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. 

REFERENCES 

  1. Andersen, F. B., Djugum, M. E. T., Sjåstad, V. S., & Pallesen, S. (2023). The prevalence of workaholism: A systematic review and meta-analysis. Frontiers in Psychology, 14, 1252373. https://doi.org/10.3389/fpsyg.2023.1252373
  2. Andreassen, C. S. (2014). Workaholism: An overview and current status of the research. Journal of Behavioral Addictions, 3(1), 1–11. https://doi.org/10.1556/JBA.2.2013.017
  3. Barrett, J., Eason, C. M., Lazar, R., & Mazerolle, S. M. (2016). Personality Traits and Burnout Among Athletic Trainers Employed in the Collegiate Setting. Journal of Athletic Training, 51(6), 454–459. https://doi.org/10.4085/1062-6050-51.7.08
  4. Bentzen, M., Lemyre, P.-N., & Kenttä, G. (2016). Development of exhaustion for high-performance coaches in association with workload and motivation: A person-centered approach. Psychology of Sport and Exercise, 22, 10–19. https://doi.org/10.1016/j.psychsport.2015.06.004
  5. Bruening, J. E., & Dixon, M. A. (2007). Work–Family Conflict in Coaching II: Managing Role Conflict. Journal of Sport Management, 21(4), 471–496. https://doi.org/10.1123/jsm.21.4.471
  6. Cairns, A. H., Singe, S. M., & Eason, C. M. (2023). Perceived Stress as an Indicator of Work–Family Conflict and Burnout Among Secondary School Athletic Trainers. International Journal of Athletic Therapy and Training, 28(4), 215–220. https://doi.org/10.1123/ijatt.2021-0112
  7. Cayton, S. J., & Valovich McLeod, T. C. (2020). Characteristics of Burnout Among Collegiate and Secondary School Athletic Trainers: A Systematic Review. Athletic Training & Sports Health Care, 12(5), 227–234. https://doi.org/10.3928/19425864-20190529-01
  8. Clark, M. A., Michel, J. S., Zhdanova, L., Pui, S. Y., & Baltes, B. B. (2016). All Work and No Play? A Meta-Analytic Examination of the Correlates and Outcomes of Workaholism. Journal of Management, 42(7), 1836–1873. https://doi.org/10.1177/0149206314522301
  9. Dixon, M. A., & Bruening, J. E. (2005). Perspectives on Work-Family Conflict in Sport: An Integrated Approach. Sport Management Review, 8(3), 227–253. https://doi.org/10.1016/S1441-3523(05)70040-1
  10. Eason, C. M., Gilgallon, T. J., & Singe, S. M. (2022). Work-Addiction Risk in Athletic Trainers and Its Relationship to Work-Family Conflict and Burnout. Journal of Athletic Training, 57(3), 225–233. https://doi.org/10.4085/JAT0348-20
  11. Goodger, K., Gorely, T., Lavallee, D., & Harwood, C. (2007). Burnout in Sport: A Systematic Review. The Sport Psychologist, 21(2), 127–151. https://doi.org/10.1123/tsp.21.2.127
  12. Graham, J. A., & Dixon, M. A. (2014). Coaching Fathers in Conflict: A Review of the Tensions Surrounding the Work-Family Interface. Journal of Sport Management, 28(4), 447–456. https://doi.org/10.1123/jsm.2013-0241
  13. Graham, J. A., & Smith, A. B. (2021). Work and Life in the Sport Industry: A Review of Work-Life Interface Experiences Among Athletic Employees. Journal of Athletic Training, 57(3), 210–224. https://doi.org/10.4085/1062-6050-0633.20
  14. Graham, J. A., & Smith, A. B. (2022). Work and Life in the Sport Industry: A Review of Work-Life Interface Experiences Among Athletic Employees. Journal of Athletic Training, 57(3), 210–224. https://doi.org/10.4085/1062-6050-0633.20
  15. Hatfield, L. M., & Johnson, J. T. (2012, April 9). Work-Family Conflict and Related Theories in NCAA Division II Sports Information Professionals. The Sport Journal. https://thesportjournal.org/article/work-family-conflict-and-related-theories-in-ncaa-division-ii-sports-information-professionals/
  16. Kristensen, T. S., Borritz, M., Villadsen, E., & Christensen, K. B. (2005). The Copenhagen Burnout Inventory: A new tool for the assessment of burnout. Work & Stress, 19(3), 192–207. https://doi.org/10.1080/02678370500297720
  17. Laskowski, K. D., & Ebben, W. P. (2016). Profile of Women Collegiate Strength and Conditioning Coaches. Journal of Strength and Conditioning Research, 30(12), 3481–3493. https://doi.org/10.1519/JSC.0000000000001471
  18. Mazerolle, S. M., Bruening, J. E., & Casa, D. J. (2008). Work-Family Conflict, Part I: Antecedents of Work-Family Conflict in National Collegiate Athletic Association Division I-A Certified Athletic Trainers. Journal of Athletic Training, 43(5), 505–512. https://doi.org/10.4085/1062-6050-43.5.505
  19. Mazerolle, S. M., Bruening, J. E., Casa, D. J., & Burton, L. J. (2008). Work-Family Conflict, Part II: Job and Life Satisfaction in National Collegiate Athletic Association Division I-A Certified Athletic Trainers. Journal of Athletic Training, 43(5), 513–522. https://doi.org/10.4085/1062-6050-43.5.513
  20. Mazerolle, S. M., Eason, C. M., Pitney, W. A., & Mueller, M. N. (2015). Sex and Employment-Setting Differences in Work-Family Conflict in Athletic Training. Journal of Athletic Training, 50(9), 958–963. https://doi.org/10.4085/1052-6050-50.2.14
  21. Mazerolle, S. M., Pitney, W. A., Casa, D. J., & Pagnotta, K. D. (2011). Assessing Strategies to Manage Work and Life Balance of Athletic Trainers Working in the National Collegiate Athletic Association Division I Setting. Journal of Athletic Training, 46(2), 194–205. https://doi.org/10.4085/1062-6050-46.2.194
  22. McMillan, L. H. W., O’Driscoll, M. P., & Burke, R. J. (2003). Workaholism: A Review of Theory, Research, and Future Directions. In C. L. Cooper & I. T. Robertson (Eds.), International Review of Industrial and Organizational Psychology 2003 (1st ed., pp. 167–189). Wiley. https://doi.org/10.1002/0470013346.ch5
  23. Naugle, K. E., Behar-Horenstein, L. S., Dodd, V. J., Tillman, M. D., & Borsa, P. A. (2013). Perceptions of Wellness and Burnout Among Certified Athletic Trainers: Sex Differences. Journal of Athletic Training, 48(3), 424–430. https://doi.org/10.4085/1062-6050-48.2.07
  24. Netemeyer, R. G., Boles, J. S., & McMurrian, R. (1996). Development and validation of work–family conflict and family–work conflict scales. Journal of Applied Psychology, 81(4), 400–410. https://doi.org/10.1037/0021-9010.81.4.400
  25. Norris, L. A., Didymus, F. F., & Kaiseler, M. (2017). Stressors, coping, and well-being among sports coaches: A systematic review. Psychology of Sport and Exercise, 33, 93–112. https://doi.org/10.1016/j.psychsport.2017.08.005
  26. Oglesby, L. W., Gallucci, A. R., & Wynveen, C. J. (2020). Athletic Trainer Burnout: A Systematic Review of the Literature. Journal of Athletic Training, 55(4), 416–430. https://doi.org/10.4085/1062-6050-43-19
  27. Olusoga, P., Bentzen, M., & Kentta, G. (2019). Coach Burnout: A Scoping Review. International Sport Coaching Journal, 6(1), 42–62. https://doi.org/10.1123/iscj.2017-0094
  28. Our Division II Story. (n.d.). NCAA.Org. Retrieved September 17, 2024, from https://www.ncaa.org/sports/2021/2/16/our-division-ii-story.aspx
  29. Our Division III Story. (n.d.). NCAA.Org. Retrieved September 17, 2024, from https://www.ncaa.org/sports/2021/2/16/our-division-iii-story.aspx
  30. Our Three Divisions. (n.d.). NCAA.Org. Retrieved September 17, 2024, from https://www.ncaa.org/sports/2016/1/7/about-resources-media-center-ncaa-101-our-three-divisions.aspx
  31. Overview. (n.d.). NCAA.Org. Retrieved September 17, 2024, from https://www.ncaa.org/sports/2021/2/16/overview.aspx
  32. Pitney, W. A., Mazerolle, S. M., & Pagnotta, K. D. (2011). Work–Family Conflict Among Athletic Trainers in the Secondary School Setting. Journal of Athletic Training, 46(2), 185–193. https://doi.org/10.4085/1062-6050-46.2.185
  33. Pope, D. G., & Pope, J. C. (2014). Understanding College Application Decisions: Why College Sports Success Matters. Journal of Sports Economics, 15(2), 107–131. https://doi.org/10.1177/1527002512445569
  34. Robinson, B. E. (1999). The Work Addiction Risk Test: Development of a Tentative Measure of Workaholism. Perceptual and Motor Skills, 88(1), 199–210. https://doi.org/10.2466/pms.1999.88.1.199
  35. Schaufeli, W. B., Taris, T. W., & Van Rhenen, W. (2008). Workaholism, Burnout, and Work Engagement: Three of a Kind or Three Different Kinds of Employee Well‐being? Applied Psychology, 57(2), 173–203. https://doi.org/10.1111/j.1464-0597.2007.00285.x
  36. Schein, E. H. (2010). Organizational Culture and Leadership, 4th Edition (4th ed.). Jossey-Bass.
  37. Scriber, K. C., & Alderman, M. H. (2005). The Challenge of Balancing Our Professional and Personal Lives. Athletic Therapy Today, 10(6), 14–17. https://doi.org/10.1123/att.10.6.14
  38. Singe, S., Cairns, A., & Eason, C. (2024). Examining Burnout Among Collegiate Athletic Trainers: The Relationship with Age, Gender, and Years of Experience. Internet Journal of Allied Health Sciences and Practice, 22(3). https://nsuworks.nova.edu/ijahsp/vol22/iss3/8
  39. Singe, S. M., Cairns, A., Eason, C. M., & Marion, P. E. (2022). Work-Family Conflict and Guilt among Athletic Trainers: Influences of Family Role Performance, Years of Experience and National Collegiate Athletic Association Level. Journal of Issues in Intercollegiate Athletics.
  40. Singe, S. M., Mydosh, C. G., Cairns, A., & Eason, C. M. (2023a). Working Hours, Sleep, and Burnout Among Athletic Trainers Employed in College Athletics: A Cross-Sectional Study. The Internet Journal of Allied Health Sciences and Practice. https://doi.org/10.46743/1540-580x/2023.2430
  41. Singe, S. M., Rodriguez, M., Cairns, A., Eason, C. M., & Rynkiewicz, K. (2023b). Work-Family Conflict and Family Role Performance Among Collegiate Athletic Trainers. Journal of Athletic Training, 58(4), 381–386. https://doi.org/10.4085/227.22
  42. Snarr, R. L., & Beasley, V. L. (2022). Personal, Work-, and Client-Related Burnout Within Strength and Conditioning Coaches and Personal Trainers. Journal of Strength and Conditioning Research, 36(2), e31–e40. https://doi.org/10.1519/JSC.0000000000003956
  43. Taylor, E. A., Huml, M. R., & Dixon, M. A. (2019). Workaholism in Sport: A Mediated Model of Work–Family Conflict and Burnout. Journal of Sport Management, 33(4), 249–260. https://doi.org/10.1123/jsm.2018-0248

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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Reducing absenteeism and turnover among part-time labor in community sport settings: A case study example and project guidelines for sport management students

Authors: Michael J. Diacin1

1Department of Kinesiology, Health, and Sport Sciences, University of Indianapolis, Indianapolis, IN, USA

 

Corresponding Author:

Michael J. Diacin, Ph.D.

1400 E. Hanna Ave.

Indianapolis, IN 46227

(317)791-5703

[email protected]

Michael J. Diacin, Ph.D., is an Associate Professor in the sport management program at the University of Indianapolis. His research interests focus on sport management pedagogy, experiential learning, and consumer incentives within spectator and participatory sport organizations.

ABSTRACT 

Part-time employees are critical to the daily operation at many sport and recreation focused businesses. Managers at many sites regularly deal with turnover and absenteeism among part-time workers. Absenteeism among the part-time workforce is problematic when less than a full staff is present to perform critical tasks. It negatively impacts customers through longer wait times and employees through increased workload. Therefore, managers in these settings should be making attempts to retain quality employees for as long as possible and offset the detrimental consequences of absenteeism. Managers could develop initiatives to ensure attendance from employees scheduled to work at times of peak customer presence as well as incentivize employees to replace absent workers on short notice. Therefore, the purpose of this work is to provide students with a case study situated within the possible employment setting of community-based sport and recreation facilities and complexes and have them develop initiatives to improve attendance and longevity of employment among part-time workers.

The application to sport management is that current students could likely work in businesses that employ part-time, seasonal workers. Commercial sport and recreation facilities and complexes exist in many locations; therefore, there is a strong likelihood that current sport management students will be working in these settings after graduation. Furthermore, they could benefit from imagining themselves overseeing a labor force of part-time workers and developing initiatives aimed at those part-time workers ranging from high school aged students to senior citizens. As future managers in these settings, students could be challenged to find ways to reduce absenteeism, fill staff shortages created by absenteeism on short notice, and retain quality workers for longer durations. The efficiency and effectiveness of the operation is highly dependent upon part-time workers; as a result, it would be worthwhile to develop initiatives to best ensure the operation is running at a maximum level of efficiency and effectiveness.

KEYWORDS: management, incentives, employees

INTRODUCTION 

Commercial sport and recreation businesses may range from single buildings to expansive multi-sport complexes. These complexes might be referred to as “sports campuses.” The size of these sites could range from an indoor facility measuring 50,000 square feet to a larger complex measuring hundreds of acres. The activities that take place within could include any assortment of team-based and individual activities. Basketball, hockey, tennis, gymnastics, soccer, flag football, cornhole, and pickleball are among the activities conducted at these sites. Regarding ownership and management of these facilities and complexes some might be owned by a municipality and managed by the municipality’s sport and recreation division. Some municipalities choose to outsource the daily management to a private company while other facilities and complexes are privately owned.

At many of these sites, a core of full-time managers directs the overall operation. The quantity of full-time employees could vary based upon the size and scope of the operation. A common aspect within these facilities and complexes is that the full-time managerial core depends on a team of part-time employees who execute many significant tasks related to customer service and maintenance. The part-time staff includes people from different age groups ranging from high school aged students to senior citizens. They receive an hourly wage, and some might receive fringe benefits such as free use of the facility (e.g., swimming pool, fitness equipment). With rare exception, part-time employees do not receive health insurance, retirement contributions, and/or other benefits that are often provided to full-time workers.

An operation in which part-time employees are heavily relied upon presents challenges to the management. Despite being counted on to execute important tasks, part-time workers are not highly compensated, nor do they receive the same benefits given to full-time staff. Unlike full-time staff, the job might not be their primary focus nor primary source of income. This population could be more likely to leave if other opportunities become available or not report for duty if other circumstances arise. Consequently, reliability and retention of part-time employees have consistently been identified as a critical issue facing managers that work in commercial sport and recreation settings (McCole, Jacobs, Lindley, & McAvoy, 2012). Consequences resulting from frequent absenteeism and rapid turnover of part-time employees could negatively impact the operation in numerous ways; therefore, management should attempt to be proactive to best mitigate the negative effects associated with frequent absenteeism and rapid turnover.

Although turnover is an inevitable aspect associated with operating any business, lessening the amount of turnover can be beneficial. The cost associated with turnover can be significant. McKinney, Bartlett, and Mulvaney (2007) identified the consumption of time and financial resources as consequences of turnover. First, there could be a cost to announce vacancies through sites that charge for posting them (e.g., classified listings in the local newspaper, websites targeting job seekers). In addition, there would be a cost associated with additional wages being paid out because a new hire could be working alongside another employee to learn the job. Since that new hire is earning a wage while working alongside another employee earning a wage, the aspect of paying two wages to do one job exists until the new hire has been fully trained and able to do a job on their own.

In addition, the cost of time spent by management on screening and interviewing candidates could be significant. Although part of the job, these activities command time, and frequent turnover means that the managerial staff is frequently spending time on screening and interviewing activities to fill vacancies. If management consistently spends time on these activities, the time spent on other aspects of the operation decreases. In a setting where there are small quantities of managerial staff and each manager “wears many hats,” retention of part-time workers would benefit management because less of their time would be dedicated to finding replacements for departed employees.

Frequent absenteeism and turnover could be especially problematic because of the negative impact to an operation when inexperienced staff is working shorthanded. For example, absenteeism could add to the workload and stress to the employee who did show up for work. In addition, there could also be a negative consequence for customers, as staff shortages could result in negative outcomes such as longer lines and wait times. If customers repeatedly have negative experiences, they might be motivated to go elsewhere to pursue their leisure interests.

On the other hand, a fully staffed operation with an experienced workforce benefits coworkers and customers. When a full contingent of experienced employees is working, no one is placed in a position of having to cover for the absent worker. In addition, the accumulation of experience increases efficiency and effectiveness within the operation. Shorter lines and shorter wait times benefit the customer. Ensuring the customer has a positive experience is critical to securing their ongoing patronage. Although absenteeism and turnover will occur, management should strive to incentivize those employees to work when scheduled as well as remain for an entire busy season (McCole et al., 2012). Management could establish various initiatives to minimize absenteeism and turnover. The details of those initiatives are expanded upon in the following section.

INITIATIVES TO REDUCE TURNOVER AND OFFSET STAFF SHORTAGES 

Commercial sport and recreation facilities are highly reliant on part-time labor to execute many important tasks. There are many circumstances that would cause these employees to miss their scheduled shift on short notice or leave the job altogether. Regardless of the legitimacy of the reason for absenteeism, such occurrences negatively impact both part-time and managerial staff, as well as customers. Therefore, a full complement of staff is needed to ensure maximum efficiency and effectiveness occurs on any given day.

These facilities and complexes are also potential employment settings for sport management students. Graduates may begin as mid-level managers in community-based sport and recreation facilities and complexes as a first job in the sport industry after graduation. Because sport management students could be working in a setting where turnover and absenteeism could be frequent, it would be worthwhile for them to engage in an exercise before entering the setting that would challenge them to think proactively and create a program designed to reduce incidents of frequent turnover and absenteeism. Although they will never eliminate absenteeism and turnover, they should be thinking proactively to minimize absenteeism as well as increase longevity among part-time employees.

Therefore, the purpose of this case study exercise is to provide students with an opportunity to engage in a managerial challenge within the possible employment setting of community-based sport and recreation facilities/complexes. It is designed to help students understand the challenges of working in settings where there is a high level of reliance upon part-time labor as well as challenge them to create a proposal designed to entice potential part-time workers to stay for a particular duration, fulfill their scheduled shifts, and/or assist in situations of absenteeism by filling shifts left open by an absent employee. The initiative could focus on a period as short as a single day to an entire peak season lasting several months. The proposal might also include focus on performance-based initiatives. For this case study exercise, the student could take the role of a mid-level manager. This mid-level manager would supervise part-time staff and reports to a higher-level full-time staff member, such as a General Manager. The proposal would be presented to the General Manager (the course instructor and/or an invited guest such as a manager of a local facility or complex).

Although it would take time and effort to create and manage such initiatives, the benefit to colleagues, customers, and the business resulting from fewer incidents of absenteeism and turnover could make the initiative worth the effort and expense. These types of facilities and complexes could generate revenues in the hundreds of thousands to several million dollars. Expenses such as utilities, maintenance, personnel, and equipment/supplies will use up most of the revenues. Therefore, the financial resources available would be limited as the quantity of dollars available for this case study exercise would be $12,000 to $18,000 annually ($1,000 to $1,500 monthly), with the fiscal year starting September 1 and ending August 31 the following year.

“Survive the Day” Initiatives

This initiative is designed to offset staffing shortages that occur when a part-time worker calls off on short notice or does not show up without any notice given. It is intended to ensure enough employees are present to execute various tasks. This initiative could be focused upon accomplishing two ideals. They are to 1) incentivize the people who are scheduled that day to show up for their shift and 2) if someone must call off, incentivize someone who wasn’t originally scheduled to take the place of the worker who called off on short notice or did not show up for work (e.g., “no call, no show”).

“Survive the Season” Initiatives

Although open for business year-round, the amount of customer activity within commercial sport and recreation facilities and complexes fluctuates based on the season. The greatest amount of customer traffic occurs during the winter months (early December through late February). Ice surfaces have been booked from the late afternoon (4pm) until late night (1am) on weekdays and booked from 6am to 1am on Saturday and Sunday. Youth association and high school hockey teams are conducting their games in the early evening. Adult leagues occupy the latter hours. In addition to the presence of these user groups, youth and high school games bring a greater amount of spectator traffic as friends, classmates, and family members of the participants attend the contests. It is also the period when public skating attendance peaks. As many as 300 customers could be admitted for a two-hour session on a Saturday or Sunday afternoon.

The ice surfaces are booked for similar hours during the months of September and October. Practice and scrimmages are typically conducted. These activities bring user groups but do not bring spectator traffic. Public skating is offered but would bring a fraction of the traffic seen during the winter months. To ensure employees are present to cover the hours in which user groups are present, a “survive the season” initiative could be designed to incentivize part-time employees to stay with the job from September through February. Contingencies could also be added. For example, employees would need to work a specified quantity of shifts/hours (especially on weekends). In addition, limits to the number of times an employee is absent from a scheduled shift, especially weekends, could be implemented.

Recognition for Performance Initiatives

This initiative would focus on rewarding employees for engaging in certain behaviors outside of the attendance-based actions. Employees who engage in quality work would be rewarded for doing so. Support for recognizing employees was revealed by Kellison, Kim, and Magnusen (2013) as they surveyed 522 part-time college aged (18-23 years old) campus recreation center employees from eleven universities to gain insight regarding factors that influenced their intentions to continue working in a part-time capacity at their respective university recreation centers. Recognition was identified as a key factor that positively influenced intentions to remain with the job/organization. Because many of the part-time workers in this case study exercise are in the age range of 18-23, these findings lend support to attempting recognition-based initiatives that have potential to retain employees.

Many organizations have a performance-based initiative in place, commonly referred to as an “employee of the month” program. This is often a competition-based system where one person is selected from the entire staff and receives the award. Various challenges to implementing initiatives where an employee is rewarded in this fashion exist. First, there is a challenge to objectively measuring and documenting the employee’s work. Because many of the part-time support staff members working in commercial sport and recreation settings do not engage in tasks that are easily quantifiable, measuring “good work” could be subject to opinion and perspective. Second, there are different employee groups, each engaging in different tasks. For instance, some of the workers are front of the house workers who are frequently interacting with customers. Others would be considered back of the house workers who do not regularly engage with customers. Consequently, there would be difficulty in comparing the performance of front of the house to back of the house workers because of the differences in their jobs. As a result, it would be the responsibility of the manager to establish parameters, standards, and/or benchmarks for each employee group.

Although an initiative for rewarding good deeds/good work is well-meaning, a system that relies on opinion, relationships, and other subjective criteria could result in more employees feeling less valued if they perceive they earned the reward but were passed over. Instead of having a competition among all employees working different jobs, an alternative is to establish the initiative so that each employee would be able to “control their own destiny.” That means each employee could receive the reward if certain benchmarks and/or standards are reached. If the commitment is made to proceed with such an initiative, an objective system of measurement is needed so that the employee could clearly understand what is expected to obtain the reward. Otherwise, employees could perceive the initiative as subjective, biased, and/or arbitrary.Regardless of the initiative(s) chosen, the proposal should include the following content:

  • The parameters/standards/actions that the employee will take (e.g., filling in for an absent employee, working “x” number of peak busyness shifts over a particular period) to receive the reward.
  • The rewards that will be given.
  • The costs are associated with implementing the initiative.
  • Argument behind why this initiative is feasible in this setting and with this workforce.
  • Identification of potential obstacles for success; why could this initiative be implemented and still not provide the desired results?

PROJECT DETAILS

The following sections for this case study exercise include further description of the setting, operating schedule, manager and part-time worker job descriptions and categories. The quantity of part-time workers hired for each area and the quantity of workers from each category that is on duty at a given time is provided. In addition, the times of day and days of week they typically work as well as the duration of their shifts are indicated.

Facility Setting and Description

The facility that will be utilized for this case study is a multi-purpose facility in which the terms “ice arena” or “hockey arena” might be used. The activities that commonly take place would be ice sports such as hockey, figure skating, and recreational skating. The facility is approximately 180,000 square feet. Two arenas that each house an ice surface of 85×200 feet are the primary activity spaces. When the ice is removed, activities can be conducted on the concrete floor. During off-peak months, various events and programs such as trade shows, exhibitions, and circuses could be conducted.

Each arena consists of stationary spectator seating in the form of metal bleachers with a seating capacity of 1,000. Each arena has six locker rooms (four for hockey teams, one for referees and one additional room to be used on an “As Needed” basis (e.g., for girls participating on boys’ youth hockey teams). There are storage areas and a large garage area where the ice resurface machines are housed. Other areas not accessible to the public include mechanical rooms where the ice cooling equipment is housed. Public areas would consist of a large lobby in which numerous benches and tables are present for the convenience of the patrons. Accessible from the lobby is the concession stand, pro shop (equipment and merchandise sales), arcade, office space, fitness center, restrooms, and two multi-purpose rooms where staff meetings, birthday parties, and team banquets could be held.

Facility Operating Schedule

Many sport and recreation related businesses are open for business seven days a week and typically see most customer activity during weeknights (after 5pm, Monday through Friday) and weekends. On weekends, activity could start as early as 6am and continue as late as midnight or 1am during the peak season. This is when staff is most needed to cover these hours. The amount of customer activity will be at its peak from early December until early March. This is the peak period for youth hockey games, which increases the amount of spectator traffic as family members attend the contests. High school programs could rent space for their practices and games as well. Their games bring additional spectator traffic. It is also peak season for public skating sessions. A public skating session on a weekend afternoon during the winter months could attract as many as 300 paying customers for a two-hour window of skating time. 

Regular business hours (e.g., Monday-Friday from 8am-5pm) are typically the periods with the least amount of customer activity. During this time, most maintenance and cleaning activities occur. Deliveries from vendors also occur during this time. Therefore, there is a need for management and custodial personnel to be present during times of minimal customer activity.

Full-Time Manager Descriptions

The facility is overseen by a general manager and additional full-time, salaried assistant managers. The general manager and assistant managers participate in various aspects of the operation. It is not uncommon for each assistant manager to not only have a primary responsibility regarding some managerial aspect, but also “wear many hats” and participate in other aspects of the operation. For example, one of the assistant managers might be responsible for overseeing tasks in connection with human resources. This person would be responsible for writing and disseminating job descriptions, screening applicants, and conducting interviews. The other assistant managers could be responsible for overseeing facility maintenance/cleanliness, the concessions operation, the pro shop/retail operation and/or marketing/programming. In addition, full-time managers participate in other aspects of the operation as they should be able to step in and assist anywhere on an “as needed” basis. This would include driving the ice resurface machine, operating cash registers, distributing rental equipment, and spot cleaning.

At least one of the full-time, salaried staff members are present when the building is open for business. This would include coverage during regular business hours as well as weeknights and weekends. It is possible that during peak times of business, more than one manager could be present. It would not be uncommon for 4-5 full-time management members to be employed at this type of facility.

Part Time Staff Descriptions

Perry (2018) identified different categories of employees that seek part-time employment in commercial sport and recreation facilities and complexes. The first category consists of individuals who are looking for some work to keep busy and gain supplementary income. A retired individual, perhaps a senior citizen, would fall into this category. The second category consists of post-college aged workers with full-time jobs who want or perhaps need a second job to help pay bills, accumulate extra savings, etc. The third category would consist of high school and college aged individuals who are looking to gain work experience and obtain income. This demographic is typically working around their schooling.

Regardless of the demographic, these jobs are often not the primary focus in the employee’s life. Other aspects are higher on the priority scale; therefore, employees might not alter other life aspects (e.g., primary job, school, family commitments) to work these jobs. Because the employee is not intending to make a career out of the part time job in this setting, this could have an impact upon attendance and performance.

These part-time workers fulfill “front of the house” and “back of the house” positions. In this setting, front of the house positions consists of duties such as cash handling/cash register operation, serving food products, collecting participation fees, distributing rental equipment, monitoring customer conduct, and being present in the event customers have questions and/or need assistance. Front of the house positions that often exist in the setting include concessions, skate staff, pro shop/merchandise sales, and front desk workers/receptionists.

Back of the house employees largely contribute to the cleanliness and upkeep of the facility. In this setting, custodians and ice resurfacing machine drivers/building attendants are common types of back of the house workers. They have little interaction with customers and in the case of custodians, often work when few to no customers are present in the facility.

These employees are paid an hourly wage and could work as little as 10 hours a week or as many as 40 hours a week. Accumulated hours are monitored so that the employee does not exceed 40 hours a week. If 40 hours in a week are exceeded, overtime compensation of one and a half times the employee’s standard hourly wage would be paid. In many cases, the hourly wage could be at or slightly above the locally mandated minimum wage.

For this case study exercise, seven part-time worker categories exist, consisting of several front and back of the house positions. Several people are on the roster within each worker category. Not everyone who has been hired and appears on the roster is working at the same time. Saturday and Sunday will be the busiest days requiring the greatest amount of part-time worker participation. Key duties, the time of day and quantity of hours per shift that employees within each of these categories are typically scheduled, and worker demographics are provided.

Concessions

Concession stand workers are responsible for preparing and serving food and beverages. “Quick serve” foods are usually prepared and then held in a warming bin or warming rollers (e.g., pizza, popcorn, hot dogs). Some facilities might possess a deep fryer, which would allow workers to prepare items such as fries, mozzarella sticks, etc. Concessions workers work when customer traffic is heaviest (evenings and weekends), except for periods when school is out of session such as winter break. High school and college aged employees are common. Post college aged adults working part-time, perhaps around another full-time day job, also staff the concessions operation. One person will be on duty most of the time. During the periods of peak customer traffic, such as public skating sessions during the winter months, two people could be scheduled to work at the same time. Shift duration is commonly 4-6 hours. There could be 6-8 employees on the roster in this area.

Skating staff

The skate staff would consist of counter/desk workers who are responsible for collecting admission fees and distributing “skate passes” to patrons participating in public skating sessions. Skate passes are often colored stickers the patron can wear on their clothing so that staff can easily see they paid their admission fee for that public skating session. They would also distribute rental skates to patrons who do not own their own set of skates. The other type of worker in the skating staff category is the “skate guard.” These individuals ensure those who enter the ice have paid their admission, indicated by the skate pass they are wearing. They also watch for and report any injuries or incidents of dangerous behavior to management. This worker group commonly consists of high school and college age individuals. Their work schedule aligns with public skating sessions, which are typically on Saturday and/or Sunday. With a two-hour skating session, for instance, workers could be scheduled for a 3.5 to 4-hour shift. This duration allows for them to be on duty before customers arrive and allows for post-session cleanup, putting skates away, etc. The roster could consist of 4-8 employees in this category. 1-2 skate guards would be on duty for each session (2 during the busiest winter sessions) as well as 1-2 counter/desk workers (2 during the busiest winter sessions).

Pro shop staff

Merchandise such as tape, water bottles, mouthguards, sticks, helmets, and other equipment is commonly sold in the “pro shop.” These workers are responsible for operating the register and assisting customers. Some light cleaning within the area is periodically assigned. In some facilities, skate sharpening is offered, and the pro shop employees will sharpen customers’ skates. During slow periods, the pro shop staff often is charged with sharpening the rental skates that will be used during the public skating sessions. The pro shop would be open during the evening and weekends. This worker group commonly consists of high school and college age individuals. The shift duration could last from 4-6 hours. On weeknights, one person would likely be on duty. During the weekends, especially the times around public skating sessions, two people could be on duty. There could be 4-6 people on the roster within this worker category.

Fitness center workers

The facility in this case study has a fitness center on site. The fitness center would include equipment that would commonly be found at commercial fitness centers, such as treadmills, elliptical trainers, and free weights. Monthly and/or annual memberships could be sold. This area could be open from early morning until late evening (6am-11pm) seven days a week. Fitness center workers would ensure members have checked in, engage in light cleaning duties and conduct minor troubleshooting of equipment. These workers do not conduct personal training sessions. During the weekday mornings and afternoons, the workers would mostly consist of senior citizens or other post-college aged adults. High school and college aged individuals would typically work evening and weekend hours. Because this area is staffed seven days a week from early morning to late evening, 6-8 individuals could be on the roster for this position. One worker would be working at a time for a shift typically lasting 4-6 hours.

After hours reception desk

These workers would be on duty after regular business hours on weekdays and on weekends. This person would likely distribute keys for the locker rooms to hockey teams, provide information to basic inquiries (e.g., assigned locker room number), answer phone calls, and serve as a point of contact for patrons who report a circumstance in need of attention, such as cleaning up a spill, restocking paper products in restrooms, etc. The desk worker would contact the manager on duty and/or other worker groups to address the need. In some facilities, the desk worker might assume duties such as collecting fees and distributing passes for public skating patrons. Workers in this group could range from high school or college students to post college aged individuals and senior citizens. One person at a time would be on duty and there could be 3-4 people on the roster in this category.

Building attendant/ice resurface machine driver

Building attendants are responsible for resurfacing the ice for each new user group. In between ice resurfacing duties, they are responsible for surface cleaning in locker rooms, restrooms, spectator seating areas, and lobbies/foyers. Restocking restrooms and mopping up spills are among cleaning and light maintenance duties that a building attendant would be expected to perform. They are scheduled during times when user groups are present in the building; therefore, the schedule consists of mostly evening and weekend work. Building attendants are required to be a minimum of 18 years old because the job includes operation of the ice resurfacing machine. Middle-aged individuals working around a primary job could also be working in this role. One person would be scheduled to work in this capacity on a weeknight; however, two people could be scheduled to work on weekends during peak times. The shift duration would likely be 7-8 hours. There could be 4-6 employees on the roster.

Custodial

Custodians are responsible for the overall cleanliness of the facility. Much of their time is spent cleaning and restocking restrooms and locker rooms, emptying trash bins and cleaning spectator seating areas. These employees typically work when the building is not full of customers so that they can engage in deep cleaning activities. Working during regular business hours (e.g., Monday-Friday 8am-5pm) is common. They could also be scheduled for late night/early mornings on Friday night into Saturday morning and Saturday night into Sunday morning as the facility will typically be full of customers when the doors open on weekend mornings. Worker demographics could vary, ranging from post college age to semi-retired individuals. Some of these employees might be working this job along with another job. There could be 2-4 people employed in this category, with one person on duty at a time. A typical shift duration could be 4-8 hours. If a special cleaning or maintenance project is planned, more than one worker from this category could be scheduled.

APPLICATION TO SPORT MANAGEMENT

Regarding the application of this work to the educational setting, sport management students could find this case study exercise useful because it gets them to imagine themselves working in a setting and engaging in challenges they could face once they enter the workforce as a full-time, managerial employee. Commercial sport and recreation facilities and complexes exist all over the world; therefore, there is at least some likelihood that some will work in these settings. Therefore, it is important to expose students to situations they could experience within possible employment settings. Having students generate content that could be used in an actual setting would be useful because many could be overseeing part-time workers from various demographics and life situations at their respective workplaces.

Students who work in these settings will not only be faced with challenges related to staffing but could also be working in settings where there are not large amounts of financial resources available to them. Many of these facilities and complexes are smaller “mom and pop” businesses that do not generate massive amounts of revenue. Therefore, they will have to find ways to address a challenge with a limited amount of money (in this case study $1,000-$1,500 monthly limit) at their disposal.

The content students create in association with this case study could take the form of a written proposal and/or an oral presentation. In order to give them the opportunity to create the most extensive proposal possible, it is suggested that students create content for each of the three initiatives (survive the day, survive the season, and performance). As a middle level manager who was hired by a superior, the student would report to that individual or perhaps several individuals who occupy a higher position in the organizational chart. For this case, the student could present the content to the instructor of the course who would represent the upper-level member of management. It is suggested that if feasible, managers from a local facility or complex be invited to participate in the presentation of the proposal. Their presence and scrutiny would add an additional layer of authenticity to the endeavor. Furthermore, the instructor might wish to reach out to managers of local facilities and complexes to see if they would like for students to create a proposal specifically for their operation. The manager would possibly first appear as a guest speaker and share details of the operation with the students. Students could use that visit to ask questions and gain a better understanding of the operation and then develop a plan for that manager.

Regarding the execution of the students’ proposal in the “real world,” it is likely that costs and personnel limitations would be presented as reasons as to why these ideas would not reach the execution stage. Therefore, part of the challenge for students is to create a plan that would be financially feasible for a small business as well as a plan that could be executed by a single manager or perhaps a small managerial team of 2-4 people. In closing, it is hoped that this case study exercise will benefit faculty seeking content to add to their courses. This case study could be executed within courses focusing on human resources management, facility management, and/or financial management. Because a large quantity of these operations exists, it is possible that students will secure employment in them. Therefore, this endeavor can help to further prepare students for managing a predominately part-time workforce within commercial sport and recreation facilities and complexes.

REFERENCES 

  1. Kellison, T. B., Kim, Y. K., & Magnusen, M. J. (2013). The work attitudes of millennials in collegiate recreational sports. Journal of Park and Recreation Administration, 31(1), 78-97.
  2. McCole, D., Jacobs, J., Lindley, B., & McAvoy, L. (2012). The relationship between seasonal employee retention and sense of community: The case of summer camp employment. Journal of Park and Recreation Administration, 30(2), 85-101.
  3. McKinney, W. R., Bartlett, K. R., & Mulvaney, M. A. (2007). Measuring the costs of turnover in Illinois Public Parks and Recreation Agencies: An exploratory study. Journal of Park and Recreation Administration, 25(1), 50-74.
  4. Perry, P. M. (2008). Finding great part-time workers. NSGA Retail Focus, 61(2), 10-11, 22.

2025-12-05T10:58:04-06:00June 3rd, 2026|Contemporary Sports Issues, General, Leadership, Research, Sports Management, Sports Studies|Comments Off on Reducing absenteeism and turnover among part-time labor in community sport settings: A case study example and project guidelines for sport management students
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Over-promised, under-delivered: Does position in the National Football League draft matter?

Authors: Dennis M. Shaffer1 and Ryanne E. Shaffer

1Department of Psychology, The Ohio State University Mansfield, Mansfield, Ohio, USA

 

Corresponding Author:

Dennis M. Shaffer, PhD

1760 University Drive

Mansfield, OH 44906

[email protected]

Dennis M. Shaffer, PhD, is a Full Professor Psychology at The Ohio State University in Mansfield, Ohio. His research interests focus on how athletes use visual information to pursue and induce collisions with targets in the environment in domains such as Frisbee catching, American football, and baseball, and how cognition and systems of perception and action interact.

Ryanne E. Shaffer is currently a senior at Twinsburg High School in Twinsburg, Ohio.

ABSTRACT 

Purpose. We investigated whether players drafted higher in the National Football League (NFL) over a ten-year period performed better in their first four years in the league, consistent with the trade value charts and rookie wage scale the NFL uses to value players. The purpose was to see whether how the NFL intuits draft values is connected to player performance.

Methods. In Study 1, we collected draft position data for each of the seven rounds of the draft over a ten-year period as well as the values for each of two different trade charts and the salaries in the rookie wage scale. We then coded data by round, third of round (top, middle, bottom), years in the league, and Pro Football Focus (PFF) grades.

Results. We found no correlation between performance and the way the NFL values draft positions and no difference in player performance and years in the league between draft positions in rounds 4 and 5. There were also no differences in player performance or years played in the among top, middle, or bottom thirds of rounds. We also found a distinct advantage in player performance for teams trading down for draft picks compared to those trading up for draft picks, contrary to the way the NFL values draft positions.

Conclusions. Our work shows several player performance-based results that contradict well-established beliefs concerning the value of draft picks in the NFL.

Applications in Sport. Trade values and rookie wage salaries are used as baselines by the NFL. The importance of drafting better players higher in the draft order have important implications for greater success for teams, executives, and players. Our work may inform strategies that might be best to use in drafting prospective players in the National Football League.

Key Words: NFL draft, trade value, intuitive beliefs, player performance.

INTRODUCTION 

The work here tested whether draft position predicts player performance once they are drafted into the National Football League (NFL). The NFL draft is set up so the team with the worst regular season record picks first, followed by the team with the second worst regular season record picking second, and so on. Picking first, though it means you finished with the worst record in the league the previous year, is an enviable position to be in at draft time as you have your pick of ~250-~275 players. The NFL draft consists of seven rounds(since 1994) of draft picks where, at least originally, every team has one pick in every round. Prevailing wisdom in this field and even if you are picking teams for any game whether athletic or not is that higher picks should be valued more than lower picks and that over time the data should bear this out.

The intuitive beliefs that the NFL, individual teams, and executives have about draft order or draft pick position can be measured in two ways. First, this may be measured by what are called ‘trade value charts,’ that define values for each one of the draft picks (3). There are a few different types of trade value charts, but most teams follow one of these versions if they want to trade draft picks with any other team. The classic version of a trade value chart is the Jimmy Johnson (JJ) chart. A more recent chart is the Rich Hill (RH) trade value chart. These charts basically provide teams with a framework or baseline from which to trade draft picks (14). The trade value charts are similar in several ways—(1) values increase exponentially in the first round from about pick 4 to pick 1 and (2) values for picks decrease for each subsequent pick. For instance, Pick #10 in the JJ chart this year was given a value that was 46.3% of Pick#1 (RH chart = 36.9%); Pick #20 was given a value that was 28.33% of Pick #1 (RH chart = 26.9%), and Pick #30 was given a value that was 20.67% of Pick #1 (RH chart = 19.6%). Consistent with this, the ratio of values in the top third : middle third of the first round is 1.824 in the JJ chart, (RH chart = 2); middle third : bottom third is ~1.49 (RH = 1.55); top third : bottom third is ~2.71 (RH = 3.13), and bottom third of 1st : top third of 2nd is 1.3 (RH = 1.5).

The second way the NFL’s intuitive beliefs about draft order may be measured is by the ‘rookie wage scale’ put forth by the player’s labor union and the NFL in 2011, which defines the parameters for what every drafted player will earn in his first four years in the league (3). For instance, this year, the number one overall draft pick will earn $48,757,500 in total value over his first 4 years; the number 10 pick will earn a little less than 55% of that (a difference of over $22M over the first four years); the number 20 pick will earn about 37% of that (a difference of almost $30M), while the number 30 pick will earn about 31.25% of that (a difference of ~$33.5M). While the percentage and salary difference is less in subsequent rounds among those picks (picks 1, 10, 20, and 30 in rounds 2-7), the importance of drafting better players higher in the draft order have important implications for building the best team, paying players the proper amount for their performance, the amount of money that is charged to a team’s salary cap, and the livelihood of the NFL executives who have a hand in drafting these players.

Previous work has investigated several avenues regarding characteristics that affect draft value, that are related to performance once in the NFL (5, 9, 15, 17, 20). The results of some of this work show how draft value does very little to affect probabilities of teams making the playoffs (9, 15), Other work has shown that college performance is a better predictor of performance once in the NFL than tests measuring physical ability (11, 20-21). While other work has shown that total yards gained by running backs in college and overall speed has been shown to be a primary predictor of both draft status and higher salaries once in the NFL over tests of physical ability and combine tests (6, 11, 16-17, 20). Additionally, predicting success based on results of athletic testing including the NFL Combine can yield complicated and somewhat mixed results (16).

The primary focus of this paper was to investigate whether what teams intuit of draft value based on grades in these trade value charts and rookie wage scales matches actual performance data for the players chosen in those spots. More specifically, our primary investigative foci in Study 1 were to: (1) analyze whether differences in player value (as given by trade value charts and the rookie wage scale) from pick-to-subsequent-pick were correlated with differences in player performance from pick-to-subsequent-pick, (2) analyze whether player performance, as measured by Pro Football Focus (PFF) grades and years spent in the league, was different among and within rounds (14), (3) analyze whether PFF grades and years in the league were different among thirds of rounds across and within rounds, (4) and analyze how the NFL valuation and PFF grades for last twelve picks of the first round compared to the first twelve picks of the second round.

STUDY 1

METHODS

Data Sets

We first used the Pro Football Reference site (20) to gather and download data for every player drafted from 2011-2020. We then used the Pro Football Focus (14) site to gather the overall season grades for each player across their first four years in the NFL. This resulted in 2,544 drafted players across 10 years. This study was approved by The Ohio State University Behavioral and Social Sciences Institutional Review Board (Study Number: 2023B0282).

Procedure

Evaluating a Player’s First Four Years in the NFL

Since we were interested in evaluating the success of teams in drafting, we evaluated player performance over the player’s first four years. This is because four years is the length of all rookie (1st year player) contracts. Additionally, the first four years provides a very good indicator of what the teams think the player can do for their team in terms of performance.

Understanding the Pro Football Focus Grading System

PFF analyzes every player on every snap, with each play receiving a grade on a scale from -2 to +2. A score of 0 represents an average or the expected execution of the player’s responsibilities, while a +2 denotes an outstanding play and a -2 indicates a critical error. These assessments are adjusted for factors such as difficulty of assignment and game context. PFF’s system includes tracking over 200 data points per play using the All-22 coaches’ film, including such aspects as player alignment, assignment, and outcome of the play from every aspect of the field (1, 14). PFF then converts these evaluations into a normalized score on a 0–100 scale.

Calculation and Coding of PFF Grades and Years in the League

While PFF normalizes plays to values ranging from 0-100, the overall grades across an entire season of plays are far more restricted, ranging from ~high 40’s-low 90’s (for the requirement of at least 10 games played per season as described below). For every player, we calculated a mean for their overall PFF grade across their first four years after being drafted. For players with a missing grade, we found which of the four years there was a missing grade for and why. If the player was injured and missed the entire year (for any year), we did not count that year for their average and averaged across their other years. For players at most positions, we used the offense or defense overall grade for the given year. Only for punters and kickers did we use the special team grade. Our threshold for counting a PFF grade for the year, was at least ten games played. Additionally, if the drafting team waived the player they drafted, we assigned that player a value of 35, as that is below the lowest grade anyone on a team who played earned across an entire year of play (with a minimum of 10 games played). If they played on a team after they were waived, we filled in the four years with the grade(s) they earned in the remaining year(s) on the subsequent team. We wanted to penalize the drafting team, but we also did not want to assign a 0 as waiving the player was an act but does not represent their PFF grade over an entire year. Additionally, this happened far less often in earlier rounds and since we were calculating means, we did not want these outliers to dramatically influence the results. We assigned a value of 45 for a player who was on an NFL roster, but not active for the minimum number of games (or did not have enough snaps to be graded by PFF). This is a lower grade than any player we graded who played during the season for at least ten games and gave us a baseline for someone who is good enough to be on the team but may not be good enough/needed to dress on game day(s). We did not gather PFF data for rounds 6 and 7 as fewer players in these rounds were active for enough games (i.e., played enough snaps) for which PFF could assign grades.

Finally, we also analyzed the number of years players were in the league. Again, in the interests of evaluating how well teams draft, we were really focused on years in the league of these players over their first four to five years. Therefore, we coded years played in the league in categories of less than two years, two to three years, four years, five years, and more than five years, and then analyzed this coded data.

Availability of Data and Material

Data may be accessed at: https://osf.io/pf5hq/?view_only=28f7350c720f430b92270c76e5b48080

RESULTS

We performed Bayesian analyses throughout the Results sections for all experiments to properly identify and balance the same evidence in favor of as we did evidence opposed to differences, in line with the recommendations of both Dienes (4) and Kruschke (7). The primary independent variables were draft round and position within the round (top, middle, or bottom third), while the primary dependent variables were years in the league and PFF mean overall grade for players’ first four years in the league. We outline each set of analyses below.

Testing for Correlations Between Differences in PFF Grades and Differences in Jimmy Johnson and Rich Hill Trade Chart Values and Rookie Salaries for Each Subsequent Pick

 If players drafted with picks 1-10 are better than players drafted with picks 10-20, and so on, then both the difference in trade chart values and rookie wage scale salaries from pick #1 to pick #2 and pick #2 to #3 and so on through the first five rounds of the draft should be highly correlated with PFF grades. Bayesian correlational analyses showed substantial to strong evidence that there was close to zero correlation between PFF grades and trade chart values, RH trade value chart: Bayes Factor in favor of the null hypothesis (BF01)= 5.594, r = .086, JJ trade value chart: BF01= 10.485, r = .014, and PFF grades and rookie wage scale salaries: BF01= 9.047, r = .043. The Bayes factors may be interpreted that it is 5.594, 10.485, and 9.047 times as likely that there is no correlation between PFF grades and RH trade chart values, JJ trade chart values, and rookie wage scale salaries, respectively, than there is a correlation (12, 24). Values of BF01 or BF10 of 0-1 = no evidence, 1-3 = anecdotal evidence, 3-10 = substantial evidence, 10-30 = strong evidence, 30-100 = very strong evidence, and >100 = decisive evidence in favor of whatever hypothesis is being tested (null (BF01) or alternative (BF10) (12, 24).

Analyzing How the NFL Values Draft Positions Based on the Rookie Wage Scale

We first established how the NFL values draft position across rounds and thirds of rounds. We used the rookie wage scale salaries for the first five rounds of the draft (the same rounds for which we calculated PFF grades for players—picks 1-165). Bayesian analyses showed decisive evidence of differences in salaries across rounds, BF10= 2.806 x 10+37, F(4, 150) = 448.83, p < .001, h2 = 0.71, Cauchy Prior with a scale of .707. Post hoc tests also indicated decisive evidence for differences among all rounds. Bayesian analysis showed substantial evidence of differences in salaries across thirds of rounds, BF10= 4.99, F(2, 150) = 88.19, p < .001, h2 = 0.07, Cauchy Prior with a scale of .707. Post hoc tests confirmed between anecdotal to substantial evidence for differences among all thirds of rounds (top, middle, and bottom). Virtually identical results were found when performing these same analyses using each trade value chart in lieu of the rookie wage scale.

Analyzing Differences in Rounds for Coded Years in League and PFF Overall Mean Grade

 Coded Years in League

 A Bayesian one-way ANOVA analyzing whether there were differences in years played in the league showed that there were: BF10= 2.806 x 10+111 (decisive evidence), > Test value F(6, 2536) = 101.93, p < .001, h2 = 0.19, Cauchy Prior with a scale of .707. Post hoc tests indicated that there was moderate to strong evidence that players drafted in round 1 remained in the league somewhat longer than players drafted in round 2, BF10= 4.312 (moderate to substantial). Players in almost all subsequent rounds remained in the league for less time than the previous round. One exception was that there was no difference in years played in the league between rounds 4 and 5, BF01 = 3.83 in favor of no difference, indicating moderate to substantial evidence in favor of no difference in years played in the league for 4th and 5th round draft picks.

 PFF Overall Mean Grade

A Bayesian one-way ANOVA analyzing whether there were differences in PFF overall mean grade showed that there were: BF10= 2.673 x 10+59 (decisive evidence), > Test value F(6, 2536) = 80.95, p < .001, h2 = 0.16, Cauchy Prior with a scale of .707. Additionally, again almost all post hoc test BF10 evidence showed decisive evidence for differences among all five rounds with values ranging from BF10 = 188.192 to 2.724 x 10+38. The one exception was that there was no difference in PFF overall mean grade between rounds 4 and 5, BF01 = 4.51 in favor of no difference, indicating moderate to substantial evidence in favor of no difference. Figure 1 shows a graph of pick position (x-axis) by PFF grade (y-axis) for picks across all ten years.

Figure 1.

Shown is a plot of the pick number by overall mean PFF grade for the first 4 years. Each symbol represents the average PFF grade across 10 years for a particular position in the draft (picks1-179).

Differences in Thirds of Rounds for Coded Years in League and PFF Overall Mean Grade

A Bayesian one-way ANOVA analyzing whether there were differences in years played in the league in terms of whether a player was chosen at the top, in the middle, or at the bottom third of the round showed decisive evidence that there is no difference: BF01= 20.998, Cauchy Prior with a scale of .707.

PFF Overall Mean Grade

A Bayesian one-way ANOVA analyzing whether there were differences in PFF overall mean grade showed decisive evidence that there also is no difference: BF01= 30.292, Cauchy Prior with a scale of .707.

Differences Among Top, Middle, and Bottom of Rounds for Coded Years in League and PFF Overall Mean Grade Round-by-Round

While our previous analyses show no differences among player longevity and PFF overall mean grade across the top, middle, and bottom of rounds, it could be that any potential differences were washed out by combining rounds for the analysis. For instance, later rounds that have lesser talented players overall may see no differences, or have more talented players at the middle and bottom of rounds than the tops of rounds, whereas earlier rounds may have more talented players at the tops of rounds than at the middle and bottom of rounds. These effects or patterns may cancel each other out by combining all rounds. Therefore, we again tested for differences among the top, middle, and bottom third of rounds, but this time did so within each round. Table 1 shows the results of these analyses.

Table 1.

Shown are Bayes factor in favor of the null hypothesis (BF01) for one-way ANOVAs testing for differences in coded years in the league and PFF overall mean grades for draft picks in rounds 1-7 (for years) and round 1-5 (for PFF grades) from 2011-2020. Values of BF01 of 1-3 = anecdotal evidence, 3-10 = substantial evidence, 10-30 = strong evidence, 30-100 = very strong evidence, and >100 = decisive evidence in favor of the null hypothesis) (12, 23).

Round 1Round 2Round 3Round 4Round 5Round 6Round 7
Years in League2.2373.2847.1034.856.1791.28510.313
PFF Grades8.54318.6256.6032.5219.475  

Predicting PFF Grades from Trade Value Charts and Evaluating Whether Trading Up into the First Round from the Second Round Warranted

Many times, draft experts will argue that some teams may “trade up” into the bottom third of the first round—that is, the last ~twelve to fifteen (~picks 18-32 or so)–from the top of the second round in order to draft a second player for whom they will have the 5th year option (1st round pick). Many teams have done this in the past. In fact, according to the values themselves, the NFL views the bottom 10-15 picks in round 1 as over 7008 times greater in value than the top 10-15 picks in round 2 , BF10= 7008.114, Cauchy Prior with a scale of .707). When we analyzed PFF grades across ten years for the “bottom third” of the first round (12 picks—21-32) and compared them to PFF grades across ten years for the “top third” of the second round (12 picks—33-44), a Bayesian one-way ANOVA showed substantial evidence that there is no difference: BF01= 6.854, Cauchy Prior with a scale of .707 (MBottom10of1st = 63.835; SDBottom10of1st = 10.642, MTop10of2nd= 64.387, SDTop10of2nd = 9.975).

STUDY 2

In Study 1 we found that the NFL values draft positions in the top of a round far more than those in the middle or bottom of a round and those in the middle of the round far more than those in the bottom of a round. However, there was substantial to decisive evidence of no differences in PFF grades across and within rounds, respectively, for top, middle, and bottom thirds of draft positions in rounds. We also found that there was no difference in PFF grades for draft positions in the top third of the 2nd round compared to the bottom third of the 1st round. This predicts that teams should not move up in a draft for players as the player performance will not be better for players drafted even 20-30 picks higher. When teams move up in the draft, they give up more draft capital for at least two reasons. First, while the values in the trade value chart might be even, typically the team moving back must give up more than one pick to do that in order to even out the trade value. Second, the team trading down must be incentivized in some ways to trade down. Sometimes, that team simply needs more players for the values to be even. Other times, the team trading down will ask for more as they are giving up an attractive draft pick. In Study 2, we sought to investigate whether the findings from the ten year period we investigated in Study 1 would predict the outcome of pick-for-pick trades in the 2021 NFL draft.

METHODS

We identified each of the draft pick-for-draft pick trades in the draft immediately after the last season for which we analyzed draft picks (the 2021 NFL draft). We evaluated each of the twenty-nine trades that are listed by the NFL that occurred during the 2021 draft (15). We did this because the 2021 draft was the first draft after the last year for which player performance data was collected so it allowed us to test whether our findings predicted future patterns and because it was the last year that would still allow us to analyze player performance in the first four years of the player’s career. We only looked at trades involving draft picks for draft picks.

Raters

We had two high school football players (MAge = 18 years, MExperiencePlayingCompetitiveFootball = 6 years) who have a strong knowledge of not only the workings of football but also a strong knowledge of the NFL, the NFL draft, and grading players.

Procedure     

We gave raters the series of trades with round(s), pick number(s), and PFF grades across years played listed for each pick. We removed the draft year and team and player names from the list. Raters were also blind to which part of the trade was the “trade up” and which part was the “trade down.” Our list consisted of Team A on one side and Team B on the other. We randomly assigned which team (“trade up” or “trade down”) was Team A and which team was Team B. We instructed raters on how PFF grades are set up and general cutoffs for what PFF grades are generally considered elite, good, above average, average, and poor. We also instructed them to decide which team “won” each trade. While they were instructed that they should use all the information available in making their decisions, they were told that the performance of the players (i.e. PFF grades) should be paramount in making their decisions.

Raters made their judgments independently from one another. They were initially seated in two different areas at the same time while they made their judgments. They then came together, compared their judgments, and went over the judgments that were different to see whether they could come to a consensus on the judgments that were different.

RESULTS

The raters initially agreed on 20/29 trades. After discussing the trades they had originally disagreed on, they came to a consensus on all 29. Of the 9 on which they initially had different answers, neither rater favored the “trade up” team; each of the 9 consisted of one rater deciding on the “trade down” team and the other deciding on “neither.” For 6/9 of the trades they eventually decided on “neither,” and in the other 3 trades they decided on “trade down.” Finally agreed-upon frequencies for each group were: Trade Up: 4, Trade Down: 19, and Neither: 6.

Since we were only interested in testing whether trading up resulted in better player performance, we were most interested in a comparison where we split the categories into the following two groups: Group 1: Trade Up and Group 2: Trade Down and Neither. We performed a Bayesian binomial test on frequencies of what the raters judged as “wins” in each category. Raters judged that there were significantly more wins in terms of better performing players for teams who traded down than for teams who traded up, BF10  = 753.471, Proportion = .832, Prior Distribution with α and β = 1. This is decisive evidence that trading down led to better performing players than trading up and may be interpreted that it is more than 753 times more likely that trading down led to better performing players than trading up. In a second analysis, we compared only trade downs versus trade ups and removed any trade that resulted in a judgement of “neither.” Raters again judged that there were significantly more wins in terms of better performing players for teams who traded down than for teams who traded up, BF10  = 39.472 , Proportion = .826, Prior Distribution with α and β = 1. This indicates very strong evidence that trading down led to better performing players than trading up. Therefore, even when solely comparing trade ups versus trade downs, it is still over thirty nine more times likely that trading down led to better performing players than trading up.

DISCUSSION 

The way the NFL values draft positions in terms of trade values and rookie salaries is not correlated at all with player performance consistent with previous work (14). While time spent in the league and overall PFF grade during their rookie contracts did, for the most part, gradually decline in subsequent rounds as trade value charts, fans, and NFL executives would all predict, this pattern was not straightforward. One large deviation from this gradual decline between rounds was between the  4th and 5th rounds, where there was moderate to substantial evidence in favor of no difference in both years played and overall PFF grades between those rounds, contradicting how the NFL values draft position. In fact, if we use trade values as a representation of people’s intuitions then we should expect far greater value out of 4th round picks than we do from 5th round picks, as a Bayesian independent-samples t-test analyzing trade values in round 4 versus round 5 found decisive evidence that round 4 values are significantly greater than round 5 values, BF10= 7.294 x 10+11, Cauchy Prior with a scale of .707. According to the values themselves, the NFL views round 4 picks as 7.294 x 10+11 greater in value than 5th round picks.

There were several other counterintuitive findings. First, there was decisive evidence of no difference among the top, middle, and bottom thirds of rounds across all rounds for both years in the league and PFF grades. Second, this evidence of no difference for both years in the league and PFF grades among the top, middle, and bottom thirds of rounds was a regularity for every round when evaluating each round individually. Third, we found that the performance of players taken in the top third of the second round was no different from the performance of players taken in the bottom third of the first round across all ten years, contradicting what the trade values tell us—that there is decisive evidence in favor of differences in those respective trade values.

When we looked at trades that occurred in 2021, we found that, in terms of quality of player(s), the large majority—over 82%–and significant number of trades did not favor the team who traded higher up in the draft where that pick had greater value. This result is consistent with what was found with the analyses finding no differences across thirds of rounds across and within rounds across our data. This has important implications for not only player performance, but also because it has been recently shown that teams getting the better end of trades increase their probability of making the playoffs (9).

There were limitations to this study. First, while PFF grades are used by NFL teams, NFL analytics sites, and content creators to assess player performance and are seen as the best tool for doing this, they are not perfect. However, while there may be an argument as to what goes into creating the absolute grades, we analyzed the grades relatively for players so the shortcomings of the grades themselves would apply to all players. A second limitation is that we used trade values and rookie wage salaries to as a measure of how general managers (GMs) of teams and the NFL as a whole assess player quality without directly asking them about how they value draft positions. However, there was a collective bargaining agreement between the NFL and player’s union that put in place the rookie wage scale in 2011 (3), and owners, GMs, and players all had their input into how this would be created. Additionally, every team in the NFL uses these trade values as the standard way to barter before, during, and after the draft. Thus, we feel this was a fair way to assess the way the NFL values draft positions.

Further research should be conducted to see how the player performance grades in the first four years connect with second contracts of players in their next 2-5 years. It may be that it takes certain players four years to blossom in the league. However, the average tenure of a player who makes the opening-day roster is ~6 years (about half of that if you include drafted players who do not make the opening day roster). Other work should focus on directly assessing the executives in charge of the teams who draft players and the intuitions they have regarding trading up or down in the draft and what goes into these decisions. It may be that player performance is not the only factor that drives this decision making.

CONCLUSION 

Differences in the way the NFL values draft positions are not associated with player performance in those respective draft positions. This occurs whether you analyze differences in values assigned to draft positions, different positions within a round, or adjacent positions across rounds. These patterns from our findings also predict the outcomes of future drafts in terms of the assets a team trading up gets compared to a team trading down.

APPLICATIONS IN SPORT

Trade values and trade value charts in the NFL are used as the baselines by which to trade draft picks. These charts serve at least two purposes. First, they give general managers a common mechanism that they generally agree on to trade draft picks. Second, it prevents desperate teams from trading away too much and prevents overly greedy teams from demanding too much. These values assign an assumed or perceived worth of the player picked in that position. The idea is that, while picks that are very close together may result in players that are of equal talent, picks that are several positions away from one another should result in better players for those picks that are higher in the draft order. This is reflected in the draft assets that teams are willing to give up to move up in the draft order. Our work shows that there are several performance-based patterns that contradict these naïve beliefs stemming from values given to players. While some teams might argue that they needed a player at a specific position over the best available player, one would expect that if teams stayed with their original pick, that across a general manager’s tenure they would be better off picking someone who provides better performance and not positional need. One should also expect that over one’s tenure the player performance is more valuable as a better player is more valuable as a trade asset. Our work may inform strategies that might be best to use in drafting prospective players in the National Football League.

ACKNOWLEDGEMENTS

Dennis M. Shaffer conceptualized the studies. For Study 1, the author oversaw investigation, methodology, and data curation. Formal analysis in the paper was conducted by this author. 

Ryanne E. Shaffer contributed to both studies, assisting with data curation for Study 1, and conducting Study 2. Dennis M. Shaffer supervised Study 2. 

The paper was drafted by Dennis M. Shaffer; the paper was reviewed and edited by Ryanne E. Shaffer. 

The authors would like to thank JD Okuma and Gavin Davis for their work as raters.

REFERENCES 

1. ALL-22 (2025). https://www.all-22.com/#/

2. All Access Football (2025). https://www.allaccessfootball.com/p/nfl-draft-pick-trade-value-chart

3. Deubert, C., Wong, G. M., & Howe, J. (2012). All four quarters: A retrospective and analysis of the 2011 collective bargaining process and agreement in the National

Football League. UCLA Entertainment Law Review, 19, 1-78. https://doi.org/10.5070/LR8191027149

4. Dienes, Z. (2024). Use one system for all results to avoid contradiction: Advice for using significance tests, equivalence tests, and Bayes factors. Journal of Experimental

Psychology: Human Perception and Performance. 50, 531-534. doi: 1        0.1037/xhp0001202.

5. Hadley, L., Poitras, M., Ruggiero, J., & Knowles, S. (2000). Performance evaluation of National Football League teams. Managerial And Decision Economics, 2, 63-70.

http://www.jstor.org/stable/3108334

6. Hartman, M. (2011). Competitive performance compared to combine performance as a valid predictor of NFL draft status. Journal of Strength & Conditioning Research, 25, S105-S106. DOI:10.1097/01.JSC.0000395746.03546.e8

7. Kruschke, J. K. (2021). Bayesian analysis reporting guidelines. Nature Human Behaviour, 5, 1282-1291. https://doi.org/10.1038/s41562-021-01177-7

8. Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8, 1-28. https://doi.org/10.1525/collabra.33267

9. Lapre, M., A., & Palazzolo, E. M. (2024). Does draft currency promote competitive balance? An empirical investigation of the National Football League 2002–2021

Journal of Sports Economics, 25, 779-801. https://doi.org/10.1177/15270025241264238

10. Lee, M. D., & Wagenmakers, E.-J. (2013). Bayesian cognitive modeling: A practical course. Cambridge University Press.

11. Lyons, B. D., Hoffman, B. J., Michel, J. W., & Williams, K. J. (2011). On the predictive efficiency of past performance and physical ability: The case of the National Football League. Human Performance, 24, 158-172. https://doi.org/10.1080/08959285.2011.555218

12. Massey, C., and Thaler, R. (2013). The loser’s curse: Decision making and market efficiency in the National Football League draft. Management Science, 59, 1479-1495. http://dx.doi.org/10.1287/mnsc.1120.1657

13. NFL (2025). https://www.nfl.com/news/2021-nfl-draft-trade-tracker-full-details-of-every-move

14. PFF football news and analysis (June, 2025). https://www.pff.com/.

15. Reynolds, Z., Bonds, T., Thompson, S., LeCrom, C. (2015). Deconstructing the draft: An evaluation of the NFL draft as a predictor of team success. Journal of Applied Sport Management, 7, https://doi.org/10.7290/jasm07bj7q.

16. Rishis, E., Johnston, K., & Baker, J. (2023). On the predictive validity of the National Football League combine: Does it forecast future success? Journal of Sports Sciences, 41,

217-231. https://doi.org/10.1080/02640414.2023.2207853

17. Simmons, R., & Berri, D. (2009). Gains from specialization and free Agency: The story from the gridiron. Review Of Industrial Organization, 34, 81-98. https://doi.org/10.1007/s11151-009-9200-9

18. Sports Reference © (June, 2025). Pro Football Reference. https://www.pro-football-reference.com

19. Spotrac © (September, 2025). https://www.spotrac.com/nfl/cba/rookie-scale.

20. Terry, N. (2007). Investing in NFL prospects: Factors influencing team winning percentage. International Advances In Economic Research, 13, 117. https://doi.org/10.1007/s11294-006-9071-x

21. Tucker, R., Lee, C., & Black, W. J. (2024). The predictive ability of the physical skills used at the NFL combine to predict draft status. The Sport Journal. https://thesportjournal.org/article/the-predictive-ability-of-the-physical-skills-used-at-the-nfl-combine-to-predict-draft-status/

22. Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., & Wagenmakers, E. J. (2011). Statistical evidence in experimental psychology: An empirical comparison using 855 t tests. Perspectives on Psychological Science, 6, 291–298. http://dx.doi.org/10.1177/1745691611406923

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2025-10-13T10:22:32-05:00April 29th, 2026|Research, Sports Coaching, Sports Management|Comments Off on Over-promised, under-delivered: Does position in the National Football League draft matter?
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Fundraising in Sports: A case study

Author: Francisco J. Quevedo1

Corresponding Author:

Francisco J. Quevedo

72 Maple Street

Watchung, NJ, 07069

[email protected]

929-208-5289 


1Department of Marketing, Rutgers, The State University of New Jersey, Newark, NJ 

Dr. Quevedo is an Assistant Professor of Marketing at Rutgers University. A UMass Amherst ’78 graduate, he got his doctorate, MBA, and CAGSB at Pace University. He taught there, and at NYU before joining Rutgers full-time in 2020. He worked corporate and developed his family’s businesses in insurance, tourism, sports, and agriculture for 33 years until returning to academia. He has taught college for 15 years and done consulting for Fortune 100 firms, NGOs, and governments in nine countries. He has worked with nonprofits for 20 years. He researches brand management and nonprofit marketing, publishing 12 articles and chapters since 2019. He received an Award for Teaching Innovation in 2023 and coordinates the CM3A consulting center at Rutgers. 

ABSTRACT

Nonprofits in general long for fundraising guidance, market and donor research, and strategic planning support from academia. Within this sector, US amateur sports could represent a $60.5 billion segment, which receives but a small portion of total donations. To help close the gap, this paper presents a case study that can serve as a model to optimize nonprofit performance based on an amateur sports organization, which combines three related studies: a time-series analysis of nonprofits in the US showing that revenues depend largely on awareness and income, and points to the need to choose the right target and put the message out to raise funds; a donor survey which showed that, individually, decisions to give are based mostly on pride, pity, PR, personal interest, and pleasure, and points to the need to craft the right appeal; and a cross-sectional, six-country analysis of a proposed structure and processes that represents the underlying theory for this paper, which showed how networking, fiscal leveraging, and a coherent narrative, supported by the proper strategy and organization, generate external influence and revenues, thus emphasizing the need to follow proper procedure to achieve the desired results. A deep dive into the scientific literature sets the stage to analyze 17 years of experience in the WSKF Sports Foundation, part of a worldwide organization that spans over 110 countries and a million members, and raised up to $3.3 million at its peak in 2015, winning 266 world medals between 2007 and 2017, thereby providing a blueprint for fundraising in sports that can extend to most nonprofit organizations.

Key Words: sponsorship, strategy, process, model, medals, nonprofit, WSKF, foundation

INTRODUCTION

This paper points to the most pressing needs of nonprofit organizations. An unpublished survey of the Center for Marketing Advantage, Advancement, and Action of Rutgers University, working with the membership of the NJ Center for Nonprofits, pinpointed the demands of private foundations; fundraising, marketing and donor research stand out as the most urgent requirements of NGOs, followed by specifics like digital marketing and communications, market research, and strategic planning. Tracking 17 years of nonprofit research and amateur sports experience, we aim to present a tested and proven model to optimize nonprofit performance with the support of three specific research studies and a wide search of the literature.

The proposed model is supported by a cross-sectional test of Koschmann, Khun & Pfaerrer’s theory (23) done by Quevedo (33), a time series analysis of the US nonprofit sector by Quevedo & Quevedo-Prince (36), and a national survey that studied the driving motives to donate by Quevedo and Lee (35), which extended prior research by Quevedo and Gopalakrishna (34) on consumer preferences applying them in the nonprofit field.

The WSKF Venezuela Sports Foundation, part of a Japanese karate federation, the World Shotokan Karate-do Federation, that spans over 20,000 clubs and over a million members in more than 100 countries, served as the basis for a six-country analysis that showed how networking, leveraging, and a coherent narrative, deployed on the shoulders of the proper strategy, organization and processes, generate external influence (press coverage and lobbying power), and lead to substantially more revenues for the organization.

These studies and experiences showed that choosing the right target, designing the right appeal, and following the right approach, strategy and processes, will boost press coverage and drive fundraising. It is not just about saying and doing the right things, nonprofits must do the right things correctly.

A key paradox in amateur sports is whether funding follows medals or medals follow funding. In the case of the WSKF Sports Foundation, winning seemed to be the key to fundraising. Winning in one championship leveraged the next championship cycle. Looking at other causes, however, we must ask, should they generate social benefits to raise funds or raise funds to generate benefits? This chicken-and-the-egg paradox (Illustration 1) is paramount in sports, since medals increase media coverage and provide bragging rights to get more funds, but then funds, and training of course, are the means to get those medals, but it may not be necessarily true in other scenarios.

Illustration # 1: Medals and Funds – A Virtuous Circle in Amateur Sports

BACKGROUND

The youth and amateur sports industry is booming. The sector’s direct spending impact was valued at $39.7 billion in 2021, says a Sports ETA’s industry report signed by Clement (6). Wintergreen Research predicted that this market would grow at a compound annual growth rate of 8.9% until 2028. The NCAA generated a record $1.22 billion of revenue in 2022 from March Madness ticket sales, merchandise and television broadcast rights. Indeed, CBS and Turner Sports will pay the NCAA up to $19.6 billion over a 22-year contract term said Morones (31). These elements can add up to a $41 billion industry which depends in good part on fundraising to survive. However, sports are but a minuscule part of the philanthropic market and dynamics, so small that they do not make the charts. Certainly, more research support is needed to develop the sector. Unfortunately, marketing literature is unable to provide meaningful guidance because scant research attention has hampered a fuller understanding of why people help, as Bendapudi, Singh & Bendapudi found (2).

Chart 1: Nonprofit Revenues in the US

The professional sports market on the other hand is projected to reach close to $85 billion this year and that may not consider royalties for branded sports clothing and memorabilia according to Statista (39). Based on these figures, we could be looking at an umbrella sports market of $126 billion in the US alone, and perhaps as much as $500 billion worldwide by extrapolation (based on US vs. world GDP). 

METHODS

Sargeant and Shang (2010) emphasized that the need for a comprehensive model for fundraising has never been greater (37). Accordingly, we aim to provide a blueprint for funding amateur sports based on both theory and practice, leaning on three specific research studies, a deep dive into the scientific literature, and 17 years of successful fundraising experience with the WSKF Venezuela Sports Foundation, and 20 years of foundational work overall. Furthermore, we aimed to answer the question “will the right target and message, the right appeal and the right approach drive fundraising success, or do we need credentials and credibility upfront to attract sponsors?”

Illustration # 2: Kushman’s et al (2012) Model for Nonprofits

The WSKF Venezuela Sports Foundation raised up to $3.3 million (at the official rate of exchange) in its peak year, 2015, when its national team won 66 world medals in Tokyo, and received 73 press mentions which reverberated throughout the web internationally. These results speak for themselves. Its model was in use since 2008, and was replicated in Japan, the US, Canada, Panama, Spain, Ireland and other countries where the organization is present. A cross-sectional study, covering six countries, tested how much a gap in the execution of the appropriate model will affect  fundraising results.

Data Analyses

Statistical analyses were performed using SPSS version 29.0.2.0 (IBM). Multiple regression was combined with factor analysis in the time series modeling of the US nonprofit sector. Pearson correlation coefficients were calculated, as were the significance and p-values once the best fitting variables were identified. The donor decision model was determined through multinomial logistic regression, considering the extensive use of categorical variables. Cronbach’s alpha, Pseudo-R2 coefficients, significance and Chi-square values were calculated as well. Compare means was used in the cross-sectional analysis of six countries represented in the WSKF Sports Foundation to validate variations in their results. 

Prior Research Studies

Traditionally, the largest source of charitable giving in the US are individuals, not corporations, with $268.28 billion in donations which represent 71% of total giving, followed by foundations ($57.19 billion or 16%), bequests ($28.72 billion or 9%), and corporations ($18.46 billion or 5%). The average annual household contribution to nonprofits stood at $2,974, according to Statista (42). The majority of charitable dollars go to churches (32%), schools and colleges (15%), human services (12%), grant-making foundations (11%), and hospitals in general (8%). Sports does not make the Top 5 in this report.

List says that the nonprofit market revolves around three major players: (1) the donors, who provide the resources to charities. These can be corporations, public institutions, individuals, and non-government organizations (NGOs); (2) charitable organizations, which attract and allocate resources; and (3) the government, which decides on the fiscal framework for individual, corporate and NGO contributions, shapes the supply of grants to the various charities, and decides which public goods it will provide directly (28).

This proposal feeds from three research studies and 17 years of fundraising experience with the WSKF Sports Foundation. First, a predictive model of the US Nonprofit Sector based on time-series analysis showed that Nonprofit Revenues (NPR) depend largely on Public Awareness, as measured by TV coverage, and on Disposable Personal Income (DPI), specifically: NPR = – 4401.542 + 528.327(DPI) +23.121(TV Coverage) + Ɛ (36). Pearson’s R came up to 0.935, significance levels were at 0.001. Confirmatory Factor Analysis reaffirmed the fit of the equation, with an R² of 0.87. These findings indicate that nonprofits must first choose their targets well. Then fundraisers must put the message out, if they wish to get funds.

The question is “what should nonprofits say?” The second reference comes from a survey of 615 respondents, using their alma mater, the ASPCA, St. Jude’s Hospital for Children, a local homeless shelter, and their church as references; considering pride, pity, PR, personal interest, and pleasure as the driving motives, testing which appeal worked best to communicate a Nonprofit Organization’s message to generate funds. These were called “The 5-Ps of Fundraising” (35). Based on the pseudo-R2 coefficients generated by Multinomial Logistic Regression, the model reflected a predictive ability of 49.7%. All criteria were statistically significant. The pleasure of giving was the strongest driver, coming out as an underlying motivator in the donating decision. Different social causes respond differently to alternate fundraising appeals, therefore, determining which appeal works best is key to success. Ignoring the key drivers in the decision to donate may lead to being both ineffective and inefficient. These findings tell fundraisers how to craft the right appeal.

The third study would show how to deliver the right appeal to the right target, and how to operate a nonprofit organization successfully. Looking into the literature, Curry, Rodin and Carlson proposed that organizations that operate on transformational approaches to fundraising have fared significantly better than those which operate on a more transactional basis, and that the greater physical proximity of the donor base of an organization would positively impact fundraising (7). Wallace said that predictive modeling has concentrated on big-donor analytics, largely aimed at the identification of potential donors (43). Nonetheless, Koschman et al. (23) presented a more detailed model for optimizing the performance of Nonprofit Organizations (Illustration 2), which in hindsight, was being used by the organization under study years before it was published. Their model became thereby the underlying theory for this case study.

Indeed, Harris says that case analysis is a valid learning tool for research in fundraising for sports (15). Accordingly, we tested the Koschman et al. (23) model on the WSKF Venezuela Sports Foundation, part of a Japanese federation that spans over 20,000 clubs and more than one million members in more than 100 countries throughout all the continents except Antarctica, using six countries (the US, Panama, Spain, Ireland, Canada, and Venezuela) to find cross sectional illustrations of how the “meaningful participation” of members, the “centripetal forces” generated by the organization and its environment, and the consolidation of an institutional image through a “coherent narrative,” worked on the basis of “authoritative texts,” to use the original labels (23), generated “external influences” and led to substantially more revenues for the organization (33). These findings in sum tell fundraisers to follow proper procedure, a solid strategy, detailed plans and professional processes to achieve the desired results, given the choice of the right target and an appropriate message and appeal.

Although a better understanding of nonprofit dynamics and of the factors that affect fundraising efficiency is essential to charity managers, policy makers, and private donors, research has focused more on the micro than the macro view, says Yi (46), and not quite on the “how to” of organizational performance. Guy and Patton say that nonprofit marketing should begin with a basic understanding of motivations and donor behavior rather than merely adopting prefabricated marketing techniques (14). Sure enough, to be competitive, charitable organizations must rely on carefully formulated promotional programs, but there is an urgent need for research to identify the prevalence and effectiveness of different messages, according to Leonhardt and Peterson (27), who add that more than 55% of all NGOs appeal to selfless consumer motives (i.e., altruism), which is appropriate. However, an important experiment revealed that appealing to more selfless vs. less selfless (i.e., reputation) motives results in consumers having a more favorable attitude toward the charitable organization. So, there is more to donating than just the desire to help, and there is more to fundraising than just asking for money to those who have it. Consumer involvement, for instance, is found to have an important effect on the decision to donate; selfless appeals promote a more positive attitude among consumers with low involvement, but not for those with high involvement with a charitable cause (e.g., animal welfare).

Furthermore, Cao  found that psychological involvement with charities affects donation intentions; seeing a picture of a sad vs. a happy person increased intentions to give among participants with lower levels of psychological involvement, whereas the reverse was true for highly involved participants (3), hence the importance for NGOs and CSR executives to understand the nature and behavioral context of their operations. Huber, Van Boven, & McGraw combine what they call the internal and external influences on donor behavior (18), pointing in the direction of this paper and related research. Donor behavior has been disaggregated by researchers like Fajardo, Townsend, and Bolander into two components: donation choice and donation amount. Donor-related appeals have a greater effect on choice, while organization-related appeals have a greater effect on the amount pledged or donated. This could lead one to conclude that presenting both types of appeals in a solicitation is ideal (10).

On an individual level, the vast majority of donors are enthusiastic and positive about the organizations they give to, and about charities in general says Wooden (45). Leonhardt says that people give money to feel the “glow” associated with being the kind of person who helps a worthy cause (26). Kemp, Kennett-Hensel, and Kees studied emotions like pride and pity in charitable appeals, focusing on sex and gender as potential emotional collateral variables (21). Utility-based models that focus on the effects of lifetime, recency, seasonality, and appeals also show that fundraising attempts should emphasize commitment rather than amount, as stated by Kim, Gupta, and Lee, (22). Sectorial research by Kamatham, Pahwa, Jiang and Kumar focused on education’s 75% success rate studied how different appeals affect fundraising; sophistication of the appeal has a positive effect on fundraising and the amount donated. Providing information on the state of a project has a positive effect on donations, corroborating reinforcement models of donor behavior; individuals share a burden when supporting charitable causes and donate at least as much as the minimum donated (20). At the strategic level, Krug and Weinberg’s Merit Axis Model links the mission of the organization, the money raised, and merit as a standard for nonprofit management (24). Pride, pleasure, and personal interest were linked by Third to the legacy effect in the college and universities context, pointing to relational fundraising and the application of CRM to nonprofit marketing (41). A unified conceptual, behavioral, and econometric framework for optimal fundraising can combine approaches from Economics, Marketing, Psychology, and Sociology, said Haruvy, Popkowski,  Leszczyc, Allenby, Belk, Eckel, Fisher, Li, Ma, Wang, and List (16), which is the intention of this paper, considering the need for developing a comprehensive model of giving behavior and nonprofit organization performance.

Although the marketization of nonprofit activities, given by the introduction of marketing practices like sales of POP and different goods and services, competing for consulting contracts, donor relations management (the philanthropic version of CRM), and social entrepreneurship has drawn criticism, according to Eikenberry and Drapal (8), fierce competition for funds and a tighter economy have given rise to innovative fundraising methods like web-based crowdfunding and what is called Cause Related Marketing or CRAM by Chaney and Dolli (5).

Little research has been published about the perhaps circular correlation between medals and funds raised. Slater’s study relates medals and press coverage (38) which in turn supports fundraising. A cross-sectional study covering Belgium, Finland, Japan, the Netherlands, and the United Kingdom by Funahashi, Shibli, Sotiriadou, Mäkinen, Dijk, and De Bosscher relates funding with sporting success (12), which seems logical. Funds allow athletes and teams to train and eat, even to rest properly, and of course to compete and classify, thereby increasing their chances of success in top-tier events. Another report by Hogan and Norton, published through the National Institutes of Health found a high direct correlation between medals and funds (17). Although correlation does not imply causation, definitely the more funds, the more medals (and vice-versa, we would add).

Fundraising will continue to be vital for sports programs and facilities to operate. However, the climate for fundraising has become more competitive as more organizations chase the same discretionary dollars, and donors become more demanding. In order to cope, fundraisers will need to readjust their strategies. Fundraisers must understand all fundraising-related elements such as the event’s purpose, target markets and donors, and methods and strategies to be employed, said a 1996 editorial in the Journal of Social Marketing. Indeed, Stier and Schneider claim that fundraising is one of the major responsibilities of sport managers in the 21st century (40).

The Case of the WSKF Sports Foundation

As mentioned, prior research showed that the secret to fundraising success lies on selecting the right target and getting the message out there (36), based on the right appeal (35), to set in motion the most effective model of nonprofit performance (33). Indeed, Koschmann et al. (23) suggested that a proper combination of networking, leveraging and communication, based on a clear strategy, and following well-targeted processes, will generate optimal press coverage and influence, and -of course- funds.

Illustration # 3: The Winning Strategy

At the WSKF Venezuela Sports Foundation, applying the Koschmann et al. (23) model, something it did four years before it was ever published, meant (1st) leaning on the athletes and their parents to network and target corporations to gain access to their Corporate Social Responsibility (CSR) programs, (2nd) leveraging fundraising efforts on the Law for the Development of Sports which created a 0.5% sports tax on profits and allowed corporations to channel half of that directly to projects accredited by the Ministry of Sports, and (3rd) appealing to pride and PR interests, considering that Charity Sport Event (CSE) fundraisers are often confronted by the donors’ lack of interest, even though those events can provide participants with a meaningful experience, as stated by Filo, Fechner and Inoue (11). The message was carried by a top-of-the-line institutional DVD presentation, a quarterly newsletter, a website, direct and digital marketing efforts, and through an aggressive media management strategy that used timely press-releases, many of them sent from Tokyo, the common championship site, to gain immediate exposure.

This strategy, born out of a Shihan-kai meeting in Cyprus in 2010, blended well with Kaplan and Norton’s (19) map format, which kicks off from an organization that strove to muster the  support of parents, athletes, and instructors to execute the fundraising process, by reaching out to the right target with the proper appeal and press support, and achieve the desired financial results, as seen on Illustration 3. The leading KPIs (Key Performance Indicators) were medals won and funds raised primarily, but press coverage was extremely important for fundraising, since it reinforced the pride and PR appeal, as were the dimensions of the donors’ database. Donor relationship management leaned on the newsletter, BUDOtips, and as many as 73 media mentions per championship cycle.

The fundraising process was detailed, starting with the identification of all possible sources of funds, since it is not all about sponsorship. Indeed, McKeever and Pettijohn stressed that nonprofit organizations derive half of their revenues quid-pro-quo (30), as Graph 1 shows; in terms of sports organizations, this 50% may come from ticket sales, broadcasting rights, advertising, memorabilia and fees charged, among other internal sources. Additional funding may come from government or NGO grants, private and corporate donors, even multilaterals; depending on a single source is myopic as Levitt (25) would most likely define it. Accordingly, the first question that nonprofit managers must ask themselves is “are we doing the things we need to do to get money, or should we be getting money for the things we do?” Some nonprofits miss this benchmarking and go straight to asking for donations without considering the monetization of things that they can do or sell to generate funds. In case of WSKF, this meant monthly fees, sales of sporting goods and memorabilia, special training sessions, and events like national and regional championships.

Chart 3: Structure of Nonprofit Revenues

Based on a clear understanding of nonprofit market dynamics and the supply of funds, and considering the Sports Law, corporate and government targets were identified, and a unique appeal was tailored for each segment. The operational planning began when all decisions had been made and defined, otherwise it could have turned into a map without destination. The organization would pursue its financial objectives through traditional fundraising means, grants, events, and crowdfunding. The technical arm, the WSKF organization, would be the one to charge fees and hold events, collecting money from attendance and participation, under foundational guidelines.

Illustration # 4: The WSKF Fundraising Process

A growing database of corporate donors was informed and nurtured with a newsletter called BUDOtips which circulated throughout the organization. A survey of athletes, parents, and instructors generated the structure of the magazine which was then tested against donors’ expectations. Four sections were created: “Budo,” dealing with principles, for the parents who sought discipline and principles for their children, and who represented over two-thirds of the membership; “Technique” for the athletes who wanted to improve their performance; “Management” for the instructors who wanted to run their clubs profitably; and “News” for the donors and for everyone; the Editorial was just an introduction and an invitation to read, as seen on the cover page below.

A growing database of corporate donors was informed and nurtured with a newsletter called BUDOtips which circulated throughout the organization. A survey of athletes, parents, and instructors generated the structure of the magazine which was then tested against donors’ expectations. Four sections were created: “Budo,” dealing with principles, for the parents who sought discipline and principles for their children, and who represented over two-thirds of the membership; “Technique” for the athletes who wanted to improve their performance; “Management” for the instructors who wanted to run their clubs profitably; and “News” for the donors and for everyone; the Editorial was just an introduction and an invitation to read, as seen on the cover page below.

Illustration # 5: The WSKF Newsletter

The results of these concerted efforts were evident. Formal fundraising began after a lack of funding left the 2005 championship cycle dry. 14 medals were won in 2007. The WSKF Venezuela Sports Foundation was created in 2008, leading to 24 world medals in Tokyo the following year. As the organization learned and matured, the medal count skyrocketed to record-breaking numbers, 50 in 2011, 42 in 2013, a record-breaking 66 in 2015, and 60 in the following cycle, 2017. Eight medals were won by a small team in the World Cup held in Cyprus in 2010. Winning led to press coverage which peaked at 73 TV, newspaper, radio and digital mentions in 2015, which reverberated throughout the web, nationally and internationally.

Chart 4: The WSKF Venezuela Medal Count

rage 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

The predictive model points fundraising and communicational efforts toward deep pockets (36), which implies choosing the right target and putting out the most appropriate message; research into donor choice (35) leads to crafting the right appeal to carry that message; and testing Koschmann et al.’s communicative framework (23, 33) guides nonprofits to follow the right strategy and proper processes, supported on networking, leveraging on legal and fiscal incentives, and on the proper media strategy. Indeed, the strategy of the WSKF Sports Foundation, knowingly or not, and ahead of its time, blended these three theories and put them into practice, combining this theoretical framework with the Kaplan and Norton’s (19) strategy map format by adapting the organizational perspective to create a network of athletes and parents to reach out to corporate donors, crafting fundraising and sports operations to leverage on the Law for the Development of Sports, and fitting the customer perspective to the media strategy, and vice-versa. The financial perspective was led by the Balanced Score Card with metrics like revenues and average sponsorship level per athlete. The Strategy Map represented in and of itself a vital authoritative paper, along with the fundraising process flowchart. Moreover, it added an interesting twist, using world championship success and feedback to fuel fundraising, as medals triggered press coverage which in turn attracted sponsors, and then their sponsorship allowed the teams and athletes to train, compete and win more medals. This created a virtuous cycle. To feed the flame, the Foundation added reverberance by hosting a “Dinner with the Champs” upon returning from Tokyo, where the press and the donors would share photo-ops with the athletes in their colors and with their medals, while receiving plaques for their support, which added more press coverage and PR opportunities.

The Foundation continued to multiply its branding efforts by adding non-sports philanthropy to its credentials, networking with several organizations like Mayor’s Offices, corporate programs (CSR), and private foundations to help the needy, thereby positioning its brand at a national level and squeezing the most out of the athletes’ medals’ appeal (Illustration 6). Again, this added more press coverage. Indeed, the WSKF Venezuela Sports Foundation showed that theory, when put into practice, gets the most out of the strategy.

CONCLUSIONS

Theory says choose your target well, craft the right appeal, and execute the right strategy correctly, following proper procedure, through a well laid out fundraising process. Strategizing will require a detailed situational analysis and brainstorm, blending the theory and the best practices into your initiatives. Choose your KPIs well; funds, medals, or outside of sports, social impact, and press coverage should be the strongest drivers; medals add leverage, they lead to press coverage, press coverage attracts sponsors and triggers pride and PR opportunities; and sponsorship allows athletes to train and participate in world events, which leads to medals, as the virtuous cycle makes another rotation. Be relentless and thorough in the execution of the strategy; and whenever and wherever possible, widen your networking circles. The more, the merrier!

Limitations and Further Research

Although the Pearson coefficient of the first study is outstanding, the donor choice research could use additional criteria like peer influence and personal commitment with the social cause to increase its predictive ability. This would make it “The 7-Ps of Fundraising” and should raise the model’s pseudo-R2. The cross-sectional study is pretty straightforward, but it also showed that not every country has such a favorable fiscal framework for sports as Venezuela, which enacted legislation that taxes corporate earnings to fund the development of sports. They finance the construction of sports complexes, sporting events, and national team competitions, both nationally and internationally. Corporate donors can channel one half of that tax directly to accredited projects; this benefits the leveraging aspect of Koschmann et al.’s model (23). Nonetheless, there are always tax incentives and breaks for donors and fundraisers in just about every country we analyzed; in the end, what donors are looking for are meaningful projects that are properly organized and well presented. Credibility is a must, and feelings and appearances matter.

It should be also mentioned that the Venezuelan socio-economic and political situation today may not be conducive to achieving the same 2007 ⎯ 2017 results that were analyzed here. Funding has been politicized, the economy has shrunk 80%, and the exchange rate has gone from Bs. 10 per US dollar, in August 2018, to Bs. 119,144,000,000,000 or 119.14 today, after the regime erased eleven zeroes from the currency to hide the mega-devaluation and hyper-inflation.

APPLICATIONS IN SPORT

Rarely has a combination of theory and practice been put together to recommend fundraisers how to balance strategy and operations; not one or two but three research studies support this paper; 20 years of foundational experience leverage them; raising up to $3.3 million a year in funds and winning 266 world medals in 10 years prove it right; an organization spanning over 110 countries and over one million members, make this a unique learning opportunity. The underlying theoretical model calls for networking among people and organizations, leveraging on legal and fiscal incentives, and communicating the right message to the right target, working on the shoulders of a clear strategy, a lean and mean organization, and a consistent fundraising process, to generate press coverage and lobbying power, and ⎯ultimately⎯ funds. The theory says choose wisely, and indeed strategy is all about choice: identify the right target, craft the right appeal, and do the right things correctly, which demands a fine-tuned organization and processes.

Now, to the question, “do we need to win medals to raise funds or raise funds to win medals?” Well, yes, credentials help fundraisers win support but in the absence of medals, the operational model and the right choices should cast a net that is wide enough to generate revenues and attract volunteers, but in the absence of results, in startup nonprofits, the founders’ accolades, and networks, can help. But appearances matter, that is why the WSKF Sports Foundation leaned on its website, a top-of-the-line DVD presentation, and its newsletter, all of which seemed bigger than life, to reach the target before the medal count skyrocketed and a virtuous cycle was created. Momentum did the rest.

It is important to remember that one half of nonprofit revenues are quid-pro-quo, coming from things nonprofit organizations do or sell (see Graph # 1). Hospitals recover medical costs, universities charge tuition, and the WSKF Sports Foundation collected fees from its membership. Income cannot depend solely on donations or grants. Nonprofits must make an effort to add to their revenue streams by monetizing their activities, something not always remembered, as our consulting efforts at Rutgers University have shown us. Private foundations struggle with lack of resources and specialized skills, but solutions are at an arm’s length.

Social Implications

The Nonprofit Sector in general, which represents 5.4% of the US economy, can benefit from  strategies that are supported by data and research, plus decades of fundraising experience at the same time. Amateur sports fundraising in particular, a $60 billion industry, can surely profit from a fresh perspective.

Eather, Wade, Pankowiak, et al.’s research suggests that community sports programs, supported by fundraising, can significantly enhance social capital and promote social cohesion by increasing trust, improving social networks, and fostering a stronger sense of community amongst participants, providing opportunities for community members –athletes, coaches, volunteers, and supporters– to interact, build relationships, and develop a shared identity (8)

Supporting fundraising in amateur sports through scientific research goes beyond securing financial resources. It fosters community spirit, enhances social connections, and provides numerous positive social and psychological benefits for both participants and volunteers. These benefits contribute to stronger, healthier, and more cohesive communities says Wheatley (44). Ultimately, if the nonprofit sector does indeed pick up the slack of governmental failure, Matsunaga and Yamauchi’s theory (29), then anything that benefits philanthropy will benefit society as a whole.

REFERENCES 

  1. Author investigates why people give. Chronicle of Philanthropy, no. 18, 2005.
  2. Bendapudi, N., Singh, S. N., & Bendapudi, V. (1996). Enhancing helping behavior: an integrative framework for promotion planning. Journal of marketing, 60(3), 33-49.
  3. Cao, X. (2016) Framing charitable appeals: the effect of message framing and perceived susceptibility to the negative consequences of inaction on donation intention. International Journal of Nonprofit & Voluntary Sector Marketing. Feb2016, Vol. 21 Issue 1, p3-12. 10p.
  4. Center for Marketing Advantage, Advancement, and Action (2022), Survey of nonprofit organizations’ needs, Rutgers University, New Jersey. Retrieved from https://www.business.rutgers.edu/cm3a 
  5. Chaney, I. and Dolli, N. (2001) Cause related marketing in New Zealand. International Journal of Nonprofit & Voluntary Sector Marketing. May2001, Vol. 6 Issue 2, p156. 8p.
  6. Clement, J. (2024). Best practices for investors exploring the youth sports industry. Forbes Magazine. Jan 25, 2024. Retrieved from https://www.forbes.com/councils/forbesbusinesscouncil/2024/01/25/best-practices-for-investors-exploring-the-youth-sports-industry
  7. Curry, J., Rodin, S. and Carlson, N. (2012) Fundraising in difficult economic times: best practices. Christian Higher Education, 11:4, 241-252, DOI: 10.1080/15363759.2011.559872 .
  8. Eather, N., Wade, L., Pankowiak, A. et al. (2023) The impact of sports participation on mental health and social outcomes in adults: a systematic review and the ‘mental health through sport’ conceptual model. Syst Rev 12, 102. Retrieved from https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-023-02264-8   
  9. Eikenberry, A. M. and Drapal Kluver, J. (2004). The marketization of the nonprofit sector: civil society at risk? Public Administration Review. 64 2, pp. 132-140. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-6210.2004.00355.x  
  10. Fajardo, T., Townsend, C. & Bolander, W. (2018) Toward an optimal donation solicitation: evidence from the field of the differential influence of donor-related and organization-related information on donation choice and amount. Journal of Marketing. Mar2018, Vol. 82 Issue 2, p142-152. 11p. 1 Chart.
  11. Filo, Kevin,, Fechner, Davidand Inoue, Yuhei (2020). Charity sport event participants and fundraising: an examination of constraints and negotiation strategies. Sport Management Review. Jun2020, Vol. 23 Issue 3, p387-400. 14p. https://doi.org/10.1016/j.smr.2019.02.005  
  12. Funahashi, H., Shibli, S., Sotiriadou, P., Mäkinen, J., Dijk, B., & De Bosscher, V. (2020). Valuing elite sport success using the contingent valuation method: a transnational study. Sport Management Review, 23(3), 548–562.
  13. Fundraising in Sports (1996). Journal of Sport Management. Apr1996, Vol. 10 Issue 2, p225-225. 1/3p.
  14. Guy, B. S., & Patton, W. E. (1989). The marketing of altruistic causes: Understanding why people help. Journal of Consumer Marketing, 6(1), 19-30.
  15. Harris, D. (2023). YMCA posts record-high grant, fundraising revenue. Journal of Business (1075-6124). 8/6/2023, Vol. 38 Issue 16, p19-24. 6p.
  16. Haruvy, E., Popkowski Leszczyc, P., Allenby, G., Belk, R., Eckel, C., Fisher, R., Li, Sherry X., Ma, Y., Wang, Y. & List, J. (2020) Fundraising design: key issues, unifying framework, and open puzzles. Marketing Letters. 2020, Vol. 31 Issue 4, p371-380. 10p. 1 Chart.
  17. Hogan, K and Norton, K. (2000). The ‘price’ of Olympic gold. Journal of Science Medical Sport. Jun;3(2):203-18. Retrieved from https://doi.org/10.1016/S1440-2440(00)80082-1
  18. Huber, M., Van Boven, L., & McGraw, A. P. (2011). Donate different: external and internal influences on emotion-based donation decisions. The science of giving: Experimental approaches to the study of charity, 179-200.
  19. Kaplan, R. S., & Norton, D. P. (2000). Having trouble with your strategy? Then map it. Focusing Your Organization on Strategy—with the Balanced Scorecard, 49(5), 167-176.
  20. Kamatham, S., Pahwa, P., Jiang, J. & Kumar, N. (2021) Effect of appeal content on fundraising success and donor behavior. Journal of Business Research. Mar2021, Vol. 125, p827-839. 13p.
  21. Kemp, E., Kennett-Hensel, P. & Kees, J. (2013) Pulling on the heartstrings: examining the effects of emotions and gender in persuasive appeals. Journal of Advertising. Spring2013, Vol. 42 Issue 1, p69-79. 11p. 5 Graphs.
  22. Kim, S., Gupta, S. & Lee, C. (2021) Managing members, donors, and member-donors for effective nonprofit fundraising. Journal of Marketing. May2021, Vol. 85 Issue 3, p220-239. 20p. 3 Charts, 8 Graphs.
  23. Koschmann, M. A., Kuhn, T. R., & Pfarrer, M. D. (2012). A communicative framework of value in cross-sector partnerships. Academy of management review, 37(3), 332-354.
  24. Krug, K. and Weinberg, C. (2011). 9.4 Relating fund raising to the merit axis. Foundations & Trends in Marketing. 2011, Vol. 6 Issue 3/4, p296-303. 8p.
  25. Levitt, T. (1960). Marketing myopia. Harvard Business Review, 38, 45–56
  26. Leonhardt, D. (2008). What makes people give? New York Times Magazine, 44. ISSN: 0028-7822.
  27. Leonhardt, J. and Peterson, R. (2019) Should charity promotions appeal to altruism? International Journal of Nonprofit & Voluntary Sector Marketing. Feb2019, Vol. 24 Issue 1, pN.PAG-N.PAG. 1p.
  28. List, John A. The market for charitable giving. Journal of Economic Perspectives. Spring2011, Vol. 25 Issue 2, p157-180. 24p. 2 Charts, 3 Graphs. Retrieved from https://www.aeaweb.org/articles?id=10.1257/jep.25.2.157
  29. Matsunaga, Y., & Yamauchi, N. (2004). Is the government failure theory still relevant? A panel analysis using US state level data. Annals of Public and Cooperative Economics, 75(2), 227-263.
  30. McKeever, B. and Pettijohn, S. (2014) The nonprofit sector in brief: public charities, giving, and volunteering. The Urban Institute. October 2014. Retrieved from https://www.urban.org/sites/default/files/publication/33711/413277-The-Nonprofit-Sector-in-Brief–.PDF
  31. Morones, S. (n.d.). Following the money in college sports. Morones Analytics. Retrieved from https://moronesanalytics.com/following-the-money-in-college-sports/
  32. Ministerio del Poder Popular para Deporte y Recreación (2011). Ley orgánica del deporte, educación física y recreación, Gaceta Oficial No. 39.741, 23 de Agosto de 2011, República Bolivariana de Venezuela. Retrieved from http://www.ind.gob.ve/wp-content/uploads/2016/06/Ley-Organica-de-Deporte-y-Educacion-Fisica-2011.pdf
  33. Quevedo, F. J. (2019). Testing Koschman, Khun & Pfaerrer’s (2012) communicative framework on a global NGO: the case of the WSKF Sports Foundation. International Journal of Recent Advances in Multidisciplinary Research, 6(10), 5248-5256.
  34. Quevedo, F. J., & Gopalakrishna, P. (2021). Rationality is overrated: brand choice is largely intuitive. Rutgers Business Review, 6(3), 312-332.
  35. Quevedo, F. J., & Lee, K. (2023). The 5-Ps of fundraising: lessons from consumer behavior to nonprofit marketing. Rutgers Business Review, 8(1), 28-38.
  36. Quevedo, F. J., & Quevedo-Prince, A. K. (2019). A predictive model for the us nonprofit market: a macro to micro perspective. Advanced Journal of Social Science, 5(1), 1-9.
  37. Sargeant, A., & Shang, J. (2010). Fundraising principles and practice (Vol. 17). John Wiley & Sons.
  38. Slater, K. (2024). More medals, more press: African media coverage of the 2022 Commonwealth games. Howard Journal of Communications, 1–20.
  39. Statista (2024) North American sports market revenues.
  40. Stier Jr., W. F. and Schneider, R. (1999). Fundraising: an essential competency for the sport manager in the 21st century. Mid-Atlantic Journal of Business. Jun-Sep99, Vol. 35 Issue 2/3, p93. 11p.
  41. Third, Rachel (2018). Act today, transform tomorrow: How a legacy appeal at Loughborough University had an unexpected legacy of its own. Journal of Education Advancement & Marketing. Autumn/Fall2018, Vol. 3 Issue 2, p182-187.6p.
  42. Urban Institute; National Center for Charitable Statistics (2020). Revenues of reporting nonprofit organizations in the U.S. from 1998 to 2016. Statista. June2020.
  43. Wallace, N. (2016). Data and the search for big donors. Chronicle of Philanthropy, 28(10), 7.
  44. Wheatley, S, (2024). Building strong communities through amateur sports: Connecting athletes locally, February 8, 2024. Team Travel Source. Retrieved from https://www.teamtravelsource.com/2024/02/08/building-strong-communities-through-amateur-sports-connecting-athletes-locally/
  45. Wooden, R. A. (2005). What makes donors give. Chronicle of Philanthropy, (05) December 2005. Retrieved from https://www.philanthropy.com/article/What-Makes-Donors-Give/171235
  46. Yi, D. T. (2010), Determinants of fundraising efficiency of nonprofit organizations: evidence from US public charitable organizations. Managerial and Decision Economics, 31: 465-475. https://doi.org/10.1002/mde.1503
2025-09-10T15:45:29-05:00January 21st, 2026|General, Olympics, Research, Sports Management, Sports Studies|Comments Off on Fundraising in Sports: A case study
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Relationship Between the National Football League (NFL) Combine Measurables and Playing Time in the 2024 NFL Rookie Class

Authors: Greg A. Ryan, Kevin Harvey, Elijah Campbell, Mark Shoebridge, Landon Overby, Joshua Sauer, & Robert L. Herron

Corresponding Author:

Robert L. Herron, Ed.D., CSCS*D, ACSM-RCEP

75 College Drive

Montevallo, AL 35115

[email protected]

205-665-6118


Authors’ Affiliation: College of Health Professions, Department of Nursing & Health Sciences, University of Montevallo, Montevallo, AL, USA.

ABSTRACT

Purpose: This study investigated the relationship between anthropometric and performance measures collected at the 2024 National Football League (NFL) Combine and playing time (PT) during the 2024 NFL regular season. Methods: Data from four anthropometric (Body Mass Index; Hand Size; Arm Length; Wingspan) and seven performance tests (40-yard Dash; 10-yard Split; Vertical Jump; Broad Jump; 3-Cone Drill; 20-Yard Shuttle; 225lb Bench Press) of 315 players were standardized into average Anthropometric Z-Scores (AZ), Performance Z-Scores (PZ) and Total Z-Scores (TZ) for analyses. PT was calculated as a player’s total number of regular season snaps during their 2024 rookie season. Pearson correlations were used to investigate the relationships (α = 0.05) between AZ, PZ, and TZ to PT. Players were also analyzed for potential relationships within each position group. Results: A significant, weak, positive correlation existed between PZ and PT (r = 0.19, p < 0.01) and TZ and PT (r = 0.20, p < 0.01) for all players. No relationship existed for AZ and PT (r = 0.02; p = 0.73). Additionally, significant relationships existed among: Offensive Line  – PZ and PT (r = 0.33, p = 0.01) and TZ and PT (r = 0.35, p < 0.01); Wide Receiver – PZ and PT (r = 0.39, p = 0.03) and TZ and PT (r = 0.46, p < 0.01); Linebacker – TZ and PT (r = 0.39, p = 0.05). Conclusions: NFL Combine performance metrics may provide insight on PT, but anthropometric measurables were not related to PT. The lack of relationship within position groups indicates the NFL Combine may not be valuable in evaluating a rookie’s success on the field. Applications in Sport: Professionals who work with prospects may choose to train Combine specific techniques to maximize a prospect’s chances of playing in the NFL. However, individualized training that focuses on position specific demands or weaknesses that are not directly measured by NFL Combine tests may be more useful in increasing PT. The NFL Combine may be a useful supplement to all factors that go into an NFL team’s decision to draft a player.

Key Words: performance testing, predictive analytics, scouting, correlational analysis, American football

INTRODUCTION

The National Football League (NFL) hosts an annual Scouting Combine in Indianapolis, Indiana of elite college football players. Only about 3% of college football players are invited to the NFL Combine and therefore represent those with the highest chance of being drafted into the NFL (4). The purpose of the NFL Combine is to allow coaches, scouts, and other team personnel representing the 32 NFL teams the opportunity to assess hundreds of players from all divisions of collegiate football.

Football has position-specific skills that are needed to excel at the highest level. However, there are similarities between each position. All positions need vertical and horizontal power, agility, and strength. During this weeklong event, players participate in a multitude of tests. These tests include anthropometric measurements (Height; Weight; Wingspan; Arm Length; Hand Size) and performance tests (40-yard dash; 10-yard split; Vertical Jump; Broad Jump; 3 Cone Drill; 20 Yard Shuttle; 225lb Bench Press). All the events in the NFL Combine have been shown to have face validity (4). NFL player personnel departments use the NFL Combine data as part of their criteria to determine whether to select a player in the upcoming NFL Draft.

While the NFL Combine tests are designed to determine that aptitude to play at the next level, research is conflicted on the ultimate usefulness of the NFL Combine in determining player performance and playing time (PT). Kuzmits and Adams (4) found no consistent significant relationship between NFL Combine tests and player performance during the years of 1999 to 2004. Research also noted that the NFL Combine from 2013 to 2015 lacked the ability to predict game performance when specifically analyzing first year game performance (3). Teramoto, Cross, and Willick (12) looked at whether the NFL Combine could predict future performance of Running Backs (RB) and Wide Receivers (WR). The results of this study were that the time on 10-yard split was the most important predictor of yards per attempt for RB while vertical jump was significantly associated with receiving yards per reception for WR. However, the measures cannot explain a large part of the variance in the future performance of RBs and WRs. Vincent et al. (15) looked at NFL Combine participants from 2005 to 2010 who then played in the NFL. Significant relationships were found between at least one NFL Combine measure and on-field success. Even though significant relationships were found the authors stated that the NFL Combine tests are only modest predictors of future performance. More recently, investigation of six physical skill tests at the NFL Combine to try and predict draft placement in the 2022 NFL Draft and showed no significant difference between drafted and nondrafted players in any of the six physical tests analyzed (14).

LaPlaca and McCullick (5) built on previous research looked at player performance from the years 2006 to 2018 and compared it to the NFL Combine from 2006 to 2016. They found that every position group, both offensive and defensive, had at least one NFL Combine test that was significantly correlated with player performance. The study made sure to disclose that even though they found significant correlations, the large sample size made it easier to find weaker correlations. A limitation that was discussed was that while the authors did use objective performance statistics such as Touchdowns scored, they also used a grading system through Pro Football Focus to determine player performance. This grading system was not purely objective because the grades are determined by multiple reviewers through the observation of game film. Therefore, the overall performance of each player was not entirely objective. Additionally, a robust study by Frank and colleagues (2) analyzed 20 years (2000-2018) of NFL Combine data and noted that for offensive positions, single measures often best predicted success, while various combinations of NFL Combine performance traits predicted success among defensive players. This study also suggested that NFL Combine data is best used in conjunction with scouting and personnel departments to supplement NFL draft decision making. Similarly, research was conducted looking at the impact of the NFL Combine on five-year performance data from the 2013-2017 NFL seasons and concluded that the NFL Combine lacked predictive ability during that timeframe (1). While historical research does exist in this field, each year provides another opportunity to determine the NFL Combine’s effectiveness in predicting success. Additionally, limited research exists discussing the relationship between NFL Combine Measurables and PT for first-year players. The primary purpose of this study was to determine if the anthropometric and performance measures of the athletes invited to the 2024 NFL Combine were related to PT during the 2024 NFL regular season.

METHODS

Participants

Participants for the data analysis in this study were college football players that participated in the 2024 NFL Combine (N = 315). Participants were also grouped by position for use of positional comparisons (Offensive Line [OL] (N = 70); Defensive Back [DB] (N = 67); Defensive Line [DL] (N = 50); Running Back [RB] (N = 29); Linebacker [LB] (N = 30); Quarterback [QB] (N = 14); Tight End [TE] (N = 16); Wide Receiver [WR] (N = 39)). All player positions were input based off their official designation at the time of the NFL Combine. Due to limited sample size (N = 6) and variations in specializations, NFL Combine athletes who were labeled Specialist (Kicker, Punter, Long Snapper) were excluded from analyses.

Procedures

Four anthropometric (Body Mass Index [BMI]; Hand Size; Arm Length; Wingspan) and seven performance measures (40-yard Dash; 10-yard Split; Vertical Jump; Broad Jump; 3-Cone Drill; 20-Yard Shuttle; 225lb Bench Press) were analyzed. BMI was calculated by the researchers using Height and Weight measurements taken at the NFL Combine. Full descriptions of the performance tests have been detailed previously by McShay (7).

The data from the NFL Combine was obtained from NFL.com/combine/tracker (8). Each participant’s scores were retrieved for every test that was completed. Standardization of data, via Z-scores, were created for every anthropometric and performance measure. The measures from the NFL Combine were standardized into averages for each player, taking each player’s combined Z-Score score and dividing by the number of NFL Combine events they participated in to account for players who did not complete every NFL Combine event. Standardized averages were created for Anthropometric Z-scores (AZ), consisting of the four anthropometric measures, Performance Z-scores (PZ), consisting of the seven performance measures, and Total Z-scores (TZ), consisting of all 11 NFL Combine measures, for analyses This method of standardization of NFL Combine data into Z-scores for analysis has previously been supported (1).

Once all NFL Combine data was standardized, researchers used Pro-football-reference.com (9) to retrieve offensive, defensive, and special teams snaps for each player during the 2024 NFL regular season. Each player’s total snap count was then combined to provide a single value to determine PT, which was used for analysis. Because of this study only requiring secondary analysis of data which is publicly available on web-based domains, which do not disclose individual’s health information, Institutional Review Board approval was not required, though the study was approved by the research institution.

Data Analyses

Pearson product moment correlations, using Statistical Product and Service Solutions (SPSS, v29.0, IBM Corporation, Armonk, NY), were used to determine the relationship (α = 0.05) between AZ, PZ, TZ to PT. Additionally, players were separated by position and Pearson product moment correlations (α = 0.05) were used to determine potential relationships within each group between AZ, PZ, and TZ, to PT. All data are presented as means ± standard deviation with 95% confidence intervals (95%CI) unless otherwise stated.

RESULTS

Descriptive Statistics

Of the 321 athletes whose data were collected, 315 were used for analysis. A total of six athletes were excluded from analysis due to their position of Specialist (punter, kicker, long snapper) because only anthropometric data was collected on this group. Of the 315 athletes used for analysis, 312 (99%) completed all anthropometric measurements. There was more variability in the performance testing, with 25 (8%) completing all seven performance events, and 263 (83.5%) completing at least one performance event. When broken down by event, 220 (69.8%) completed the 40yd (4.73 ± 0.31s) with a 10yd split (1.63 ± 0.11s), 227 (72.1%) completed the VJ (34.0 ± 4.3in), 220 (69.8%) completed the BJ (117.9 ± 9.0in), 78 (24.8%) completed the 3C (7.30 ± 0.40s), 89 (28.3%) completed the PRO (4.44 ± 0.28s), and 100 (31.8%) completed the BP (21.9 ± 5.6reps). When examining snaps played over the 2024 regular season, 239 (75.9%) players went on to play at least one snap, with 224 (71.1%) averaging more than one snap per game over the course of the season.

Anthropometric Correlation Analysis

The results of the correlation analysis for AZ and PT are presented in Figure 1. Pearson product moment correlation coefficients were calculated for the relationship between average AZ and PT for all players and separated by position group. No significant overall relationship existed for AZ and PT (n = 312; r = 0.02; p = 0.73).

Additionally, no significant relationships existed among position groups: OL (n = 70; r = 0.13; p = 0.29); RB (n = 29; r = 0.21; p = 0.29); WR (n = 37; r = 0.24; p = 0.16); TE (n = 16; r = 0.39; p = 0.14); QB (n = 13; r = 0.02; p = 0.95); DL (n = 50; r = -0.10; p = 0.52); LB (n = 30; r = 0.19; p = 0.33); DB (n = 67; r = -0.02; p = 0.89).

  Performance Correlation Analysis

The results of the correlation analysis for PZ and PT are presented in Figure 2. Pearson product moment correlation coefficients were calculated for the relationship between average PZ and PT for all players and separated by position group. A significant, weak, positive correlation existed between PZ and PT (n = 263; r = 0.19, 95%CI [0.07, 0.31]; p < 0.01). The positive direction of this relationship indicates that players who performed better at the NFL Combine played more snaps during the 2024 NFL regular season.

When separated by position groups, significant, positive relationships existed for the following groups: OL (n = 61; r = 0.33, 95%CI [0.09, 0.54]; p = 0.01); WR (n = 34; r = 0.39, 95%CI [0.06, 0.65]; p = 0.03). The positive direction of these relationships indicates that OL and WR who performed better at the NFL Combine accumulated more snaps during the 2024 NFL Regular season. No significant correlations were noted for: RB (n = 25; r = 0.31; p = 0.14); TE (n = 12; r = 0.07; p = 0.15); QB (n = 7; r = -0.39; p = 0.40); DL (n = 43; r = 0.30; p = 0.06); LB (n = 26; r = 0.31; p = 0.13); DB (n = 55; r = -0.02; p = 0.89).

Total Correlation Analysis

The results of the correlation analysis for TZ and PT are presented in Figure 3. Pearson product moment correlation coefficients were calculated for the relationship between average TZ and PT for all players and separated by position group. A significant, weak, positive correlation existed between TZ and PT (r = 0.20, 95%CI [0.08, 0.31]; p < 0.01) for all players. The positive direction of this relationship indicates that players who had higher average TZ scores played more snaps in the 2024 NFL regular season.

When separated by position groups, significant, positive relationships existed for the following groups: OL (n = 61; r = 0.35, 95%CI [0.11, 0.56]; p < 0.01); WR (n = 34; r = 0.46, 95%CI [0.15, 0.69]; p < 0.01); LB (n = 26; r = 0.39, 95%CI [0.01, 0.68]; p = 0.05). The positive direction of these relationships indicates that players in these position groups who had higher average AZ scores played more snaps in the 2024 NFL Regular season. No significant correlations were noted for: RB (n = 25; r = 0.31; p = 0.14); TE (n = 12; r = 0.07; p = 0.85); QB (n = 7; r = -0.24; p = 0.61); DL (n = 43; r = 0.30; p = 0.06); DB (n = 55; r = 0.06; p = 0.70).

Discussion

The main finding of this study is that PZ and TZ may have a weak relationship to PT in a player’s first year in the NFL. There was no relationship between a player’s AZ and subsequent PT across all athletes nor when separated by position group. The study did find a significant weak positive correlation between average PZ and PT for all players. However, when separated by position groups significant, positive relationships existed for OL and WR. Finally, there was a significant weak positive correlation between TZ and PT for all players. When separated by position groups, significant, positive relationships existed for OL, WR, and LB.

There could be many reasons why these relationships exist for WR, LB, and OL. Previous movement analysis research for NFL players by position found that WR had highest in-game velocity and highest total running volume by an offensive position (6). Therefore, the 40-yard dash and 10-yard split may carry more importance among WR. The same study showed that LB had the most high-velocity efforts and high-velocity distance in game compared to all other positions. LB also showed the largest variability across player-games which is likely due to the roles that LB perform which include rushing the QB, play in space and cover offensive players, or primarily to tackle an opponent. Additionally, OL noted a positive relationship in the current study, with better NFL Combine performances leading to more PT.  While previous research (11) has noted that OL have worse NFL Combine values compared to other positions, the nature of the OL position may lend itself to more direct relationships from NFL Combine performance, since these athletes require multidirectional power over limited space. The positional findings in the current study do support previous research that noted relationships between NFL Combine performance metrics and PT among WR (40-yard Dash, Vertical Jump), LB (40-yard Dash, 20-yd Shuttle) and OL (20-yard Shuttle, Vertical Jump) (1, 2).

The NFL is not the only sport that uses a combine to test and evaluate future players’ abilities. Teramoto et al. (13), investigated the National Basketball Association (NBA) scouting Combine to determine whether the NBA Combine could predict PT. The study showed that the NBA Combine metrics had minimal correlation with long-term performance. In the NBA, it was found that certain anthropometrics had slightly better predictive power than athletic tests, which contrasts with what researchers found about the 2024 NFL Combine. Both in the NFL and NBA Combine researchers have proposed that performance in college or in game is the biggest predictor of draft position and future performance (11, 13).

There are limitations associated with this study. As reported in the results only 25 (8%) of all prospects completed all seven performance events. Increasingly, players are opting out of some or all the NFL Combine process, due to injury concern, agent decision, recovering from an injury during the season, or to focus on performing well at individual workouts, where more variables can be controlled by that athlete. In the season being analyzed in this study, five of the first six picks in the NFL Draft did not participate in the NFL Combine process, which could impact these findings. A larger, more complete sample from all NFL Combine athletes would comprise a better representation of their athletic performance. Finally, players that played zero snaps their first year due to injury were included in analysis, due to limitations among researchers to determine the extent of every injury or whether a player was not on the field due to injury or coaching decisions. A player that may have had strong AZ, PZ, and TZ scores, but did not play during their rookie season because of injury, which would have impacted the relationship between those variables and PT.

CONCLUSIONS

Many studies have been conducted over the last 20 years to determine if and how NFL Combine measurables can predict performance in the NFL (1-6, 10, 12, 14, 15). These studies have found mostly found minimal relationships overall, though stronger relationships among certain position groups. Despite the general scientific consensus that the NFL Combine is not a strong predictor of future NFL success, a multitude of NFL Combine “prep courses” exist, with athletes paying for training specifically to improve in NFL Combine measurables. There has been scientific skepticism about these courses and their impact on performance at the NFL Combine and its translation to improved draft status or playing time. While these courses claim that they will improve an athlete’s chance of getting drafted, there is currently no scientific evidence to these claims (1, 4, 10). Training programs that focus on a prospect’s position specific demands or individual weaknesses that are not directly measured by NFL Combine tests may be more useful in increasing PT for that athlete. The results of the current study support the previous work in the literature, but do note that some position groups (OL, WR, LB) may benefit by improving NFL Combine-specific performance in the lead up to the NFL Combine and Draft.

APPLICATIONS IN SPORT

The results from the current study suggest PT among NFL rookies during the 2024 regular season could not be strongly predicted with data collected during the NFL Combine. However, due to the relationships that were found, specifically withing certain position groups, it may be important for athletes in those positions to train specifically for those performance tests to have a better chance at playing in their first year. The data can be important for NFL player personnel departments who may use data collected during the NFL Combine to influence drafting decisions. Due to the significant, but variable, nature of the relationships found in the current study, it appears that the NFL Combine may be a useful supplement to scouting, film analysis, interviews, and other factors that go into an NFL team’s decision to draft a player. However, it is apparent that there is more to determining PT during a rookie season than just superlative measurables collected during the NFL Combine.

REFERENCES 

  1. Cook, J., Ryan, G. A., Snarr, R. L., & Rossi, S. (2020). The relationship between the National Football League scouting combine and game performance over a 5-year period. Journal of Strength and Conditioning Research, 34(9), 2492–2499. https://doi.org/10.1519/JSC.0000000000003676
  2. Frank, D., King, M., Dennard, C., & Macnamara, B. (2023). Discriminant function analysis reveals which combination of measures from the NFL scouting combine predict NFL performance. Journal of Expertise.
  3. Hedlund, D. P. (2018). Performance of future elite players at the National Football League scouting combine. Journal of Strength and Conditioning Research, 32(11), 3112–3118. https://doi.org/10.1519/JSC.0000000000002252
  4. Kuzmits, F. E., & Adams, A. J. (2008). The NFL combine: Does it predict performance in the National Football League? Journal of Strength and Conditioning Research, 22(6), 1721–1727. https://doi.org/10.1519/JSC.0b013e318185f09d
  5. LaPlaca, D. A., & McCullick, B. A. (2020). National Football League scouting combine tests correlated to National Football League player performance. Journal of Strength and Conditioning Research, 34(5), 1317–1329. https://doi.org/10.1519/JSC.0000000000003479
  6. Lyons, B., Hoffman, B., Michel, J., & Williams, K. (2011). On the predictive efficiency of past performance and physical ability: The case of the National Football League. Human Performance, 24(2), 158–172. https://doi.org/10.1080/08959285.2011.555218
  7. McShay, T. (2016, February 27). Todd McShay’s guide to every combine drill. ESPN. http://www.espn.com/espn/feature/story/_/id/14837586/todd-mcshay-guide-every-combine-drill-nfl-draft
  8. NFL.com. (2025). Combine tracker. https://www.nfl.com/combine/tracker
  9. Pro-Football-Reference.com. (2025). Total snaps. https://www.pro-football-reference.com/
  10. Robbins, D. W. (2010). The National Football League (NFL) combine: Does normalized data better predict performance in the NFL draft? Journal of Strength and Conditioning Research, 24(11), 2888–2899.
  11. Sanchez, E., Weiss, L., Williams, T., Ward, P., Peterson, B., Wellman, A., & Crandall, J. (2023). Positional movement demands during NFL football games: A 3-year review. Applied Sciences, 13(16), 9278. https://doi.org/10.3390/app13169278
  12. Teramoto, M., Cross, C. L., & Willick, S. E. (2016). Predictive value of National Football League scouting combine on future performance of running backs and wide receivers. Journal of Strength and Conditioning Research, 30(5), 1379–1390. https://doi.org/10.1519/JSC.0000000000001202
  13. Teramoto, M., Cross, C. L., Rieger, R. H., Maak, T. G., & Willick, S. E. (2018). Predictive validity of National Basketball Association draft combine on future performance. Journal of Strength and Conditioning Research, 32(2), 396–408. https://doi.org/10.1519/JSC.0000000000001798
  14. Tucker, R., Lee, C., & Black, W. J. (2024). The predictive ability of the physical skills used at the NFL combine to predict draft status. The Sport Journal, 24.
  15. Vincent, L. M., Blissmer, B. J., & Hatfield, D. L. (2019). National scouting combine scores as performance predictors in the National Football League. Journal of Strength and Conditioning Research, 33(1), 104–111. https://doi.org/10.1519/JSC.0000000000002937
2025-09-05T08:46:38-05:00January 7th, 2026|General, Research, Sports Management, Sports Studies|Comments Off on Relationship Between the National Football League (NFL) Combine Measurables and Playing Time in the 2024 NFL Rookie Class
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