Identifying Self-Awareness of Leadership Abilities Using 360 Degree Feedback Method: A Case Study of Collegiate Rowers

Authors: Stephen Cadoux1, Kimberly Shaffer2

1Department of Clinical Psychology, Antioch University New England, Keene, NH, USA

2Department of Sport & Exercise Science, Barry University, Miami, FL, USA

 

Corresponding Author:

Kimberly Shaffer

[email protected]

Stephen Cadoux, MS, is a Clinical Psychology Doctoral student at Antioch University New England. His research interests focus on sports neuropsychology, leadership development, and neurocognitive effects of stress.

Kimberly Shaffer, Ph.D., CMPC is an Associate Professor and program director of the Sport, Exercise & Performance Psychology Program at Barry University. Kimberly’s areas of research interest include athlete identity, transition from sport & core values of performers. 

ABSTRACT 

Self-awareness is one of the most vital characteristics to effective leadership, yet it is a trait rarely measured within leaders. Without self-awareness, leaders place themselves in a position of weakness that can negatively impact their team’s performance. Using a Female NCAA Division II Rowing Team (n= 7), and their coaches (n=2) this study aimed to identify if captains of a collegiate athletic team are self-aware of their leadership abilities. The study was conducted via the Multifactor Leadership Questionnaire (MLQ) and a research technique known as the 360-degree feedback method. Teammates and coaches completed the MLQ about their team captain(s) leadership abilities. Meanwhile, the captain(s) rated their own leadership using the MLQ. Results from the three participant groups were compared to evaluate self-awareness (S-A) of the captain(s). S-A was determined if the Captain(s) self-reported scores are within the standard deviation of the scores of the Coaches and Teammates. Results suggested differences in the S-A of the two captains is, such that Captain X scores were found to be outside the SD of either the Coaches and/or the teammates in six of the twelve leadership subcategories, while Captain Y self-reported scores outside of their coaches and/or teammates SD on 4 different subcategories. The applied nature of this study is valuable for creating leadership programs within collegiate athletic departments and provides a quantitative model for assessing self-awareness in leadership.

Keywords: coaching, NCAA athletics, peer assessment

INTRODUCTION 

Leaders are critical to the functioning of any group, team, or organization. For teams to be successful, they require motivation, hard work, social and task cohesion, and swift decision making (1, 3, 8, 17). Over the past 60 years, there have been over 60 different leadership theories; each aiming to define leadership into distinct and unique concepts (8, 17). 

Presently, the most validated and widely used theory is the Transactional and Transformational Leadership Theory (TTLT) (3). The TTLT involves dividing leadership into two areas: Transactional leadership and Transformational leadership (3). Avolio and Bass modified TTLT to include Passive/ Avoidance behavior (1).

According to TTLT Avoidant/ Passive leaders are more passive and reactive. Avoidant leaders “avoid specifying agreements, clarifying expectations, and providing goals and standards” (1). Individuals with this style can negatively impact those around them and hurt the team’s overall performance. Within Avoidant behavior, are two sections: Management-by-Exception: Passive (MBEP) and Laissez-Faire (LF). Individuals who are high in MBEP wait until an issue arises before acting while leaders high in LF go one step further and fail to ever intervene in issues (1).

The second major category of leadership within the TTLT is Transactional leadership. Transactional leadership is based on exchanging rewards for goal completion, good performances, and desired behavior (3). These leaders clearly lay out the expectations they have for their subordinates, and they encourage their followers to perform to the best of their abilities (1). Transactional leadership is based on Contingent Reward (CR) and Management-by- Exception: Active (MBEA) (Avolio & Bass, 2004). Leaders’ high in CR offer rewards in exchange of one’s service; celebrating the accomplishments of their team and its members to reinforce positive behavior teams accomplishments Conversely, MBEA minded leaders focus on past failures, mistakes, and irregularities. These leaders set a specific standard that all members must meet and any deviation from this standard is confronted (1).

The third category of leadership in the TTLT is Transformational. Transformational leaders are viewed as the highest level of leaders (3). These individuals “connect with followers and appeal to their strengths in order to best challenge them to be more productive” (14, p. 62).

Avolio and Bass added “5 I’s” under the Transformational leadership (1). The 5 I’s are Idealized Attributes (IA), Idealized Behaviors (IB), Inspirational Motivation (IM), Intellectual Stimulation (IS), and Individual Consideration (IC; 1). Both IA and IB fall under the subset of Idealized Influence. Leaders with high Idealized Influence are leaders who consider others needs before their own and are people who others want to emulate (1). Inspirational Motivation (IM) leaders use their leadership to motivate those around them. Intellectual Stimulation (IS) leaders help fuel their follower’s intellectual mental efforts. They help their followers to be more innovative and creative as well as stimulate new ideas, thoughts, and solutions. Lastly, Individual Consideration (IC) leaders focus on their groups need for achievement and growth. They accomplish this by acting as a peer mentor and coaching figure to those around them (1).

The increase in leadership research has been primarily led by Industrial-Organizational psychology (I/O), focusing on improving for-profit businesses, personnel, and staff (5). In contrast, the field of sports has not received comparable levels of research attention or financial investment (16). This disparity has created several gaps in sport leadership research, particularly within the sub-category of leader self-awareness.

Self-awareness is arguably the most important aspect of leadership (9). Despite extensive leadership research in sports, self-awareness is rarely measured (7). Most leaders are not self-aware of their own abilities or talents (7). Without self-awareness, captains are at a disadvantage when it comes to leading their teams to victory. With the amount of money, time, and energy put into these sports teams, captains cannot have large flaws in their leadership.

While there are many ways to measure self-awareness, the 360 Degree Feedback method is not as widely used as it requires more empirical evidence. The 360 Degree Feedback method was designed for the use of providing business managers and executives more accurate feedback on their performance (5). This method involves having the leader (ratee) score their abilities on a survey or questionnaire. The organization then has several staff, peers, and supervisors anonymously complete that same survey about the ratee. This provides the organization with not only how the leader views themselves, but also how the rest of the organization and team view them. The organization can then provide the leader with structured feedback. Using 360-degree feedback has been found to provide more accurate feedback, enhance self-awareness, and can increase self-perceptions in individuals (4).

While the 360 Degree Feedback model is being utilized within the business world, the use of this method has also branched into other academic areas, including sport psychology.  Consultant groups, such as Amplos, have applied the method to identify development within coaches and athletes at various Power 5 athletic institutions (15). Although the method has proven successful in applied settings, it lacks validity in the scientific community and needs empirical evidence to further support its success.

The purpose of the proposed study is to use the Multifactor Leadership Questionnaire (MLQ; 1), and the 360 Degree Feedback Method (6) to identify if collegiate team captains are self-aware (S-A) of their leadership abilities. This study explored three hypotheses: (1)Captains would rate themselves as having higher Transformational and Transactional Leadership as compared to the scores of the coaches and teammates. (2) Captains would rate themselves as having lower Avoidant Leadership as compared to the scores of the coaches and teammates. (3) Captains would have an inverse relationship between the scores of MBEA and MBEP.

METHODS 

Participants

Participants consisted of both male (n=1) and females (n=8) involved in a NCAA Division II rowing team located in South Florida. Ages varied within the three participant categories as both young collegiate athletes and older coaches participated in this study. The Coaches (n=2) had a mean age of 33.50 (SD= ±12.02), the Captains (n=2) had a mean age of 21.50 (SD= ±2.12), and the Teammates (n=5) had a mean age of 21.60 (SD= ±2.30). The Teammate group consisted of 5 participants; however, each Captain rated the other captain and were thus included in the “Teammate” participant group during data collection. With the captains included in the Teammate participant group, the Teammates (n=7) had a mean age of 21.14 (SD= ±2.03).

Procedures

The study began with participant recruitment. Recruitment was conducted via email. Upon recruitment of the rowing team, individual athletes, captains, and coaches were recruited as well. Once recruitment had completed, the study was conducted virtually via an online video call explanation session in which participants received all directions verbally. The PI gave a brief explanation of the purpose of the study, following initial instructions, the PI explained the directions for the consent form, the demographic questionnaire and the MLQ questionnaire (all of which were provided via an online Qualtrics survey link). Participants were instructed to complete one MLQ questionnaire form for each of their participating team captains. After completion of the study, participants were thanked for their time.

 Instruments

Demographic Questionnaire

Demographic questionnaires were created by the PI and were administered to all study participants. Each participant group had its own distinct demographic questionnaire. These questionnaires were used to gather additional data about the participants that the MLQ does not specifically ask for. This data included both personal and athletic information.

Multifactor Leadership Questionnaire

The shortened version of the Multifactor Leadership Questionnaire (MLQ) was used (11). This 45-item self-reporting questionnaire is designed to assess an individual’s leadership abilities, leadership style, and the outcomes of their leadership (11).

The MLQ measures leadership by dividing the subject into three categories: Transactional Leadership, Transformational Leadership, and Passive/ Avoidant Leadership Within these three categories, the MLQ measures these styles using twelve subcategories. Transactional Leadership is divided into CR and MBEA (11). Transformational Leadership is made up of IA, IB, IM, IS, and IC (1). Passive/ Avoidant Leadership is divided into MBEP and LF (1). The last area that the MLQ measures is the outcomes of leadership; this is separated into Extra Effort (EE), Effectiveness (EFF), and Satisfaction (SAT). The MLQ uses a five point-Likert scale ranging from zero (Not at all) to four (Frequently, if not always). The questionnaire’s Cronbach’s coefficient alphas range from 0.63 to 0.92 with an internal consistency above 0.70.

Data Analyses

All data was analyzed using the IBM SPSS Statistics program. A descriptive analysis was conducted to find the means and standard deviations of the self-reported scores. S-A is determined if the captain’s self-reported scores are within the standard deviation of the scores collected from their Coaches and Teammates (1, 11).

RESULTS

Captains
The two captains tested in this study will be labelled as “Captain X” and “Captain Y”. Captain X is an American citizen who has been rowing for 10 years. She has been Captain of her team for 1 year and was also the Captain of her High School rowing team. She believes that her team is highly successful and believes that she has directly influenced the performances of her team. She also describes herself as self-aware of her abilities. Captain Y is an international student studying in the United States. Captain Y has been rowing for only two years, not having rowed in high school. Captain Y also believes her team is highly successful and her leadership abilities directly influence the team’s overall results. She also describes herself as self-aware of her leadership abilities.


Coaches
The coaching staff consisted of a male, American head coach with 12 years of coaching experience and a female, Eastern European assistant coach with four years’ experience. Both Coaches have Coached Captain X for three years and Captain Y for two years. Both Coaches also believe that their team is having a successful season and that their Team Captains are a direct result of that success.


Captain X
As seen below in table 1, Captain X’s self-reported scores were found to be outside the SD range of the scores of their Coaches and/or Teammates in six of twelve leadership subcategories. The first is IM. Captain X (m=4, ±0) self-reported themselves as higher than the scores of the teammates (m=3.30, ±0.48), while the Coaches (m=3.12, ±1.24) rated Captain X between the two groups. Within Intellectual Stimulation, Captain X (m=3.75, ±0) rated themselves higher than both the Coaches (m=2.87, ±0.53) the Teammates (m=3.30, ±0.44). In CR, Captain X (m=3.50, ±0) rated themselves as higher than the Coaches (m=2.25, ±0) while their teammates (m=3.05, ±0.51) scored between them. In MBEA, Captain X (m=2.25, ±0) ranked themselves as higher than the Coaches (m=1.87, ±0.17) but were not outside the scores provided by the Teammates (m=1.65, ±1.16). In EE, Captain X (m=4.00, ±0) scored higher than the rankings of the Teammates (m=3.13, ±0.69) while the Coaches (m=3.16, ±1.17) scored between both of the groups. The last category is EFF, where Captain X (m=4.00, ±0) rated themself higher than the SD of the Teammates (m=3.30, ±0.48). The Teammates scores were not outside the SD range of the Coaches (m=3.37, ±0.88).

Table 1

Mean scores and Standard Deviation’s for Captain X’s MLQ 360-Degree Feedback Test

 IA (SD)IB (SD)IM* (SD)IS* (SD)IC (SD)CR* (SD)MBEA* (SD)MBEP (SD)LF (SD)EE* (SD)EFF* (SD)SAT (SD)
Captain X3.50 (0)3.50 (0)4.00 (0)3.75 (0)2.75 (0)3.50 (0)2.25 (0)1.00 (0)0.25 (0)4.00 (0)4.00 (0)4.00 (0)
Coaches (n=2)3.12 (0.88)3.37 (0.88)3.12 (1.24)2.87 (0.53)2.75 (0.70)2.25 (0)1.87 (0.17)1.25 (1.76)1.00 (1.41)3.16 (1.17)3.37 (0.88)3.25 (1.06)
Teammates (n=6)3.35 (0.57)3.50 (0.46)3.30 (0.48)3.30 (0.44)3.30 (0.77)3.05 (0.51)1.65 (1.16)1.08 (0.61)0.60 (0.57)3.13 (0.69)3.30 (0.48)3.40 (0.65)
Note: *Captains scores are outside the SD for one or both groups

Table 2

Mean scores and Standard Deviation’s for Captain Y’s MLQ 360-Degree Feedback Test

 
 IA (SD)IB* (SD)IM (SD)IS (SD)IC (SD)CR (SD)MBEA* (SD)MBEP* (SD)LF (SD)EE (SD)EFF (SD)SAT* (SD)
Captain Y2.75 (0)4.00 (0)3.50 (0)3.00 (0)3.00 (0)2.75 (0)2.25 (0)0.25 (0)0.75 (0)3.00 (0)3.25 (0)4.00 (0)
Coaches (n=2)3.25 (0.70)3.37 (0.53)3.50 (0.70)3.12 (0.17)3.12 (0.17)3.25 (0.70)2.87 (0.53)0.75 (1.06)0.50 (0.70)3.50 (0.70)3.50 (0.70)3.00 (2.00)
Teammates (n=6)2.91 (0.54)3.33 (0.30)3.08 (0.78)2.70 (0.96)3.33 (0.46)3.04 (0.88)2.54 (1.30)1.00 (0.61)0.62 (0.41)3.27 (0.57)3.33 (0.43)3.08 (0.37)
Note: *Captains scores are outside the SD for one or both groups

Figure 1

Captain X 360-Degree Feedback Data

Figure 2

Captain Y 360-Degree Feedback Data

Captain Y

As seen in Table 2, Captain Y’s self-reported scores are outside the SD range of the reported scores of the Coaches and/or Teammates in only four of twelve leadership subcategories. The first is IB. Captain Y (m=4, ±0) rated themselves higher than both their Teammates (m=3.33, ±0.30) and Coaches (m=3.37, ±0.53). In MBEA, Captain Y (m=2.25, ±0) rated themselves below the SD of the Coaches (m=2.87, ±0.53). Another category of difference is MBEP. Captain Y (m=0.25, ±0) rated themselves lower than the SD of the teammates (m=1.00, ±0.61). Neither group’s scores were outside the SD provided by the Coaches (m=0.75, ±1.06). The last difference is in the subcategory of SAT. Captain Y (m=4.00, ±0) self-reported scores higher than the SD of both the Coaches (m=3.00, ±0) and Teammates (m=3.08, ±0.37).

DISCUSSION

The collected data suggests Captain Y and Captain X differ in their leadership strengths and level of S-A. Captain X scores were found to be outside the SD of either the Coaches and/or the teammates in six of the twelve leadership subcategories, while Captain Y self-reported scores outside of their coaches and/or teammates SD on 4 different subcategories. Captain X’s scores were outside the SD of both the Coaches and Teammates for only one subcategory, Leadership. While Captain Y had two subcategories, Idealized Behavior and Satisfaction, that were outside the SD range of both the Teammates and Coaches scores.

Most interesting is the evaluation of SD of scores. The SD for several Coach and Teammate scores varied greatly. An example of this wide-ranging SD can be found on Table 1 with the Coaches having a SD of 1.76 (m=1.25) on MBEP and on Table 2 with the Teammates having a SD of 1.30 (m= 2.53) on MBEA. These wide-ranging SD display a divide in the perspective the Coaches and Teammates have on the Captains. Captain X and Y scored different than the mean scores both the Coaches and Teammates in almost all of the Leadership subcategories. However, the large SDs kept the Captains within the range to be labeled “self-aware” according to Avolio and Bass (1). These large SDs argue neither the Coaches or Teammates were unified in their beliefs of the Captains. Some participants within their groups believed that their captains were excellent leaders who provided crucial support to their team. While some participants saw their captains as less effective and, sometimes, borderline detrimental to their teams. It furthers interest that the Coaches, with a group size of 2, were also divided on their Captains in several categories. While the data suggests that these Captains are self-aware of their leadership, this self-awareness does not come without scrutiny. This can be best seen in Figures 1 and 2.

Another interesting point is within Captain X and Y’s belief in the Outcomes of their Leadership. Represented in the MLQ as EE, EFF, and SAT, Captain X rated herself as a “4” for all three categories, while Captain Y rated herself as the following: 3 (EE), 3.25 (EFF), and 4 (SAT). While Captain X has stronger belief that their leadership causes more positive outcomes for their team than Captain Y, they each rated themselves as a “4” in satisfaction. Meaning, they each believe their Teammates and Coaches are satisfied with their leadership abilities. However, this cannot be the case due to the wide-ranging SD’s found in many subcategories. It can be inferred, even without major differences from both their Teammates and Coaches in the SAT category, Captains may be incorrect about their teammate’s opinions of their leadership. They believe their team celebrate their leadership, while there is not a unified belief on their abilities. In addition, a high level of perceived satisfaction may inhibit captains’ motivation to grow or further develop their leadership abilities, as they may mistakenly believe their current performance is sufficient. This tendency aligns with patterns of social loafing, where individuals reduce effort or avoid self-improvement when they perceive their contributions as adequate and unchallenged (2, 10).

While the MLQ does not label the leadership style of Captains, it does infer trends and likelihoods. Within the scores collected, Captain X views themselves as a Transformational leader who directly, and positively, influences their teams’ performances. While Captain Y does not fit directly into Transformational, Transactional, or Avoidant Leadership. Captain Y rated herself as an amalgamation of both transformational and transactional leadership styles, specializing in having a strong moral code who may occasionally act as a parental figure to many of their teammates (IB).

As stated previously, this study had three hypotheses. The first hypothesis was that the Captains would rate themselves as having higher Transformational and Transactional Leadership when compared to the scores of the Coaches and Teammates. This hypothesis was not true with either Captains. The second hypothesis was the Captains would rate themselves as having lower Avoidant Leadership when compared to the scores of the Coaches and Teammates. This hypothesis was true only for Captain Y. The last hypothesis was that Captains will have an inverse relationship between the scores of MBEA and MBEP. This was found to be true in both Captains.

Limitations & Future Directions

While this study had several strengths, the main being the first empirical test of the 360 Feedback method, it of course is not without weakness. The first being a small sample size. While the MLQ does not give a specific sample size to use to make it effective, merely using one team (n=9) is small nonetheless. Future studies of this nature should look to include various teams from different sport types, genders, age and experience levels. To ensure validity, the items of the MLQ were not re-worded for each distinct participant group. All items of the MLQ were phrased “I am…”. While the items were worded correctly for the captains, all coaches and teammates had to reword the items in their heads as they were not responding to these questions about themselves. Furthermore, the MLQ is not a sport specific questionnaire. While it is a statistically valid and reliable questionnaire, it was designed to be used with a general population base. It was not specifically designed for athletes.

Other limitations to consider, are the social pressures of collegiate teammates. Despite the confidential and anonymous nature of the study, teammates may have felt unconscious pressure to identify their captains as having higher levels of positive leadership to avoid drama, feelings of guilt, or confrontations from the team (2).

Outside of adjustments to sample size, and inclusion of a sport specific questionnaire, future research should include a qualitative component to capture nuances of leadership, as well as a debriefing session with both coaches and captains. This level of transparency about how the captain is doing in the coaches and teammates eyes could provide a mechanism for change and promote open dialogue between all parties.

Lastly, the population used in this study were proficient in the English language, it was not their first language. With many international students and coaches used in this study, it is unknown if there were any difficulties understanding, reading, or comprehending the items they were tasked with completing.

CONCLUSION 

This study provides an empirical look at leadership and perceptions of different stakeholders about how team captain’s lead. Ultimately, one of the biggest takeaways is the large variance in opinions about the captains. Not just the difference in perception from the captains themselves to the ratings of the athletes and coaches, but the differences of how each individual teammate viewed the ability of the captain.  While the goal was to analyze the self-awareness of collegiate sport captains, the take home was more centered around the unique perception and individual nature to each athlete of what makes a great leader. This is supported in various studies regarding the notion that there is no one-size-fits-all approach to leadership (9, 12, 13, 17) Simply because an individual is elected, or selected, as a captain, that does not automatically make them an excellent leader and unanimously beloved.

APPLICATIONS IN SPORT

Applied implications of this study are vast within the realms of research and consulting practices. First, it provides a framework for future 360-Degree Feedback Method studies to take place. As previously stated, this method of research is underutilized in the realm of Sport Psychology research. Additionally, the data collected from this study may be used to update leadership education programs, creating importance for Self-Awareness training and identification within students, athletes, and leaders. Use of this data can also be used to stress the importance of team building and team cohesion. This study’s data found that the team’s coaches and teammates had dramatically different opinions on the leadership of their captains. This dramatic difference within the groups can be harmful to a team’s cohesion and performance, stressing the importance of this research study.

REFERENCES 

  1. Avolio, B. J., & Bass, B. M. (2004). Multifactor leadership questionnaire. Mind Garden.
  2. Bratton, V. K., Dodd, N. G., & Brown, F. W. (2011). The impact of emotional intelligence on accuracy of self‐awareness and leadership performance. Leadership & Organization Development Journal, 32(2), 127–149. https://doi.org/10.1108/01437731111112971
  3. Burns, J. M. (1978). Leadership. Harper & Row.
  4. Carlson, M. S. (1998). 360-degree feedback: The power of multiple perspectives. Popular Government, 63(2), 38–49.
  5. Carson, M. (2006). Saying it like it isn’t: The pros and cons of 360-degree feedback. Business Horizons, 49(5), 395–402. https://doi.org/10.1016/j.bushor.2006.01.004
  6. Drew, G. (2009). A “360” degree view for individual leadership development. Journal of Management Development, 26(7), 581–592. https://doi.org/10.1108/02621710910972698
  7. Eurich, T. (2017, September). Increase your self-awareness with one simple fix [Video]. TEDxMileHigh. https://www.ted.com/talks/tasha_eurich_increase_your_self_awareness_with_one_simple_fix
  8. Fleishman, E. A., Mumford, M. D., Zaccaro, S. J., Levin, K. Y., Korotkin, A. L., & Hein, M. B. (1991). Taxonomic efforts in the description of leader behavior: A synthesis and functional interpretation. The Leadership Quarterly, 2(4), 245–287. https://doi.org/10.1016/1048-9843(91)90016-U
  9. George, B., Sims, P., McLean, A. N., & Mayer, D. (2007). Discovering your authentic leadership. Harvard Business Review, 85(2), 1–8.
  10. Ghaleb, B. (2024). Social loafing: Understanding, mitigating, and enhancing group performance. International Journal of Scientific Multidisciplinary Research, 2(9), 1321-1328. https://doi.org/10.55927/ijsmr.v2i9.10975
  11. Muenjohn, N., & Armstrong, A. (2008). Evaluating the structural validity of the Multifactor Leadership Questionnaire (MLQ), capturing the leadership factors of transformational-transactional leadership. Contemporary Management Research, 4(1), 3–14. https://doi.org/10.7903/cmr.704
  12. Northouse, P. G. (2016). Leadership: Theory and practice (7th ed.). SAGE Publications.
  13. Pienaar, J., & Nel, P. (2017). A conceptual framework for understanding leader self-schemas and the influence of those self-schemas on the integration of feedback. SA Journal of Human Resource Management, 15, 1–11. https://doi.org/10.4102/sajhrm.v15i0.772
  14. Robbins, J. E., & Madrigal, L. (2017). Sport, exercise, and performance psychology: Bridging theory and application. Springer Publishing Company.
  15. Shaffer, J. (2018). 360 review: Self, teammate, and coach evaluation for personal development. Synergy Performance: A Division of Synergy Group.
  16. Wagstaff, C. R. D., Fletcher, D., & Hanton, S. (2012). Positive organizational psychology in sport: An ethnography of organizational functioning in a national sport organization. Journal of Applied Sport Psychology, 24(1), 26-47. https://doi.org/10.1080/10413200.2011.589423
  17. Warrick, D. (2011). The urgent need for skilled transformational leaders: Integrating transformational leadership and organization development. Journal of Leadership, Accountability, and Ethics, 8(5), 11–26.
2026-04-15T11:28:29-05:00May 6th, 2026|General, Sport Education, Sports Coaching, Sports Studies|Comments Off on Identifying Self-Awareness of Leadership Abilities Using 360 Degree Feedback Method: A Case Study of Collegiate Rowers

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

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

Relative Age Effect Among Olympic Medalists: Evidence from Ten Summer and Winter Olympic Games held between 2000 and 2018 

Authors: Christiana E. Hilmer, Michael J. Hilmer1

Corresponding Author:

Christiana Hilmer, PhD 

5500 Campanile Drive 

San Diego, CA 92182-4485 

[email protected] 

619-301-9388 


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

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

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

ABSTRACT

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

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

INTRODUCTION

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

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

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

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

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

METHODS

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

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

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

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

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

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

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

RESULTS

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

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

                      

 

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

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

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

+  εi                            (2)

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

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

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

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

Discussion

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

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

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

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

CONCLUSIONS

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

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

APPLICATIONS IN SPORT

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

REFERENCES 

  1. Awosoga, D., & Chow, M. (2024). Peaks and primes: Do athletes get one shot at glory? Significance, 21(3), 6–9.
  2. Baker, J., Janning, C., Wong, H., Cobley, S., & Schorer, J. (2014). Variations in relative age effects in individual sports: Skiing, figure skating and gymnastics. European Journal of Sports Science, 14, 183–190. https://doi.org/10.1080/17461391.2012.671369
  3. Barnsley, R. H., & Thompson, A. H. (1988). Birthdate and success in minor hockey: The key to the NHL. Canadian Journal of Behavioral Science, 20(2), 167–176. https://doi.org/10.1037/h0079927
  4. Cobley, S., Baker, J., Wattie, N., & McKenna, J. (2009). Annual age-grouping and athlete development: A meta-analytical review of relative age effects in sport. Sports Medicine, 39(3), 235–256. https://doi.org/10.2165/00007256-200939030-00005
  5. Costa, A. M., Marques, M. C., Louro, H., Ferreira, S. S., & Marinho, D. A. (2013). The relative age effect among elite youth competitive swimmers. European Journal of Sports Science, 13(5), 437–444. https://doi.org/10.1080/17461391.2012.742571
  6. Cummins, L. F. (2007). Figure skating: A different kind of youth sport. Journal of Clinical Sport Psychology, 1(4), 390–401. https://doi.org/10.1123/jcsp.1.4.390
  7. Glamser, F. D., & Vincent, J. (2004). The relative age effect among elite American youth soccer players. Journal of Sport Behavior, 27(1), 146–151.
  8. Hilmer, C. E., & Hilmer, M. J. (2020). Does confirmation bias exist in judged events at the Olympic Games? Journal of Quantitative Analysis in Sports, 17(1), 1–10. https://www.degruyterbrill.com/document/doi/10.1515/jqas-2019-0043/html
  9. Joyner, P. W., Lewis, J. S., Dawood, R. S., Mallon, W. J., Kirkendall, D. T., & Garrett, W. E. Jr. (2017). Relative age effect: Beyond the youth phenomenon. American Journal of Lifestyle Medicine, 14(4), 429–436. https://doi.org/10.1177/1559827617743423
  10. Longo, A. F., Siffredi, C. R., Cardy, M. L., Aquilino, G. D., & Lentini, N. A. (2016). Age of peak performance in Olympic sports. Journal of Human Sport and Exercise, 11(1), 31–41. https://doi.org/10.14198/jhse.2016.111.03
  11. Musch, J., & Grondin, S. (2001). Unequal competition as an impediment to personal development: A review of the relative age effect in sport. Developmental Review, 21(2), 147–167. https://doi.org/10.1006/drev.2000.0516
  12. Patiño, B. A. B., Varon-Murcia, J. J., Cardenas-Contreras, S., Castro-Malaver, M. A., & Martinez, J. (2024). Scientific production on the relative age effect in sport: Bibliometric analysis of the last 9 years (2015–2023). Retos, 52, 623–638.
  13. Raschner, C., Muller, L., & Hildebrandt, C. (2012). The role of a relative age effect in the first Winter Youth Olympic Games in 2012. British Journal of Sports Medicine, 46(14), 1038–1043. https://doi.org/10.1136/bjsports-2012-091535
  14. Thompson, A. H., Barnsley, R. H., & Stebelsky, G. (1991). Born to play ball: The relative age effect and Major League Baseball. Sociology of Sport Journal, 8(2), 146–151. https://doi.org/10.1123/ssj.8.2.146
  15. Wattie, N., Schorer, J., & Baker, J. (2015). The relative age effect in sport: A developmental systems model. Sports Medicine, 45(1), 83–94. https://doi.org/10.1007/s40279-014-0248-9
  16. Weglarczyk, S. (2018). Kernel density estimation and its application. ITM Web of Conferences, 23, 00037. https://doi.org/10.1051/itmconf/20182300037
  17. Werneck, F. Z., Coelho, E. F., de Oliveira, H. Z., Ribeiro Jr., D. B., Almas, S., de Lima, J. R. P., Matta, M., & Figueiredo, A. J. (2016). Relative age effect in Olympic basketball athletes. Science and Sports, 31(3), 158–161. https://doi.org/10.1016/j.scispo.2015.08.004
  18. Zetaruk, M. N. (2000). The young gymnast. Clinics in Sports Medicine, 19(4), 757–780. https://doi.org/10.1016/s0278-5919(05)70236-2
2025-08-26T10:08:11-05:00December 23rd, 2025|General, Olympics, Research, Sports Health & Fitness, Sports Studies, Sports Studies and Sports Psychology|Comments Off on Relative Age Effect Among Olympic Medalists: Evidence from Ten Summer and Winter Olympic Games held between 2000 and 2018 

Understanding the Decline of Lacrosse Officials in the Midwest: A Study on Retention Challenges and Stakeholder Influence

Authors: Nicholas Zoroya1, Joshua Greer2, Carla Blakey3

Corresponding Author:

Nicholas Zoroya

20932 Hasenclever Dr., South Lyon, MI 48178

(248)420-9200, [email protected]


1 Madonna University

2 Cumberland University

3 University of Alabama

ABSTRACT

Purpose:

This study examines the ongoing decline of lacrosse officials in the Midwest by exploring how stakeholder behavior, organizational support, and personal motivations affect officials’ decisions to continue or leave the profession. The goal is to identify key factors contributing to attrition and provide practical recommendations for improving retention.

Methods:

A mixed-methods survey design was used to collect data from 55 lacrosse officials who had officiated in the Midwest within the past five years. Participants responded to a series of closed-ended questions assessing demographics, officiating experience, and interactions with coaches, fans, and players. Open-ended responses were also collected to contextualize and support quantitative results. Data were analyzed using descriptive statistics, and illustrative quotes were used to reinforce common trends.

Results:

Most participants were White males over the age of 40, with more than a decade of officiating experience. While abuse from players was reported less frequently, officials indicated that verbal abuse from coaches and fans occurred often and significantly impacted their officiating experience. Additionally, officials expressed mixed feelings about the support they receive from associations and assignors. Despite these challenges, most participants reported a strong personal connection to the game and cited their passion for lacrosse and desire to give back as primary reasons for continuing. A subset of respondents, however, acknowledged that negative experiences have made them consider leaving the profession.

Conclusions:

Findings highlight the important role personal passion plays in keeping officials engaged despite a lack of institutional support and ongoing negative stakeholder interactions. Without meaningful changes to reduce abuse and increase organizational support, the officiating pipeline will remain vulnerable. The study also raises concerns about the lack of demographic diversity in lacrosse officiating, warranting further exploration.

Applications in Sport:

The results have practical implications for lacrosse governing bodies, assignors, and administrators. Improving sideline behavior, increasing compensation, offering mentorship, and expanding recruitment efforts to underrepresented groups could significantly improve retention and build a more sustainable and inclusive officiating workforce.

Key Words: officiating, lacrosse, referee retention, stakeholder behavior, sport management

INTRODUCTION

The shortage of sports officials, particularly in youth and high school sports, is a pressing issue that threatens organized athletics’ operational integrity and sustainability. The National Federation of High School Associations (NFHS) found that around 50,000 individuals have stopped serving as high school officials since the onset of the pandemic in 2020 (Niehoff, 2022). This decline can be attributed to several interrelated factors, including occupational stress, abuse from spectators, insufficient support systems, and inadequate training opportunities for officials.

Literature Review

The shortage of sports officials is increasingly attributed to the rising incidence of verbal and physical abuse directed at referees by players and spectators. Research indicates that abusive behavior, particularly at the grassroots level, significantly contributes to high turnover rates, with negative experiences reducing officials’ willingness to continue in the profession (Dawson et al., 2021; Rayner et al., 2016). Dawson et al. (2021) highlight the alarming decline in the number of qualified officials, stressing that this culture of abuse not only affects officials but also threatens the integrity of competitive sports. Additionally, issues such as harassment and discrimination, especially against female officials, further intensify attrition, creating a hostile environment that undermines the overall health of sports communities (Marshall et al., 2022; Webb et al., 2020).

In addition, the lack of adequate support, resources, and effective training opportunities exacerbates attrition, as many organizations fail to provide the necessary infrastructure to sustain officials’ careers (Webb et al., 2020; Tingle et al., 2014). Insufficient professional development and an aging workforce further compound the issue, necessitating innovative strategies to attract and retain younger officials (Ryan et al., 2014; Barnhill et al., 2018; Pierce et al., 2021). This literature emphasizes the multifaceted challenges in officiating and highlights the critical need for systemic changes to address the issues of abuse, support, and recruitment.

The Decline of Lacrosse Officials 

The decline of lacrosse officials in the Midwest has raised concerns regarding the sustainability of officiating in growing sports leagues. In recent years, the shortage of qualified officials has emerged as a critical issue. Lacrosse, a sport that has enjoyed significant regional growth in the Midwest, now faces challenges similar to those observed in other sports arenas (Ridinger et al., 2017). The decline in the number of lacrosse officials not only impedes game integrity but also affects the overall development of the sport. Existing literature has shown that multifaceted factors, including motivational changes, psychosocial stressors, and insufficient support structures, play essential roles in the retention and attrition of referees (Livingston & Forbes, 2016; Ridinger, 2015).

Negative Stakeholder Behavior

The decline in the number of lacrosse officials in the Midwest can be tied to negative stakeholder behavior, particularly from parents, coaches, and fans. This trend is troubling, as officials play a critical role in maintaining the integrity and safety of the game. The psychological impact of abuse from various stakeholders on referees cannot be overstated. Studies indicate that officials often experience significant stress and mental health challenges due to verbal abuse and aggression directed at them during games, which can lead to a decline in their overall job satisfaction and motivation (Breslin et al., 2022; Giel & Breuer, 2021).

It is important to note that the abuse received by officials, from players, coaches, and spectators, is frequently normalized within many sports environments. Research in sports such as rugby and football demonstrates that officials often report feeling overwhelmed by hostility from these groups (Webb et al., 2019; Webb et al., 2018). This hostility not only affects the officiating experience but can also deter potential new referees from entering the field. Furthermore, the retention rates of officials are directly influenced by the social interactions they have with these stakeholder groups, and the lack of positive reinforcement or sportsmanship has been shown to exacerbate dropout intentions (Giel & Breuer, 2021).

The influence of these stressors is particularly notable in the context of youth sports, where the pressure from parents and coaches can create a toxic atmosphere for officials trying to enforce rules and manage games. Coaches, in their roles, often have a substantial impact on how players perceive referees, which in turn affects the emotional atmosphere during matches (Webb, 2020). If coaches model negative behaviors, such as disrespect towards referees, it can lead to a cycle of abuse where players mimic these actions, further isolating officials and intensifying their negative experiences (Webb et al., 2018).

Interventions aimed at increasing awareness and promoting mental health support among referees are essential in addressing this decline. Recommendations have been made for mental health training for stakeholders to improve the overall environment surrounding officiating and reduce instances of abuse (Breslin et al., 2022). Additionally, stakeholder education on the consequences of negative behaviors towards officials can help reshape perspectives and foster a more respectful sporting culture. Such measures would not only help in maintaining a robust pool of lacrosse officials but also promote a healthier, more inclusive environment for all participants in the sport.

Abuse
Abuse, both verbal and physical, is a significant contributor to officiating attrition, with numerous studies highlighting its impact on officials’ mental health and intentions to quit. Brick et al. (2022) found that nearly all Gaelic Games officials surveyed (94.29%) had encountered verbal abuse, and almost one in four (23.06%) had experienced physical abuse during their careers. Verbal abuse was shown to be frequent and directly linked to mental health issues and quitting intentions, with distress acting as a mediating factor. Similarly, Webb et al. (2018) documented the prevalence of both verbal and physical abuse in rugby league, finding that emotional abuse (i.e., intimidation, swearing, and threats) and physical aggression (i.e., pushing and hitting) significantly reduced job satisfaction. These hostile environments, particularly when abuse is persistent and unaddressed, contribute to officials leaving their roles.

The impact of abuse on officiating extends across various sports and levels. For instance, Ridinger et al. (2017) revealed that 42% of 2,485 high school referees identified abuse as the most significant challenge in their roles, and 10% cited abuse as a factor in their intention to quit. This aligns with findings from Kavanagh et al. (2021), who reported that abuse in youth soccer led to emotional exhaustion and burnout among officials. Tingle et al. (2014) also noted that the normalization of verbal abuse within sports culture exacerbates the negative effects on officials, especially for newcomers lacking support systems. Collectively, these studies underscore the need for sports organizations to implement proactive abuse prevention measures and institutional support to mitigate attrition and improve the officiating experience.

Unsupportive Interactions
Unsupportive social dynamics play a critical role in officials’ decisions to leave their positions. Warner et al. (2013) examined the effects of problematic peer interactions and inadequate mentoring in sports such as lacrosse, revealing how these relational shortcomings contribute to officiating attrition. When officials lack meaningful support from mentors or peers and feel disconnected from a broader officiating community, their engagement and satisfaction decline. The Referee Retention Scale (Ridinger et al., 2017) identifies several social factors that contribute to retention, including several factors that address a sense of community and mentoring support. These elements reflect the importance of fostering interpersonal relationships that reinforce a positive officiating experience (Table 1).

Table 1
 Key Factors Contributing to Referee Retention

Factor NameDescription
Administrator ConsiderationLevel of perceived fairness and consideration from assigners and administrators
MentoringSupport and encouragement from a mentor or a friend to become involved with officiating
Sense of CommunityPerceived sense of belonging to a supportive community of officials
Lack of StressInfrequent encounters with stressful situations related to officiating

Note. Adapted from Ridinger, L. L., Kim, K. R., Warner, S., & Tingle, J. K. (2017). Development of the Referee Retention Scale. Journal of Sport Management, 31(5), 514–527.

In addition to interpersonal issues, organizational shortcomings also undermine retention efforts. Warner et al. (2013) highlighted how insufficient policy frameworks and administrative neglect exacerbate attrition, particularly when officiating structures fail to proactively address the evolving needs of officials. The Referee Retention Scale provides a methodological foundation for identifying these structural deficiencies. Notably, factors such as “Administrator Consideration” and “Lack of Stress” underscore the role of fair management practices and manageable work environments in referee satisfaction. Furthermore, Livingston and Forbes (2016) and Ridinger (2015) emphasize the necessity of aligning recruitment and retention strategies with officials’ motivations and expectations. Collectively, these findings stress that without intentional and sustained institutional support, officiating organizations risk ongoing loss of personnel due to preventable burnout and disengagement.

Referee Retention

Research on referee retention has provided useful insights into the systemic and individual challenges impacting officiating roles. Ridinger et al. (2017) developed the Referee Retention Scale to assess factors such as job satisfaction, perceived organizational support, and the prevalence of abuse, all of which are directly linked to declining retention rates. Their work underscores that referee attrition is often precipitated by issues that extend beyond the administrative domain and delve into psychosocial and environmental stressors. Similarly, Livingston and Forbes (2016) investigated the evolving motivations of amateur sport officials and confirmed that changes in personal goals and external support diminish retention levels over time. Their study, although centered on Canadian officials, provides a framework that is applicable to the Midwest context, where similar socio-organizational dynamics are at play.

Ridinger (2015) compared the experiences of baseball umpires and lacrosse officials, revealing common constraints such as economic shortages and inadequate mentorship. This comparative analysis highlights that lacrosse officials, in particular, face challenges that are exacerbated by limited training opportunities and the absence of community-based support systems. In other research pertinent to community sports, Baxter et al. (2021) examined the experiences of female volunteer officials, outlining barriers and motivators that resonate with broader issues affecting retention. Although focused on gender-related dimensions of officiating, their findings reinforce the notion that organizational policies and social support are crucial to sustaining a committed officiating workforce.

The literature clearly indicates that the decline of lacrosse officials in the Midwest is a complex phenomenon influenced by issues of retention, support deficiency, and exposure to abuse. By synthesizing insights from multiple studies, this review stresses the importance of a comprehensive strategy that includes recruitment, retention, and preventive measures to improve the working environment for lacrosse officials. Future research and policy changes informed by these findings will be crucial in reversing the downward trend and ensuring the long-term sustainability of lacrosse officiating.

Conclusion

Despite a growing body of literature on officiating attrition, few studies have examined the distinct cultural and geographic dynamics affecting lacrosse officials in emerging regions like the Midwest. The reviewed research highlights a multifaceted crisis, with lacrosse serving as a representative case of the broader challenges afflicting youth and high school sports. Across regional and national contexts, verbal abuse and safety concerns have emerged as key contributors to attrition. In the Midwest, the shortage of lacrosse officials is impeding sport development and compromising game quality.

National survey findings from NASO and NFHS reinforce the severity of the crisis, revealing that a majority of new officials depart within three years due to burnout, safety concerns, and undervaluation. While recent initiatives, such as the NFHS National Officials Consortium Summit and the #BecomeAnOfficial campaign, represent positive steps forward, the literature suggests that these efforts must be part of a broader, coordinated strategy. Interventions focused on stakeholder education, mental health support, structured mentorship, and the public acknowledgment of officials’ contributions are necessary to reverse current trends. Sustaining officiating in lacrosse will require systemic change, cultural realignment, and a renewed commitment to valuing those who enforce the rules and protect the integrity of the game.

METHODS

Purpose

The purpose of this study is to examine the underlying causes of the declining number of lacrosse officials in the Midwest. Specifically, it seeks to determine how stakeholder interactions, support structures, and personal motivations influence officials’ decisions to remain active in the field. The study is designed to inform retention strategies and stakeholder education efforts.

Methodology

Participants

Participants in this study were 55 lacrosse officials who officiated games across the Midwest region of the United States. Eligibility criteria required participants to have officiated lacrosse at any level (youth, high school, college, or club) within the past five years in a Midwest state. Participants were predominantly male and white, and ranged in age from 25 to 72 years old, with officiating experience spanning from less than 1 year to over 30 years. Participation was voluntary, and no compensation was provided.

Procedures

Data was collected via an anonymous online survey distributed through Qualtrics. Recruitment was conducted through email invitations sent to lacrosse officiating associations, assignors, and personal networks within the officiating community, as well as through social media posts targeting officials in the Midwest. The survey remained open for three weeks, with one reminder sent midway through the collection period. Prior to data collection, the study received Institutional Review Board (IRB) approval from Madonna University. Participants provided informed consent at the beginning of the survey.

The survey consisted of both closed and open-ended questions. Closed-ended items collected demographic information (age, gender, race/ethnicity, years of officiating experience) and information on perceived challenges in officiating (e.g., pay, scheduling, respect from stakeholders). Open-ended questions invited participants to elaborate on their experiences, including reasons for continuing or discontinuing officiating and suggestions for improving the officiating experience.

Data Analysis

Quantitative data were analyzed using descriptive statistics (frequencies, percentages, means) to summarize participant demographics and the prevalence of key issues identified by officials. Open-ended responses were reviewed to identify illustrative quotes that reinforced or provided examples of the quantitative findings. Qualitative responses were not formally coded or thematically analyzed but were used to add narrative context to the statistical results.

RESULTS

A total of 55 lacrosse officials from the Midwest region completed the survey. Participants ranged in age from 23 to 67 years (M = 45.8, SD = 11.2), with the majority identifying as male (85%) and White/Caucasian (94%). Officials reported working across multiple states, most commonly Indiana, Illinois, Michigan, Ohio, and Wisconsin. On average, participants had 14.3 years of officiating experience, with nearly all officiating at the youth and high school levels (92%). Additionally, 64% reported officiating collegiate lacrosse, and 9% officiated at the professional level.

Officials were asked about their experiences with negative interactions from various stakeholders. Verbal abuse from coaches was reported as occurring “sometimes” by 58% of respondents and “often” by 16%. Similar patterns emerged regarding fans and parents, with 49% reporting “sometimes” and 22% reporting “often” experiencing verbal abuse. Abuse from players was less frequent, with 51% of officials reporting “rarely” and 38% reporting “sometimes.” Despite these negative interactions, officials rarely reported fearing for their personal safety, with 74% indicating “never” and 18% “rarely” feeling unsafe while officiating.

Perceptions of support from officiating associations were mixed. While 42% of respondents felt “often” supported by their associations, 33% reported “sometimes” feeling supported, and 25% “rarely.” When asked how often they considered quitting due to negative experiences, 56% reported “never” considering leaving officiating, 24% “rarely,” 11% “sometimes,” and 9% “often.”

Qualitative responses provided further insight into officials’ motivations and concerns. Officials frequently cited a love for the game, a desire to give back to the sport, camaraderie with fellow officials, and ensuring opportunities for young athletes as primary reasons for continuing to officiate. One participant explained, “I won’t stop until my body no longer allows me to officiate,” while another noted, “If associations or assignors supported officials more, I’d feel better about continuing.” Conversely, low pay, spectator abuse, insufficient support from associations, and the physical demands of officiating as they age were commonly cited factors contributing to potential attrition.

Discussion

The findings of this study provide a nuanced look into the factors influencing lacrosse officials’ retention in the Midwest. Despite frequent reports of verbal abuse from coaches, players, and fans, many respondents reported continuing to officiate due to intrinsic motivations such as a love of the sport and a desire to give back. This aligns with prior research emphasizing passion and sport commitment as key drivers of officiating persistence. Finding joy in officiating can lead to better psychological outcomes, fostering an environment where officials are more likely to continue their engagement with the sport (Carson et al., 2020).

However, respondents also highlighted significant deterrents to retention, including low compensation, lack of recognition, poor treatment from stakeholders, and limited support from assigning organizations. These challenges are consistent with broader officiating literature identifying unsupportive environments and abuse as predictors of attrition. Research supports the notion that the challenges of managing player dynamics and external pressures, such as crowd noise, significantly impact officials’ performance and mental states (Carter et al., 2024). Therefore, the emotional and psychological investment in sport, empowered by both passion and commitment, is essential in nurturing a sustained career in officiating.

Interestingly, while many officials expressed dissatisfaction with aspects of the officiating experience, few indicated plans to immediately stop officiating, suggesting a complex interplay between commitment, tolerance for negative experiences, and practical constraints.

The demographic homogeneity of the sample raises additional concerns. The overwhelming representation of older White men suggests potential gaps in recruitment or retention efforts targeting women and racial minorities. Given lacrosse’s growing popularity and emphasis on inclusion, this lack of diversity warrants further investigation and intervention.

Collectively, these findings reinforce the need for officiating associations and lacrosse governing bodies to implement more robust training, mentorship, and support systems. Addressing verbal abuse, improving communication, and recognizing officials’ contributions may improve retention. Ultimately, sustaining a high-quality officiating workforce requires addressing both systemic challenges and individual experiences.

Future Research

While this study offers valuable insight into the experiences of lacrosse officials in the Midwest, it also highlights several opportunities for future research. First, the demographic composition of respondents (predominantly White, male, and middle-aged or older) suggests a need to explore barriers to entry and advancement for underrepresented groups in officiating. Investigating the experiences of women, racial minorities, and younger officials could help identify structural or cultural factors limiting diversity in the officiating pipeline.

Additionally, future research could expand beyond the Midwest to assess whether similar trends exist nationally or vary by region. Comparative studies across different competitive levels (youth, high school, collegiate, professional) may also reveal distinct challenges and support mechanisms. Finally, longitudinal research could track officials over time to better understand career trajectories, burnout risk, and retention strategies. Together, these avenues of inquiry can build a more comprehensive understanding of officiating challenges and inform evidence-based recruitment and retention initiatives.

CONCLUSIONS

This study sheds light on the complex realities facing lacrosse officials across the Midwest, revealing a profession challenged by inadequate pay, lack of respect from key stakeholders, inconsistent scheduling practices, and minimal institutional support. Despite these hurdles, officials overwhelmingly cited their love of the game, passion for supporting athletes, and commitment to the sport as primary motivators for continuing their work. This finding underscores a critical dynamic: lacrosse officiating, particularly in under-resourced regions, is being sustained largely by the intrinsic dedication and personal investment of its officials rather than by systemic support or professional incentives.

Without this fierce passion for the sport, it is likely that attrition would be even higher. Many participants described tolerating negative treatment, logistical difficulties, and low compensation solely because of their deep-rooted connection to lacrosse. While this dedication is admirable, it raises serious concerns about sustainability and burnout. The profession cannot rely indefinitely on goodwill and personal sacrifice without addressing the structural and cultural issues contributing to official dissatisfaction and turnover.

These findings highlight the urgent need for action to support and retain lacrosse officials and ensure the sport’s long-term sustainability. Ultimately, this study emphasizes that lacrosse officiating in the Midwest stands at a crossroads.

APPLICATION IN SPORT

The findings of this study have clear implications for lacrosse governing bodies, officiating associations, assignors, coaches, and athletic administrators seeking to address the shortage of officials. First, targeted efforts to reduce verbal abuse and improve sideline behavior are critical for creating a more supportive environment that encourages retention. Educational workshops for coaches, parents, and athletes focused on respecting officials may help shift cultural norms and reduce negative interactions.

Second, the study highlights the need for stronger mentoring and peer support systems within officiating communities. Developing formal mentorship programs that connect new officials with experienced referees could foster a greater sense of belonging and resilience, improving retention among newer and younger officials. Assigning bodies should prioritize community-building activities, recognition initiatives, and accessible professional development opportunities to sustain engagement.

Additionally, improving compensation and scheduling practices may directly influence retention by addressing key logistical frustrations reported by officials. Providing consistent game assignments, clear communication, and timely pay can increase satisfaction and encourage officials to remain active longer.

Finally, the demographic homogeneity observed in this study signals an urgent need to broaden recruitment efforts to underrepresented groups, including women and racial minorities. Intentional outreach, training scholarships, and inclusive recruitment messaging may help diversify the officiating pipeline and ensure the sport’s continued growth. Implementing these strategies can help sport leaders, administrators, and policy makers foster a more sustainable, inclusive, and supportive officiating environment in lacrosse and beyond.

REFERENCES 

  1. Avalos, B. L. (2020). “Friday night is their Super Bowl”: A relational investigation regarding occupational stress among American high school football officials. Communication & Sport, 8(4–5), 655–676. https://doi.org/10.1177/2167479520968932
  2. Barnhill, C. R., Martinez, J. M., Andrew, D. P. S., & Todd, W. (2018). Sport commitment, occupational commitment and intent to quit among high school sports officials. Journal of Amateur Sport, 4(1), 45–68. https://doi.org/10.17161/jas.v4i1.6459
  3. Baxter, C., Smith, J., & Thompson, R. (2021). Female volunteer community sport officials: A scoping review and research agenda. European Sport Management Quarterly, 21(3), 187-203. https://doi.org/10.1080/16184742.2021.1877322
  4. Breslin, G., Shannon, S., Cummings, M. P., & Leavey, G. (2022). An updated systematic review of interventions to increase awareness of mental health and well-being in athletes, coaches, officials, and parents. Systematic Reviews. https://doi.org/10.1186/s13643-022-01932-5
  5. Brick, N. E., Breslin, G., Shevlin, M., & Shannon, S. (2022). The impact of verbal and physical abuse on distress, mental health, and intentions to quit in sports officials. Psychology of Sport and Exercise, 60, 102162. https://doi.org/10.1016/j.psychsport.2022.102162
  6. Carson, F., Dynon, N., Santoro, J., & Kremer, P. (2020). Examining negative emotional symptoms and psychological wellbeing of Australian sport officials. International Journal of Environmental Research and Public Health, 17(21), 8265. https://doi.org/10.3390/ijerph17218265.
  7. Carter, T. B., Gorczynski, P., Coady, C. J., Cunningham, I., Mascarenhas, D., Grant, M., Sullivan, P., Webb, T., Livingston, L. A., & Hancock, D. J. (2024). Implementing a scoping review to explore sport officials’ mental health. Frontiers in Sports and Active Living, 6, 1436149. https://doi.org/10.3389/fspor.2024.1436149
  8. Dawson, P., Webb, T., & Downward, P. (2021). Abuse is not a zero-sum game! The case for zero tolerance of match official physical and verbal abuse. European Journal of Sport Science, 21(2), 266–274. https://doi.org/10.1080/17461391.2021.1881619
  9. Giel, T., & Breuer, C. (2021). The general and facet-specific job satisfaction of voluntary referees based on the model of effort-reward imbalance. European Sport Management Quarterly. https://doi.org/10.1080/16184742.2021.1964090
  10. Kavanagh, E., Brown, L., & Jones, I. (2021). ‘You’re not a real man’: The experiences of male sports officials in emotionally challenging environments. Sport Management Review, 24(2), 266–289. https://doi.org/10.1016/j.smr.2020.03.004
  11. Livingston, M., & Forbes, S. (2016). Factors contributing to the retention of Canadian amateur sport officials: Motivations, perceived organizational support, and resilience. International Journal of Sports Science & Coaching, 11(2), 406-421. https://doi.org/10.1177/1747954116644061
  12. Marshall, S., McNeil, N., Seal, E., & Nicholson, M. (2022). The “boys’ club,” sexual harassment, and discriminatory resourcing: An exploration of the barriers faced by women sport officials in Australian basketball. International Review for the Sociology of Sport, 57(6), 889–906. https://doi.org/10.1177/10126902221137802
  13. Niehoff, K. (2022, February 16). With loss of 50,000 officials, NFHS organizes consortium to find solutions. National Federation of State High School Associations. https://www.nfhs.org/articles/with-loss-of-50-000-officials-nfhs-organizes-consortium-to-find-solutions/
  14. Pierce, D. A., Sherman, G., Mechelin, K. J., & Kryder, B. (2021). Innovate sports officiating with design thinking. Case Studies in Sport Management, 10(1), 26–35. https://doi.org/10.1123/cssm.2020-0029
  15. Rayner, M., Webb, T., & Webb, H. (2016). The occurrence of referee abuse in rugby union: Evidence and measures through an online survey. International Journal of Sport Management Recreation & Tourism, 21(1), 1–17. https://doi.org/10.5199/ijsmart-1791-874x-21d
  16. Ridinger, L. (2015). Contributors and constraints to involvement with youth sports officiating. Journal of Amateur Sport, 1(2), 94-110. https://doi.org/10.17161/jas.v1i2.4946
  17. Ridinger, L., Warner, S., Tingle, J. K., & Kim, K. (2017). Development of the Referee Retention Scale. Journal of Sport Management, 31(6), 635-647. https://doi.org/10.1123/jsm.2017-0065
  18. Ryan, T. D., Sosa, J., & Thornton, M. A. (2014). Influences of training on individual outcomes for high school sports officials. SAGE Open, 4(2). https://doi.org/10.1177/2158244014532475
  19. Tingle, J. K., Warner, S., & Sartore-Baldwin, M. (2014). The experience of former women officials and the impact on the sporting community. Sex Roles, 71(1), 7–20. https://doi.org/10.1007/s11199-014-0366-8
  20. Warner, S., Tingle, J. K., & Kellett, P. (2013). Officiating attrition: The experiences of former referees via a sport development lens. Journal of Sport Management, 27(4), 316-328. https://doi.org/10.1123/jsm.27.4.316
  21. Webb, T., Dicks, M., & Thelwell, R. (2018). An explorative investigation of referee abuse in English rugby league. Journal of Applied Sport Management, 10(2), 54-71. https://doi.org/10.18666/jasm-2017-v10-i2-8834
  22. Webb, T., & Rayner, M., & Thelwell, R. (2019). An examination of match officials’ perceptions of support and abuse in rugby union and cricket in England. Managing Sport and Leisure. https://doi.org/10.1080/23750472.2019.1605841
  23. Webb, T., Rayner, M., & Thelwell, R. (2018). An explorative investigation of referee abuse in English rugby league. Journal of Applied Sport Management. https://doi.org/10.18666/jasm-2017-v10-i2-8834
  24. Webb, T. (2020). The future of officiating: Analyzing the impact of COVID-19 on referees in world football. Soccer and Society. https://doi.org/10.1080/14660970.2020.1768634
  25. Webb, T. (2020). Sports match official research: An evolving narrative, positioning future research. Managing Sport and Leisure, 25(1–2), 1–4. https://doi.org/10.1080/23750472.2020.1762304
  26. Webb, T., Dicks, M., Thelwell, R., van der Kamp, J., & Rix‐Lièvre, G. (2020). An analysis of soccer referee experiences in France and the Netherlands: Abuse, conflict, and level of support. Sport Management Review, 23(2), 214–226. https://doi.org/10.1016/j.smr.2019.03.003
2025-07-21T14:29:22-05:00December 9th, 2025|Contemporary Sports Issues, General, Research, Sport Training, Sports Coaching, Sports Studies|Comments Off on Understanding the Decline of Lacrosse Officials in the Midwest: A Study on Retention Challenges and Stakeholder Influence
Go to Top