ISSN: 1543-9518

Examining Work Addiction, Burnout and Work-Family Conflict in Sport Organizations

Authors: Alexandrya H. Cairns1, Danielle Earnest2, Stephanie M. Singe3

1PhD, ATC, Assistant Professor, Department of Health and Movement Sciences, Southern Connecticut State University

2BS, Athletic Training Student, Department of Kinesiology University of Connecticut

3PhD, ATC, FNATA, Professor, Department of Kinesiology, University of Connecticut

 

Corresponding Author:

[email protected]

ABSTRACT 


Purpose: The culture of National Collegiate Athletics Association (NCAA) Division I (DI) athletics can stimulate a culture that appears to “greedy” placing high demands on the time and energy of those working within the sport organization. These intense demands create the potential for experiences of work addiction, burnout, and work-family conflict among sport professionals. We aimed to examine the overall experiences of work addiction, burnout, and work-family conflict within the NCAA DI sport organization. Methods: We used an online cross-sectional survey (Qualtrics, Provo, UT) composed of demographics, measurement tools for work addiction, burnout, and work-family conflict. Each of the scales have strong internal consistency as reported by Cronbach’s alpha scores. The study was distributed to certified athletic trainers (AT), coaches, and sport performance coaches (SPC) working full-time in their position at an NCAA DI institution. Results: There was no significant difference in reported scores on the BWAS between athletic trainers and coaches (U = 3952.00, p = .160), and no significant difference was found between sport performance coaches and athletic trainers (U = 5894.00, p = .879). A significant difference of burnout levels between athletic trainers and coaches was revealed (U = 3559.50, p = .017) andno significant difference discovered in the reported levels of burnout between athletic trainers and sport performance coaches (U = 5483.00, p = .313). There was no significant difference between athletic trainers and coaches for work-family conflict (U = 4483.00, p =.939), or sport performance sport performance coaches and athletic trainers (U= 5576.50, p = .416). Conclusions: Our results indicate that work addiction and work-family conflict are experienced similarly across the sport organization. Athletic trainers were found to experience higher levels of burnout compared to coaches, but similar levels to sport performance coaches. Application in Sport: Implementing policies that address work and family strain coaches, athletic trainers, and sport performance coaches can face working in sport is important. Although overall burnout was low, athletic trainers were at greater risk; thus addressing the factors causing them to have greater levels of burnout than other 2 stakeholder groups is important.

Key Words: stress, role strain, workplace dynamics, organization conflict

INTRODUCTION 

Working within a collegiate sport organization places high demands on an individual, regardless of the role they play within that organization. The demands of the individual working in sport can include long working hours (+40 hours a week) that extend into nights and weekends (Laskowski & Ebben, 2016; Mazerolle et al., 2011; Scriber & Alderman, 2005; Singe et al., 2023b). Working hours are often accompanied by the need to be physically present, limiting flexibility and autonomy over work scheduling (Laskowski & Ebben, 2016; Mazerolle et al., 2011; Scriber & Alderman, 2005; Singe et al., 2023b). Organizational culture represents the underlying beliefs, values, and assumptions within an organization (Schein, 2010). The culture within sport organization has been described as one that is influenced by commercialization which has led to pressures to win at all costs due to the financial implications (Pope & Pope, 2014). Coaches, athletic trainers, and others working in sport organizations can feel the pressures associated with this culture, which can increase their stress, and influence their perceptions of work saliency, work-family conflict, and burnout.

Work addiction is a preoccupation with work (Andreassen, 2014; Robinson, 1999); and can be conceptualized as an individual who prioritizes their work over other responsibilities, which can lead to work-family conflict (Eason et al., 2022). Working in sport may have an influence on experiences of work addiction, particularly if the expectations around success and commitment hinge on prioritizing work. Coaches, athletic trainers, and sport performance coaches all contribute to the mission of the sport organization yet have very different and unique roles. Thus, the level of work addiction each of these individuals working in sport may demonstrate could vary, as well as the influence it may have on burnout and work-family. Research has examined experiences of burnout and work-family conflict among coaches and athletic trainers, independently, but not simultaneously (Graham & Smith, 2021; Singe et al., 2022). Organizational factors unique to sport are perhaps keys to understanding why burnout and work-family conflict occur, and better understanding if the role assumed in the sport organization can contribute.

LITERATURE REVIEW

Working Within the Sport Organization

The National Collegiate Athletic Association (NCAA) is the governing body that administers intercollegiate athletics in the United States. The NCAA is subdivided into three different divisions to create a fair playing field where teams are competing with schools at a similar level. Many factors separate the three subdivisions including media attention, airtime, and of course resources centered around finances and scholarship (Overview, n.d.). The NCAA Division I (DI) schools typically house the largest student bodies and possess the greatest number of athletic scholarship opportunities largely attributed to their large athletic budgets. Working within the NCAA DI setting comes with increased pressures and stress (Singe et al., 2022; Taylor et al., 2019) , particularly for coaches as they must produce through wins as well as retain students in their programs (Norris et al., 2017; Singe et al., 2022). The NCAA DI programs have large budgets which has the potential to play a significant role in the pressures and stress faced by those who are employed in the division.

At the NCAA Division II (DII) level student-athletes are offered scholarships to participate, but the number per sport is much less than the NCAA DI setting (Our Division II Story, n.d.).The expectations of those student-athletes participating at this level are somewhat less than the NCAA DI level, as time demands are slightly less (Our Division II Story, n.d.). The overall philosophy of the NCAA DII setting is one about balance, in which student-athletes are pushed to excel in their sport, but also in the classroom and campus community (Our Division II story, n.d.).

The NCAA Division III (DIII) level does not award scholarships generated from athletic participation (Our Three Divisions, n.d.), and has been described as a setting that encourages student first, and athlete second. Since there are no athletic scholarships offered, the budgets within these programs are much less than the other two divisions. The demands and expectations within the NCAA DIII setting are much less than and considered to be the most well-balanced collegiate experience (Our Division III Story, n.d.).

Working in the intercollegiate setting has been described as high-pressure, demanding, and one that can increase feelings of stress. Work addiction, burnout, and challenges with work-life balance have been found to occur for those working in intercollegiate sport, including coaches, athletic administrators, sports information specialists, and athletic trainers (Dixon & Bruening, 2005; Eason et al., 2022; Graham & Smith, 2022; Hatfield & Johnson, 2012). Causative factors linked to these challenges of working in sport include culture expectations within the workplace, time demands, inflexible work schedules, travel, and role incongruence. Sport is founded on the premise of teamwork and each member of the team has a critical role to support team success. Coaches, athletic trainers, and sport performance coaches are key members within the intercollegiate setting with unique roles supporting the student-athlete. Each has different roles, responsibilities, and expectations, and evidence that suggests those working in the intercollegiate setting are challenged to push beyond their work saliency leaving them vulnerable to work addiction, burnout, and work-family conflict. 

Work Addiction and Sport

Workaholism is conceptualized as something that occurs when a person becomes completely engulfed in their work, investing their time and energy in their work life (McMillan et al., 2003). Those who display characteristics of a workaholic are prone to experiences of increased stress, burnout, and work-family conflict (Clark et al., 2016; Eason et al., 2022). One’s career has been associated with higher experiences of workaholism, such as sport as the culture is one of sacrifice, expectations to put in long work hours, and choosing work over one’s personal life (Dixon & Bruening, 2005; Graham & Dixon, 2014). Workaholics have a high involvement in their work (i.e. working long hours), have a hard time disengaging from work, and feel compelled or driven to work (McMillan et al., 2003). Working harder than perhaps their job requires workaholics will then start neglecting their lives outside of their jobs (Schaufeli et al., 2008).

Coaches, athletic trainers, and sport performance coaches all must work long hours; in fact, athletic trainers have reported working 60+ hour work weeks, extending into nights and weekends (Bruening & Dixon, 2007; Singe et al., 2023b; Snarr & Beasley, 2022). These long working hours reported by individuals working in sport have been attributed to burnout and work-family conflict (Eason et al., 2022), and recently have been suggested to be perhaps driven by work addiction ( Eason et al., 2022) or associated with it (Taylor et al., 2019). Work addiction can be explained as an individual factor that can be attributed to one’s experiences of work-family conflict or burnout, and job demands such as long hours can be an organizational construct that influences work-family conflict or burnout (Cayton & Valovich McLeod, 2020; Eason et al., 2022). What is unknown is the aspects such as the navigation of long working hours and personal attributes of a coach, athletic trainer, or sport performance coach necessary to be successful members working in intercollegiate athletics.

Work addiction has seven core components or symptoms: salience, mood modification, tolerance, withdrawal, conflict, relapse, and problems. These symptoms have been developed into a scale, the Bergen Work Addiction Scale (BWAS) as outlined by Andreassen et al. (2014) salience (the activity dominates thinking and behavior), tolerance (increasing amounts of the activity are required to achieve initial effects), mood modification (the activity modifies/improves mood), relapse (tendency for reversion to earlier patterns of the activity after abstinence of control), withdrawal (occurrence of unpleasant feelings when the activity if discontinued or suddenly reduced), conflict (the activity comes into conflict with personal life, needs, and relationships), and problems (caused by being greatly engaged in the activity).

Experiences of Burnout in Athletics

Burnout is one of the many identified stressors of those working in athletics largely attributed to the long working hours, high workloads, and demands (Singe et al., 2023b). Burnout has been defined as the degree of physical and psychological fatigue experienced by a person that can be attributed to personal, work, or client-related stress (Cairns et al., 2023; Kristensen et al., 2005). Organizational factors have been identified in being the greatest influence over experiences of burnout (Barrett et al., 2016). Individual factors such as personality have also been observed to influence burnout as well. Burnout has been positively associated with role strain, neuroticism, and work-family conflict (Barrett et al., 2016; Cayton & Valovich McLeod, 2020). The demanding environment of athletics involves high emotional involvement, stress, responsibility, and time restraints (Cayton & Valovich McLeod, 2020; Mazerolle et al., 2008). Furthermore, the organization commonly inadequately compensates their employees while still expecting them to work long hours with inadequate numbers of staff, a lack of control over scheduling, and limited time off (Bruening & Dixon, 2007; Cayton & Valovich McLeod, 2020). The combination of these factors places those working within the sport organization at an increased risk of experiencing burnout. Positive relationships have been observed between burnout, work-family conflict, and intention to leave, while negative relationships have been observed with job and life satisfaction for those experiencing burnout (Mazerolle et al., 2008).

Due to the predispositions those working in sport face, burnout has been widely studied in sport. Those working in sports have been shown to experience moderate levels of burnout (Cairns et al., 2023; Singe et al., 2023a; Snarr & Beasley, 2022). However, there have been slight fluctuations in reported levels of burnout since the pandemic with levels of burnout lessening (Cairns et al., 2023). Sport professionals also tend to report high levels of personal and work-related burnout (Singe et al., 2023a; Taylor et al., 2019). Levels of personal burnout have a positive relationship with working hours and a negative relationship with hours of sleep (Singe et al., 2023a). Men and women report similar levels of burnout, suggesting that gender is not a significant predictor of experiences of burnout (Cairns et al., 2023). Incorporating coping strategies such as social support, continuing education, and self-care in addition to organizational support have all been associated with decreased levels of burnout in sport (Singe et al., 2023a; Snarr & Beasley, 2022).

Work-family Conflict

Work-family conflict defined as a form of inter-role conflict. The conflict occurs when the general demands of, time devoted to, and strain created by the job interfere with performing family-related responsibilities (Netemeyer et al., 1996). With the high demands concerning time and presence associated with working in sport, work-family conflict is a prominent area of interest within the sport organization. Work-family conflict has been framed as a complex construct that is explained by individual, organizational/structural, and socio-cultural factors (Dixon & Bruening, 2005). This integrated approach to the exploration of work-family conflict within sport is increasingly important as studies have shown the presence of work-family conflict across the sport organization regardless of factors such as job, age, sex, or family/marital status. (Bruening & Dixon, 2007; Mazerolle et al., 2008) .

While work-family conflict is experienced regardless of demographic factors, there have been increased levels of work-family conflict associated with marital and parental statuses. Those who are married with children are more likely to experience greater levels of work-family conflict (Singe et al., 2022). Setting has also been seen to play a role in the experiences of work-family conflict with those working in collegiate athletics reporting higher levels than those in the secondary setting (Mazerolle et al., 2015). Experiences of work-family conflict among those working in the sport organization have also been seen to be above average (Mazerolle et al., 2015). Previous research has also suggested that working within the NCAA DI setting increases experiences of work-family conflict (Singe et al., 2022). This is supported by findings that those working in the NCAA DI setting report greater levels of work-family conflict compared to those working in the NCAA DIII setting which could likely be attributed to the increased demand of the DI setting (Singe et al., 2022). Beyond intense professional demands, long working hours, lack of control over work schedules, and unbalanced workloads were all also related to increased conflict at the DI level (Mazerolle et al., 2011). Within the sport organization, four types of conflict have been found attributing to work-family conflict: time, energy, attention, and emotional spillover (Graham & Smith, 2022). However, several organizational and personal strategies help establish work-family balance. As an organization, the implementation of staffing policies and the creation of a supportive work environment help in reducing experiences of work-family conflict (Mazerolle et al., 2011). Individual management strategies can be broken down into personal factors and individual strategies on the professional level. Individual strategies involve the incorporation of teamwork, boundary setting, prioritization, and integration of family with work (Mazerolle et al., 2011). Personal factors focus greatly on the separation and work and life as well as the establishment of a support network (Mazerolle et al., 2011).

Purpose

Despite the growing body of research dedicated to the examination of these constructs within the sport organization, there remains a need for a better understanding of the varied experiences held by different stakeholders within the organization. Additionally, the exploration of work-addiction within the sport organization is novel. Therefore, the purpose of this study was to examine overall experiences of burnout, work addiction, and work-family conflict within sport organizations. Additionally, this study seeks to compare these experiences among the various stakeholders within the sport organization. Given this information, we hypothesized the following:

H1a– Coaches will report greater levels of work addiction compared to athletic trainers.

H2b– Athletic trainers will report greater levels of work addiction compared to sport performance coaches.

H2a– Athletic trainers will report greater levels of burnout compared to coaches.

H2b– Athletic trainers will report greater levels of burnout compared to sport performance coaches.

H3a– Athletic trainers will report greater levels of work-family conflict compared to coaches.

H3b– Athletic trainers will report greater levels of work-family conflict compared to sport performance coaches.

H4a– Work addiction and work-family conflict will have a positive relationship.

H4b– Work addition and burnout will have a positive relationship. 

METHODS 

Study design

The study design is a web-based cross-sectional study (Qualtrics, Provo, UT). Data was collected using a self-reported online questionnaire evaluating sleep, self-care, work-family conflict, work addiction, and burnout among NCAA Division I collegiate athletic trainers, coaches, and sport performance coaches. Approval for this study was obtained from the institutional review board (IRB) prior to data collection, which occurred over a four-week period in the Fall of 2023.

Procedures

Prior to survey distribution, we completed a face validity process; 3 athletic trainers took the survey for the purposes of the process. No changes were made to the survey based on the face validity feedback. Two email reminders were sent at the 1-week and 3-week marks, reminding participants to complete the survey.

Participants

The target population for the current study were NCAA Division I (DI) athletic trainers, sport performance coaches, and coaches. A list of all NCAA DI institutions was created using the NCSA college recruiting website (n = 363). From the list of institutions offering NCAA DI athletics programs, the individual athletics websites were accessed to create a list of emails for those individuals identified as an athletic trainer, sport performance coach, or a head or assistant coach. We were able to identify 13,412 email addresses across the 3 stakeholder groups. Our power analysis indicated a requirement of 258 respondents, which resulted in 86 participants from each stakeholder (group). Strata randomization was utilized since we did not have a complete list of all possible participants, thus phases of distribution were utilized and represented in Figure 1.  

Figure 1. Recruitment and Data Screening

Sample

A total of 153 athletic trainers (51.5%), 59 coaches (19.9%), and 78 sports performance coaches (26.3%) completed this research study. Of the participants, 166 were female (55.9%), 121 male (40.7%), and 2 preferred not to answer (0.7%). The mean age of the participants in this study was 33 ± 9, with ages ranging from 22 – 70 years. Participants on average had 10 ± 9 years of experience, with an average of 5 ± 6 years working at their current institution. On average, participants worked 55 ± 16 hours per week. Complete demographic data is shown in Table 1.

Table 1. Participant Demographics

DemographicScore
Gender, n (%)
    Male121 (40.7)
    Female166 (55.9)
    Prefer not to answer2 (0.7)
Highest level of education, n (%)
    Bachelor’s Degree48 (16.2)
    Master’s Degree237 (79.8)
    Doctorate5 (1.7)
Primary Role, n (%)
    Head Coach18 (6.1)
    Associate Coach9 (3.0)
    Assistant Coach34 (11.4)
    Head Athletic Trainer15 (5.1)
    Associate Athletic Trainer37 (12.5)
    Staff/Assistant Athletic Trainer99 (33.3)
    Director, Sport Performance (Conditioning)23 (7.7)
    Strength and Conditioning Coach51 (17.2)
Marital status, n (%)
    Single137 (46.1)
    Cohabitating28 (9.4)
    Married117 (39.4)
    Separated2 (0.7)
    Divorced3 (0.7)
    Widowed1 (0.3)
    Engaged3 (1.0)
Spouse employment status n (%)
    Employed, full-time210 (70.7)
    Employed, part-time18 (6.1)
    Does not work/stay at home25 (8.4)
Children, n (%)
    0202 (70.0)
    Currently Pregnant8 (2.7)
    121 (7.1)
    233 (11.1)
    3+27 (8.8)
Group Identity, n (%) 
    Single Female112 (37.7)
    Single Male33 (11.1)
    Married Female43 (14.5)
    Married Male80 (26.9)
    This does not apply to me20 (6.7)

Instrumentation

The online survey was hosted in Qualtrics and included 36-items not including the demographic questions. Participants completed 13 demographic questions, prior to the 3 scales (i.e. Copenhagen Burnout Inventory (CBI), Bergen Work Addiction Scale, and Work-Family Conflict), which were not altered as they are valid instruments.  

Burnout. Burnout was measured using the CBI as a tool that demonstrates reliability (α=.85-.87) and had been used previously to measure burnout among athletic trainers (α=.88) (Kristensen et al., 2005; Naugle et al., 2013). The scale included 3 subscales: personal (n=6-items), work-related (n=7-items), and client-based burnout (n=6-items). Participants use a 5-point Likert scale 0 (never/almost never/low degree), 25 (seldom/low degree), 50 (somewhat or sometimes), 75 (often/high degree), and 100 (always/high degree). The scale is summed for an overall burnout score, with a higher score indicating a higher level of burnout (0 is low, 100 is severe).

Work addiction. The Bergen Work Addiction Scale (BWAS) was used to measure work addiction (α=.78) among our sample. The scale has 7-items, each representing an aspect, or symptom of work addiction (salience, mood modification, tolerance, withdrawal, conflict, relapse, and problems – Table 6). The 7-items are assessed using a 5-point Likert scale, 1 (never) to 5 (always). The responses are summed (range 7 to 35), and a score of 4 (often) or 5 (always) on 4 of 7 items indicates a high risk for work addiction.

Work-family conflict. Work-family conflict scale was assessed using the scale previously validated by Netemeyer et al. (α=.90). The 10-item scale evaluates the bi-directional nature of the construct; 5-items for work-family conflict (WFC) and 5-items for family-work conflict (FWC). Participants indicated their responses on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). Sample questions include: “WFC®The demands of my job interfere with my home and family life,” and “FWC®The things I want to do at home do not get done because of the demands of my job.”

Data analyses

The data collected via Qualtrics was transferred to Excel by Microsoft Corporation. Following the completion of data collection, it underwent a filtration process to remove incomplete responses, defined as those failing to complete the required scales or the survey itself as per the scale validation. Subsequently, the filtered data was imported into SPSS, version, etc., for statistical analysis. Demographic information such as age, gender, and marital status were obtained through specific questions, and these demographic variables were subjected to descriptive and frequency analyses. The outcomes are presented as mean and standard deviation or frequency. Validated scales were assessed using means due to the non-parametric nature of the data analysis at hand, and Cohens d is reported for effect size.

RESULTS

Participant Demographics

Participants were 51.5% athletic trainers (n = 153), 19.9% were coaches (n=59), and 26.3% were sports performance coaches (n = 78). The average age of the participants was 33 ± 9 and they had been working in their respective roles for an average of 11± 9. They self-reported working 55 ± 17 hours per week (at the time of data collection).

Stakeholders and Work-Addiction

The mean score on the BWAS across all three stakeholder groups was 20.71 ± 4.57. Table 2 represents the mean scores on the BWAS, reported by each stakeholder group. Athletic trainers reported a score of 20.84 ± 4.51, whereas coaches reported a mean score of 20.05 ± 4.85. There was no significant difference in reported scores on the BWAS between athletic trainers and coaches (U = 3952.00, p = .160, d= 0.11). Additionally, sport performance coaches reported a mean score of 20.96 ± 4.50, and no significant difference was found between sport performance coaches and athletic trainers (U = 5894.00, p = .879, d= -0.010). Furthermore, across all three stakeholders, 80 were found to be workaholics while 210 (38%) were found not to be work addicted. Among athletic trainers, 45 of the 153 (29%) respondents were found to be workaholics. Of coaches, 12 of the 59 (20%) respondents were found to be workaholics. Among sport performance coaches, 23 of 78 (29%) respondents were found to be workaholics.

Stakeholders and Burnout

Across all three stakeholder groups, participants reported low levels (46.27 ± 16.04) on the CBI, additionally mean scores of 54.9 5 ± 17.24 on the personal-related subscale, 49.99 ± 18.87 on the work-related subscale, and 33.25 ± 18.67 on the client-related subscale. Table 2 represents the mean scores on the CBI and subscales, reported by each stakeholder group. Athletic trainers reported a mean score of 48.07 ± 16.42 on the CBI, while coaches reported a mean score of 41.99 ± 15.89 on the CBI. A significant difference of burnout levels between athletic trainers and coaches was revealed (U = 3559.50, p = .017, d= -0.16).Additionally, sport performance coaches reported a mean score of 45.97 ± 14.92. There was no significant difference discovered in the reported levels of burnout between athletic trainers and sport performance coaches (U = 5483.00, p = .313, d= -0.06).

Table 2: Comparison of Reported Scale Scores by Stakeholder

StakeholderCBI (Mean±SD)BWAS (Mean±SD)WFC (Mean±SD)
Athletic Trainers48.07±16.4220.84±4.5137.66±9.26
Coaches41.99±15.8920.05±4.8537.64±10.52
Sports Performance45.97±14.9220.96±4.5037.86±9.49

Stakeholders and Work-Family Conflict

The mean score across all stakeholders on the WFC scale was 37.71 ± 9.56. Athletic trainers reported a mean of 37.66 ± 9.26, whereas coaches reported a mean of 37.64 ± 10.52. There was no significant difference between athletic trainers and coaches (U = 4483.00, p =.939, d= -0.05).Furthermore, sport performance coaches reported a mean of 37.86 ± 9.49, and no significant difference was found between sport performance coaches and athletic trainers (U= 5576.50, p = .416, d= -0.06).

Variable relationships

Correlation matrices revealed a moderate positive correlation (.507) between work addiction and work-family conflict. Work addiction and burnout also resulted in a moderate positive relationship (.573).

DISCUSSION

Inferences has been made that working in sport can lead to experiences of burnout and work-family conflict, as well as that to be a productive member of the team one must be addicted to their role. Our purpose was to explore the experiences of work-addiction, burnout, and work-family conflict among athletic trainers, coaches, and sport performance coaches. This aim was directed at better understanding around one’s role in the sport organization and experiences of these constructs. As predicted work addiction, regardless of stakeholder position, leads to increased levels of burnout and work-family conflict. Uniquely, athletic trainers and coaches experience higher levels of burnout than sport performance coaches.

Stakeholders and Work-Family Conflict

We did not find any significant differences among our samples and experiences of WFC. The total mean score on the WFC scale is comparative to other studies examining WFC among athletic trainers work in the sport industry (Mazerolle et al. 2011; Pitney et al. 2011; Singe et al. in press). Our sample was largely represented by those who do not have children (70%); which could explain why we did not find any differences among our sample regarding experiences of WFC. Time is often a large facilitator of WFC, despite our sample reporting 55 hours per week, many did not have children another facilitator of WFC (Mazerolle et al., 2008; Pitney et al., 2011; Singe et al., 2023a). Perhaps working long hours has less of an impact on the individual when additional family responsibilities are not present, and one can focus on work and personal interests.

Stakeholders and Burnout

Overall, this sample of individuals working in the sport organization are experiencing low levels of burnout. Low levels of burnout does not imply that our sample is not experiencing it; however quantifiably it is lower. The literature over the last 5 years has suggested that coaches and athletic trainers are experiencing higher levels of burnout (Goodger et al., 2007; Singe et al., 2024; Singe et al., 2023a). We found that athletic trainers reported higher levels of burnout compared to coaches, but similar levels of burnout to sport performance coaches. Moderate levels of burnout have recently been reported among athletic trainers  (Singe et al., 2023a); however, fluctuations in experiences have been observed over the past 3 years with levels varying between moderate and low (Cairns et al., 2023; Oglesby et al., 2020; Singe et al., 2023a). Sport performance coaches have yet to be identified within the literature regarding burnout; our sample reported similar levels of burnout as athletic trainers. Similar to athletic trainers, sport performance coaches have high demands placed upon them, and they are invested in the success of their athletes as well as log long hours in the workplace (Bentzen et al., 2016; Olusoga et al., 2019).

Stakeholders and Work-Addiction

Our overall sample is not classified as a workaholic; however, both athletic trainers and sport performance coaches demonstrate a larger sample (29%) of those who would be classified as such. Workaholics may work long hours but that is by choice and perhaps not as a necessity (Andersen et al., 2023). Although our sample reports working excessive hours (55), they do not self-identify as workaholics. Moreover, we did not find significant differences between stakeholders. These findings suggest that work addiction is likely an individualized factor, and not necessarily an outcome of working in sport organization. As detailed in the work-family conflict framework of Bruening and Dixon (2005, 2007), there are individual, organizational, and sociocultural outcomes of experiences of work-family conflict.  

Variable relationships

Positive relationships were found between work addiction and both burnout and work-family conflict. The correlations found between the experiences of these constructs are consistent with those observed in previous studies examining these constructs in athletic trainers (Eason et al., 2022). These results make it apparent that experiences of work addiction, work-family conflict, and burnout occur at the same time. Previously stated, work-addiction can be attributed to experiences of work-family conflict and burnout. In this case all stakeholders are experiencing all three constructs.

We predicted there to be positive relationships between work addiction, burnout, and work-family conflict. Work addiction is yet another construct that is experienced by those working in the sport organization. This study adds to the literature that there are no differences in work-family conflict and burnout across athletic trainers, coaches, and SPCs. Yet, there are notable differences when it comes to burnout. Coaches and SPCs are experiencing work-family conflict, and work-addiction similarly to athletic trainers. This speaks to the sport organization as a whole; all employees are encountering these constructs. We suggest the sport organization investigate and assess reasons employees are work-addicted and have work-family conflict, to improve job and life satisfaction.

ATs experienced higher levels of burnout compared to coaches, and SPCs. There are many reasons this may be, the number of athletes per employee, responsibilities, and medical roles. However, in this sample athletic trainers reported low levels of burnout, though higher than coaches and SPCs, not quite as high as levels in recent literature (Barrett et al., 2016).

Consideration for Future Research and Study Limitations

The findings of this study expand upon the growing body of literature examining the constructs of work-addiction, burnout, and work-family conflict within the sport organization, yet limitations on these findings remain. Our study received 297 usable responses, which is a lower response rate than anticipated. Due to these factors, we recognize that these findings may not represent the experiences of all of those working within the sport organization. Our database was established using publicly available information therefore a complete list of all athletic trainers, coaches, and sports performance coaches at the DI level was unable to be obtained. Therefore, the results of this study may not represent the experiences of the entirety of NCAA DI athletic trainers, coaches, and SPCs. Our study also only examined those working within the NCAA DI setting; thus, those working in the DII, DIII, NAIA, or other collegiate levels may not have similar experiences with these constructs. Furthermore, those working in secondary schools or other settings also may not identify with the findings of this study.

Further research should include the investigation of work-addiction, burnout, and work-family conflict at all levels of collegiate athletics as well as those in secondary schools and alternate settings. Currently, the literature has examined these constructs within the sport organization solely focused on the experiences of athletic trainers, creating a need for future research among coaches and sports performance coaches on these constructs. Additionally, the study of work addiction within the sport organization is a novel issue, so further research is necessary to gain a better understanding of work addiction within athletics.

CONCLUSION 

This study sought to further our knowledge of the experiences of athletic trainers, coaches, and sport performance coaches in the DI setting, regarding work-addiction, burnout, and work-family conflict. Experiences were nearly universal across the sport organization except for athletic trainers experiencing greater levels of burnout compared to coaches. Positive relationships were also observed between levels of work addiction and both burnout and work-family conflict. The findings of this study suggest that these constructs are prominent issues across the sport organization. Given the prevalence across the sport organization, increased implication of both personal and organizational strategies may be necessary as a means of mitigating the impact of these issues (Cairns et al., 2023; Singe et al., 2022). This study serves as a preliminary exploration into the variance of experiences of work addiction, burnout, and work-family conflict across the sport organization stakeholders.

APPLICATIONS IN SPORT

Athletic trainers reported significantly different levels of burnout compared to coaches and sport performance coaches; thus we believe that understanding the specific role stressors for the athletic trainer can help address potential programs to prevent burnout. For example, wellness programs or a workload redistribution may be warranted for athletic trainers.  We did not find any differences among work-family conflict among any of the grups, which suggests more broad based policies that are family-friendly may help athletic trainers, coaches, and sport performance coaches (family-leave, time-off policies). Work addiction was a risk factor for both burnout and work-family conflict among our stakeholders, thus individuals and supervisors should be aware of the signs of burnout, but also encourage stress and boundary management,  as well as healthy work habits to prevent issues around burnout and conflicts between work and home. 

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2025-12-09T16:14:43-06:00June 17th, 2026|Contemporary Sports Issues, General, Leadership, Sports Health & Fitness, Sports Management, Sports Studies, Sports Studies and Sports Psychology|Comments Off on Examining Work Addiction, Burnout and Work-Family Conflict in Sport Organizations
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An Analysis of Carbon Emissions from College Football Recruiting Visits

Authors: Jeffrey J. Fountain1, Thomas Wuerzer2, & Peter S. Finley1

1Department of Management, Nova Southeastern University, Fort Lauderdale, FL, USA

2Department of Public Administration and Real Estate Development, Nova Southeastern University, Fort Lauderdale, FL, USA

 

Corresponding Author:

Jeffrey J. Fountain, Ph.D.

3301 College Avenue

Fort Lauderdale, FL 33314

[email protected]

954-262-8129

Jeffrey Fountain, Ph.D., and Peter Finley, Ph.D., are Professors of Sport Management at the H. Wayne Huizenga College of Business and Entrepreneurship at Nova Southeastern University. Their research interests focus on issues in college athletics.

Thomas Wuerzer, Ph.D., is Professor in the Department of Public Administration & Real Estate Development at Nova Southeastern University. His research focus is on Geographic Information Systems.

ABSTRACT 

Recruiting college football players to come play for a National Collegiate Athletic Association (NCAA) Power-5 school is highly competitive, with each school inviting recruits nationwide on official campus visits. By estimating the carbon emissions generated, this study examined the environmental impact of official recruiting visits (n = 7,045) to Power-5 schools from 2013 to 2020. Using Geographic Information Systems (GIS) to geocode recruits’ hometowns and calculate travel distances, a Recruit Visit Carbon Footprint (RVCF) was calculated to approximate the CO2eq emissions for each visit. The analysis focused on the 23 Power-5 schools with over 250 reported official visits. The findings revealed substantial variability in RVCF among schools, with 15 of the 23 schools increasing their carbon footprint in the latter years of the study. Still, the higher-spending athletic departments tended to have lower RVCFs. The findings provide valuable insights into the environmental impact of recruiting activities and highlight the importance of addressing this overlooked aspect of college sports’ carbon emissions.

KEYWORDS: Carbon Footprint, Power-5, Recruiting, Official Visit, College Football

INTRODUCTION 

As societal awareness of the environmental impact of both mega sporting events and routine contests (regular season games) has increased, many sports organizations, teams, and sponsors have come to understand the need to assess the carbon footprint they create (10). As noted by Dolf et al. (13), several researchers have stressed that sports events are worth investigating to leverage broader change because of the high-profile nature of such events, because they are capable of creating real and meaningful action (11, 19). Several athletic departments have promoted their initiatives throughout the last decade and publicized their efforts to reduce and offset their environmental impact by tracking and reducing carbon dioxide-equivalent emissions (CO2eq). The typical path toward claiming to be carbon neutral for college athletic departments is to assess the environmental impact of the day-to-day operations, home game operations, and off-campus travel for official tournaments and games. However, it is important to recognize that the carbon footprint begins long before sporting events are played; for college sports, this goes back to the initial recruitment phase of the athletes, which typically requires them to travel as part of the recruiting process.

In 2020, the Power-5 conferences included the Atlantic Coast Conference (ACC), Big 12 Conference, Big Ten Conference, Pacific 12 Conference, and the Southeastern Conference (SEC). Over the years, the number of Power-5 schools increasing their investment in recruiting athletes has grown, with 38 of the 52 public Power-5 schools reporting a significant growth in overall athletic department recruiting expenditures (37). One extreme example was the University of Georgia’s athletic department, which increased its overall recruiting budget from $308,000 in 2005 to $4.5 million by 2022 (23).

Recruiting

Each recruit is permitted one official visit per school, extendable only if there is a change in the coaching staff, with each visit lasting no more than 48 hours or one weekend (29). Visits are classified by the funding source; when the host school covers expenses such as transportation, lodging, meals, and entertainment for the recruit and their parents or guardians, it is deemed an official visit (29). Historically, recruits were limited to five official visits; however, this cap was removed in 2023, allowing unlimited visits while maintaining the “only one visit per school” rule (30).

College football recruiting visits often feature expensive, extravagant events designed to attract recruits (12, 24, 36). The financial commitment to a recruiting weekend at Clemson University in the fall of 2019, during which the Tigers brought eleven prospects to campus (they would eventually sign ten of them), ended with a total bill of $85,000 (32). While the NCAA prohibits media from attending recruiting events or interacting with prospects while on campus, the expenditures from that weekend provided insight into the itinerary, which included travel by professional car service to and from local airports, flights to Greenville-Spartanburg, and transportation to the campus, about 40 miles away. In addition, two charter buses were used to transport prospects and their families to the finest restaurants in the area, including a steakhouse about 45 minutes from campus (32). Another example was the University of Texas spending over $280,000 during a single weekend in June 2022 to host nine recruits, including highly touted quarterback Arch Manning (20).

Carbon Footprinting

The concept and measurement of an “ecological footprint” was introduced by Wackernagel and Rees (34) and originally quantified the land and sea area necessary to support human populations. Subsequent adaptations of this concept have focused on the “carbon footprint,” which estimates the land required to sequester CO2 emissions attributable to human activities. This notion has evolved into broader assessments such as the “life cycle impact,” which evaluates the environmental impact of products and services throughout their life cycles (31).

Research by Čuček et al. (9) and Pandey et al. (31) expanded the scope of assessment to include calculating sustainability metrics and measuring energy, water, and ecological impacts. These studies defined a carbon footprint as “the quantity of Greenhouse Gases (GHGs), expressed in terms of CO2 equivalents, emitted by an individual, organization, process, product, or event within a specified boundary” (31) and as “a quantitative measurement describing the appropriation of natural resources by humans,” (9). This study adopted these definitions to evaluate the carbon footprint of prospective college football players while making their official recruiting visits to college campuses.

Attempts to measure carbon footprint related to sports have historically focused on major events and the travel of sports teams. Examples include the findings that approximately 560 tons of CO2eq was created during the 2004 Football Association (FA) Cup Final in the United Kingdom (one soccer game) (4), 1,260 tons of CO2eq for the 2004 Wales Rally (an Autosport’s event over four days) (5), and 144,120 tons of CO2eq for the stages of the Tour de France held in the United Kingdom in 2007 (the Prologue and Stage One) (6). Most studies focused solely on the carbon footprint of spectators, though a limited number of studies examined participants, such as teams and staff members.

The environmental impact of all college activities, including collegiate sports has garnered significant attention (28). However, there appears to be no available research that has explicitly focused on the environmental impact (carbon footprint) produced throughout the college football recruiting season. Therefore, the researchers sought to explore and determine the approximate carbon emissions produced during official college football recruiting visits from Power-5 schools. This study utilized the reported official recruiting visits between 2013 and 2020. Using Geographic Information Systems (GIS) to conduct spatial analysis of multimodal travel, including car and plane trips, the researchers were able to calculate the approximate travel distances and corresponding carbon footprint of each recruit.

The Recruit Visit Carbon Footprint (RVCF) was created as a proxy measure utilizing prior carbon footprinting research of sport tourism. This approach enabled a systematic exploration of three primary research questions.

RQ1: Which Power-5 schools generated the largest RVCF between 2013 and 2020?

RQ2: Did RVCF totals increase or decrease over time?

RQ3: Was there a correlation between each school’s financial, recruiting, and performance variables and their RVCF?

METHODS 

Data Collection

Data on official recruiting visits, published by 247sports.com, was collected for the years 2013 to 2020. Previous research has utilized data from 247sports.com, recognizing it as a well-established source of college football recruiting information (21, 27, 35). The dataset included dates of official school visits and recruits’ hometowns. Prior research also utilized GIS to geocode locations such as athletes’ hometowns or high school locations for analysis (1, 26, 38). GIS geocoding takes a specific location, such as addresses or towns, and references it as a mapped location. Therefore, this study geocoded each football recruit’s hometown, the location of each college visited, and the closest major airport to calculate the approximate travel distances for spatial analysis.

The study utilized ESRI ArcPRO 3.5 (Esri, Redlands, CA, USA) software with the Business Analyst extension to geocode the dataset. To focus on the highest-producing RVCF programs and to make the data set more manageable, a minimum threshold of 250 visits was established. Of the 64 Power-5 schools, 23 (35.9%) met the 250-visitor threshold, totalling 7,045 reported official visits. The travel routes for each visit were then calculated using GIS to determine the most efficient mode of travel. Driving directly to the school was the most efficient mode for 1,636 visits. Typically, these distances were around 200 miles or less to the campus. For recruits living over 200 miles from the visiting campus, if their distance from their home to an airport necessitated a long drive followed by a flight, driving was deemed more efficient due to the extensive travel time involved in flying to the campus. For the remaining 5,409 visits, air travel was deemed the most efficient mode. For these visits, three travel distances were calculated: 1) the drive from the recruits’ hometown to the nearest major airport, 2) the flight miles from that airport to the nearest major airport to the campus they visited, and 3) the drive from that airport to the campus. These distances were doubled to account for the return trip and integrated into a travel matrix to approximate CO2eq emissions from transportation.

Additionally, financial data for athletic departments (i.e., Football Revenue, Football Recruiting was sourced from the Knight-Newhouse College Athletics database (25), team performance was sourced from ESPN.com (16). The descriptions and summary statistics for these variables are provided in Table 1. Utilizing these variables allowed for additional analysis to explore potential correlations between an athletic department’s RVCF and financial data, performance data, and recruiting data.

Table 1 Descriptive Analysis of Variables: Mean and Standard Deviation
VariableDescriptionMeanSD
FB_TotalRevTotal Revenue from Football$66,518,526$25,205,244
Mens_TotalRevTotal Revenue from all Men’s Sports (including Football)$84,428,967$25,300,581
FB_MensRev%Football’s Revenue as a Percentage of all Men’s Sports Revenues77.40%11.17%
Dept_TotalRevTotal Revenue from the entire Athletic Department$125,143,966$31,108,327
FB_DeptRev%Football’s Revenue as a Percentage of the entire Athletic Department Revenues52.50%13.10%
Mens_RecruitExpTotal Recruiting Expenses from all Men’s Sports (including Football)$1,391,362$704,861
Dept_RecruitExpTotal Recruiting Expenses from the entire Athletic Department$1,878,962$855,080
FB_OpsExpTotal Operation Expenses for Football$5,683,499$2,558,649
Mens_OpsExpTotal Operation Expenses for all Men’s Sports (including Football)$8,800,193$4,035,500
Dept_OpsExpTotal Operating Expenses for the entire Athletic Department$12,787,529$5,068,156
FB_TotalExpTotal Expenses for the entire Football Program$33,846,192$11,218,516
Mens_TotalExpTotal Expenses for all Men’s Sports Programs (including Football)$53,035,310$13,927,935
FB_MensExp%Football Expenses as a Percentage of all Men’s Sports Expenses63.18%7.58%
Dept_TotalExpTotal Expenses for the entire Athletic Department$116,141,712$27,071,219
FB_DeptExp%Football Expenses as a Percentage of the entire Athletic Department Expenses63.18%7.58%
Win_PercentageFootball teams Win Percentage62.43%19.97%
    

Recruit Visit Carbon Footprint

Calculating CO2eq emissions from travel can vary depending on the methods and formulas used. In this study, the researchers approximated the RVCF utilizing established methods from prior sport tourism carbon footprint research. The framework by Franchetti and Apul (18) required three boundaries. 1) Temporal Boundary, which refers to the period used for analysis, which, in this study, included Power-5 official recruiting visits from 2013 to 2020. 2) Organizational Boundary, which defines the measured entity, ensuring that only emissions produced from the designated entity are included. Here, it refers to the travel for a single recruit’s official visit to a Power-5 school. 3) Operational Boundary, which is based on the scope of emissions, including direct emissions, indirect emissions, and indirect products. The operational boundary was set at direct emissions only for this study.

In order to operationalize the boundaries, calculations were used to approximate each recruit’s carbon footprint as they travelled from their hometown to their selected school for an official recruiting visit. Cooper’s (2020) approximation of the University of Tennessee’s football gameday tourism carbon footprint was used as a framework for this study. The method for approximating the carbon footprint of sport tourism was applied to the dataset to calculate the approximated total amount of CO2eq emissions produced by each recruiting visit. The total carbon footprint of each visit was calculated by considering direct emissions from transportation (car and flight miles), food consumption per day, waste per day, and hotel stays (8, 14). The EPA formula for the average gasoline-powered passenger vehicle (3.91 × 10^-4 metric tons CO2eq per mile) was applied and converted into kilograms (15). For air travel emissions, the formula (air miles × 0.24 × 1.891) combined the Blue Sky Model formula and the Carbon Fund’s radiative forcing factor (1.891) to provide a total CO2eq per person per pound figure, which was then converted to kilograms (2, 3). Hotel accommodation emissions were calculated using Filimonau’s (17) factor of 11.65 kg CO2eq per night, multiplied by two to account for the typical two-night stay during a recruiting visit. For food and waste emissions, factors from Cooper’s (7) study were used: 7.4 kg CO2eq per person per day for food and 1.1 kg CO2eq per day for waste, multiplied by two for the typical 48-hour visit. Utilizing these formulas allowed the researchers to approximate the RVCF for each reported recruiting visit.

RESULTS AND DISCUSSION

Over the eight years, the top 23 highly-visited schools collectively emitted 2.3 million kg of CO2eq, averaging 328 kg CO2eq per recruiting visit. For context, the global average annual CO2eq emission per person is approximately 4.7 tons (4,263 kg), according to the IEA (22). Thus, the CO2eq for a single 48-hour recruiting visit represents about 7.7% of the average person’s global annual CO2eq footprint.

Table 2 provides a breakdown of RVCF variables along with the means and totals for all 23 schools to address RQ1, “Which Power-5 schools generated the largest RVCF between 2013 and 2020?” Washington State (n = 276) reported the highest total RVCF at 171,489.84 kg CO2eq, and the highest mean RVCF at 621.34 kg CO2eq. In contrast, the University of South Carolina (n = 263) had the smallest carbon footprint, with a total RVCF of 55,621.71 kg CO2eq and an average RVCF per visit of 211.49 kg CO2eq. All official visits to Washington State and South Carolina are depicted using GIS maps in Figure 1, which shows Washington State attracted several recruits from the Midwest, Florida, and Texas. At the same time, South Carolina only invited a few recruits who required a long-distance flight to visit Columbia, South Carolina.

Table 2   RVCF by school for all reported official visits from 2013 to 2020  
Schooln% Drove (No Flight)Car (No Flight)Car  (To/From Airport)FlightHotelFoodWasteMSDTotal
Washington State2762.17%564.4821,782.22137,796.796,405.954,084.79855.60621.34362.98171,489.84
Oregon2813.20%667.817,322.00150,332.796,522.004,158.79871.10604.54323.99169,874.50
Nebraska3735.90%1,437.039,203.29131,283.868,657.325,520.391,156.30421.60171.96157,258.20
Alabama37817.99%6,138.0121,504.7481,842.828,773.375,594.391,171.80330.34219.30125,025.12
Minnesota32815.55%1,543.639,279.4494,019.657,612.874,854.391,016.80360.75190.85118,326.78
Louisville3437.87%1,814.239,356.0792,166.147,961.025,076.391,063.30342.38189.87117,437.16
Oklahoma31521.27%7,627.739,318.5385,483.067,311.144,661.99976.50364.79187.59115,378.96
Tennessee35614.89%5,866.3411,129.2172,691.178,262.755,268.791,103.60293.04213.71104,321.87
Texas A&M32746.18%15,370.2818,162.7656,491.357,589.664,839.591,013.70314.50223.19103,467.35
Washington25122.31%2,329.335,626.1481,958.425,825.713,714.80778.10399.33250.68100,232.49
Ohio State30126.58%7,676.345,596.1469,715.536,986.204,454.79933.10316.82223.4995,362.10
Arkansas32515.38%4,563.0713,222.0664,152.597,543.244,809.991,007.50293.23166.9795,298.46
Indiana27315.38%3,307.9013,293.6462,085.006,336.324,040.39846.30329.34169.1389,909.57
Florida33328.53%8,658.696,782.7656,932.067,728.924,928.391,032.30258.45179.7686,063.13
Miami30139.53%3,920.904,735.8663,566.686,986.204,454.79933.10280.01263.3084,597.54
Florida State31714.20%3,929.026,600.1159,945.347,357.564,691.59982.70262.44182.3083,506.32
Auburn31335.14%9,914.5514,971.1242,452.127,264.724,632.39970.30256.25161.7180,205.21
Georgia27033.70%6,709.3215,279.3141,800.056,266.693,995.99837.00276.78196.7074,888.37
Penn State25429.53%9,210.7316,663.5938,471.375,895.343,759.20787.40289.49192.6574,787.61
Mississippi State29356.31%17,224.2017,413.1326,745.766,800.524,336.39908.30250.61176.9073,428.32
Kentucky27419.71%4,331.997,066.4249,895.236,359.534,055.19849.40264.81146.5772,557.76
LSU30038.00%7,465.215,491.1341,783.316,962.994,439.99930.00222.53146.7367,072.63
South Carolina26332.70%7,526.485,361.7631,921.566,104.233,892.40815.30211.49128.8055,621.71
Total7,04523.57%137,797.27255,161.411,633,532.66163,514.32104,265.8721,839.48328.91203.012,316,111.00
Note: Car, Flight, Hotel, Food, Waste, Mean, Standard Deviation, and Total are in kg CO2eq

To explore the second research question, “Did RVCF totals increase or decrease over time?” the dataset needed to be segmented. During this time period college football programs did not get an entirely new roster of players each year; consequently, examining each year’s change would yield varying results based on how many recruits the school needed that year. Rosters typically turn over every 4 to 5 years. Therefore, with eight years of data available, the dataset was subdivided into two four-year periods (2013-2016 and 2017-2020) to better examine changes over a longer period of time.

Table 3 shows the schools with the largest changes in their mean RVCFs. Fifteen schools experienced an increase in mean RVCF between the two time periods. Ohio State had the largest increase in mean difference (MD = 74.77 kg CO2eq), with its mean RVCF rising from 280.80 kg CO2eq in 2013-2016 to 355.57 kg CO2eq in 2017-2020. Oregon saw the largest overall increase in total RVCF, increasing 29,617.65 kg CO2eq during the latter period. Figure 2 utilizes GIS maps to depict all recruiting visits to Ohio State for each period, highlighting an expanded recruiting range that targeted more players from Texas and the Western United States. Conversely, eight schools showed a reduction in mean RVCF between the two time periods, with the University of Miami experiencing the largest decrease in mean difference (MD = -61.96 kg CO2eq). Although Washington State’s mean reduction was not as considerable as the bottom three schools, it had the largest total reduction in RVCF, decreasing by 19,562.28 kg CO2eq between the two periods.

Table 3 Largest Mean Difference in RVCF between the two time periods
 2013-2016 2017-2020 
SchoolsnTotalM nTotalM DifferenceMD
Ohio State15643,804.86280.80 14551,557.24355.57 7,752.3974.77
Penn State10225,847.68253.41 15247,683.11313.70 21,835.4460.30
Oregon12270,128.43574.82 15999,746.07627.33 29,617.6552.51
Florida St.16546,656.85282.77 15236,535.52240.37 -10,121.32-42.40
Arkansas15349,311.35322.30 17245,987.11267.37 -3,324.24-54.93
Miami15146,944.88310.89 15037,339.37248.93 -9,605.51-61.96
Note: Totals and Means are in kg CO2eq

Wuerzer et al. (38) identified county-level geographical hotspots in the United States overproducing elite college football talent, necessitating migration to other states to find available roster spots on Power-5 football teams. Consequently, Power-5 schools in regions with minimal elite talent and far from these hotspots must expand their recruiting efforts, increasing their RVCF. Schools that rely heavily on air travel for recruiting will naturally have a larger carbon footprint, as air travel is the primary contributor to total RVCF. This is evident from the top three schools with the highest total RVCF also have the lowest percentages of recruits visiting within driving distance to their campuses (Washington State (2.17%), Oregon (3.20%), and Nebraska (5.90%)). Despite this, schools still make strategic choices in their recruiting practices. For example, as shown in Figure 1, Washington State invited several recruits from Florida, a state with prominent county-level recruiting hotspots, instead of focusing on nearby regions or closer recruiting hotspots in California and Texas.

A Pearson correlation coefficient analysis was conducted to address research question three: “Were there any correlations between schools’ financial, recruiting, and performance variables and their RVCF?” The analysis identified two significant correlations, both negative: Total RVCF and Athletic Department Total Annual Revenue [r(176) = -.202, p = .007] and Athletic Department Total Annual Expenses [r(176) = -.198, p = .008]. These findings suggest that athletic departments with higher revenues and expenses tend to have lower RVCFs. This could be attributed to the fact that Power-5 programs with substantial financial resources often have well-established and highly regarded football programs, enabling them to attract top recruits from within a closer geographical range. Consequently, these programs would be less dependent on long-distance recruiting, which typically requires greater air travel, the primary contributor to a school’s RVCF, thereby lowering their overall RVCF.

Overall, these findings highlight the multifaceted nature of college football recruiting, shaped by a complex interplay of positional needs, recruits’ availability, and recruits’ geographical location. The competitive nature of Power-5 college football recruiting requires substantial time and effort to build top-tier recruiting classes, prompting many schools to expand their recruiting reach over time, which subsequently increases their RVCF. The findings show that 15 of the 23 schools increased their RVCF over the two periods. Given the fierce competition for elite talent, it is unlikely that any football program would willingly reduce its recruiting-related carbon emissions if it jeopardizes on-field performance. This creates a significant challenge for universities wanting to adopt more sustainable operations.

CONCLUSION 

This study provides a substantial initial assessment of the carbon footprint associated with college football recruiting. By utilizing GIS for recruits’ hometowns, college locations, and nearest major airports to calculate travel distances, the researchers provided an approximation of each school’s RVCF Recruiting Visit Carbon Footprint (RVCF). The findings reveal substantial variability in RVCF among schools, highlighting the different levels of environmental impact of recruiting. The study also found that higher-spending athletic departments tended to have lower RVCFs, suggesting that successful programs may not need to extend their recruiting reach as widely.

However, several limitations must be acknowledged. The data for this study came from a third-party recruiting website, thus allowing for only an approximate carbon footprint for each official visit. Also, various models and formulas can be used to estimate CO2eq emissions from travel, but each carries assumptions and biases. Moreover, policy changes during the study period, such as the NCAA’s 2016 rule change allowing schools to cover travel costs for up to two parents or guardians accompanying a recruit, could result in a higher actual carbon footprint than the reported RVCF from this study (33). More detailed research is essential for a more accurate and comprehensive understanding of the carbon emissions associated with college football recruiting. Unfortunately, without a governing body mandating standardized reporting of recruiting carbon emissions using consistent formulas, it will remain difficult to fully assess and compare the carbon emissions of different athletic departments.

APPLICATIONS IN SPORT

For universities aiming to reduce their athletic department’s carbon footprint, including all recruiting activities in their calculations is crucial. A comprehensive approach would enable the development of effective strategies that promote sustainability without sacrificing athletic success. Athletic departments can better incorporate sustainability into their planning and decision-making processes by understanding the true carbon footprint generated by each sport, school, and conference.

ACKNOWLEDGMENTS
This research was supported by a college-level seed grant focused on sustainability issues from the Huizenga College of Business and Entrepreneurship’s Societal Impact Seed Grant program.

REFERENCES 

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2025-10-13T15:18:13-05:00May 27th, 2026|Contemporary Sports Issues, Research, Sports Studies, Sports Studies and Sports Psychology|Comments Off on An Analysis of Carbon Emissions from College Football Recruiting Visits
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Accreditation, Curriculum, and Competition: An Explanatory Case Study of Sport Sales Education in Undergraduate Sport Management Programs

Authors: Joshua S. Greer1, Nicholas Zoroya2, and Tim Wilson3

1Cumberland University

2Wayne State University

3Middle Tennessee State University

 

Corresponding Author:

Joshua S. Greer

[email protected]

Joshua S. Greer. https://orcid.org/0009-0005-2890-1673

We have no known conflict of interest to disclose.

ABSTRACT 

This explanatory mixed-methods case study explored the relationship between accreditation, curriculum design, and student performance in sport sales education within undergraduate sport management programs. Using archival data from the 2024–2025 National Collegiate Sports Sales Championship (NCSSC), the study compared outcomes among 25 institutions, including COSMA- and non-accredited programs. Quantitative analysis found no significant relationship between accreditation status and Top-10 finishes in either the Ticket Sales or Corporate Partnerships divisions (p > .05). Qualitative findings indicated that student performance was more closely associated with experiential learning depth, faculty expertise, and the integration of customer relationship management and analytics tools. Grounded in Experiential Learning Theory, Competency-Based Education, Human Capital Theory, and Communities of Practice, the study concludes that accreditation provides useful structure but does not independently predict competitive success. Program-level factors such as applied pedagogy, simulation-based learning, and industry partnerships appear to be stronger indicators of professional readiness and employability in sport sales.

KEYWORDS: Experiential Learning Theory, Competency-Based Education, Human Capital Theory, Communities of Practice

INTRODUCTION 

The goal of supporting positive outcomes for younger people (i.e., generativity; Erikson, 1950) is one that is both widely and cross-culturally relevant, yet despite this, the understanding for how to best support young people and the strategies employed to do so are still in flux. Only recently have developmental psychology and social research begun to place an emphasis on fostering positive outcomes for youth, as opposed to the prevention of negative outcomes and problematic behaviors (Larson, 2000). Within the areas of social and developmental research, this emphasis has led to the creation of diverse approaches to and philosophies of developmental youth programming (Lerner et al., 2011), which often provide opportunities for life skill development (i.e., explicit positive youth development). That said, the translation of such knowledge to spaces where youth development is view as a secondary priority, such as sport, tends to be challenging (Jones et al., 2011).  The primary aim of the present pilot study was to test a grounded theory of implicit positive youth development through sport by examining the impact of peer, coach, and parental relationships on youth sport experiences in a small, single-organization sample. In doing so, the present study offers a novel examination of the collective social climate (i.e., PYD climate) and its relationship to athlete developmental outcomes. We hypothesized the following:

  • Athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) will be predicted by positive changes to the ratings of the coach-athlete relationship, peer cohesion, and parental involvement across a sport season.

At two time points (e.g., beginning of the season, end of the season), athletes’ ratings of their relationships with their coach, peer cohesion, and parental involvement were collected.  Subsequently, athletes’ perceptions of skill development across four areas (e.g., personal and social skills, cognitive skills, goal setting, initiative) were regressed on changes to the relationship variables. Both the coach-athlete relationship and parental involvement were shown to significantly predict social skill development, not only offering partial support for a theory of implicit PYD through sport and underscoring the critical developmental role of relationship building in sport but also pointing to the need for stakeholders to prioritize a high-quality social climate in the sport context to better support youth development.

LITERATURE REVIEW

Historically, adolescence and adolescent development has been regarded as a period during which youth are at risk and laden with problematic behaviors (Benson et al., 2006), therefore implying that the role of adults was to manage and prevent the problems that arise from adolescent development, also known as a deficit-focused approach to youth development (Clonan et al., 2004; Lerner, 2005). However, preventing such problems through a focus on treatment or intervention often failed to yield positive results (Catalano et al., 2008). Appearing concurrently with positive psychology’s focus on human strengths and flourishing, positive youth development theory offered that youth are “resources to be developed,” presenting a path toward positive youth outcomes through youth enrichment and the promotion of adolescent strengths (Lerner, Almerigi, et al., 2005). Positive youth development is a broad term, but generally refers to “processes, approaches, and instances” (Lerner et al., 2011) which seek to optimally prepare young people for adulthood, with the targeted outcomes being well-being and the fulfillment of their potential (Catalano et al., 2008). Contexts which aim to support positive youth development vary widely, to include agricultural programming (Lerner, Lerner, et al., 2005), volunteer and service programming (McBride et al., 2011), tutoring (Worker et al., 2019), aquatics (Storm et al., 2017), adventure-based programming (Sibthorp & Morgan, 2011), and sport (Bruner et al., 2021).

Youth sports are generally touted as tools for healthy and positive development, yet research aimed at validating this claim or understanding the processes by which it occurs is ambiguous (Holt et al., 2017). PYD theory was developed outside of the sport context (Lerner, Lerner, et al., 2005) and researchers have struggled to apply PYD models and measures to sporting contexts (Jones et al., 2011). One reason for this may be that PYD researchers have failed to acknowledge keyfeatures of the sport environment (Holt et al., 2017). In a systematic review of qualitative data, Holt and colleagues (2017) proposed that PYD through sport occurs via two distinct pathways. In the first, programs offer explicit education to youth sport participants aimed at life skill development. In the second pathway, PYD occurs implicitly via positive relationships with coaches, peers, and parents (i.e., the creation of a ‘PYD climate’). Holt and colleagues concluded that further research is needed to not only investigate the validity of this framework but also understand additional nuances for when and how PYD may occur through explicit and implicit factors. The need for further research was bolstered by a systematic review of sport-based PYD programming, conducted by Whitley and colleagues (2019), who concluded the benefit of explicit PYD programming in sport is not clear enough to support the implementation of a standardized intervention. Therefore, while the field’s understanding of how to best implement explicit PYD programming through sport is still evolving, there also exists a need to test the proposed model of implicit PYD through positive relationships within sport. While the specific role positive relationships play in supporting PYD within sport is unclear, it is generally accepted that these relationships are all valuable, if not necessary, for positive athlete outcomes (Burns et al., 2019).

Coach-Athlete Relationship

Arguably the primary relationship in the sporting context (Jowett, 2017), the dyadic relationship between coach and athlete has been shown to be instrumental to numerous athlete outcomes. In a systematic review of the coach-athlete relationship literature, Nikolina and Đorić (2023) reported that a positive coach-athlete relationship was not only predictive of increased motivation, satisfaction, and performance, but also protective from athlete stress, burnout, and negative affect. Davis and Jowett (2014) have reported that the quality of the coach-athlete relationship is directly related to athlete positive and negative affect. Furthermore, in a systematic review of the literature, McShan and Moore (2023) found that a positive coach-athlete relationship, as reported by coaches, was associated with coach’s beliefs of fostering an environment supportive of athlete life skill development. In Holt and colleague’s (2017) grounded theory of implicit PYD, the authors posit that strong, positive relationships between athletes and coaches can create a developmentally supportive social environment.

Peer Cohesion

Paralleling the coach-athlete relationship research, research on the role of peer relationships in the sport environment have shown these relationships to be highly influential on athlete experiences and outcomes (Smith & Ullrich-French, 2020).  Peer support has been shown to be related to elite sport participation, athlete motivation, and reduced withdrawal from sport (Sheridan et al., 2014). Additionally, researchers have shown that peer cohesion is not only associated with performance (Carron et al., 2002; Filho et al., 2014), but also athlete need satisfaction and learning (Erikstad et al., 2018). Furthermore, Smith and Ulrich-French (2020) have posited that peer relationships in the sport context are likely to be influential to individual athlete development, to include character, moral, social, and life skill development. In proposing strong peer relationships as influential of an implicit PYD climate, Holt and colleagues (2017) highlighted how strong peer relationships in the sport context often result in feelings of belongingness and support, which may provide developmental benefit.

Parental Involvement

While not always directly involved in the training environment, researchers have shown that parents are highly influential to youth athletes’ experiences and outcomes in sport. Youth who perceive their parents as satisfied with their performance and who experience low parental pressure are more likely to report sport enjoyment and positive affect (Dorsch et al., 2021). Additionally, parental involvement has also been associated with youth sport enjoyment, perceptions of competence, and self-esteem (Dorsch et al., 2021). Parental involvement in sport has also been found to be associated with youth athlete need satisfaction (Felber Charbonneau & Camiré, 2020). Furthermore, parental involvement in sport has also been connected to athletes’ development, to include socialization and value adoption (Danioni et al., 2017). In their grounded theory model, Holt and colleagues (2017) highlighted the reinforcing role that parental involvement plays to creating a PYD climate; while coaches may be responsible for delivering lessons and values to athletes in the sport context, the authors noted that it is important that parents support, not contradict, these messages.

Study Aims

In their grounded theory model, Holt and colleagues (2017) posited that these three relationships (i.e., coaches, peers, parents) collectively create a social climate supportive of implicit positive youth development. Therefore, the primary aim of the present study was to examine the impact of peer, coach, and parental relationships on youth sport experiences and youth athletes’ perceptions of developmental skills gained, thereby piloting a test of Holt and colleagues’ (2017) grounded theory model. Should these relationships be predictive of positive youth development, it could be expected that athletes who experience positive changes to these relationships (e.g., increased peer cohesion, increased parental involvement) across a sport season should also receive increased benefit from their participation compared to athletes whose relationships did not improve. As such, we hypothesized that athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) would be predicted by positive changes to the ratings of their peer relationships, coach-athlete relationships, and parental involvement across a sport season.

METHODS 

Participants

Participants included 67 youth athletes from a competitive soccer club in the northwest region of the United States. In total, 41 athletes (Mage = 11.85) completed data collection at both time points. Participants represented 13 teams from four separate age categories. Additionally, 65.9% of the athletes identified as white and 61.0% of the athletes identified as boys.

Measures

Coach-Athlete Relationship Questionnaire (CART-Q)

To measure athlete perceptions of their relationship with their coach, the Coach-Athlete Relationship Questionnaire (CART-Q; Jowett & Ntoumanis, 2004) was utilized. The 11-item scale measured the nature of the athlete’s relationship with their coach (a = 0.97). Using a seven-point Likert scale, athletes rated their agreement with statements such as, “I trust my coach.”

Youth Sport Environment Questionnaire (YSEQ)

Athletes’ perceptions of their relationship with teammates were measured utilizing the Youth Sport Environment Questionnaire (YSEQ; Eys et al., 2009). The scale, which has been shown to be both valid and reliable, measured group cohesion and peer relationship quality. The YSEQ contains 16 statements, such as, “I am happy with my team’s level of desire to win” (a = 0.93). Athletes rated their agreement with these statements utilizing a seven-point Likert scale.

Parental Involvement in Sport Questionnaire (PISQ)

The Parental Involvement in Sport Questionnaire (PISQ; Lee & MacLean, 1997) is a valid and reliable 19-item scale (a = 0.87), which captures athletes’ perceptions of parental involvement across three subscales: directive behavior, praise and understanding, and active involvement. Utilizing a five-point Likert scale, athletes rated their level of agreement with statements such as, “Do your parents push you to practice harder?”

Youth Experience Survey for Sport (YES-S)

Employed only at the second time point, the short form Youth Experience Survey for Sport (YES-S; MacDonald et al., 2012; Sullivan et al., 2015) is 16-item scale that measured the perceptions of athletes’ experiences participating in sport across the previous season, and was utilized in the present study to operationalize PYD. The scale measures whether athletes perceived any benefit to their participation across four subscales: personal and social skills (a = 0.78), cognitive skills (a = 0.78), goal setting (a = 0.81), and initiative (a = 0.71). Athletes rated their agreement with statements such as, “I learned to push myself” on a five-point Likert scale.

Procedure

Ahead of the start of the summer season, the first author attended the club’s tryouts and parent meetings to share information about the study and recruit participants. During this time, parental consent was obtained through the completion of a written consent form and household demographic survey. The first survey was completed electronically one month into the summer season.  Subsequently, 14 weeks later, the research team returned to conduct the second survey during the final week of the fall season. At both time points, the surveys collected demographic information, athlete perceptions of relationships with their coach, peer cohesion, and parental involvement. At the second time point, the survey collected measurements of athletes’ perceptions of their experiences playing sport across the previous season, particularly focused on skills gained.

The dataset contained 0.3% missingness, and results of an MCAR test were not significant (X2(1386) = 0.00, p = 1.00), suggesting data was missing at random. For cases with missingness, scales were prorated based on completed items. Descriptive statistics were calculated for each scale and notable demographic differences are reported in Table 1. For each of the relationship variables (i.e., CART-Q, PISQ, YSEQ), a difference score was calculated (MT2 – MT1) to measure changes in these relationships across the season. While the utilization of difference scores has been criticized for its negative, summative impact on reliability (Edwards, 1994), researchers have noted that difference scores can be an appropriate choice in research, particularly for nonrandomized, theory-driven analyses (Castro-Schilo & Grimm, 2018). Assumptions testing revealed issues regarding multicollinearity as there was a high correlation between coach-athlete relationship and the peer cohesion change scores (r = 0.801), which resulted in unstable beta coefficients. This instability indicated that the presence of the peer cohesion variable in the model was distorting the estimation of other predictors, undermining the reliability and interpretability of the model. As such, the peer cohesion variable was removed from primary analyses. Following this, we regressed the four subscales of the YES-S (i.e., personal and social skills, cognitive skills, goal setting skills, initiative) on changes in relationship quality across the season, while controlling for age, race, and gender.

Table 1

Sample Characteristics and Descriptive Statistics

   CART-QYSEQPISQYES-S Social SkillsYES-S Cog. SkillsYES-S Goal SettingYES-S Initiative
Variablen%T1 – M(SD)T2 – M(SD)T1 – M(SD)T2 – M(SD)T1 – M(SD)T2 – M(SD)T2 – M(SD)T2 – M(SD)T2 – M(SD)T2 – M(SD)
Age            
1037.35.61(1.24)*5.97(1.47)*4.25(2.01)*5.08(1.98)*2.39(0.18)2.91(0.45)3.58(0.52)3.67(0.58)4.25(0.58)4.58(0.52)
11922.05.46(1.73)6.36(0.39)4.74(1.52)5.53(0.83)3.02(0.60)3.13(0.52)4.00(0.60)3.69(1.05)4.00(0.85)4.50(0.45)
122048.86.10(0.40)5.96(0.85)5.10(0.75)5.30(0.91)*2.92(0.60)3.25(0.74)*4.17(0.75)3.53(1.16)3.93(0.90)4.25(0.59)
13922.05.71(1.04)*5.15(1.26)* †4.69(1.40)4.89(1.18)3.16(0.69)3.30(0.58)4.03(0.57)3.56(0.69)4.25(0.57)4.43(0.66)
Gender            
Boy2561.06.03(0.61)6.17(0.69)†4.91(1.03)*5.26(0.89)*2.92(0.62)*3.24(0.66)*4.12(0.66)3.72(0.85)4.11(0.71)4.43(0.41)
Girl1639.05.56(1.43)5.41(0.99)†4.81(1.42)5.22(1.25)3.01(0.62)3.17(0.62)3.96(0.89)3.35(1.19)3.90(0.94)4.27(0.76)
Race            
White2765.95.77(1.13)5.97(0.83)4.75(1.21)*5.24(1.03)*2.95(0.61)3.14(0.57)4.07(0.69)3.52(1.04)3.99(0.85)4.43(0.54)
Black12.4          
Asian49.85.50(1.38)5.41(1.85)4.77(1.85)*5.30(1.64)*2.74(0.90)3.29(0.90)4.00(0.35)3.94(0.43)3.94(0.43)3.94(0.66)
Hispanic49.86.27(0.45)5.86(1.12)5.50(0.89)5.55(0.74)2.99(0.57)3.41(0.83)4.50(0.41)4.25(0.54)4.69(0.47)4.63(0.32)
Other512.26.13(0.31)5.65(1.20)5.05(0.83)4.99(1.04)2.99(0.45)3.31(0.52)3.80(0.89)3.15(1.29)3.80(0.94)4.15(0.74)
Total41100.05.84(1.02)5.87(0.99)4.87(1.18)*5.24(1.03)*2.96(0.62)*3.21(0.64)*4.06(0.67)3.58(1.00)4.03(0.80)4.37(0.57)

Notes. n = 41; CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement; YSEQ = Ratings of Peer Cohesion; YES-S = Perceptions of Developmental Experiences, *Difference is significant between time points; †Difference is significant between groups.

RESULTS

The model examining personal and social skills was significant and explained 45.4% of variance in the outcome (R2 = 0.454, F(5,34) = 5.664, p < 0.001).

Regression Results for Perceptions of Social Skills Gained by Athletes

    95% CI 
VariablebbSELLULp
Intercept 0.7741.268-1.8023.3500.546
Gender-0.129-0.1750.184-0.5500.1990.348
Age0.3690.2910.1100.0670.5150.012
Race-0.024-0.0080.042-0.0940.0780.858
DCART-Q0.4820.2500.0740.0990.4000.002
DPISQ0.3260.3820.1600.5800.7070.022

Notes. n = 41; R2= 0.454, F(5,34) = 5.664, p < 0.001; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.

**When ran independently due to the existing multicollinearity, change to peer cohesion was also a significant predictor of personal and social skills (R2 = 0.317, F(4,35) = 4.063, p = 0.008).

Within this model, both changes to the coach-athlete relationships (b= 0.482, p = 0.002) and changes to parental involvement (b= 0.326, p = 0.022) across the season were significant predictors of personal and social skills. Additionally, the covariate age was also a significant predictor of personal and social skills (b = 0.369, p = 0.012). The model examining cognitive skills explained 25.1% of the variance, however was only marginally significant (R2 = 0.251, F(5,34) = 2.275, p = 0.069). Within this model the change in coach-athlete relationship was a statistically significant predictor (b= 0.403, p = 0.022), whereas changes to parental involvement was not (b= 0.158, p = 0.330).

Table 3

Regression Results for Perceptions of Cognitive Skills Gained by Athletes

    95% CI 
VariablebbSELLULp
Intercept 2.0482.221-2.4656.5610.363
Gender-0.155-0.3150.323-0.9720.3420.337
Age0.1430.1690.193-0.2240.5610.389
Race-0.066-0.0320.074-0.1820.1190.670
DCART-Q0.4030.3120.1300.0480.5760.022
DPISQ0.1580.2770.280-0.2920.8450.330

Notes. n = 41; R2= 0.251, F(5,34) = 2.275, p = 0.069; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.

** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.

The models predicting goal setting skills (R2 = 0.183, F(5,34) = 1.528, p = 0.207) and initiative (R2 = 0.185, F(5,34) = 1.542, p = 0.203) were not statistically significant.

DISCUSSION 

The present study provides partial support to Holt and colleague’s (2017) proposition that there is an implicit pathway of PYD in sport that takes place through positive relationships. In particular, changes to the coach-athlete relationship significantly predicted youth athletes’ perceptions of social skills and cognitive skills gained; and changes to perceptions of parental involvement also predicted social skills gained. Additionally, when analyzed separately due to issues of multicollinearity, changes to peer cohesion also significantly predicted social skill perceptions. As such, data in the current study reinforce the importance of relationships within the sport environment, and extend previous research by highlighting their value to the specific area of PYD through sport.

While research has shown the coach-athlete relationship to be associated with motivation (Adie & Jowett, 2010), collective-efficacy (Hampson & Jowett, 2014), and team cohesion (Turman, 2003), its role in the social and cognitive development of athletes is less understood. That said, research has shown that coaches seem to intuitively understand the developmental value of a positive coach-athlete relationship as coaches have reported a positive relationship with their athletes led to social and emotional development and resilience (White & Bennie, 2015). Furthermore, Davis and colleagues (2019) proposed a bidirectional relationship between communication skills and the coach-athlete relationship, where communication skills not only helped to improve the relationship, but also improved as a product of a high-quality coach-athlete relationship. When examining the more expansive literature on the impact of a high-quality relationships, researchers have documents that teacher-student relationships can promote cognitive development (Davis, 2003) and social adjustment (Dong et al., 2021) through positive and trusting learning environments. Data in the current study suggest coaches hold a responsibility to ensure the development and sustainment of positive relationships in the sport environment to support similarly positive developmental outcomes for youth athletes. This is particularly important as social skills have been shown to be associated with academic performance (Sung & Chang, 2010), increased mental health (Greenberg et al., 2003), wellbeing (Sancassiani et al., 2015), and self-esteem (Riggio et al., 1990).

The present study also highlights the important yet specific role that parents play in positive youth development through sport. Parental styles have been shown to be associated with social skill development; youth with democratic and permissive parents have been shown to score higher on social skills measures than those with neglectful or authoritative parents (Salavera et al., 2022). As such, it could be hypothesized that parents with more developmentally supportive parenting styles are more likely to be involved in their child’s sport and supportive of their child’s social skills. That said, data in the current study suggests the need to delineate the roles of parents and coaches, as these relationships may provide different benefits for youth. For example, Knight and colleagues (2011) reported that athletes consistently prefer parents to fill a supportive and encouraging role, as opposed to a coaching role. This is supported by data in the current study in that while change to parental involvement predicted athletes’ perceptions of social skill development, it did not predict their cognitive skill perceptions.

Finally, it is important to note that girls rated their relationship with their coach significantly lower than their peers who identified as boys; and older athletes were also significantly less likely to rate their coach-relationships higher than younger athletes. As such, should there exist any developmental benefit to high-quality, coaching relationships, the present findings would suggest that girls and older youth athletes are less likely to receive those benefits. Given that a positive coach-athlete relationship can be protective from poor mental health outcomes for girl athletes specifically (Massey et al., 2024), it is important that positive coach-athlete relationships are prioritized for female athletes, particularly adolescent female athletes. Furthermore, it is generally accepted that as athletes get older, the sporting environment shifts from a focus on fun to a focus on competition. Be that as it may, research has shown that the true shift lies within how athletes are treated; Kipp and Bolter (2020) found that while both older and younger athletes equally perceived their sporting environments to be focused on effort and learning, older athletes were more likely to report being punished or disciplined for mistakes. It is possible that such climates explain the decreasing trend of the coach-athlete relationship observed in the present study. Speaking strictly to the proposed developmental role of the coach-athlete relationship within sport, the present findings would offer that sports become less beneficial and developmentally supportive over time.

Despite the present study’s value to the literature base on PYD through sport, its small, homogenous sample limits its generalizability. In addition to being predominantly white, the sample derived from a singular, pay-to-play soccer organization within an affluent community. Additionally, the present sample predominantly identified as boys, which may parallel youth sport participation trends, but limits the generalizability of the findings to non-boy athlete populations. The age rage of the sample was also limited, clustered into the soccer organizations U11 and U13 age groupings, and as such, the findings may be in part reflective of the natural development occurring in this age range.

Furthermore, most athletes in the present study were satisfied with their relationship with their coach and peers, and the mean parental involvement score was slightly above the midpoint of the scale. Depending on sport or community context, it is possible that more athletes would report more dissatisfaction with these relationships or less parental involvement, thereby affecting the nature of the findings. With respect to age and gender differences, it is possible that these differences could be explained by confounding variables, such as coach gender, competition level, or position, which could not be differentiated in the present study due to the small sample size. Lastly, while multicollinearity necessitated the removal of the peer cohesion variable from the analyses, it should be acknowledged that doing so also limits the completeness of the model by excluding a theoretically important dimension of the sport environment, and one which should continue to be examined in this line of research.  As such, future studies should not only continue to examine the nuanced roles of parents and coaches in sport-based PYD, but also peer relationships, and doing so in larger and more diverse samples.

CONCLUSION 

The social context of the sport environment, which includes coaches, parents, and peers, plays a significant role in shaping athletes’ perceived development through sport. In the present study, athletes’ perceived social skill development was significantly predicted by positive changes to the coach-athlete relationship and parental involvement. The quality of the coach-athlete relationship also emerged as a meaningful predictor of athletes’ perceived cognitive development, highlighting the broader developmental impact of adult figures in the sport context. Furthermore, while peer cohesion was omitted in analyses due to multicollinearity, its interconnectedness with the coach-athlete relationship should be acknowledged, and researchers should continue to utilize it as a variable of interest as theory would dictate. Taken together, these findings underscore the importance of considering the full network of sport-based relationships when seeking to support athletes’ development through sport participation.

APPLICATIONS IN SPORT

In addition to providing support for Holt and colleagues’ (2017) theory of implicit PYD through sport, the present study highlights the interconnected nature of youth sport’s social context. We offer the following recommendations to stakeholders seeking to utilize these findings to develop their youth sport organization’s PYD climate:

  • Provide coaches with education and training that supports their development of communication and relationship-building skills (see Barnett et al., 1992; Jowett & Cockerill, 2003).
  • Provide education and clear expectations for parents’ involvement in the organization, as well as opportunities for involvement (see Knight et al., 2011).

Prioritize relationship building and psychological safety at the outset of the season, to include team-building activities and the development of team norms, rituals, and goals (see Carron et al., 1997; Senécal et al., 2008).

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Appendix A
Supplemental Materials

Table 4

Correlation Matrix of Study Variables

Variables1234567
1. Age       
2. CART-Q-0.34*      
3. PISQ0.150.23     
4. YSEQ-0.140.66**0.31*    
5. Social Skills0.140.62**0.35*0.47**   
6. Cognitive Skills-0.050.40*0.160.160.66**  
7. Goal Setting0.030.43**0.130.42**0.57**0.70** 
8. Initiative-0.100.53**0.180.47**0.51**0.40*0.70**

Notes. * Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed); CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement, YSEQ = Peer Relationships

Table 5

Regression Results for Perceptions of Goal Setting Skills Gained by Athletes

   95% CI for B  
VariablebSELLULbp
Intercept2.0531.862-1.7315.836 0.278
Gender-0.2280.271-0.7790.322-0.1400.405
Age0.1860.162-0.1430.5150.1960.259
Race0.0110.062-0.1150.1370.0280.863
DCART-Q0.2300.1090.0080.4510.3690.042
DPISQ0.1750.235-0.3020.6510.1240.462

Notes. R2= 0.183, p = 0.207; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement

** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.

Table 6

Regression Results for Perceptions of Initiative Gained by Athletes

   95% CI for B  
VariablebSELLULbp
Intercept4.0001.3151.3286.671-0.1203.043
Gender-0.1380.191-0.5270.2500.062-0.723
Age0.0420.114-0.1910.2740.0350.365
Race0.0100.044-0.0790.0990.3890.221
DCART-Q0.1710.0770.0150.3270.0872.224
DPISQ0.0860.166-0.2510.423-0.1200.520

Notes. R2= 0.185, p = 0.203; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement

** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.

2026-04-09T15:25:29-05:00April 9th, 2026|Contemporary Sports Issues, Leadership, Research, Sports Coaching, Sports Studies and Sports Psychology|Comments Off on Accreditation, Curriculum, and Competition: An Explanatory Case Study of Sport Sales Education in Undergraduate Sport Management Programs
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The Role of Sport Relationships in Positive Youth Development

Authors: Jim P. Arnold1 and William V. Massey1

1Department of Kinesiology, College of Health, Oregon State University

 

Corresponding Author:

Jim P. Arnold

[email protected]

Jim P. Arnold https://orcid.org/0009-0004-2282-1915
William V. Massey, Ph.D. https://orcid.org/0000-0002-4002-3720
We have no known conflicts of interest to disclose.

ABSTRACT 

Purpose. Research on positive youth development (PYD) through sport remains unclear and speculative (Whitley et al., 2019). It has been suggested that sport-based PYD can occur implicitly through positive relationships (Holt et al., 2017). The present pilot study examined the impact of changes in the coach-athlete relationship, peer cohesion, and parental involvement on PYD outcomes across a sport season in a sample of youth soccer participants (N = 41, Mage = 11.85, 61% boys).

Methods. Athletes responded to surveys rating their relationships with coaches, parents, and peers at two time points, and additionally reported their perceptions of developmental skills gained across the sport season. A difference score was calculated for each relationship variable to measure change across the season. Four developmental outcomes (i.e., personal and social skills, cognitive skills, goal setting skills, initiative) were regressed on changes in relationship quality across the season, while controlling for age, race, and gender.

Results. Changes to the coach-athlete relationship (b= 0.482, p = 0.002) and parental involvement (b= 0.326, p = 0.022) were significant predictors of perceptions of social skill development (R2 = 0.454, F(5,34) = 5.664, p < 0.001), supporting a relationship-based model of PYD in sport. Significant age and gender differences in ratings of the coach-athlete relationship were also discovered.

Conclusions. The present study not only offers partial support to a Holt and colleagues’ (2017) theory of implicit PYD through sport but also highlights the need important developmental role of relationship building in in the sport context.

Applications in Sport. Organizations should prioritize positive sport relationships through education, training, and programming, as poor or absent relationships may undermine the envisioned benefits of sport. In particular, the present study highlights the need for positive parental involvement, which may require stakeholders to work with parents to define their role expectations.

KEYWORDS: youth sport, positive youth development, sport relationships, coaching, parental involvement

INTRODUCTION 

The goal of supporting positive outcomes for younger people (i.e., generativity; Erikson, 1950) is one that is both widely and cross-culturally relevant, yet despite this, the understanding for how to best support young people and the strategies employed to do so are still in flux. Only recently have developmental psychology and social research begun to place an emphasis on fostering positive outcomes for youth, as opposed to the prevention of negative outcomes and problematic behaviors (Larson, 2000). Within the areas of social and developmental research, this emphasis has led to the creation of diverse approaches to and philosophies of developmental youth programming (Lerner et al., 2011), which often provide opportunities for life skill development (i.e., explicit positive youth development). That said, the translation of such knowledge to spaces where youth development is view as a secondary priority, such as sport, tends to be challenging (Jones et al., 2011).  The primary aim of the present pilot study was to test a grounded theory of implicit positive youth development through sport by examining the impact of peer, coach, and parental relationships on youth sport experiences in a small, single-organization sample. In doing so, the present study offers a novel examination of the collective social climate (i.e., PYD climate) and its relationship to athlete developmental outcomes. We hypothesized the following:

  • Athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) will be predicted by positive changes to the ratings of the coach-athlete relationship, peer cohesion, and parental involvement across a sport season.

At two time points (e.g., beginning of the season, end of the season), athletes’ ratings of their relationships with their coach, peer cohesion, and parental involvement were collected.  Subsequently, athletes’ perceptions of skill development across four areas (e.g., personal and social skills, cognitive skills, goal setting, initiative) were regressed on changes to the relationship variables. Both the coach-athlete relationship and parental involvement were shown to significantly predict social skill development, not only offering partial support for a theory of implicit PYD through sport and underscoring the critical developmental role of relationship building in sport but also pointing to the need for stakeholders to prioritize a high-quality social climate in the sport context to better support youth development.

LITERATURE REVIEW

Historically, adolescence and adolescent development has been regarded as a period during which youth are at risk and laden with problematic behaviors (Benson et al., 2006), therefore implying that the role of adults was to manage and prevent the problems that arise from adolescent development, also known as a deficit-focused approach to youth development (Clonan et al., 2004; Lerner, 2005). However, preventing such problems through a focus on treatment or intervention often failed to yield positive results (Catalano et al., 2008). Appearing concurrently with positive psychology’s focus on human strengths and flourishing, positive youth development theory offered that youth are “resources to be developed,” presenting a path toward positive youth outcomes through youth enrichment and the promotion of adolescent strengths (Lerner, Almerigi, et al., 2005). Positive youth development is a broad term, but generally refers to “processes, approaches, and instances” (Lerner et al., 2011) which seek to optimally prepare young people for adulthood, with the targeted outcomes being well-being and the fulfillment of their potential (Catalano et al., 2008). Contexts which aim to support positive youth development vary widely, to include agricultural programming (Lerner, Lerner, et al., 2005), volunteer and service programming (McBride et al., 2011), tutoring (Worker et al., 2019), aquatics (Storm et al., 2017), adventure-based programming (Sibthorp & Morgan, 2011), and sport (Bruner et al., 2021).

Youth sports are generally touted as tools for healthy and positive development, yet research aimed at validating this claim or understanding the processes by which it occurs is ambiguous (Holt et al., 2017). PYD theory was developed outside of the sport context (Lerner, Lerner, et al., 2005) and researchers have struggled to apply PYD models and measures to sporting contexts (Jones et al., 2011). One reason for this may be that PYD researchers have failed to acknowledge keyfeatures of the sport environment (Holt et al., 2017). In a systematic review of qualitative data, Holt and colleagues (2017) proposed that PYD through sport occurs via two distinct pathways. In the first, programs offer explicit education to youth sport participants aimed at life skill development. In the second pathway, PYD occurs implicitly via positive relationships with coaches, peers, and parents (i.e., the creation of a ‘PYD climate’). Holt and colleagues concluded that further research is needed to not only investigate the validity of this framework but also understand additional nuances for when and how PYD may occur through explicit and implicit factors. The need for further research was bolstered by a systematic review of sport-based PYD programming, conducted by Whitley and colleagues (2019), who concluded the benefit of explicit PYD programming in sport is not clear enough to support the implementation of a standardized intervention. Therefore, while the field’s understanding of how to best implement explicit PYD programming through sport is still evolving, there also exists a need to test the proposed model of implicit PYD through positive relationships within sport. While the specific role positive relationships play in supporting PYD within sport is unclear, it is generally accepted that these relationships are all valuable, if not necessary, for positive athlete outcomes (Burns et al., 2019).

Coach-Athlete Relationship

Arguably the primary relationship in the sporting context (Jowett, 2017), the dyadic relationship between coach and athlete has been shown to be instrumental to numerous athlete outcomes. In a systematic review of the coach-athlete relationship literature, Nikolina and Đorić (2023) reported that a positive coach-athlete relationship was not only predictive of increased motivation, satisfaction, and performance, but also protective from athlete stress, burnout, and negative affect. Davis and Jowett (2014) have reported that the quality of the coach-athlete relationship is directly related to athlete positive and negative affect. Furthermore, in a systematic review of the literature, McShan and Moore (2023) found that a positive coach-athlete relationship, as reported by coaches, was associated with coach’s beliefs of fostering an environment supportive of athlete life skill development. In Holt and colleague’s (2017) grounded theory of implicit PYD, the authors posit that strong, positive relationships between athletes and coaches can create a developmentally supportive social environment.

Peer Cohesion

Paralleling the coach-athlete relationship research, research on the role of peer relationships in the sport environment have shown these relationships to be highly influential on athlete experiences and outcomes (Smith & Ullrich-French, 2020).  Peer support has been shown to be related to elite sport participation, athlete motivation, and reduced withdrawal from sport (Sheridan et al., 2014). Additionally, researchers have shown that peer cohesion is not only associated with performance (Carron et al., 2002; Filho et al., 2014), but also athlete need satisfaction and learning (Erikstad et al., 2018). Furthermore, Smith and Ulrich-French (2020) have posited that peer relationships in the sport context are likely to be influential to individual athlete development, to include character, moral, social, and life skill development. In proposing strong peer relationships as influential of an implicit PYD climate, Holt and colleagues (2017) highlighted how strong peer relationships in the sport context often result in feelings of belongingness and support, which may provide developmental benefit.

Parental Involvement

While not always directly involved in the training environment, researchers have shown that parents are highly influential to youth athletes’ experiences and outcomes in sport. Youth who perceive their parents as satisfied with their performance and who experience low parental pressure are more likely to report sport enjoyment and positive affect (Dorsch et al., 2021). Additionally, parental involvement has also been associated with youth sport enjoyment, perceptions of competence, and self-esteem (Dorsch et al., 2021). Parental involvement in sport has also been found to be associated with youth athlete need satisfaction (Felber Charbonneau & Camiré, 2020). Furthermore, parental involvement in sport has also been connected to athletes’ development, to include socialization and value adoption (Danioni et al., 2017). In their grounded theory model, Holt and colleagues (2017) highlighted the reinforcing role that parental involvement plays to creating a PYD climate; while coaches may be responsible for delivering lessons and values to athletes in the sport context, the authors noted that it is important that parents support, not contradict, these messages.

Study Aims

In their grounded theory model, Holt and colleagues (2017) posited that these three relationships (i.e., coaches, peers, parents) collectively create a social climate supportive of implicit positive youth development. Therefore, the primary aim of the present study was to examine the impact of peer, coach, and parental relationships on youth sport experiences and youth athletes’ perceptions of developmental skills gained, thereby piloting a test of Holt and colleagues’ (2017) grounded theory model. Should these relationships be predictive of positive youth development, it could be expected that athletes who experience positive changes to these relationships (e.g., increased peer cohesion, increased parental involvement) across a sport season should also receive increased benefit from their participation compared to athletes whose relationships did not improve. As such, we hypothesized that athletes’ perceptions of positive outcomes obtained through sport participation (e.g., social skills, goal setting skills) would be predicted by positive changes to the ratings of their peer relationships, coach-athlete relationships, and parental involvement across a sport season.

METHODS 

Participants

Participants included 67 youth athletes from a competitive soccer club in the northwest region of the United States. In total, 41 athletes (Mage = 11.85) completed data collection at both time points. Participants represented 13 teams from four separate age categories. Additionally, 65.9% of the athletes identified as white and 61.0% of the athletes identified as boys.

Measures

Coach-Athlete Relationship Questionnaire (CART-Q)

To measure athlete perceptions of their relationship with their coach, the Coach-Athlete Relationship Questionnaire (CART-Q; Jowett & Ntoumanis, 2004) was utilized. The 11-item scale measured the nature of the athlete’s relationship with their coach (a = 0.97). Using a seven-point Likert scale, athletes rated their agreement with statements such as, “I trust my coach.”

Youth Sport Environment Questionnaire (YSEQ)

Athletes’ perceptions of their relationship with teammates were measured utilizing the Youth Sport Environment Questionnaire (YSEQ; Eys et al., 2009). The scale, which has been shown to be both valid and reliable, measured group cohesion and peer relationship quality. The YSEQ contains 16 statements, such as, “I am happy with my team’s level of desire to win” (a = 0.93). Athletes rated their agreement with these statements utilizing a seven-point Likert scale.

Parental Involvement in Sport Questionnaire (PISQ)

The Parental Involvement in Sport Questionnaire (PISQ; Lee & MacLean, 1997) is a valid and reliable 19-item scale (a = 0.87), which captures athletes’ perceptions of parental involvement across three subscales: directive behavior, praise and understanding, and active involvement. Utilizing a five-point Likert scale, athletes rated their level of agreement with statements such as, “Do your parents push you to practice harder?”

Youth Experience Survey for Sport (YES-S)

Employed only at the second time point, the short form Youth Experience Survey for Sport (YES-S; MacDonald et al., 2012; Sullivan et al., 2015) is 16-item scale that measured the perceptions of athletes’ experiences participating in sport across the previous season, and was utilized in the present study to operationalize PYD. The scale measures whether athletes perceived any benefit to their participation across four subscales: personal and social skills (a = 0.78), cognitive skills (a = 0.78), goal setting (a = 0.81), and initiative (a = 0.71). Athletes rated their agreement with statements such as, “I learned to push myself” on a five-point Likert scale.

Procedure

Ahead of the start of the summer season, the first author attended the club’s tryouts and parent meetings to share information about the study and recruit participants. During this time, parental consent was obtained through the completion of a written consent form and household demographic survey. The first survey was completed electronically one month into the summer season.  Subsequently, 14 weeks later, the research team returned to conduct the second survey during the final week of the fall season. At both time points, the surveys collected demographic information, athlete perceptions of relationships with their coach, peer cohesion, and parental involvement. At the second time point, the survey collected measurements of athletes’ perceptions of their experiences playing sport across the previous season, particularly focused on skills gained.

The dataset contained 0.3% missingness, and results of an MCAR test were not significant (X2(1386) = 0.00, p = 1.00), suggesting data was missing at random. For cases with missingness, scales were prorated based on completed items. Descriptive statistics were calculated for each scale and notable demographic differences are reported in Table 1. For each of the relationship variables (i.e., CART-Q, PISQ, YSEQ), a difference score was calculated (MT2 – MT1) to measure changes in these relationships across the season. While the utilization of difference scores has been criticized for its negative, summative impact on reliability (Edwards, 1994), researchers have noted that difference scores can be an appropriate choice in research, particularly for nonrandomized, theory-driven analyses (Castro-Schilo & Grimm, 2018). Assumptions testing revealed issues regarding multicollinearity as there was a high correlation between coach-athlete relationship and the peer cohesion change scores (r = 0.801), which resulted in unstable beta coefficients. This instability indicated that the presence of the peer cohesion variable in the model was distorting the estimation of other predictors, undermining the reliability and interpretability of the model. As such, the peer cohesion variable was removed from primary analyses. Following this, we regressed the four subscales of the YES-S (i.e., personal and social skills, cognitive skills, goal setting skills, initiative) on changes in relationship quality across the season, while controlling for age, race, and gender.

Table 1

Sample Characteristics and Descriptive Statistics

   CART-QYSEQPISQYES-S Social SkillsYES-S Cog. SkillsYES-S Goal SettingYES-S Initiative
Variablen%T1 – M(SD)T2 – M(SD)T1 – M(SD)T2 – M(SD)T1 – M(SD)T2 – M(SD)T2 – M(SD)T2 – M(SD)T2 – M(SD)T2 – M(SD)
Age            
1037.35.61(1.24)*5.97(1.47)*4.25(2.01)*5.08(1.98)*2.39(0.18)2.91(0.45)3.58(0.52)3.67(0.58)4.25(0.58)4.58(0.52)
11922.05.46(1.73)6.36(0.39)4.74(1.52)5.53(0.83)3.02(0.60)3.13(0.52)4.00(0.60)3.69(1.05)4.00(0.85)4.50(0.45)
122048.86.10(0.40)5.96(0.85)5.10(0.75)5.30(0.91)*2.92(0.60)3.25(0.74)*4.17(0.75)3.53(1.16)3.93(0.90)4.25(0.59)
13922.05.71(1.04)*5.15(1.26)* †4.69(1.40)4.89(1.18)3.16(0.69)3.30(0.58)4.03(0.57)3.56(0.69)4.25(0.57)4.43(0.66)
Gender            
Boy2561.06.03(0.61)6.17(0.69)†4.91(1.03)*5.26(0.89)*2.92(0.62)*3.24(0.66)*4.12(0.66)3.72(0.85)4.11(0.71)4.43(0.41)
Girl1639.05.56(1.43)5.41(0.99)†4.81(1.42)5.22(1.25)3.01(0.62)3.17(0.62)3.96(0.89)3.35(1.19)3.90(0.94)4.27(0.76)
Race            
White2765.95.77(1.13)5.97(0.83)4.75(1.21)*5.24(1.03)*2.95(0.61)3.14(0.57)4.07(0.69)3.52(1.04)3.99(0.85)4.43(0.54)
Black12.4          
Asian49.85.50(1.38)5.41(1.85)4.77(1.85)*5.30(1.64)*2.74(0.90)3.29(0.90)4.00(0.35)3.94(0.43)3.94(0.43)3.94(0.66)
Hispanic49.86.27(0.45)5.86(1.12)5.50(0.89)5.55(0.74)2.99(0.57)3.41(0.83)4.50(0.41)4.25(0.54)4.69(0.47)4.63(0.32)
Other512.26.13(0.31)5.65(1.20)5.05(0.83)4.99(1.04)2.99(0.45)3.31(0.52)3.80(0.89)3.15(1.29)3.80(0.94)4.15(0.74)
Total41100.05.84(1.02)5.87(0.99)4.87(1.18)*5.24(1.03)*2.96(0.62)*3.21(0.64)*4.06(0.67)3.58(1.00)4.03(0.80)4.37(0.57)

Notes. n = 41; CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement; YSEQ = Ratings of Peer Cohesion; YES-S = Perceptions of Developmental Experiences, *Difference is significant between time points; †Difference is significant between groups.

RESULTS

The model examining personal and social skills was significant and explained 45.4% of variance in the outcome (R2 = 0.454, F(5,34) = 5.664, p < 0.001).

Regression Results for Perceptions of Social Skills Gained by Athletes

    95% CI 
VariablebbSELLULp
Intercept 0.7741.268-1.8023.3500.546
Gender-0.129-0.1750.184-0.5500.1990.348
Age0.3690.2910.1100.0670.5150.012
Race-0.024-0.0080.042-0.0940.0780.858
DCART-Q0.4820.2500.0740.0990.4000.002
DPISQ0.3260.3820.1600.5800.7070.022

Notes. n = 41; R2= 0.454, F(5,34) = 5.664, p < 0.001; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.

**When ran independently due to the existing multicollinearity, change to peer cohesion was also a significant predictor of personal and social skills (R2 = 0.317, F(4,35) = 4.063, p = 0.008).

Within this model, both changes to the coach-athlete relationships (b= 0.482, p = 0.002) and changes to parental involvement (b= 0.326, p = 0.022) across the season were significant predictors of personal and social skills. Additionally, the covariate age was also a significant predictor of personal and social skills (b = 0.369, p = 0.012). The model examining cognitive skills explained 25.1% of the variance, however was only marginally significant (R2 = 0.251, F(5,34) = 2.275, p = 0.069). Within this model the change in coach-athlete relationship was a statistically significant predictor (b= 0.403, p = 0.022), whereas changes to parental involvement was not (b= 0.158, p = 0.330).

Table 3

Regression Results for Perceptions of Cognitive Skills Gained by Athletes

    95% CI 
VariablebbSELLULp
Intercept 2.0482.221-2.4656.5610.363
Gender-0.155-0.3150.323-0.9720.3420.337
Age0.1430.1690.193-0.2240.5610.389
Race-0.066-0.0320.074-0.1820.1190.670
DCART-Q0.4030.3120.1300.0480.5760.022
DPISQ0.1580.2770.280-0.2920.8450.330

Notes. n = 41; R2= 0.251, F(5,34) = 2.275, p = 0.069; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement.

** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.

The models predicting goal setting skills (R2 = 0.183, F(5,34) = 1.528, p = 0.207) and initiative (R2 = 0.185, F(5,34) = 1.542, p = 0.203) were not statistically significant.

DISCUSSION 

The present study provides partial support to Holt and colleague’s (2017) proposition that there is an implicit pathway of PYD in sport that takes place through positive relationships. In particular, changes to the coach-athlete relationship significantly predicted youth athletes’ perceptions of social skills and cognitive skills gained; and changes to perceptions of parental involvement also predicted social skills gained. Additionally, when analyzed separately due to issues of multicollinearity, changes to peer cohesion also significantly predicted social skill perceptions. As such, data in the current study reinforce the importance of relationships within the sport environment, and extend previous research by highlighting their value to the specific area of PYD through sport.

While research has shown the coach-athlete relationship to be associated with motivation (Adie & Jowett, 2010), collective-efficacy (Hampson & Jowett, 2014), and team cohesion (Turman, 2003), its role in the social and cognitive development of athletes is less understood. That said, research has shown that coaches seem to intuitively understand the developmental value of a positive coach-athlete relationship as coaches have reported a positive relationship with their athletes led to social and emotional development and resilience (White & Bennie, 2015). Furthermore, Davis and colleagues (2019) proposed a bidirectional relationship between communication skills and the coach-athlete relationship, where communication skills not only helped to improve the relationship, but also improved as a product of a high-quality coach-athlete relationship. When examining the more expansive literature on the impact of a high-quality relationships, researchers have documents that teacher-student relationships can promote cognitive development (Davis, 2003) and social adjustment (Dong et al., 2021) through positive and trusting learning environments. Data in the current study suggest coaches hold a responsibility to ensure the development and sustainment of positive relationships in the sport environment to support similarly positive developmental outcomes for youth athletes. This is particularly important as social skills have been shown to be associated with academic performance (Sung & Chang, 2010), increased mental health (Greenberg et al., 2003), wellbeing (Sancassiani et al., 2015), and self-esteem (Riggio et al., 1990).

The present study also highlights the important yet specific role that parents play in positive youth development through sport. Parental styles have been shown to be associated with social skill development; youth with democratic and permissive parents have been shown to score higher on social skills measures than those with neglectful or authoritative parents (Salavera et al., 2022). As such, it could be hypothesized that parents with more developmentally supportive parenting styles are more likely to be involved in their child’s sport and supportive of their child’s social skills. That said, data in the current study suggests the need to delineate the roles of parents and coaches, as these relationships may provide different benefits for youth. For example, Knight and colleagues (2011) reported that athletes consistently prefer parents to fill a supportive and encouraging role, as opposed to a coaching role. This is supported by data in the current study in that while change to parental involvement predicted athletes’ perceptions of social skill development, it did not predict their cognitive skill perceptions.

Finally, it is important to note that girls rated their relationship with their coach significantly lower than their peers who identified as boys; and older athletes were also significantly less likely to rate their coach-relationships higher than younger athletes. As such, should there exist any developmental benefit to high-quality, coaching relationships, the present findings would suggest that girls and older youth athletes are less likely to receive those benefits. Given that a positive coach-athlete relationship can be protective from poor mental health outcomes for girl athletes specifically (Massey et al., 2024), it is important that positive coach-athlete relationships are prioritized for female athletes, particularly adolescent female athletes. Furthermore, it is generally accepted that as athletes get older, the sporting environment shifts from a focus on fun to a focus on competition. Be that as it may, research has shown that the true shift lies within how athletes are treated; Kipp and Bolter (2020) found that while both older and younger athletes equally perceived their sporting environments to be focused on effort and learning, older athletes were more likely to report being punished or disciplined for mistakes. It is possible that such climates explain the decreasing trend of the coach-athlete relationship observed in the present study. Speaking strictly to the proposed developmental role of the coach-athlete relationship within sport, the present findings would offer that sports become less beneficial and developmentally supportive over time.

Despite the present study’s value to the literature base on PYD through sport, its small, homogenous sample limits its generalizability. In addition to being predominantly white, the sample derived from a singular, pay-to-play soccer organization within an affluent community. Additionally, the present sample predominantly identified as boys, which may parallel youth sport participation trends, but limits the generalizability of the findings to non-boy athlete populations. The age rage of the sample was also limited, clustered into the soccer organizations U11 and U13 age groupings, and as such, the findings may be in part reflective of the natural development occurring in this age range.

Furthermore, most athletes in the present study were satisfied with their relationship with their coach and peers, and the mean parental involvement score was slightly above the midpoint of the scale. Depending on sport or community context, it is possible that more athletes would report more dissatisfaction with these relationships or less parental involvement, thereby affecting the nature of the findings. With respect to age and gender differences, it is possible that these differences could be explained by confounding variables, such as coach gender, competition level, or position, which could not be differentiated in the present study due to the small sample size. Lastly, while multicollinearity necessitated the removal of the peer cohesion variable from the analyses, it should be acknowledged that doing so also limits the completeness of the model by excluding a theoretically important dimension of the sport environment, and one which should continue to be examined in this line of research.  As such, future studies should not only continue to examine the nuanced roles of parents and coaches in sport-based PYD, but also peer relationships, and doing so in larger and more diverse samples.

CONCLUSION 

The social context of the sport environment, which includes coaches, parents, and peers, plays a significant role in shaping athletes’ perceived development through sport. In the present study, athletes’ perceived social skill development was significantly predicted by positive changes to the coach-athlete relationship and parental involvement. The quality of the coach-athlete relationship also emerged as a meaningful predictor of athletes’ perceived cognitive development, highlighting the broader developmental impact of adult figures in the sport context. Furthermore, while peer cohesion was omitted in analyses due to multicollinearity, its interconnectedness with the coach-athlete relationship should be acknowledged, and researchers should continue to utilize it as a variable of interest as theory would dictate. Taken together, these findings underscore the importance of considering the full network of sport-based relationships when seeking to support athletes’ development through sport participation.

APPLICATIONS IN SPORT

In addition to providing support for Holt and colleagues’ (2017) theory of implicit PYD through sport, the present study highlights the interconnected nature of youth sport’s social context. We offer the following recommendations to stakeholders seeking to utilize these findings to develop their youth sport organization’s PYD climate:

  • Provide coaches with education and training that supports their development of communication and relationship-building skills (see Barnett et al., 1992; Jowett & Cockerill, 2003).
  • Provide education and clear expectations for parents’ involvement in the organization, as well as opportunities for involvement (see Knight et al., 2011).

Prioritize relationship building and psychological safety at the outset of the season, to include team-building activities and the development of team norms, rituals, and goals (see Carron et al., 1997; Senécal et al., 2008).

REFERENCES 

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2. Barnett, N. P., Smoll, F. L., & Smith, R. E. (1992). Effects of enhancing coach-athlete relationships on youth sport attrition. The Sport Psychologist, 6(2), 111-127. https://doi.org/10.1123/tsp.6.2.111

3. Benson, P. L., Scales, P. C., Hamilton, S. F., & Sesma Jr., A. (2006). Positive youth development so far: Core hypotheses and their implications for policy and practice. Search Institute Insights & Evidence 3(1), 1-13.

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Appendix A
Supplemental Materials

Table 4

Correlation Matrix of Study Variables

Variables1234567
1. Age       
2. CART-Q-0.34*      
3. PISQ0.150.23     
4. YSEQ-0.140.66**0.31*    
5. Social Skills0.140.62**0.35*0.47**   
6. Cognitive Skills-0.050.40*0.160.160.66**  
7. Goal Setting0.030.43**0.130.42**0.57**0.70** 
8. Initiative-0.100.53**0.180.47**0.51**0.40*0.70**

Notes. * Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed); CART-Q = Coach-Athlete Relationship; PISQ = Parental Involvement, YSEQ = Peer Relationships

Table 5

Regression Results for Perceptions of Goal Setting Skills Gained by Athletes

   95% CI for B  
VariablebSELLULbp
Intercept2.0531.862-1.7315.836 0.278
Gender-0.2280.271-0.7790.322-0.1400.405
Age0.1860.162-0.1430.5150.1960.259
Race0.0110.062-0.1150.1370.0280.863
DCART-Q0.2300.1090.0080.4510.3690.042
DPISQ0.1750.235-0.3020.6510.1240.462

Notes. R2= 0.183, p = 0.207; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement

** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.

Table 6

Regression Results for Perceptions of Initiative Gained by Athletes

   95% CI for B  
VariablebSELLULbp
Intercept4.0001.3151.3286.671-0.1203.043
Gender-0.1380.191-0.5270.2500.062-0.723
Age0.0420.114-0.1910.2740.0350.365
Race0.0100.044-0.0790.0990.3890.221
DCART-Q0.1710.0770.0150.3270.0872.224
DPISQ0.0860.166-0.2510.423-0.1200.520

Notes. R2= 0.185, p = 0.203; DCART-Q = Change in Coach-Athlete Relationship; DPISQ = Change in Parental Involvement

** When ran independently due to the existing multicollinearity, change to peer cohesion was not a significant predictor of cognitive skills.

2025-10-01T13:40:31-05:00March 18th, 2026|Leadership, Research, Sport Education, Sport Training, Sports Coaching, Sports Studies and Sports Psychology|Comments Off on The Role of Sport Relationships in Positive Youth Development
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Relative Age Effect Among Olympic Medalists: Evidence from Ten Summer and Winter Olympic Games held between 2000 and 2018 

Authors: Christiana E. Hilmer, Michael J. Hilmer1

Corresponding Author:

Christiana Hilmer, PhD 

5500 Campanile Drive 

San Diego, CA 92182-4485 

[email protected] 

619-301-9388 


1Both: Department of Economics, San Diego State University, San Diego, CA 

Christiana E. Hilmer, Ph.D., is a Professor of Economics at San Diego State University in San Diego, CA.  Her research interests include the economics of sports, applied econometrics, labor economics, and resource and environmental economics.   

Michael J. Hilmer, Ph.D., is a Professor of Economics at San Diego State University in San Diego, CA.  His research interests include the economics of sports, labor economics, and the economics of education. 

ABSTRACT

This study examines the Relative Age Effect (RAE) among 4,453 individual Olympic medalists from ten Olympic Games (five Summer and five Winter) held between 2000 and 2018. We analyze athletes’ birth quarters and ages at the time of competition to assess patterns by gender, event type, and medal outcome. Using descriptive statistics, regression analysis, a Pearson 𝜒2 test, and a logit model, we find that athletes in judged and combat events tend to be younger, while those in skill and endurance events tend to be older. Gold medalists are, on average, younger than bronze medalists and more likely to be born in the first half of the year. These results confirm the presence of RAE at the highest level of sport and suggest that early developmental advantages persist among Olympic medalists. The findings have implications for athlete development systems and elite sport selection criteria. 

Key Words: Athlete Development; Birth Quarter; Elite Sport, Logit Analysis, Pearson 𝝌𝟐 test 

INTRODUCTION

The Relative Age Effect (RAE) refers to the phenomenon in which individuals born earlier in a selected period, typically a calendar year, tend to benefit from developmental advantages over their younger peers within the same cohort.  These advantages may include earlier physical growth, cognitive maturity, and better access to competitive opportunities.  This concept was described by Barnsley and Thompson (3) in Canadian youth hockey, where players born in the first half of the year were disproportionately over-represented.  RAE has since been documented across various sports, including professional baseball (Thompson, Barnsley, and Stebelsky (14)), elite youth soccer (Glamser and Vincent (7)), youth swimming (Costa et al. (5)) and basketball (Werneck et al. (17)).  Extensive empirical evidence over the last three decades has confirmed its presence in multiple athletic and academic domains (Musch and Grondin (11); Patiño et al. (12)). Researchers have also explored alternative approaches to identifying RAEs by comparing athletes’ relative ages at the time of competition (Zetaruk (18) and Longo et al. (10)). Yet little is known about whether RAE endures at the pinnacle of sports performance. 

Many past studies have focused on youth and amateur athletes, where selection systems, age-based groupings, and physical maturation exert considerable influence.  However, less is known about whether RAE persists at the highest levels of athletic achievement.  The Olympic Games, which represent peak international competition, provide a valuable lens to explore whether early developmental advantages have long-term consequences that extend into elite performance.   

The Olympic context introduces additional layers of complexity.  Events vary widely in physical demands, skill development, and peak performance age.  For instance, judged events such as gymnastics and ice skating often feature younger athletes (Zetaruk (18) and Cummins (6)) while skill and endurance events, such as archery, cross-country skiing, and the marathon typically feature older athletes (Longo et al. (10)).  Seasonal differences between Summer and Winter Games, and gender specific trajectories, also warrant attention. 

Although prior research has examined RAE in Olympic contexts, findings have been mixed.  Baker et al. (2) find evidence of the RAE in skiing, snowboarding, and Nordic combined, find no evidence for figure skaters, and report an atypical pattern in gymnastics.  Joyner et al. (9) find evidence of RAE across multiple sports but note variation by gender and season.  Raschner et al. (13) analyzed data from the first Winter Youth Olympic Games and found evidence of RAE in both genders and across strength, endurance, and technique-related sports.  This study differs by focusing exclusively on Olympic medalists – those who reached the highest level in their sport – to determine whether RAE persists not just in participation, but in podium success. 

This study analyzes 4,453 individual medalists from ten Olympic games (five Summer and five Winter) between 2000 and 2018. We classify events into six categories (timed, judged, skill, endurance, strength, and combat), and examine both the athletes’ age at the time of competition and their birth quarter. The central research questions are (1) Are Olympic medalists disproportionately born in the earlier quarters of the calendar year? (2) Does the probability of winning a gold medal vary by birth quarter? and (3) Are athletes’ ages at the time of competition systematically associated with event type, gender, or Olympic season? This study expands the literature by analyzing RAE by event type among Olympic medalists across both Summer and Winter Games. 

METHODS

This study examines 4,453 medalists (gold, silver, and bronze) from ten Olympic Games held between 2000 and 2018 – five Summer Games (Sydney 2000, Athens 2004, Beijing 2008, London 2012, Rio de Janeiro 2016) and five Winter Games (Salt Lake City 2002, Turin 2006, Vancouver 2010, Sochi 2014, PyeongChang 2018).  Data were compiled from official Olympic databases during 2019.  Athlete biographies were consulted to ensure accuracy regarding birthdates, event categories, and medal results.  Medalists disqualified as of December 2019 due to doping violations were excluded from this analysis.  

Athletes were categorized by type of event into six mutually exclusive groups: timed/weight/measured, judged, skill, endurance, strength, and combat. Hilmer and Hilmer (8) apply these same categories to investigate the presence of confirmation bias in judged events at the Olympic Games.  The first category is timed/weight/measured, where competitors start together and medal winners are determined by that individual competition (henceforth referred to, for lack of a better term, as “timed events”), such as the 100-meter dash, canoe, and downhill skiing.  Judged events rely on subjective scoring either fully (ie, figure skating) or partially (ie, mogul skiing).  The next category is skill events such as archery, shooting, and table tennis.  The fourth category is endurance events that take a relatively long time to complete, such as biathlon, cross-country skiing, and the marathon.  Strength is the fifth category of event, which includes weightlifting, shot put, and hammer throw.  The final category of events is combat, which includes boxing, judo, taekwondo, and wrestling.  Team sports were not included in this analysis because we are interested in an individual’s age and birth quarter at the time of competition.  A team is comprised of a variety of individuals with various birth dates, which makes it difficult to isolate the impact of birth quarter and age at the time of competition.  Thus, team events such as soccer, softball, basketball, and relays are excluded from this analysis. Age was calculated in days at the time of competition, and birth quarters were based on the calendar year: Q1 (January-March), Q2 (April-June), Q3 (July-September), and Q4 (October-December). 

Table 1 presents the breakdown of the medal winners for each of the Olympic Games held between 2000 and 2018.  The Summer Olympics have the bulk of the athletes, with 78% of the medal winners, while 22% of the medal winners compete in the Winter Games.  The number of athletes winning individual medals has increased steadily over the years.  Individual sports added to the Olympic Games during this time were skeleton in 2002, BMX racing in 2008, and golf in 2016. 

The dependent variables are either type of medal, gold, silver or bronze, and how old the athlete is in days at the time of competition.  The independent variables are quarter of birth (Q1 = Jan-Mar, Q2 = Apr – June, Q3 = Jul – Sept, Q4 = Oct – Dec), gender, season, and event type (timed, judged, skill, endurance, strength, combat). Table 2 presents the percentage of competitors in the types of events, medals earned, and quarter of birth, broken down by male and female medal winners and Summer and Winter Games.  As evident from Table 2, the timed category has the most competitors with 45% of the medal winners, ranging from 40% in the Summer Games to 60% in the Winter Games.  Skill, Strength, and Combat award all of their medals in the Summer Games.  Judged events comprise 10% of the medals, while skill has 11% of the medals.  The endurance category has 7% of the medals overall but it is an important component of the Winter Games, with almost a quarter of the medals earned falling within this category.  

Under random distribution, one would expect medals to be evenly divided among the three categories. According to Table 2, bronze medals account for 36% of the overall awards.  Similarly, we would expect the athletes’ birth quarters to be split evenly, with each having 25% of the medal winners if there is no presence of RAE. The first quarter has the most medal winners at 26%, while the last quarter has the least amount of medal winners at 23%, which is a statistically significant difference with a z-score of 3.07 and a p-value of 0.0022. 

Table 3 provides means and standard deviations for how many days old the medalists were when they competed in their event.  The average age of a medalist is 26.3 years old with a standard deviation of 4.8 years, with men at an average of 26.57 and with women at 25.94.  This is similar to the finding of Longo et al. (10), who analyzed all competitors from the 2012 Summer Olympics and found men were an average of 27 years old and women were an average of 26.2 years old.  Awosoga and Chow (1) find that the peak age for a track and field athlete is just under 27 years old, that finalists were on average 16 months older than the average competitor, and medalists were just one month older than the average participant. On average, the youngest medalists are those who compete in judged events, while the oldest medalists compete in skill and endurance events.  This holds across males and females and for the Summer and Winter Games. The age of the medalists is distributed fairly consistently between gold, silver, and bronze medals with the gold medalists being around 100 days younger than either silver or bronze medalists for the entire sample.  Males are older than females by 228 days while Winter medalists are older than Summer medalists by 241 days.   

Figure 1 is a kernel density function that depicts the age in days of the medalist by the type of event.  A kernel density function is a non-parametric method for visually representing the distribution of the data. Unlike a histogram, it is a smooth representation of the probability distribution function (Weglarczyk (16)) and is more informative than summary statistics because it shows the entire distribution of the data.  Judged events have the youngest athletes with the mass of the distribution primarily in the lower end of the age distribution.  Endurance has the bulk of its mass to the right of all of the other distributions, while skill events exceeds all of the other events at the very top of the age distribution.  Figure 2 compares the distributions for males and females.  Females have more medalists at the lower end of the distribution but the distributions are nearly identical at the top end of the age distribution.  Figure 3 is a kernel density function for the Winter and Summer Games.  The distribution for the Summer Games lies to the left of that for the Winter Games, suggesting that Summer medalists are younger than Winter medalists.  

RESULTS

Table 4 provides our first look into the presence of an RAE within Olympic medal winners with a two-way table between birth quarter and type of medal.  The Pearson 𝜒2 test statistic for differences among the categories is 14.12 with a p-value of 0.028.  The Cramér’s V p-value of 0.0398 suggests that the observed association between birth quarter and medal type is unlikely to occur by chance.  Taken together, these results suggest that there is a statistical relationship between birth-quarter and type of medal.  The expected count is in parentheses and suggests that gold medal winners are over-represented for the first and second quarters of the year.  All statistical analysis for this paper is performed in STATA.    

Another option for analyzing the birth quarter of a medalist is to empirically assess whether it impacts their probability of winning a gold medal.  To accomplish this, we estimate a logit model of the form 

                      

 

(1) where gold is 1 if athlete i received a gold medal and 0 if they earned a silver or bronze medal, Q1, Q2, and Q3 are the quarter of their birth of individual i, with the fourth quarter as the omitted category, and εi is the error term.  The marginal effects are the change in the probability of the athlete winning a gold medal relative to the omitted category 

Table 5 presents the marginal effects from the logit model in equation (1).  Athletes who are born in the second quarter are 4.3% more likely to win a gold medal relative to those born in the fourth quarter at a 5% significance level.  Athletes born in the first and third quarters are not statistically more likely to win a gold medal than those born in the fourth quarter.   

In addition to examining how birth quarter impacts the medal received, we perform an empirical analysis to assess if the age of the athlete, measured in how many days old they were when they competed in their event, statistically differs for gender, type of Games, category of events, and medal type.  The most inclusive model takes the form: 

+  εi                            (2)

where εi is the error term. Each of the explanatory variables is binary with the value being 1 if the individual has the characteristic in the named variable and 0 otherwise.  For example, the variable male will equal 1 if the athlete is male and 0 if the athlete is female.  The omitted categories for this model are female, Winter, timed events, and bronze medal.  This model is estimated using multiple linear regression with robust standard errors. Because all of the independent variables are binary, this regression model tests for differences in means between the explanatory variables, holding the other included variables constant. 

The first column in Table 6 presents the results for the general model. These results suggest that, on average, males are older than females by 262 days, while Summer medalists are an average of 230 days younger than Winter medalists.  Judged medalists are on average younger than timed medalists by 1090 days, skill medalists are older than timed medalists by 1002 days, endurance medalists are older than timed medalists by 848 days, and combat medalists are younger than timed medalists by 245 days. Gold medalists are an average of 151 days younger than bronze medalists and silver medalists are not statistically different in age than bronze medalists.

The results found in the initial model generally hold for models that estimate male and females separately. The statistical significance for event type for the model with only males is similar to the general model, but the magnitudes differ.  For example, skill medalists are an average of 1,369 days older than timed medalists for the male-only model, while the difference was 1002 days for the full model. The other difference is that gold and silver medalists are not statistically different in age than bronze medalists.  In the female-only model, athletes who medal in judged events are an average of 1,374 days younger than those who medal in timed events, while in the full model the difference was 1090 days.  Female skill medalists are an average of 560 days older than female timed medalists while endurance medalists are 891 days older than timed medalists.  Strength and combat medalists are not statistically different than timed medalists in age.  For females, gold medalists are an average of 225 days younger than bronze medalists. 

Summer and Winter Games models estimated separately follow a similar pattern to the general model in the first column.  In both the Summer and Winter Games, males are statistically older than females, judged medalists are statistically younger than timed medalists, and endurance athletes are statistically older than timed athletes.  In the Summer Games, skill medalists are statistically older than timed medalists and combat medalists are statistically younger than timed medalists.  Summer athletes who win a gold medal are an average of 158 days younger than those athletes who win bronze medals.  Together, these results suggest that the results are generally consistent across males and females as well as Summer and Winter Games.    

Discussion

Our findings affirm the presence of the RAE among Olympic medalists in terms of both birth quarter and competition age.  A Pearson 𝜒2 test for a difference between birth quarter and medals found a statistically significant relationship between the two variables.  We also found that athletes born in Q2 are more likely to win a gold medal relative to those born in Q4.  This echoes patterns identified in youth and elite-level sports by previous researchers (Joyner et. al., 2017; Musch and Grondin, 2001).  These results suggest that the developmental advantages conferred by earlier birth within a competitive cohort persist even at the highest levels of sport. 

The variation in age across event types aligns with existing literature suggesting that events with aesthetic or acrobatic elements, like gymnastics or figure skating, tend to feature younger athletes (Zetaruk, (18) and Cummins (6)), while events requiring cumulative physical or technical development, such as endurance or skill-based events are dominated by older competitors (Longo et. al (10)).  This supports evidence of distinct developmental trajectories across Olympic disciplines.  These findings contribute to a broader understanding of how structural factors such as age-grouping policies and youth sport calendars may contribute to influence athlete development long after initial talent identification.  This finding may support a revision of the youth categorization system and selectors to mitigate the effects of RAE.

We can interpret these patterns using the Developmental Systems Model (Wattie et al., 2015), which posits that RAE arises from interacting individual (e.g., birthdate, maturation), task (e.g. sport type), and environmental (e.g. selection policies) constraints.  Our findings reflect all three of these inputs. From the individual perspective, older athletes may possess more maturity and resilience.  From the task perspective, certain disciplines favor youth, such as gymnastics and figure skating, while other disciplines favor experience, such as equestrian and long-distance running.  From the environmental perspective, qualification systems often reinforce early selection biases that persist all the way up to the Olympic Games.   

This study has several limitations.  Our data only includes athletes who received medals at the Olympic Games, allowing us to examine RAE for those who have achieved the highest pinnacle of their sport.  The broader population of Olympic participants may not exhibit the same patterns as medalists.  Another caveat is that team events and relays were omitted, despite the possibility that such formats may dilute or amplify RAE effects due to different selection or substitution dynamics.  Finally, the analysis does not account for cross-national or cultural variation in athlete development systems, which could meaningfully shape RAE patterns.  Future research should address these gaps by examining a more comprehensive athlete pool, including non-medalists, and incorporating institutional and cultural context.

CONCLUSIONS

This study provides evidence that the RAE persists among Olympic medalists in the Summer and Winter Games held between 2000 and 2018.  Medalists in judged and combat events tend to be younger, while those in skill and endurance events tend to be older, confirming widely held beliefs about athlete development pathways.  Additionally, athletes born in the second quarter of the year are statistically more likely to win a gold medal than those born later in the year, reinforcing the influence of birth timing, even at the elite level.

Our results demonstrate that the effects of age-based selection advantages are not confined to youth or amateur competition but may have enduring implications for performance outcomes at the pinnacle of sport.  These insights underscore the importance of re-evaluating current age-grouping structures in sport development systems.  Policymakers, coaches, and sporting organizations should consider how age-based selection mechanisms might inadvertently limit long-term talent development by favoring relatively older athletes.  By acknowledging and addressing these structural biases, it may be possible to create more equitable opportunities for younger athletes within a given cohort, ultimately enhancing both inclusivity and performance sustainability. 

APPLICATIONS IN SPORT

To mitigate the impact of RAE, sporting bodies and youth development programs should consider pilot programs that rotate cutoff dates or cluster athletes by biological age rather than birthdate alone (see Wattie et al. (15) and Cobley et al. (4)).  Musch and Grondin (11) suggest varying cutoff dates for different sports, allowing youth participants to choose the sport with the most favorable cutoff date for them.  Raschner et al. (13) suggest a limit on the number of participants by each birth year across two-year age groups. Future research could explore how the dynamics of RAE evolve over an athlete’s career trajectory and examine whether similar effects are observable in non-medalists or team events.    

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2025-08-26T10:08:11-05:00December 23rd, 2025|General, Olympics, Research, Sports Health & Fitness, Sports Studies, Sports Studies and Sports Psychology|Comments Off on Relative Age Effect Among Olympic Medalists: Evidence from Ten Summer and Winter Olympic Games held between 2000 and 2018 
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