduction of a new stadium. There is less agreement on the duration of this positive impact on attendance. In early literature by Noll (26), the stadium novelty effect was estimated to last somewhere between seven and eleven years. More recently, Hamilton and Kahn (16) estimate a much shorter three-year duration of this temporary surge in attendance. Others suggest the temporary upward shift is followed by a return to the original attendance levels with limited long-term benefits (14, 36). Howard and Crompton (18) conclude that the initial stadium novelty effect is limited often to just a single year with eventual declines after the first year in the new facility after studying NFL, MLB, NBA and NHL leagues. Most recently, Bradbury (5) suggested a new stadium will bring an initial surge in attendance that breaks down over the initial ten-year period.

One motivation for new stadium construction and renovation is the fan’s experience based upon the facility and its service environment. It must be noted, however, that sport fans can vary in their degree of fandom and their subsequent expectations during game attendance. Both Hoehn and Szymanski (17) and Porat (30) detail a spectrum from casual to involved or committed. Meanwhile, Samra and Wos (33) provide a fan typology including temporary, devoted, and fanatical.

A seminal question to ask is ‘how do fans derive value from the ballpark experience?’ To varying degrees fans value the quality of on-the-field performance. They also value the experience of a game delivered in a safe, clean, and exciting environment provided by a new stadium. Frequently the call for greater amenities is made in the argument for building a new stadium. In fact, it is asserted that new stadiums may become attractions within themselves regardless of team performance (1, 18). The new stadium setting incorporates features that modern, state-of-the-art facilities are expected to have. The ‘stadium as an attraction’ position suggests that fans immerse themselves in the new stadium atmosphere regardless of team performance. In essence, the team’s performance may not be great, but the atmospherics of the stadium creates a pleasurable experience worth the cost and worthy of repeatedly returning for another game. In short, some fans place greater value on the on-field product, whereas others place it on the atmosphere and conditions of the stadium.

While fan experience is vital, the fan base’s devotion to the team and team brand will certainly influence their willingness to attend games. Some teams are known to have loyal fans and seemingly have little trouble reaching stadium capacity. The Chicago Tribune ranked all 30 major league baseball teams by team value (34). Not surprisingly, there is a significant correlation (r = .66) between this team valuation and average team attendance since 1980 (3). These estimated team valuations are provided in Table 1.

Table 1: MLB Teams Ranked by Team Valuation (with Corresponding Fan Attendance) 

Rank Team 2024 Valuation ($B) Average Home Attend (1980-2023) 
New York Yankees 5.59 2,986,328 
Arizona Diamondbacks 4.28 2,353,169 
Los Angeles Dodgers 3.75 3,333,426 
Chicago Cubs 3.67 2,619,327 
Boston Red Sox 3.6 2,583,650 
San Francisco Giants 3.21 2,501,129 
New York Mets 2.48 2,486,904 
St. Louis Cardinals 2.235 2,998,742 
Philadelphia Phillies 2.22 2,339,642 
10 Houston Astros 2.19 2,167,333 
11 Atlanta Braves 2.165 2,297,852 
12 Los Angeles Angels 2.04 2,737,988 
13 Washington Nationals 2.0 1,760,801 
14 Texas Rangers 1.84 2,285,151 
15 San Diego Padres 1.65 2,084,153 
16 Seattle Mariners 1.62 2,009,274 
17 Chicago White Sox 1.54 1,845,744 
18 Toronto Blue Jays 1.53 2,460,458 
19 Minnesota Twins 1.52 1,982,394 
20 Baltimore Orioles 1.46 2,425,704 
21 Cleveland Indians 1.375 1,843,168 
22 Colorado Rockies 1.36 2,769,199 
23 Detroit Tigers 1.33 2,144,746 
24 Cincinnati Reds 1.325 2,016,894 
25 Oakland Athletics 1.3 1,769,573 
26 Milwaukee Brewers 1.29 2,132,008 
27 Pittsburgh Pirates 1.26 1,679,759 
28 Miami Marlins 1.14 1,464,552 
29 Kansas City Royals 1.1 1,845,441 
30 Tampa Bay Rays 1.03 1,400,312 

New Stadiums in MLB

While an expensive and disrupting proposition, building a new stadium is somewhat common in Major Lague Baseball. From 1980-2023, 23 new stadiums were built for non-expansion or relocation teams. Additionally, 5 other new stadiums were built for new franchises (including the Montreal Expos move to Washington, DC). The timing of new stadium constructions between 1980 – 2023 is presented in Figure 1. An overview of the stadiums themselves is provided in the Appendix.

Figure 1: Newly-Built Major League Baseball Stadiums by Year (1980-2023) 

Over a thirty-year span the positive impacts of the new stadium effect have been measured by researchers using a variety of methods. Calculating the aggregate impact of new stadiums in Major League Baseball, Fort (11) provides a methodology typical of this research that specifies the difference between the first year in the new stadium and the previous five-year’s averages for those teams that built new stadiums. Fort (11) finds the change in attendance for a select period to be a positive net increase of 624,000 fans for teams that built new stadiums. Conversely, those teams that did not build new stadiums realized a net increase of 96,000 fans over the same time period.

While this common approach speaks to the impact new stadiums have on league attendance, debate continues as to the team and market specific nature of the stadium novelty effect and how to best measure them. Recently, van Ours (35) employed a ‘difference-in-differences’ (DiD) method with a sample of 8 Dutch stadiums. Here, the researchers used a control group to establish an initial model, then introduced new stadium data and observed the change or difference between the two in a two-way fixed effect regression.

This study also uses the ‘difference in differences’ (DiD) method. Unlike prior studies that use attendance per team while also employing time-dependent independent variables, this study uses percentage change in attendance from the immediate prior year for each team including those with new stadiums. Using marginal (or percentage) change in attendance from the prior year marks a deviation from prior studies that use nominal annual attendance as the dependent variable with additional prior years attendances as independent variables. Using prior attendance as independent variables, as in time series modeling, generates significant multi-collinearity concerns and effectively overfits most lagged time series or autoregressive moving average (ARIMA) models. Using nominal change in attendance from one year prior does not carryover anticipated attendance which tends to overfit the model. Further, the use of marginal annual change mitigates the effects of wide variations in both stadium and market sizes across the vast time horizon studied here.

METHODS

This study uses Major League Baseball attendance records, team on-field performance, and new stadium construction data from the 1980 through 2023 playing seasons. In all, 30 teams are represented in the total data set with 23 new stadiums built during that 40+ year time span. The initial subject pool includes Major League Baseball (MLB) team attendance and performance data from 1979 through 2023 which were accessed and downloaded from the data aggregator baseball-reference.com (a depository for sports performance data). The data analysis plan for this study consisted of three stages.

Stage One

The purpose of Stage One is to collect team performance and fan attendance data. Refinements will be made to the data where warranted. For example, team relocations or the awarding of expansion teams do not offer a before-and-after scenario to analyze the stadium novelty effect. So, the data for these years will be excluded. In addition, data attached to seasons that experienced work stoppages are also excluded as it is assumed attendance figures tied to these reasons are atypical for a variety of reasons (such as fan resentment, etc.). Finally, fan attendance data during the COVID-19 period were eliminated as fan attendance limits, public health concerns, and lingering fan apprehension to attend group events impacted game attendance.

Stage Two

The purpose of Stage Two is to develop a base model to predict fan attendance in the absence of a new stadium using the difference-in-difference methodology. Then, team performance and team salary data for each year and team is regressed on the percentage change in team attendance from year to year (the dependent variable). This model can be used to predict attendance and will be later extended to include the effects of new stadiums in Stage Three.

Stage Three

The purpose of Stage Three is to add new stadium attendance observations to the base model along with the addition of a dummy variable to identify these figures as attached to the introduction of a new stadium. It is here that the final results are calculated and the summary findings advanced.

PRESENTATION OF DATA ANALYSIS

Stage One – Refining the Sample Size

Team performance and attendance data were downloaded by team and year from 1980-2023 (inclusive). 26 teams played from 1980-1992, with expansion to 28 teams in 1993, and then again to 30 teams in 1998. Counting each team during this time span, there are 1,288 observations in the initial data set. As previously noted, this study uses a ‘difference-in-differences’ or DiD approach. Bradbury (5) states “a primary concern with DiD comparisons is the selection of control units that are devoid of treatment effects; therefore, it is imperative to exclude observations of teams that may be experiencing novelty influences from existing venues or entering new markets through team relocations and league expansions.” For this reason, new stadium observations were omitted for expansion franchises, including Colorado (1993), Florida (1993), Tampa Bay (1998), and Arizona (1998). Additionally, the relocation of the Montreal Expos to Washington, DC in 2005 was also omitted given the new stadium in a new market had no comparable previous season attendance data.

Impact of Labor Disputes. During the timeline of the study, there were two significant work stoppages (1981 and 1994) due to labor-management disputes. These years pose two challenges observed in the data.

During each strike year, the dependent variable (percent change in attendance) was (on average) noticeably lower than expected.

During the year following the 1981 strike (1982), the dependent variable was (on average) noticeably greater than expected.

These two anomalies lead to an uncontrollable externality that isn’t explained by performance, marketing, or stadium effects and warrant exclusion. As such, the seasons of 1981, 1982, and 1994 are excluded from this analysis.

Impact of COVID-19 Global Pandemic. The 2020 MLB regular season was reduced to 60 games and played without fans. The post-season was played at neutral sites (Globe Life Field Arlington, TX; Minute Maid Park in Houston, TX; Petco Park in San Diego, CA; and Dodger Stadium in Los Angeles, CA). Given the lack of fans (and attendance data), the 2020 season was excluded from this analysis.

Impact of Pent-Up Demand Following Global Pandemic. The lingering effects of COVID seem to decline during the 2022 season as evidenced by the spike in game attendance. This behavioral change by fans caused the dependent variable (percent change in attendance) to be greater than expected for the 2022 season. As illustrated in Figure 2, the reader will note the high and low spikes in average percent change in attendance. These ‘dips’ and ‘spikes’ represent externalities outside the scope of this study. As such, the 2022 season was also excluded from this analysis.

Figure 2: Average Percent Change in MLB Attendance by Year (1980-2023) 

Tracking the Revisions to the Sample. Collectively, five MLB seasons (1981, 1982, 1994, 2020, and 2022 we excluded from this analysis for the reasons noted above. Additional data adjustments included accounting for individual abnormal ‘outlier’ observations. Individual observation outliers are identified using Mahalonabis Distance2 analysis (15). In doing so, 117 observations are found to be structurally outside of the norm and were also excluded from this analysis. The final data set consists of 1,001 observations for study analysis. A summary of refinement process that affected the sample size is provided in Table 2.

Table 2: Summary of the Refined Sample Size Used in This Analysis 

 Existing Stadiums New Stadiums TOTAL 
All Years 1,206 23 1,228 
Excluding franchise expansion, relocations, strike and COVID effected years. 1095 23 1118 
Final sample excluding outliers. 978 23 1,001 

Stage Two – Creating the Base Model to Predict Attendance (Without New Stadium Data)

Following a difference-in-differences (DiD) methodology (see 5, 35), this stage creates a base model to predict attendance in the absence of any new stadiums. This base model specifies the predictive ability of team variables (such an on-field player performance and player salaries) on attendance. Team performance and salary data from each eligible team and year (i.e., where no new stadium or major stadium renovations occurred) is regressed on the percentage change in attendance (dependent variable). This base model will first be used to predict attendance while later this base model will be extended to include the effect of new stadiums.

While year-over-year marginal change in attendance is the dependent variable, the independent variables include team statistics for offense, defense, and pitching as well as total player payroll (see Table 3 for list of variables). Prior literature has incorporated a limited selection of performance variables and team salary and lagged prior year attendance to predict attendance. Our approach is to incorporate 28 performance variables simultaneously:

Team (4 variables)

Offense (13 variables)

Pitching (6 variables)

Defense (5 variables)

By doing so, the model is able to construct a broader test of variables which may affect attendance. As an economic growth component, payroll suggests that greater player payrolls translate into better on-field performance which impacts attendance (21). It should be noted that the model specification does not incorporate time dependent variables as one might find in a time series analysis. Thus, there is not a controlling element for economic inflation or timely building trends that may emerge over a 40-year time horizon. While league expansion has taken place, study does not use new stadiums as there is no pre- and post-construction paired data.

Table 3: Independent Variables Used in Base Model  

Variable Categories  Variable Description 
Team:  Salary Estimated player payroll. (Standardized) 
  Win Percentage Total wins divided by games played. 
  Home Win Percentage Total wins divided by games played at home only. 
  Run Difference Average difference in runs scored vs runs allowed. 
    
Offense:  Runs Scored per game Average runs scored per game. 
  Hits Number of hits in the year. 
  Doubles Number of doubles in the year. 
  Triples Number of triples in the year 
  Home Runs Number of home runs in the year. 
  Runs Batted In Number of Runs-Batted-In in the year. 
  Stolen Bases Number of bases stolen in the year. 
  Caught Stealing Times caught stealing in the year. 
  Batter Walks Number of walks in the year. 
  Batter Strike Outs Total batter strike outs in the year. 
  Team Batting Average Number of hits divided by at bats for the team. 
  On-Base Percentage Times reached base divided by plate appearances. 
  Slugging Percentage Percentage of hits weighted by based reached. 
    
Pitching:  Runs Allowed Per game Average runs allowed per game. 
  Team ERA Average runs given up divided by 9. 
  Hits Allowed Hits allowed by pitchers in a year. 
  Home Runs Allowed Home runs allowed in a year. 
  Walks Allowed Walks allowed in the year. 
  Strike Outs Pitched Strike outs pitched in the year. 
    
Defense:  Defensive Efficiency Estimate of balls in play that result in converted outs. 
  Assists Assists made in the year. 
  Errors Committed Errors committed in the year. 
  Double Plays Turned Double Plays made in the year. 
  Fielding Percentage (Putouts + Assists) / (Putouts + Assists + Errors) 

Using IBM’s SPSS (version 29.0.1.0) a liner regression is performed using a stepwise entry method for variable selection. This method allows the most attractive variables to be entered into the model first, while consecutively testing, dropping, and adding variables until the best-fitting model emerges.

Stage Three – Creating the Extended Model to Include New Stadium Data

Once a base model is estimated, new stadium attendance observations are added to the sample along with a dummy variable coded for new stadium observations. As noted earlier, 23 new stadiums (observations) are added during this stage which are reflected in this new variable. The new variable that is built into the model during this stage accounts for the presence of a new stadium, coded by ‘1’ while all other observations (existing stadiums) are coded ‘0’. If the stadium novelty effect exists, then the regression coefficient (beta) for the new dummy variable will be significant and the model fit (r2) will improve. Similar to Stage Two, the dependent variables were retained by using a stepwise entry method for variable selection. This stage provides a comparative model directed by the difference-in-difference approach.

RESULTS

Predictive Models

Base Model Without New Stadium Data. A primary goal of this study is to measure the stadium novelty effect while controlling for the influence of team performance and player salaries. During Stage Two, a base model is estimated using a stepwise regression which retained the best predictive variables and strongest model fit. The sample under investigation for base-mode specification has 978 observations resulting in an adjusted r2 fit of .198 and significant F statistic. (see Table 4).

Table 4: Base Model Fit Statistics and Coefficient Estimates 

R R Square Adjusted R Square Std. Error of the Estimate   
0.450 0.202 0.198 0.155   
      
 Sum of Squares Df Mean Square F Sig. 
Regression 5.579 1.116 46.429 <.001 
Residual 22.012 916 .024   
Total 27.591 921    
  Unstandardized Coefficients (Beta) Std. Error Standardized Coefficients (Beta) t Sig. VIF 
(Constant) -.977 .113  -8.627 <.001  
Winning Percentage .957 .084 .372 11.345 <.001 1.35 
Salary -.043 .007 -.251 -5.862 <.001 2.112 
Strikeouts / Game .025 .007 .159 3.648 <.001 2.189 
Hits .000 .000 .102 3.146 .002 1.197 
Stolen Bases .000 .000 .083 2.645 .008 1.120 

Extended Model Including New Stadium Data. Upon the addition of new stadium observations during Stage Three, the extended model demonstrates an increase in model fit (r2) from .198 to .230. Moreover, the new stadium dummy variable is significant (.001) and strong when compared to the other variable’s standardized betas, at .216, only “winning percentage” and “batting average” serve as better predictors of changes in attendance from year to year. (see Table 5).

Table 5: Extended Model (with New Stadium Variable) Fit Statistics and Coefficient Estimates 

R R Square Adjusted R Square Std. Error of the Estimate     
.486 0.236 0.230 0.157      
       
       
 Sum of Squares Df Mean Square F Sig.  
Regression 7.085 1.012 41.199 <.001  
Residual 22.921 933 .025    
Total 30.006 940     
       
       
  Unstandardized Coefficients (Beta) Std. Error Standardized Coefficients (Beta) t Sig. VIF 
(Constant) -.730 .072  -10.201 <.001  
Winning Percentage .950 .093 .359 10.240 <.001 1.502 
New Stadium .274 .037 .216 7.480 <.001 1.016 
Salary -.050 .007 -.277 -6.627 <.001 2.138 
Strike Outs / Game .023 .007 .139 3.362 <.001 2.075 
RBIs .000 .000 .151 3.937 <.001 1.789 
Walks (Hitter) .000 .000 -.109 -3.019 .003 1.600 
Stolen Bases .000 .000 .073 2.396 .017 1.124 

The Magnitude of Stadium Novelty Effects

In this study we define the year prior to a new stadium as a “base-year” and then compare attendance in the new stadium to the base-year. This comparative process found an average change in attendance of 29.6% during the first year of play in a newly-constructed stadium. This 29.6% increase in attendance equates to an average increase of 762,263 fans for a new stadium’s inaugural season. Meanwhile, average marginal change for each successive year remains positive until year 21 as illustrated in Figure 3. By comparison, the average annual change in attendance increases for non-new stadium observations was just 2.36%, or an average increase of 63,553 fans for the study timeframe.

Figure 3: Average Percentage Change in Fan Attendance by Stadium Age 

As other studies indicate, attendance attributed to a new stadium is greatest during the first year and diminishes over time. In fact, based on study data new MLB attendance appears to decay at a rate of 1.19% per year after the introduction of the new stadium given the correlation of stadium age (in years) and percent change in attendance (r = .84). While it is unclear if all the factors contribute to attendance decay, it is plausible that the newness or novelty of the stadium diminishes while its new amenities become outdated and/or worn out. This study appears to provide a longer and slower decline in attendance extending Noll (26) that finds the stadium novelty effect is between seven and eleven years and dismisses the one-to-three-year effects that Hamilton and Kahn (16), Voight (36), Greenberg and Gray (14), and Howard and Crompton (18) all find.

A novelty of these findings is the approach used by defining the dependent variable as percent change in attendance in an effort to remove externalities that cannot be controlled across franchises. Annual attendance models using nominal annual attendance fail to capture the effect of stadium size variations and the size of the attendance variable which overweighs time-series data and can capture a very large portion of systemic error from year to year.

The Impact of On-Field Team Performance

This study further advances the current literature on stadium novelty effects by testing numerous team performance variables. Prior studies included a limited number of team performance variables such as “winning percentage” or “playoff appearances” (22). This study’s initial variable pool of 28 performance-related variables offers a more exhaustive list of performance metrics to (assumedly) better capture the influence of team performance on attendance in the presence of stadium novelty effects. In doing so, we find that five variables play a significant role in determining attendance, including: (a) winning percentage (b=.354, <.001); (b) strikeouts per game (b=.139, <.001); (c) RBIs (b=.151, <.001); (d) walks by hitter (b=-.109, .003); and (e) stolen bases (b=.073, .017). Meanwhile, team player salary (b=-.277, <.001), while a significant variable, appears to be negatively associated with attendance change. This finding is unusual and unexpected based on common perceptions that higher paid athletes tend to attract more attention.

As noted, a team’s winning percentage is found to be a key performance driver to attendance. As one can imagine, teams that perform better attract more fans. Data suggests that there is a significant correlation (r = .477) between winning percentage and home attendance figures (3). Likewise, “team ERA” is negatively associated with attendance (r = -.208) and “team batting average” is positively correlated with attendance (r = .221). In short, fans generally show up in greater numbers when teams improve on-field performance. On average, teams realize a modest 1.2% increase in home winning percentage a year after the new stadium is built, which is consistently found in other research (see 19, 20, 29, 31, 37).

CONCLUSIONS

This research builds further support for the impact new stadiums have on short-term fan attendance and financial outcomes. The building of a new stadium can be expected to increase season attendance by 29.3% for the first year of play. That elevated first-year attendance does not last forever. Rather, it tends to decline by approximately 1% per year for the next 20 years. During this entire 20-year span, overall fan attendance tends to remains higher than would have been predicted had the new stadium not been built in the first place.

By (a) modifying the dependent variable to a percent change in attendance and (b) including many more performance indicators as dependent variables, this study adds to the richness of the ongoing research into stadium novelty effects. Limitations of the study include the lack of multi-sport applications as this study focuses on Major League Baseball and does not include other professional sports such as soccer, football, or basketball. In addition, it does not include developmental and/or non-professional leagues.

Moreover, we do not account for cultural trends that may occur promoting or detracting from new stadium construction. Notably, over the time horizon, stadiums have moved from large capacity multi-use facilities to smaller ‘baseball-only’ spaces. Also, there is an increasing trend to re-locate stadiums outside of dense urban areas, Finally, the trend of sprawling multi-business complex models has also added to the art of new stadium construction. Today, new stadiums are built with an economic ecosystem surrounding the facility to include dining, entertainment, and other hospitality venues such as hotels. Finally, the model outlined in this research, while demonstrating sufficient fit statistics, fails to capture all the variation in marginal attendance change on a year-over-year basis. As such, future research should seek to include additional independent variables that can improve the model.

Stadium novelty effects are real and substantial. This study presents a new method to be used to measure and predict their impact on total attendance in any sport and at any level (college, professional, etc.).

APPLICATION IN SPORT

A baseball stadium is a fixed asset with an anticipated lifespan. No stadium lasts forever in its original form. At some point, a stadium must be remodeled or replaced to meet the needs of current consumers or fans may shy away from attending games. New stadiums can help grow attendance, diversify the fan base, and develop new revenue streams to help teams compete financially in Major League Baseball. While, new stadiums represent new branding opportunities, they also offer teams the opportunity to reach new audiences with improved and updated amenities. These benefits likely translate to greater financial outcomes for the team, however the financial debate is complicated affecting many stakeholders. While team owners may be obvious benefactors, the financial incentives offered by local governing bodies reflect a mutual perceived benefit from the broader tax-paying community.

As noted above, the introduction of a new stadium tends to trigger a large increase in first year attendance (over 29%) and while that figure tends to decline over time, the net result is that total attendance tends to stay higher than it would have been in the absence of new stadium construction for the next 20 years. This suggests local governments should be willing to consider some level of public financing for stadium construction for a minimum of 20 years, and possibly longer.

For teams that played in the 1980 MLB season, 6 teams continue to play in their original (albeit updated) stadiums: Boston Red Sox; Chicago Cubs; Kansas City Royals; Los Angeles Angels; Los Angeles Dodgers; and Oakland Athletics. Sixteen MLB teams have occupied 2 stadiums over this period while 3 teams have played in 3 different home stadiums over this 40+ year period. One team (the Montreal Expos) relocated to Washington, DC.

At the time of this writing, 3 new MLB ballparks have been projected including the Oakland A’s new park in Las Vegas with an estimated price tag of $1.75 billion as well as new parks in Tampa Bay and Kansas City. Meanwhile, the Chicago White Sox are exploring new park opportunities (9, 12). Beyond Major League Baseball, new stadium construction is viewed as an integral part of any team brand and fan-base strategy. At least five new Minor League Baseball parks have been built since 2020 including: Beloit Sky Carp’s ABC Supply Stadium; Kannapolis Cannon Ballers’ Atrium Health Ballpark; Worcester Red Sox’ Polar Park; Rocket City Trash Pandas’ Toyota Field; and the Wichita Wind Surge’s Riverfront Stadium (23, 25). It will be interesting to see the impact of these new stadiums on fan attendance in their respective cities.

The issue of new stadium construction and/or the massive remodel of existing baseball stadiums is also taking place in NCAA Division I baseball. The Board of Regents of Georgia State University (located in downtown Atlanta) have approved the construction of a new downtown baseball stadium in the footprint of the old Atlanta-Fulton County Stadium. The new stadium will allow the team to play closer to campus than their current stadium which is located 12 miles from their center-city location (13). Old Dominion University will play its entire 2025 baseball season in away games and/or nearby minor league stadiums (as available) as it remodels its on-campus baseball stadium (24).

Over the last decade, many schools in the Southeastern Conference (such as the University of Florida, University of Kentucky, Mississippi State University, and the University of South Carolina) have greatly expanded, or even replaced, their college baseball stadiums. This wave of stadium updates is expected to continue and spread to other sports and facilities. These new stadiums may possibly extend the research on stadium novelty effects into college sports.

Sports fans have many options for their time, attention, and entertainment dollar. Teams cannot assume casual fans will continue to attend games just because it is part of the local culture. Increasingly demanding fans want an updated fan experience, even in historical stadiums like Wrigley Field in Chicago or Fenway Park in Boston. This study demonstrates that overall attendance goes up when new MLB stadiums are built. While this spiked year-one attendance may decline modestly each year, this ‘decline’ is from an elevated number of fans due to the introduction of new stadium in prior years. So, in an interesting way, the ‘bonus attendance’ of the new stadium provides the cushion (or pays for) the modest reductions in attendance over time. Then, at some point in the future, the team may begin discussions of replacing their now 30-year-old stadium (again).

CONCLUDING REMARKS

When baseball fans wax poetically about their memories of MLB games from their childhoods, these descriptions are not limited to their favorite players. Embedded in these memories are the sights-and-sounds of the stadium, such as the glow of the lights for a night game, the call of the popcorn vendors, or the smell of a hot dog cooking on the grill. Enhancing the in-stadium fan experience is an integral part of success in the sports industry of today.

As noted earlier, 3 MLB teams have played in 3 different home stadiums over the timeframe of this study:

Atlanta Braves: Atlanta-Fulton County Stadium to Turner Field to the current Truist Park.

Minnesota Twins: Metropolitan Stadium to the Hubert Humphry Metrodome to the current Target Field.

Texas Rangers: Arlington Stadium to The Ballpark at Arlington to the current Globe Life Field.

It will be interesting to see the lifespan of these newer stadiums. When Atlanta-Fulton County Stadium, Metropolitan Stadium and Arlington Stadium were all originally constructed, no one could dream of the day when these shining new stadiums would be replaced. Living decades in the future, we know ‘the rest of the story.’ These stadiums have been replaced … and their replacement stadiums have been replaced. The long-term cycle continues.

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2025-05-16T09:56:27-05:00July 26th, 2025|General, Sport Education, Sports Facilities, Sports Management|Comments Off on The Novelty of New Stadiums: Evidence from 40 Years in Major League Baseball 
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