ISSN: 1543-9518

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 
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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 

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

Authors: Rachel Berkowsky1, MS, Stephanie Singe1, PhD

Corresponding Author:

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

2095 Hillside Rd U-1110, Storrs, CT 06269

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


1University of Connecticut Department of Kinesiology, Storrs, CT

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

ABSTRACT

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

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

INTRODUCTION

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

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

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

WFC and FWC

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

NCAA Coaches and Mental Health

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

Previous Research on WFC and FWC in the Sport Setting

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

Purpose and Hypotheses

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

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

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

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

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

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

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

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

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

METHODS

Research Design

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

Respondents

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

Procedures

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

Instrumentation

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

Data Analysis

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

RESULTS

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

Participant Demographics

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

WFC and FWC

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

Gender and WFC

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

Gender and FWC

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

Marital Status and WFC

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

Marital Status and FWC

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

Parental Status and WFC

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

Parental Status and FWC

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

Years of Experience and WFC

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

Years of Experience and FWC

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

Discussion

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

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

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

Work-Family Conflict

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

Family-Work Conflict

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

Limitations and Future Research

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

CONCLUSIONS

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

APPLICATION IN SPORT

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

REFERENCES 

1. Greenhaus, J.H., & Beutell, N.J. Sources of conflict between work and family roles. The Academy of Management Review. 1985;10(1):76–88.

2. Graham, J.A., & Smith, A.B. Work and Life in the Sport Industry: A Review of Work-Life Interface Experiences Among Athletic Employees. Journal of Athletic Training. 2022;57(3):210–24.

3. Mazerolle, S.M., Bruening, J.E., Casa, D.J., & Burton, L.J. Work-family conflict, part II: Job and life satisfaction in national collegiate athletic association division I-A certified athletic trainers. Journal of Athletic Training. 2008;43(5):513–22.

4. Eason, C.M., Cairns, A.H., & Singe, S.M. The Impact of the Number of Student Athletes on Burnout and Work-Family Conflict of High School Athletic Trainers. The Sport Journal. 2022;24:1–13.

5. Cairns, A.H., Singe, S.M., & Eason, C.M. Perceived Stress as an Indicator of Work-Family Conflict and Burnout Among Secondary School Athletic Trainers. International Journal of Athletic Therapy and Training. 2023;28(4):215–20.

6. Singe, S.M., Rodriguez, M., Cairns, A.H., Eason, C.M., & Rynkiewicz, K.M. Work-Family Conflict and Family Role Performance Among Collegiate Athletic Trainers. Journal of Athletic Training. 2023;58(4):381–6.

7. Singe, S.M., Cairns, A.H., Rynkiewicz, K.M., & Eason, C.M. Examining Professional Identity among Collegiate Athletic Trainers and its Relationship with Work-family Conflict. The Internet Journal of Allied Health Sciences and Practice. 2023;21(3).

8. Pitney, W.A., Mazerolle, S.M., & Pagnotta, K.D. Work-family conflict among athletic trainers in the secondary school setting. Journal of Athletic Training. 2011;46(2):185–93.

9. Eason, C.M., Gilgallon, T.J., & Singe, S.M. Work-Addiction Risk in Athletic Trainers and Its Relationship to Work-Family Conflict and Burnout. Journal of Athletic Training. 2022;57(3):225–33.

10. Sagas, M., & Cunningham, G.B. Work and Family Conflict Among College Assistant Coaches. International Journal of Sport Management. 2005;6(2):183–97.

11. Dixon, M.A., & Bruening, J.E. Work–Family Conflict in Coaching I: A Top-Down Perspective. Journal of Sport Management. 2007;21(3):377–406.

12. Bruening, J.E., & Dixon, M.A. Work–Family Conflict in Coaching II: Managing Role Conflict. Journal of Sport Management. 2007;21(4):471–96.

13. Dabbs, S.M., Graham, J.A., & Dixon, M.A. A Socio-Cultural Perspective of the
Work-Life Interface of College Coaches: A Cohort Analysis. Journal of Issues in Intercollegiate Athletics. 2016;9(1).

14. Our Division III Story [Internet].; 2025 [cited June 2, 2025]. Available from: https://www.ncaa.org/sports/2021/2/16/our-division-iii-story.aspx.

15. NCAA Demographics Database [Internet].; 2024 [updated October; cited June 11, 2025]. Available from: https://www.ncaa.org/sports/2018/12/13/ncaa-demographics-database.aspx.

16. Division III Finances [Internet].; 2025 [cited June 2, 2025]. Available from: https://www.ncaa.org/sports/2021/5/11/division-iii-finances.aspx.

17. Netemeyer, R. G., Boles, J. S., & McMurrian, R. Development and validation of work–family conflict and family–work conflict scales. Journal of Applied Psychology. 1996;81(4):400–10.

18. Gutek, B.A., Repetti, R., & Silver, D. Nonwork roles and stress at work. In C. Cooper & R. Payne (Eds.), Causes, coping, and consequences of stress at work. New York: Wiley; 1988.

19. Gutek, B.A., Searle, S., & Klepa, L. Rational versus gender role explanations for work-family conflict. Journal of Applied Psychology. 1991;76(4):560–8.

20. Judge, T.A., Boudreau, J.W., & Bretz, R.D. Job and life attitudes of male executives. Journal of Applied Psychology. 1994;79(5):767–82.

21. NCAA coaches report increased focus on mental health, detail personal challenges. [Internet].; 2023 [updated January 26; cited May 29, 2025]. Available from: https://www.ncaa.org/news/2023/1/26/media-center-ncaa-coaches-report-increased-focus-on-mental-health-detail-personal-challenges.aspx.

22. Mazerolle, S.M., Pitney, W.A., & Eason, C.M. Experiences of Work-Life Conflict for the Athletic Trainer Employed Outside the National Collegiate Athletic Association Division I Clinical Setting. Journal of Athletic Training. 2015;50(7):748–59.

23. Knight, C.J., Reade, I.L., Selzler, A.M., & Rodgers, W.M. Personal and situational factors influencing coaches’ perceptions of stress. Journal of Sports Sciences. 2013;31(10):1054–63.

24. Wright, S.A., Walker, L.F., & Hall, E.E. Effects of workplace stress, perceived stress,
and burnout on collegiate coach mental health outcomes. Frontiers in Sports and Active Living. 2023;5:974267.

25. Ferreira, J.G., Rodrigues, F., Sobreiro, P., Silva, M., Santos, F.J., Carvalho, G., Hernández Mendo, A., & Rodrigues, J. Social support, network, and relationships among coaches in different sports: a systematic review. Frontiers in Psychology. 2024;15:1301978.

26. Dixon, M.A., & Sagas, M. The relationship between organizational support, work-family conflict, and the job-life satisfaction of university coaches. Research Quarterly for Exercise and Sport. 2007;78(3):236–47.

27. Mazerolle, S.M., Pitney, W.A., Goodman, A., Eason, C.M., Spak, S., Scriber, K.C., Voll, C.A., Detwiler, K., Rock, J., Cooper, L., & Simone, E. National Athletic Trainers’ Association Position Statement: Facilitating Work-Life Balance in Athletic Training Practice Settings. Journal of Athletic Training. 2018;53(8):796–811.

28. Mazerolle, S.M., Pitney, W.A., Casa, D.J., & Pagnotta, K.D. Assessing strategies to manage work and life balance of athletic trainers working in the National Collegiate Athletic Association Division I setting. Journal of Athletic Training. 2011;46(2):194–205.

2025-07-15T09:23:26-05:00November 25th, 2025|General, Research, Sport Education, Sports Coaching, Sports Studies|Comments Off on Examining Work-Family Conflict and Family-Work Conflict among Collegiate Coaches at the NCAA Division III Level 
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The Evolving Role of Technology and Analytics in Coaching: Transforming Practices and Enhancing the Impact on the Profession

Authors: Lawrence W. Judge1, Matt Moore2

1College of Health, Ball State University

2 College of Social Work, University of Kentucky 

 

 

Corresponding Author: 

Dr. Matt Moore

Associate Dean of Academic and Student Affairs

College of Social Work

University of Kentucky

601 Patterson Office Tower

Lexington, KY 40506

[email protected] 

ABSTRACT 

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

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

Introduction

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

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

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

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

Current Roles of Technology in Coaching

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

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

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

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

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

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

Future Roles of Technology in Coaching

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

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

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

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

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

Balancing Technology with Traditional Coaching Practices

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

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

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

The Role of Relationships in Coaching

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

Integrating Human-Centered and Data-Driven Approaches

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

Challenges in Balancing Innovation with Tradition and the Road Ahead

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

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

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

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

Applications in Sport

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

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

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2025-06-09T14:04:33-05:00November 14th, 2025|Commentary, Research, Sport Education, Sport Training, Sports Coaching, Sports Studies|Comments Off on The Evolving Role of Technology and Analytics in Coaching: Transforming Practices and Enhancing the Impact on the Profession
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Extending the curve: A Closer Look at High-Velocity Measures in the Power Clean.

Wei Qian Lim1, MS, David Smith2, Eric D. Magrum2, PhD,
The George Washington University1, Washington, DC
James Madison University2, Harrisonburg, Virginia

Editor’s Note: Table 1 was incorrectly published. This has been corrected. Tables 2 and 3 were reformatted during the revision process.

Corresponding Author:

Eric D. Magrum

261 Bluestone Dr.

Harrisonburg, VA

22807

540-568-6957

[email protected] :

Abstract
Purpose: This study examines the validity and reliability of a commercially available velocity-based training device, GymAware, when measuring barbell velocity during submaximal power cleans. While GymAware has been validated for slower movements, limited research has assessed its accuracy at higher velocities, particularly in Olympic weightlifting derivatives.
Methods: Ten resistance-trained participants completed two sets of five repetitions at 40%, 50%, and 60% of their perceived one-repetition maximum in the power clean. Mean and peak barbell velocity were recorded using GymAware and compared to a motion capture system as the criterion measure. Data were analyzed for reliability using intraclass correlation coefficients and validity through correlation and regression analysis.


Results: Mean velocity measurements from GymAware demonstrated strong agreement with motion capture across all loads, with correlations exceeding 0.85 and an intraclass correlation coefficient of 0.85, indicating good reliability. However, peak velocity measurements exhibited greater variability, with a systematic overestimation of 0.37 m/s and a lower reliability coefficient (0.31). Linear regression models confirmed that GymAware accounted for 88% of the variance in mean velocity but only 44% in peak velocity, suggesting less precision in high-velocity movements.


Conclusion: GymAware provides reliable and valid measurements of mean barbell velocity but has limitations in accurately assessing peak velocity during rapid weightlifting movements. Coaches and practitioners should prioritize mean velocity when utilizing velocity-based training for performance monitoring.

Application in Sports: Velocity-based training offers an efficient method for tracking performance and adjusting training loads. GymAware’s ability to measure mean velocity reliably makes it a useful tool for monitoring training adaptations and providing immediate feedback to athletes. However, practitioners should be cautious when interpreting peak velocity data, particularly in high-velocity Olympic weightlifting derivatives, and consider alternative methods for precise assessment.

Introduction
Resistance training is a well-documented modality for improving force production, power, lean body mass, and overall athletic performance (10-11,13,20,27). For these reasons resistance training has become synonymous with athlete preparation. Before the technological renaissance, tracking athletes’ progress and assessing program effectiveness was almost entirely comprised of assessing progressive overload via number of repetitions completed or through the manipulation of external load lifted (15,19,22). However, these more traditional methods come with several challenges, making it difficult to assess program effectiveness. Specifically, athlete’s perceived exertion, range of motion, and different pacing strategies can confound practitioners’ ability to assess meaningful changes as it relates to physiological adaptations resultant resulting from training (12,18,19,22). Because of this, numerous efforts have been made to leverage technological tools to enhance the assessment of training efficacy.


Recent technological advancements have popularized the tracking of barbell velocity, termed velocity-based training (VBT), and highlighted its usefulness in gauging training efficacy. VBT is utilized for a multitude of reasons, including but not limited to predicting 1 repetition maximum (1RM) without the accumulation of excessive fatigue and increased risk of injury, monitoring training performance and neuromuscular fatigue, and providing immediate kinematic feedback potentially leading to enhanced training outcomes (1,3,-4,7,-8,10-11,18,23,24,26,28). As with any technological tool, measures of validity and reliability are paramount to assess the meaningfulness of the data provided. Providing reliable data is important for coaches and athletes alike, to accurately assess the physiological changes associated with training programs, as well as make appropriate alterations when needed.


For over 20 years, GymAware (GYM) has been considered the gold standard of linear positional transducers (LPT). LPT’s function by measuring displacement of a barbell as well as the time taken to complete said displacement. By using this data, the LPT computes several variations of barbell velocity and power (average, peak, etc.) (17). Previous research suggests that the GYM is both highly valid and reliable at slow velocities (0.3-0.7 m/s) . (3-5,7-9,14,15,21). However, few studies have examined the reliability and validity of the GYM during low load, high velocity weightlifting or plyometric movements (0.7+ m/s). Studies that have investigated GYM at these velocities report that the GYM system typically underreports peak velocity and power outputs at lower loads and higher velocity (2,6,14).


Askow et al. (2) examined the reliability and validity of GYM software at both 60 and 80% of 1RM back squats. They found that GYM tends to underestimate peak velocity by 11.6% and software is not the most accurate measure of barbell velocity during high velocity movements. Despite this, Askow and his team of researchers still reported high levels of reliability at high velocities (2). Orange et al. (17) reported excellent reliability for both peak and mean velocity measurements at a range of different percentages of 1RM in the back squat and bench press with interclass correlations (ICCs) ranging from 0.96 to 0.99. Lorenzetti et al. (14) found that GYM was both reliable and valid at tracking bar velocity at 70% of 1RM and during a ballistic jump squats; however, they found much higher reliability and validity at lower velocities when compared to the high velocity jump squat plyometrics. A systematic review of LPTs and linear velocity transducers (LVT) corroborated these findings and reported that LPTs, including the GYM, were valid and reliable in measuring velocity during powerlifting and weightlifting movements . (25).
Another review on the subject highlights the need for independent investigations of velocity-based sensors to examine higher velocity lifts such as Olympic weightlifting derivatives (1.2-1.6 m/s) (16). Due to their unique utility and force-velocity characteristics, weightlifting movements , such as the snatch, clean and jerk, are routinely utilized in sport performance settings around the globe. An essential element of these lifts is how fast the weight moves. Few studies have compared such devices to a criterion measure, namely motion capture (25). However, existing research on devices like the GYM Power Tool suggests high validity and reliability when measuring velocity during high-velocity barbell movements. Orange et al. (17) reported excellent reliability of GYM for back squats and bench presses, with ICCs ranging from 0.96 to 0.99 for velocity, suggesting that it could similarly perform well in more dynamic lifts. There is limited research on the reliability and validity of LPDT when measuring velocity during Olympic lift derivatives. Thus, the current study will address the gap in the literature and extend our understanding of the validity and reliability of VBT devices at higher velocities. Specifically, the purpose of this study is to examine the reliability and validity of GYM compared to Qualisys Motion Capture during the power clean.

Methods
The study was carried out with 10 participants (Table 1). Participants had at least one year of prior experience strength training, defined as an average of two training sessions per week. Subjects were between the ages of 18-40, technically proficient in the clean, not pregnant, free of known cardiovascular, metabolic, or renal disease, and free of injuries. After giving written consent, technical proficiency in the clean was determined during a familiarization session prior to data collection.


Table 1. Participant Characteristics 

SexAge (years) (mean ± SD)Height (m) (mean ± SD)Weight (kg) (mean ± SD)Predicted 1RM (kg) (mean ± SD)
Male (n=5)23.4 ± 4.41.74 ± 0.0683.3 ± 9.8106.6 ± 24.5
Female (n=5)22.0 ± 0.71.62 ± 0.0672.6 ± 22.661.2 ± 17.0
Total (n=10)22.7 ± 3.11.68 ± 0.0978.0 ± 17.483.9 ± 31.1


For a clean, participants had to lift the barbell in one smooth move from the floor, catching the barbell in a front rack position. Feet were to be shoulder width apart or just outside shoulder width at the catch. The participants were cued to move the weight as quickly as possible while staying under control. Participants with working weights lighter than what could be provided with bumper plates, the lift began from a hang at mid-shin height.


During the familiarization session participants were asked to complete a health history questionnaire before height and weight were taken. After a general warm up that consisted of 50 jumping jacks, 10 bodyweight squats, 5 jump squats and 5 cleans with the empty barbell, the participants provided a perceived 1RM (ex. 200 lbs.). 50% of the participants’ perceived 1RM was loaded onto the barbell (ex. 50% of 200 lbs. = 100 lbs.). The participant was then asked to perform 1 set of 5 repetitions, at which point the research team determined if technical proficiency was sufficient (binary yes or no).


Participants who met the inclusion criteria and demonstrated proficiency in the clean were invited back for a lifting session. The session began with the same general warm-up detailed above. Participants whose schedules permitted both sessions to be completed consecutively (familiarization + lifting) were not asked to perform the warmup prior to the lifting session. In total, participants completed six sets: two sets of five repetitions at 40%, 50%, and 60% of perceived 1RM (ex. 200lbs 1RM: 40% = 80lbs, 50% = 100 lbs., and 60% = 120lbs). Each set began with the signal “You may begin your lift.” Participants were instructed to fully stop and/or set down the bar at the end of each repetition for at least a one count to prevent the use of momentum and allow for a distinct ending to each repetition. This was reinforced with a count of “one” between each repetition. Participants were given three minutes to rest between each set.
Qualisys motion capture system was used as a gold standard/criterion reference. The motion capture set-up consisted of six cameras: three from the Miqus M3 series and three from the Oqus series. Six reflective markers were attached to the barbell. Two markers were attached to either end of the bar, while four markers were attached in square configuration on the collar of the barbell (Figures 1 and 2). The data were recorded with the software QTM 2020-2 Build 5710, with a frequency of 100 Hz. The limits for standard deviation for wand length calibration were 0.3 and 0.5 mm.



The GYM RS, placed on the ground between the pad and platform, was tethered to the shaft of the barbell close to the four reflective markers (see Figures 1 and 3). The GYM RS device was connected via Bluetooth to the free version of the GYM iOS application (Version 4.0.1). GYM RS records at 50 Hz. Peak and mean velocity (m/s) for each repetition were hand recorded from the application into a Microsoft Excel spreadsheet.


Velocity data were exported from the Qualisys Track Manager (QTM) software to Microsoft Excel. The beginning of the lift was determined by the inflection of barbell velocity denoted by an increase of 0.01 m/s for three consecutive frames. The end of the concentric portion of the lift was determined by the first maximum velocity value or crest of velocity curve. Corresponding with GYM, mean concentric velocity (m/s) was determined by averaging marker velocities over the entire concentric portion of the lift. Peak concentric velocity (m/s) was calculated by averaging the individual velocities of each marker over a sample period of 20 milliseconds immediately preceding peak velocity.


Participants stood on a wooden platform with the barbell resting on black foam pads on either side of the platform. Unless the participant’s working weight utilized change plates or the empty bar, the clean started from the black foam pads. If not, the clean started from a hang at mid-shin height. The materials were a 20 kg bar, Rouge change plates between 0.5 and 5 kg, 2.5 and 5 lb. plates, as well as 25 and 45 lb. bumper plates. Working weights for each participant were calculated to get as close to 40%, 50%, and 60% of perceived 1RM.

Results
Data was collected for 10 participants during a single data collection session. Subjects completed six sets: two sets of five repetitions at 40%, 50%, and 60% of perceived 1RM. Mean and peak velocity was recorded using GYM and Qualisys motion capture software for each repetition. There was a total of 60 data points per participant, resulting in 600 total data points.
3.1 Validity

Figure 4. Scatter plots expressing the peak and mean bar velocities at 40, 50, and 60% of one repetition maximum as measured by GYM and Qualisys motion capture systems. Error is defined as the difference between the GYM measurements and Qualisys measurements, with cooler colors representing less error and hotter colors representing more error. Dashed line represents a perfect linear fit that assumes no variance between the two devices. All correlations were statistically significant with a p<0.05

Scatter plots for peak velocity at each percentage of 1RM showed varied levels of correlation between GYM and Qualisys. At 40% of 1RM r=0.706, at 50% r=0.512, and at 60% r=0.703. Each of the aforementioned correlations reached statistical significance at the 0.05 level and indicate a moderate correlation between the GYM and Qualisys measurements of bar velocity. 50% of 1RM demonstrated the highest variability (Figure 4).


The mean velocity measurements between the two systems demonstrated stronger correlations across all load percentages. At 40% r=0.958, at 50% r=0.938, and at 60% r=0.871. All correlations were statistically significant (p<0.05) and indicate a consistent, strong relationship between GYM and Qualisys when assessing mean bar velocity (Figure 4).
GYM software tended to overpredict peak barbell velocities at all intensities by 0.37 m/s on average, while only over predicting mean barbell velocity by 0.09 m/s (Figure 5).

Table 2. Comparison of Linear Regression Model Results for GYM and Qualisys Motion Capture System at Different Percentages of Perceived One Repetition Max

Load (%1RM)R2F-statistic
Mean Velocity (MV)Peak Velocity (PV)Mean Velocity (MV)Peak Velocity (PV)
40%0.920.501073.6697.15
50%0.880.26723.4934.83
60%0.760.51301.48101.32
All data0.880.442086.1234.72

*All data was significant with a p-value<0.001.

A linear regression model indicated a significant relationship between mean and peak bar velocity as reported by the GYM when compared to Qualisys tracking software. Mean velocity linear regression: F (1,293) = 2086.61, p<0.001, R2 = 0.88. Peak velocity linear regression: F (1,293) = 97.15, p < 0.001, R2 = 0.44. This model indicates that across all percentages of 1RM tested, GYM software was able to account for 88% of the variance in mean bar velocity and only 44% of peak bar velocity.
When parsed out and compared by loads, the data highlights a closer relationship between mean velocity measures as compared to peak velocity measures (Table 2.) At 40% 1RM: Mean velocity: F (1,293) = 1073.66, p < 0.001, R² = 0.92; Peak velocity: F (1,293) = 97.15, p < 0.001, R² = 0.50. At 50% 1RM: Mean velocity: F (1,293) = 723.49, p < 0.001, R² = 0.88; Peak velocity: F (1,293) = 34.83, p < 0.001, R² = 0.26. At 60% 1RM: Mean velocity: F (1,293) = 301.48, p < 0.001, R² = 0.76; Peak velocity: F (1,293) = 101.32, p < 0.001, R² = 0.51.
3.2 Reliability

Table 3. Intraclass Correlation Coefficients for mean and peak barbell velocity measurements.

 Mean Barbell VelocityPeak Barbell Velocity
ICC (95% CI)0.848 (0.341-0.941)0.306 (-0.092-0.632)
F-statistic23.64.8
p-value0.002610.128

The ICCs were calculated to assess the reliability of mean and peak barbell velocity measurements. A two-way random-effects model with absolute agreement (ICC (A,1)) was used for both metrics. Mean barbell velocity had an ICC of 0.848 (0.341–0.941), with an associated F-test indicating statistical significance (F (296, 4.22) = 23.6, p = 0.00261). These calculations indicate good reliability. Peak barbell velocity had an ICC of 0.306 (-0.092–0.632), with a non-significant F-test (F (299, 2.69) = 4.8, p = 0.128). This ICC value indicates poor reliability.
The coefficients of variation (CV) were calculated to assess the relative variability in mean and peak values for both GYM and Qualisys datasets. For the mean values, the CV was 17.06% for GYM and 20.46% for Qualisys. For the peak values, the CV was 10.75% for GYM and 15.37% for Qualisys, with GYM showing the lowest relative variability among all measures.

Discussion
The findings of this study offer valuable insight into the reliability and validity of GYM as a VBT tool. While GYM demonstrated strong validity in tracking mean barbell velocity across all intensities, it was substantially less accurate when assessing peak barbell velocity. These results highlight important considerations for practitioners when using GYM as a training tool.
There was a strong correlation observed between GYM and Qualisys for mean velocity measurements, highlighting the reliability of GYM. The ICC for mean velocity (0.848) reflects good reliability, supporting its use by coaches and athletes where consistent data is essential for assessing training adaptations and adjusting programs accordingly. This finding demonstrates that GYM’s mean velocity measure is capable of providing practitioners with insightful data that can reliably indicate changes in athletes’ performance capabilities. For example, this means that a positive change of 0.15 m/s in an athletes mean clean velocity at a given load is likely due to changes in the athletes’ performance capabilities, as opposed to the measurement error associated with the VBT tool. This is rather important when competitive success has such slim margins and even more important when resistance training programs are dictated by real time data collected by VBT tools. These findings are consistent with prior research that has identified GYM as a reliable tool for monitoring barbell velocity during traditional resistance training exercises (17). Importantly, this examination focused on high velocity movements, hence the loads of 40-60%, and extended the range of velocities studied within the literature.
Despite this, GYM had a moderate correlation and systematically overestimated barbell velocity limiting its application. GYM had a mean bias of +0.37 m/s when assessing peak velocity suggesting that GYM may not offer the precision required for accurately evaluating peak velocity during rapid, explosive movements. What is perhaps more concerning is the poor ICC for peak velocity (0.306), indicating low reliability for this metric.. For example, if an athlete were to improve peak barbell velocity by 0.15 m/s, the same amount as with their mean velocity, we wouldn’t be able to confidently attribute this change to a performance improvement due to the low reliability.


These findings agree with previous research that has identified similar discrepancies in GYM’s accuracy. In Lorenzetti et al. (14), the GYM device showed a higher root mean square error (RMSE) of 0.06 m/s when assessing peak barbell velocity during ballistic jump squats compared to slower squat movements. This higher RMSE suggests that the device was less accurate in measuring peak velocity during higher velocity, explosive jumps. The study found the mean difference between GYM and the reference method (motion capture) to be -0.05 m/s, further indicating potential measurement errors in high-velocity movements. These results highlight that peak velocity measurements may be prone to greater variability in ballistic exercises. Additionally in Askow et al. (2), the GYM device consistently underestimated peak barbell velocities by 11.6% (or -0.13 m/s) when compared to a more accurate criterion measure. This bias was particularly evident during high-velocity movements, indicating that the device may not be as precise for measuring peak velocity in such contexts. The underestimation suggests a systematic error that could limit the utility of GYM for tracking performance improvements in peak velocity during explosive lifts. These values along with our data showcase that GYM may not be an effective tool at assessing peak barbell velocity at lower loads/higher barbell velocities.


This study also reinforces the importance of context when interpreting data from VBT devices. Contrary to our ICC data, the coefficients of variation (CV) highlight the consistency of GYM for both mean velocity (17.06%) and peak velocity (10.75%). Interestingly, this statistic suggests that peak velocity is more reliable when compared to mean velocity; however, this is likely due to the systematic overestimation of both peak and mean barbell velocity by GYM. Utilizing both ICC and CV’s the data supports the notion that GYM has strong reliability for mean velocity, however peak velocity measures capture by GYM leave something to be desired. These data suggest that practitioners should use mean barbell velocity measurements to achieve the best results, especially when utilizing VBT to monitor fatigue, track progress, and adjust training intensity in real time. Should practitioners have a penchant for peak velocity measures, the authors strongly encourage practitioners to run in-house statistics to understand what constitutes a meaningful change as compared to a change within the VBT’s measurement error.
Findings align with the broader literature discussing VBT devices and explore a gap in the literature by examining high-velocity movements while highlighting aspects that have practical significance. Future investigations should explore GYM’s performance with other high velocity movements such as the snatch or jerk, to better understand its broader applications. Importantly, while these results contribute to the growing body of evidence, it is important to situate the use of VBT within the broader training context and provide guidance to practitioners.

Application in Sport
The authors contend that reliable VBT tools can be leveraged by practitioners. First, VBT tools provide a cost-effective and time efficient avenue to collect data and highlight changes as a result of the training prescription. VBT data may be leveraged as biofeedback and a load modulation technique but only in synchrony with more traditional loading prescription (% of 1RM/% of set/rep best). Important to note, these strategies utilize VBT tools as a secondary data stream to inform when load changes may be needed and not as a primary load prescriber. Coaches must retain load prescription responsibilities, while utilizing their eyes and ears (in addition to VBT tools) to skillfully make load adjustments when needed. Practitioners must also bear in mind that VBT tools are inaccurate when estimating 1RM, therefore other methods for estimating are necessary. Perhaps the most compelling reason for utilizing VBT tools resides in their ability to potentiate participant performance. The presence of VBT devices may improve athlete motivation and training intent, which is paramount for optimal training. While VBT tools generally provide a positive return on investment, the practitioners’ eyes and ears should remain the primary data source which guide training decisions while VBT tools serve a supportive role. Based on available data, it would be shortsighted to rely solely on VBT tools to make real-time training decisions.


In conclusion, this study demonstrates that GYM provides reliable and valid measurements for mean barbell velocity during submaximal power cleans. As a result, practitioners may leverage GYM’s strengths, particularly its ability to provide immediate feedback and monitor mean velocity, while remaining cognizant of its limitations for high-velocity movements. This approach may allow for the effective integration of VBT tools to enhance training decisions, outcomes and athletic performance.

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2025-10-28T09:21:32-05:00October 17th, 2025|Research, Sport Training, Sports Exercise Science, Sports Studies|Comments Off on Extending the curve: A Closer Look at High-Velocity Measures in the Power Clean.
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