ISSN: 1543-9518

Match Demands and Positional Differences of NCAA Division II Women’s Soccer Players Over a Competitive Season

Authors: Joanne Spalding1, Jacob L. Grazer2

1Department of Health & Human Performance, Georgia College & State University, Milledgeville, GA, USA

2Department of Exercise Science & Sports Management, Kennesaw State University, Kennesaw, GA, USA

 

Corresponding Author:

Joanne Spalding, PhD, CSCS, CPSS, ACSM-EP

231 West Hancock Street

Milledgeville, GA 31061

[email protected]

478-445-2135

Joanne Spalding, PhD is Assistant Professor in Exercise Science at Georgia College and State University. Her research interests include long term athletic development and monitoring at the club, high school, and college level with an emphasis on neuromuscular fatigue.

Jacob L. Grazer, PhD, CSCS, USAW-1, ACSM-EP is currently a faculty member in the Department of Exercise Science and Sport Management at Kennesaw State University. Jacob’s research interests include: accentuated eccentric loading, countermovement jump analysis, and sport technology.

ABSTRACT 

Purpose:  There has been a growing interest in American women’s college soccer with the majority of research focused on the NCAA Division I level.  With 250+ Division II programs competing in 2024, there is an underrepresentation of this group in the literature. The purpose of this study is to examine the physical demands in Division II women’s soccer and analyze whether these physical demands vary by position.

Methods:  Twenty-two Division II female soccer field players from one DII college team were monitored over an 18-match season wearing GPS devices, a technology that is more commonly used at prevalent at Division I universities. Field players were divided into one of four positions: (1) Center Back Defenders; (2) Outside Back Defenders; (3) Midfielders; and (4) Forwards. The GPS devices provided data on: (a) total distance traveled; (b) total distance traveled per minute; (c) high-speed running distance traveled per minute; and (d) sprint distance traveled per minute. Due to NCAA Division II substitution rules, the focus in this study is the relative variables calculated as distance traveled per minute.

Results: Descriptive statistics for each GPS measurement are calculated based on the position played.  Then, Analysis of Variance (ANOVA) is used to identify any differences in player workload physical demands based on the position played. For distance traveled during a match, Center Back Defenders traveled the least distance compared to other positions. Similarly, for high-speed running distance, Center Back Defenders traveled the least distance compared to other positions. Finally, for Sprint Distance per minute, both Forwards and Outside Back Defenders traveled more distance compared to Center Back Defenders and Midfielders.

Conclusions: Findings suggest that the physical demands of Division II women’s soccer differ by position. Center Back Defenders tend to ‘stay at home’ and defend the goal while traveling less distance and sprinting less often. Forwards and Outside Back Defenders tend to spend more time sprinting compared to other players.

Applications in Sport: Coaches and practitioners can use this data when designing training programs to ensure their athletes are well-prepared for the differing physical demands of their sport based on the position played.

KEYWORDS: college soccer, GPS, match demands, women’s soccer, physical match performance

INTRODUCTION 

Over the past several years, there has been an increase in the number of peer-reviewed research articles focusing on the physical demands of women’s soccer. The majority of the research that has been conducted has focused on athletes who compete at the professional and international levels (E. Choice et al., 2022). Recently, there has been a growing interest in American college women’s soccer, with the majority of this research focused on National Collegiate Athletic Association (NCAA) Division I level. This can likely be attributed to the fact that wearable technology has become more prevalent at the collegiate Division I level making it easier to collect information regarding the physical demands experienced by female collegiate athletes. However, with 250+ Division II programs competing in the 2024 season, this significant population has been under-represented in research efforts to examine the physical demands on the athlete when competing.  As such, further analysis is needed to better understand the physical demands on athletes participating in the various levels of collegiate women’s soccer. 

Soccer is a high intensity, intermittent sport that requires running, jogging, walking, repeated sprints, jumping and change of direction (Al-Hazzaa et al., 2001; Bloomfield et al., n.d.; Wisløff et al., 2004). Matches consist of two 45-minute halves with a 15-minute halftime period. For international competition, the Fédération Internationale de Football Association (better known as FIFA) sanctions allow for five substitutions per game with a typical squad size of 26 players. However, NCAA soccer permits an unlimited number of players to be on the official roster during the regular season. Substitutions rules for the NCAA also differ from FIFA in that the number of substitutions is unlimited per game, and players are allowed to re-enter the match once in the second half. This difference is notable because it may influence the physical demands placed on players during match play.

Current literature has explored match play data at various playing levels and reports that there are differences in match-play demands at different levels in women’s soccer (E. Choice et al., 2022). A perspective review by Vescovi et al (2021) reported that there is a linear increase in total distance covered across playing levels ranging from youth (~7,500m), collegiate (~9,500m), to professional levels (~10,200m). A systematic review of women’s soccer also identified that playing demands increase between playing levels, and that there are positional differences in terms of total distance covered, high-speed running distance, and sprint efforts where midfielders covered greater total distances and sprint efforts compared to central defenders (Alexander, 2014; E. E. Choice et al., 2023).

Challenges arise when comparing research due to variations of inclusion criteria and determination of velocity thresholds. In addition to inclusion criteria varying, values are typically reported in total distances rather than relative to time spent playing on the field. As highlighted previously, the unique NCAA substitution rules make it challenging to fully grasp the physical demands that are placed on the athletes since it is uncommon for someone to play a full 90-minute match in women’s collegiate soccer. Due to the potential influence of substitution rules on physical demand, more research is needed to gain a greater understanding of the relative demands with respect to playing time. 

Very few studies have investigated the match demands of NCAA DII women’s soccer (E. E. Choice et al., 2023; Gentles et al., 2018). Gentles et al. (2018) assessed and compared accelerometry data to data collected using GPS signal. Although total distance and distance covered in specific velocity zones were reported, statistical analyses were not made comparing playing positions. Whereas Choice et al., (2023) investigated the external and internal load of players, including positional and time-specific differences.  

Due to the lack of research in women’s soccer in general, and specifically at the lower playing levels, there is a need for more research. The purpose of this study is to examine the physical demands in Division II women’s soccer and analyze whether these physical demands vary by position.

SOCCER TEAM ROLES AND ROLE EXPECTATIONS

Given this study examines the possible differences in physical demands on soccer athletes based on the position played, it is advantageous to clearly outline each position and the expectation of athletes who play that position. Each position carries distinct responsibilities within a team’s formation and can change based on the formation. This study focused on a team that utilized the 4-3-3 formation where positions are separated into center backs, outside backs (right and left), midfielders (center and attacking), and Forwards (central striker and right/left wing) (see Figure 1).

Figure 1 – Visual Presentation of 4-3-3 Soccer Formation

Center Back Defenders

Center back defenders are primarily responsible for defensive organization and central coverage. When in a defensive scenario, center backs are responsible for defending the opposing forwards, intercepting through balls, contesting aerial duels and tackling to gain possession. In possession, center backs initiate playing out from the back, with passes to midfielders or out to forwards, and or switching play across the back line.

Outside Back Defenders

Outside backs (right and left) provide width for the back line and help with transitional play from the defensive half to the attacking half. Out of possession, outside backs typically deal with the opposing forward or winger depending on the opposition’s formation. Similar to center backs, outside backs with attempt to win aerial duels and tackles. In possession, outside backs will look to support the team by overlapping the forward, dribbling the ball up the field, delivering crosses and well as helping maintain possession in the attacking half. 

Midfielders

The center midfielders are composed of two defensive midfielders and one attacking midfielder. The defensive midfielders attempt to shield the back line, break up opposition attacks, and in possession help to maintain and build possession from the defensive to attacking half. The attacking midfielder helps the forward and midfielders with defensive duties and in possession looks to support the forwards, maintain possession as well provide goals and assists. 

Forwards

Forwards help to provide width and are important for stretching the oppositions defense. They will primarily defend the oppositions outside back and recover into midfield positions when out of possession. In possession they will aim to beat defenders one-on-one, deliver crosses and create shooting opportunities. When a player is in the central striker position, they are the focal point of the attacking line and are primarily there to convert scoring opportunities by finishing inside and around the 18-yard box. In possession, the central striker will look to hold the ball up allowing midfielders and other forwards to join the play. They will attempt to make runs to disrupt the defensive lines and apply pressure to the opposition center backs. 

METHODS 

Participants  

A total of 22 NCAA Division II female soccer players participated in this study. Participants were athletes from a team already using a GPS athlete monitoring system. Players were assigned 1 of 4 positions by the team’s coaching staff (Sporis et al., 2009).

  1. Center Backs (CB) (n = 4)
  2. Outside Backs (OB) (n = 5)
  3. Midfielders (MID) (n=6)
  4. Forwards (FWD) (n= 7). 

Procedures  

A total of 18 regular season matches were included in this analysis from a single competitive season. Global Positional System (GPS) devices (TITAN Sports, Titan2, Houston, TX, USA) sampling at 10 Hz were used to track player movement during competition.  Data included 234 observations (n= 79 FWD, n= 70 MID, n= 50 OB, n= 35 CB). The team used for this study played 1-4-3-3 formation (please note 1 = goalkeeper) during all matches included in the analyses. All matches were official NCAA matches with two 45-minute halves and a 15-minute halftime period. All players were informed of the risks and benefits of this study and voluntarily signed an informed consent. This study was approved by the Institutional Review Board.  

Variables for Analysis

Data was collected on the following variables to better understand the physical demands on each player based on position played (see Andersson, 2010).

  • Total Distance – The total distance a player covers during a game. This value is needed to determine the distance covered related to minutes played.
  • Relative Total Distance (TDREL) – The distance covered per unit of time, expressed as meters per minute (m/min).
  • Relative High-Speed Running Distance (HSRREL) – Distance covered per minute while running at high speeds, in this case, over 15 kilometers per hour (km/h), or 9.3 miles per hour.
  • Relative Sprint Distance (SDREL) – Distance covered per minute while sprinting, usually above a higher threshold. In this case, over 18 kilometers per hour (km/h) or 11.2 miles per hour.

Using the Global Navigation Satellite System to Measure Match Load  

GPS units were worn in a harness that secured the device between the scapulae (i.e., shoulder blades). The units were turned on 10 minutes before being placed in the harness to ensure sufficient GPS signal was attained per manufacturer’s instructions. Due to the substitution rules in NCAA college soccer, calculating the variables per minutes played was deemed the optimal solution for comparing workload across playing position.

RESULTS 

Three separate one-way ANOVA analyses with Bonferroni corrections were conducted to determine if there were differences between positions for Total Distance (TDREL), High Speed Running Distance (HSRREL) and Sprint Distance (SDREL). Statistical analyses were performed in SPSS (Version 27.0.1). Alpha level was set at p <0.05. Eta-squared effect sizes were calculated to determine magnitude of differences for each variable. For pairwise comparisons, Cohen’s d effect sizes were calculated to determine magnitude of difference between playing positions (Hopkins, 2002).  Descriptive statistics for match physical performance for this specific team are summarized in Table 1.

Table 1 – Match Performance Data

Variables Midfielders (n=70) Forwards (n=79) Outside Back Defenders (n=50) Center Back Defenders (n=35) Total (n=234) 
Minutes Played per Match 51.6 ± 13.6 52.8 ± 15.1 52.1 ±13.9 68.0 ±19.9 57.1 ± 15.6 
Total Distance (m) 5,643.9 ± 1,435.0 5,507.9 ± 1,214.1 5,506.3 ±1,373.8 6365.1 ± 1884.9  Greatest total distance covered due to greatest minutes played5,855.8 ±1,476.8 
Relative Total Distance (m/min) The distance covered per unit of time, usually expressed as meters per minute (m/min). 110.4 ± 14.2 Significantly greater than Center Back Defender106.8 ± 14.4   Significantly greater than Center Back Defender106.8 ±12.6  Significantly greater than Center Back Defender93.8 ± 11.6 105.9 ± 14.5 
Relative High Speed Running Distance (m/min)  Distance covered per minute while running at high speeds, in this case, over 15 kilometers per hour (km/h), or 9.3 miles per hour.  10.06 ± 3.45  Significantly greater than Center Back Defender12.96 ±4.01 Significantly greater than Center Back Defender 13.18 ± 4.21 Significantly greater than Center Back Defender7.53 ± 2.90   11.33 ± 4.26 
Relative Sprint Distance (m/min)  Distance covered per minute while sprinting, usually above a higher threshold. In this case, over 18 kilometers per hour (km/h) or 11.2 miles per hour3.45 ± 1.98  6.06 ± 2.64 Significantly greater than Center Back Defender  Significantly different than Midfielders5.53 ± 1.85 Significantly greater than Center Back Defender  Significantly different than Midfielders3.05 ± 1.92  4.72 ± 2.53 

 Relative Total Distance (TDREL)

A one-way ANOVA revealed a significant difference in positions for TDREL, F(3, 230) = 11.9, p = <0.001. An effect size (η2=0.135) indicated a medium effect. Post Hoc analysis indicated that there were mean differences between FWD and CB (p = <0.001), MID and CB (p = <0.001), and OB and CB (p = <0.001).  

Relative High-Speed Running Distance (HSRREL)

There were statistically significant difference for HSRREL, F(3, 230) = 23.73 p < 0.001, with a large effect (η2=0.23). Post Hoc analysis showed mean differences between FWD and MID (p = <0.001), FWD and CB (p = <0.001), MID and OB (p = <0.001), MID and CB (p = <0.008) and OB and CB (p = <0.001) as seen in Figure 2. 

Relative Sprint Distance (SDREL)

Finally, there were statistically significant differences for positions in SDREL, F=(3,230), 25.547, p <0.001 with a large effect η2=0.25 Post Hoc analysis revealed mean differences for FWD and MID (p = <0.001), FWD and CB (p = <0.001), MID and OB (p = <0.001), OB and CB (p = <0.001). 

DISCUSSION 

The purpose of this study is to determine the physical demands for NCAA Division II Women’s Soccer and to assess positional differences and physical demands relative to minutes played.  The study’s results include the average demands during match play of each field position as previously shown in Table 1. The main findings of this study indicate that center backs accumulate the greatest total distance, however, this is mainly attributable to extended playing time as they frequently remained on the field 15-17 minutes longer than other positions. When normalized for minutes outside backs, midfielders and forwards covered greater total distance and high-speed running distance than center backs. In addition, outside backs and forwards demonstrated greater sprint distance per minute compared to both center backs and midfielders. 

The average total distance covered for players included in analyses was 5,855 ± 1,476 meters, which differs from prior findings by Choice et al. (2023) who saw starters cover 9,463 ± 2,591 meters. It should be noted that the analysis by Choice et al. (2023) only included players who participated in over 50% of match play. Findings from this study are similar to results reported by Gentles et al. (2018) who reported findings of 5,480 ± 2,350 meters and average minutes played of 45.32 ± 26.01 which are similar to minutes played in this study.  The maximum distance covered by an individual in this study was 9,810 meters, which is much lower than distance covered by the sample in Gentles (13,850 meters) and Choice (12,054 meters), but similar to distances reported in Division I athletes, 9,786 meters (Sausaman, 2019). Both maximum and average distance covered varied across levels may indicate that more research is needed across differing playing levels. Also, from a practical point of view, this should help sport practitioners implement appropriate programs that develop players for both maximum and mean distances, regardless of minutes played.  

To our knowledge, this is the first study to report TDREL (105 ± 14.5 m/min), HSRREL (11.3 ± 4.2 m/min) and SDREL (4.72 ± 2.53) in NCAA Division II athletes. Gentles (2018) reported total distance accumulated at velocity zones 15.00 – 19.99 kph and 20.00-24.99 kph but not relative distances. Due to the substitution rules in NCAA College soccer, there is a high variance in match-to-match playing variables. Other studies have only included players who have played over 50% of the match or the entire match (E. E. Choice et al., 2023), whereas other research only included participants who completed the match in its entirety (Alexander, 2014; Sausaman et al., 2019).  If training programs are only based on average totals, then the whole picture may not be clear when designing training programs for those not participating in 90 minutes. Therefore, reporting data per minute may be more beneficial as reserves need to be trained throughout the season to compensate for minutes not played.  

There was a difference in positional demands for HSRREL, with FWDs covering higher distances than MIDs and CBs. Whereas OBs covered more HSRREL than CBs and MIDs, and MIDs only covered more distance than CBs. Although there was no statistical difference between OBs and FWDs, OBs covered slightly more distance per minute. Although the lack of statistical difference between FWDs and OBs may be due to formation and playing style. In the 4-3-3 OB and FWDs may have similar positional demands as OBs are often part of the attack by providing width higher up the field. Formation on the field was not controlled for in this study so more research is warranted to understand positional differences across a variety of formation styles. 

Results also showed that FWDs covered more SDREL than MIDs and CBs, and OBs covered more than CBs. Although variables are different within this study, results show similar trend within the literature, that forwards cover more HSR distance and sprint distance than midfielders (Sausaman et al., 2019; J. Vescovi, 2013). It should also be noted that CB and OB positions are typically grouped together as a singular position group (commonly reported as defender) (Stolen et al., 2005; J. D. Vescovi, 2012).

This study provides further evidence, particularly in women’s soccer literature, that there is a need to differentiate between central and outside defenders when evaluating physical demands during competition due to the different physical demands during competition (Alexander, 2014; Harkness-Armstrong et al., 2022). It is also important to note that in NCAA Division I studies, players covered more high-speed running and sprint distance on average compared to NCAA division II athletes. This indicates that the Division I game may be played at a faster pace, especially in those critical moments of the game (defensive recovery run, sprinting to goal etc.) As this study reports HSR and sprint distance per minute, it prevents the authors from making additional conclusions or comparisons.  

There are limitations to the study that may have impacted the results.  The data for this study was collected for one team playing one season of competition.  The level of soccer players at all NCAA level can vary greatly across teams and conferences.  Therefore, further research is needed to confirm the current findings. Differences in match demands as well as positional demands may be a result of opposition level, formations, match status, and match location.  As such, additional research is needed to assess these factors to determine their possible influence on the physical demands of Division II women’s soccer.  

CONCLUSION 

This study examined the physical demands of women’s soccer at the NCAA Division II level and analyzed differences among position groups. Unlike previous research, this study assessed Relative total distance (TDREL), relative high-speed running distance (HSRREL), and relative sprint distance (SDREL), providing average values for all players and specific positions to describe match demands. The findings highlight differences in physical demands based on position played. The findings from this study emphasize the importance of evaluating workload relative to minutes played and recognizing the unique demands of each position at the NCAA Division II level. Such insights provide a framework for tailoring training, conditioning and substitution strategies that reflect position-specific requirements rather than relying on team-wide averages.  Future research should continue to examine how positional workloads vary across divisions and competitive levels, as well as how these demands interact with injury risk, recovery and long-term players development. 

APPLICATIONS IN SPORT

These findings provide an insight into general and positional demands of women’s soccer at the NCAA Division II level. These results are particularly valuable to coaches who may strategically utilize the NCAA substitution rules to manage player workloads. Caution should be exercised when applying published findings from other levels of play and relying on total averages as positional differences can substantially influence match demands.

The results emphasize the importance of aligning athlete’s physical attributes with the requirements of their playing positions. Forwards and outsides backs perform the highest sprinting and high-speed running demands per minute and may require players that have higher acceleration and sprint capacities. On the other hand, players that have less high-speed and sprinting demands, but positional awareness may be better suited at center back. 

This may have implications for recruitment. Coaches may want to target athletes with performance profiles that align with the positional demands, rather than relying on general fitness. In regard to player development, training and conditioning programs should be tailored to the requirements of each position moving beyond a one-size-fits-all approach. For example, midfielders may benefit from training designed to sustain higher per-minute workloads, while forwards and outside backs should focus on repeated spring ability as well as increasing top speed. 

Finally, these results may extend to tactical decision making and injury prevention. Coaches may rotate outside backs and forwards more frequently to preserve sprint performance later in matches. These findings provide sport practitioners with evidence-based guidance to optimize recruitment, training and match management in NCAA division II soccer. 


REFERENCES 

  1. Alexander, R. (2014). Physical and Technical Demands of Women’s Collegiate Soccer. Electronic Theses and Dissertations, 2421. https://dc.etsu.edu/etd/2421
  2. Al-Hazzaa, H., Almuzaini, K., Al-Refaee, S., Sulaiman, M., Dafterdar, M., Al-Ghamedi, A., &  Al-Khuraiji, K. (2001). Aeronic and anaerobic power characteristics of Saudi elite soccer players. Journal of Sports Medicine & Physical Fitness, 41(1), 54–61.
  3. Andersson, H. A., Randers, M. B., Heiner-Møller, A., Krustrup, P., & Mohr, M. (2010). Elite female soccer players perform more high-intensity running when playing in international games compared with domestic league games. Journal of Strength & Conditioning Research, 24(4), 912–919. https://doi.org/10.1519/JSC.0b013e3181d09f21
  4. Bloomfield, J., Polman, R., & O’Donoghue, P. (n.d.). Physical demands of different positions in FA Premier League soccer.
  5. Choice, E. E., Tufano, J. J., Jagger, K., L., & Cochrane-Synman, K. C. (2023). Match-Play External Load and Internal Load in NCAA Division II Women’s Soccer. Journal of Strength & Conditioning Research, 37(12), 633–639. https://doi.org/10.1519/JSC.0000000000004578
  6. Choice, E., Tufano, J., Jagger, K., Hooker, K., & Cochrane-Snyman, K. C. (2022). Differences across Playing Levels for Match-Play Physical Demands in Women’s Professional and Collegiate Soccer: A Narrative Review. Sports, 10(10), 141. https://doi.org/10.3390/sports10100141
  7. Gentles, J., Coniglio, C., Besemer, M., Morgan, J., & Mahnken, M. (2018). The Demands of a Women’s College Soccer Season. Sports, 6(1), 16. https://doi.org/10.3390/sports6010016
  8. Harkness-Armstrong, A., Till, K., Datson, N., Myhill, N., & Emmonds, S. (2022). A systematic review of match-play characteristics in women’s soccer. PLOS ONE, 17(6), e0268334. https://doi.org/10.1371/journal.pone.0268334
  9. Hopkins, W. G. (2002). A scale of magnitude for effect statistics. http://www.sportsci.org/resource/stats/effectmag.html.
  10. Sausaman, R. W., Sams, M. L., Mizuguchi, S., DeWeese, B. H., & Stone, M. H. (2019). The Physical Demands of NCAA Division I Women’s College Soccer. Journal of Functional      Morphology and Kinesiology, 4(4), 73. https://doi.org/10.3390/jfmk4040073
  11. Sporis, G., Jukic, I., Ostojic, S., & Milanovic, D. (2009). Fitness profiling in soccer: Physical and physiologic characteristics of elite players. Journal of Strength & Conditioning Research, 23(7), 1947–1953. https://doi.org/10.1519/JSC.0b013e3181b3e141
  12. Stolen, T., Chamari, K., Castagna, C., & Wisl??ff, U. (2005). Physiology of Soccer: An Update. Sports Medicine, 35(6), 501–536. https://doi.org/10.2165/00007256-200535060-00004
  13. Vescovi, J. (2013). Motion Characteristics of Youth Women Soccer Matches: Female Athletes in Motion (FAiM) Study. International Journal of Sports Medicine, 35(02), 110–117. https://doi.org/10.1055/s-0033-1345134
  14. Vescovi, J. D. (2012). Sprint profile of professional female soccer players during competitive matches: Female Athletes in Motion (FAiM) study. Journal of Sports Sciences, 30(12), 1259–1265. https://doi.org/10.1080/02640414.2012.701760
  15. Wisløff, U., Castagna, C., Helgerud, J., Jones, R., & Hoff, J. (2004). Strong correlation of maximal squat strength with sprint performance and vertical jump height in elite soccer players: Figure 1. British Journal of Sports Medicine, 38(3), 285–288. https://doi.org/10.1136/bjsm.2002.002071

2025-09-25T15:33:55-05:00February 4th, 2026|Research, Sport Education, Sport Training, Sports Coaching, Sports Health & Fitness, Women and Sports|Comments Off on Match Demands and Positional Differences of NCAA Division II Women’s Soccer Players Over a Competitive Season
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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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Baraniuk, C. (2015). Rise of the AI sports coach. New Scientist, 227(3035), 22-23.

Bennett, B., & Szedlak, C. (2023). Aligning online and remote coaching with the digital age: Novel perspectives for an emerging field of research and practice. International Journal of Sports Science and Coaching, 19(2), 882-893.

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Bishop, C., Well, J., Ehlert, A., Turner, A., Coughlan, D., Sachs, N., & Murray, A. (2023). Trackman 4: Withing and between-session reliability and inter-relationships of launch monitor metrics during indoor testing in high-level golfers. Journal of Sports Sciences, 41(23), 2138-2143.

Carson, F., & Collins, D. (2016). The future for the science of coaching: Bright, but with a significant glint? Current Opinion in Psychology, 16, 103-108.

Catapult. (2023). Sports technology trends: Performance optimization with wearable systems. https://www.catapult.com/blog/trends-in-sports.

Cronin, C., Whitehead, A. E., Webster, S., & Huntley, T. (2019). Transforming, storing, and consuming athletic experiences: A coach’s narrative of using a video application. Sport, Education, & Society, 24(3), 311-323.

Driska, A. P., Gould, D., & Pierce, S. (2017). Learning to coach through experience: Conditions that influence reflection. Physical Education and Sport Pedagogy, 22(1), 18-34.

Gin, W. (2018). Big data and labor: What baseball can tell us about information and inequality. Journal of Information Technology & Politics, 15(1), 66-79.

Goudsmit, J., Otter, R., Stoter, I., van Holland, B., van der Zwaard, S., de Jong, J., & Vos, S. (2022). Co-operative design of a coach dashboard for training, monitoring, and feedback. Sensors, 22(23), 9073-9092.

Hawk-Eye Innovations. (2023). NBA and Sony’s Hawk-Eye Innovations launch strategic partnership powering next-generation tracking technology. Hawk-Eye Innovations. https://www.hawkeyeinnovations.com/news/4155239/nba-and-sonys-hawkeye-innovations-launch-strategic-partnership-powering-next-generation-tracking-technology.

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Judge, L. W., Cheetham, P. J., Fox, B., Schoeff, M. A., Wang, H., Momper, M., & Dickin, D. C. (2021). Using sport science to improve coaching: A case study of Felisha Johnson’s Road to Rio. International Journal of Sports Science & Coaching, 16(3), 848-861.

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Müller, A., Lahkar, B. K., Dumas, R., Reveret, L., & Robert, T. (2022). Accuracy of a markerless motion capture system in estimating upper extremity kinematics during boxing. Frontiers in Sports and Active Living, 4, 939980. https://doi.org/10.3389/fspor.2022.939980

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Rajasinghe, D., Garvey, B., Smith, W. A., Burt, S., Barosa-Pereira, A., Clutterbuck, D., & Csigas, Z. (2022). On becoming a coach: Narratives of learning and development. Coaching Psychologist, 18(2), 4-19.

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Sohail, M., Talha, M., & Ali, M. (2022). Information technology and its’ modernization, the Internet, and sport psychology. Journal of Sport Psychology, 31(2), 83-92.

Sperlich, B., Duking, P., Leppich, R., & Holmberg, H. (2023). Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: A brief SWOT analysis. Frontiers in Sport and Active Living, 5, 1-6.

Stanescu, R. (2018). The new on-court tennis software: Perspective in training process. Conference Proceedings of eLearning and Software for Education, 3, 341-345.

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

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

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

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

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

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

Corresponding Author: 

Jason Hughes, Ph.D., M.S.,  

1704 Weeksville Rd.  

Elizabeth City, NC 27909 

[email protected] 

252-335-3488 

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

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

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

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

ABSTRACT 

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

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

INTRODUCTION 

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

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

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

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

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

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

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

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

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

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

METHODS 

Participants 

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

Measures 

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

Perfectionism 

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

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

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

Sport Specialization 

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

Time of Sport Specialization 

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

Data Analysis 

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

RESULTS 

Results for Perfectionistic Concerns 

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

 

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

 

Results for Perfectionistic Strivings 

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

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

DISCUSSION 

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

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

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

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

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

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

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

LIMITATIONS 

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

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

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

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

CONCLUSION 

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

APPLICATIONS IN SPORT AND FUTURE RESEARCH 

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

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

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

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

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

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

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2025-05-22T15:03:47-05:00October 31st, 2025|Research, Sport Education, Sport Training, Sports Coaching, Sports Exercise Science|Comments Off on A Comparison of Perfectionism and Time of Sport Specialization of Division-1 Athletes 
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Managerial practices and coach satisfaction: A summer camp recreation and athletics case study 

Author: Jimmy Smith1

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

 

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

Corresponding Author: 

Jimmy Smith, Ph.D.

Gonzaga University

502 E. Boone Ave

Spokane, WA 99258

[email protected]

509-313-3483

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

ABSTRACT 

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

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

INTRODUCTION 

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

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

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

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

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

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

EMPIRICAL SETTING

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

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

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

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

LITERATURE REVIEW

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

Challenges Faced by Camp Staff

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

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

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

Leadership and Managerial Practices in Camps

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

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

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

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

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

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

METHODS 

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

Research Design

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

Data Collection

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

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

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

Data Analysis

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

RESULTS 

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

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

Communication

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

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

Guidelines

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

Mission

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

Satisfaction

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

Statistical Analysis

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

Significant Differences

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

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

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

Non-Significant Differences

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

Summary of Findings

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

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

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

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

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

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

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

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

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

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

DISCUSSION 

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

Key Findings

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

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

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

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

Bridging the Gap in Existing Research

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

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

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

CONCLUSION 

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

APPLICATIONS IN SPORT

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

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

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

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

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

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

LIMITATIONS AND FUTURE DIRECTIONS

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

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

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

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

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

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

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

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

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

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