ISSN: 1543-9518

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

Authors: Stephen Cadoux1, Kimberly Shaffer2

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

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

 

Corresponding Author:

Kimberly Shaffer

[email protected]

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

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

ABSTRACT 

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

Keywords: coaching, NCAA athletics, peer assessment

INTRODUCTION 

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

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

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

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

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

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

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

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

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

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

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

METHODS 

Participants

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

Procedures

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

 Instruments

Demographic Questionnaire

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

Multifactor Leadership Questionnaire

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

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

Data Analyses

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

RESULTS

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


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


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

Table 1

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

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

Table 2

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

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

Figure 1

Captain X 360-Degree Feedback Data

Figure 2

Captain Y 360-Degree Feedback Data

Captain Y

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

DISCUSSION

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

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

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

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

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

Limitations & Future Directions

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

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

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

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

CONCLUSION 

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

APPLICATIONS IN SPORT

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

REFERENCES 

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

Authors: Jim P. Arnold1 and William V. Massey1

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

 

Corresponding Author:

Jim P. Arnold

[email protected]

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

ABSTRACT 

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

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

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

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

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

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

INTRODUCTION 

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

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

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

LITERATURE REVIEW

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

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

Coach-Athlete Relationship

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

Peer Cohesion

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

Parental Involvement

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

Study Aims

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

METHODS 

Participants

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

Measures

Coach-Athlete Relationship Questionnaire (CART-Q)

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

Youth Sport Environment Questionnaire (YSEQ)

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

Parental Involvement in Sport Questionnaire (PISQ)

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

Youth Experience Survey for Sport (YES-S)

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

Procedure

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

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

Table 1

Sample Characteristics and Descriptive Statistics

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

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

RESULTS

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

Regression Results for Perceptions of Social Skills Gained by Athletes

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

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

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

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

Table 3

Regression Results for Perceptions of Cognitive Skills Gained by Athletes

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

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

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

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

DISCUSSION 

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

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

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

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

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

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

CONCLUSION 

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

APPLICATIONS IN SPORT

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

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

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

REFERENCES 

1. Adie, J. W., & Jowett, S. (2010). Meta‐perceptions of the coach–athlete relationship, achievement goals, and intrinsic motivation among sport participants. Journal of Applied Social Psychology, 40(11), 2750-2773. https://doi.org/10.1111/j.1559-1816.2010.00679.x

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

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

4. Bruner, M. W., McLaren, C. D., Sutcliffe, J. T., Gardner, L. A., Lubans, D. R., Smith, J. J., & Vella, S. A. (2021). The effect of sport-based interventions on positive youth development: A systematic review and meta-analysis. International Review of Sport and Exercise Psychology, 16(1), 368-395. https://doi.org/10.1080/1750984X.2021.1875496

5. Burns, L., Weissensteiner, J. R., & Cohen, M. (2019). Supportive interpersonal relationships: a key component to high-performance sport. British Journal of Sports Medicine, 53(22), 1386-1389. https://doi.org/10.1136/bjsports-2018-100312

6. Carron, A. V., Colman, M. M., Wheeler, J., & Stevens, D. (2002). Cohesion and performance in sport: A meta analysis. Journal of Sport and Exercise Psychology, 24(2), 168–188. https://doi.org/10.1123/jsep.24.2.168

7. Carron, A. V., Spink, K. S., & Prapavessis, H. (1997). Team building and cohesiveness in the sport and exercise setting: Use of indirect interventions. Journal of Applied Sport Psychology, 9(1), 61–72. https://doi.org/10.1080/10413209708415384

8. Castro-Schilo, L., & Grimm, K. J. (2018). Using residualized change versus difference scores for longitudinal research. Journal of Social and Personal Relationships, 35(1), 32-58.

9. Catalano, R. F., Toumbourou, J. W., & Hawkins, J. D. (2008). Positive youth development in the United States: History, efficacy, and links to moral and character education. In L. Nucci & T. Krettenauer (Eds.) Handbook of moral and character education. Routledge. https://doi.org/10.4324/9780203114896

10. Christiansen, E. D., & von Eye, A. (2005). Positive youth development, participation in community youth development programs, and community contributions of fifth-grade adolescents: Findings from the first wave of the 4-H study of positive youth development. The Journal of Early Adolescence, 25(1), 17-71. https://doi.org/10.1177/0272431604272461

10. Clonan, S. M., Chafouleas, S. M., McDougal, J. L., & Riley-Tillman, T. C. (2004). Positive psychology goes to school: Are we there yet? Psychology in the Schools, 41(1), 101–110. https://doi.org/10.1002/pits.10142

11. Danioni, F., Barni, D., & Rosnati, R. (2017). Transmitting sport values: The importance of parental involvement in children’s sport activity. Europe’s Journal of Psychology, 13(1), 75–92. https://doi.org/10.5964/ejop.v13i1.1265 

12. Davis, H. A. (2003). Conceptualizing the role and influence of student-teacher relationships on children’s social and cognitive development. Educational Psychologist, 38(4), 207-234. https://doi.org/10.1207/S15326985EP3804_2

13. Davis, L., & Jowett, S. (2014). Coach–athlete attachment and the quality of the coach–athlete relationship: Implications for athlete’s well-being. Journal of Sports Sciences, 32(15), 1454–1464. https://doi.org/10.1080/02640414.2014.898183

14. Davis, L., Jowett, S., & Tafvelin, S. (2019). Communication strategies: The fuel for quality coach-athlete relationships and athlete satisfaction. Frontiers in Psychology, 10, 2156. https://doi.org/10.3389/fpsyg.2019.02156

15. Dong, Y., Wang, H., Luan, F., Li, Z., & Cheng, L. (2021). How children feel matters: Teacher–student relationship as an indirect role between interpersonal trust and social adjustment. Frontiers in Psychology, 11, 581235. https://doi.org/10.3389/fpsyg.2020.581235

16. Dorsch, T. E., Wright, E., Eckardt, V. C., Elliott, S., Thrower, S. N., & Knight, C. J. (2021). A history of parent involvement in organized youth sport: A scoping review. Sport, Exercise, and Performance Psychology, 10(4), 536. https://doi.org/10.1037/spy0000266

17. Edwards, J. R. (1994). Regression analysis as an alternative to difference scores. Journal of Management, 20(3), 683-689.

18. Erikson, E. H. (1950). Childhood and society. W. W. Norton & Co.

19. Erikstad, M. K., Martin, L. J., Haugen, T., & Høigaard, R. (2018). Group cohesion, needs satisfaction, and self-regulated learning: A one-year prospective study of elite youth soccer players’ perceptions of their club team. Psychology of Sport and Exercise, 39, 171–178. https://doi.org/10.1016/j.psychsport.2018.08.013

20. Eys, M., Loughead, T., Bray, S. R., & Carron, A. V. (2009). Development of a cohesion questionnaire for youth: The Youth Sport Environment Questionnaire. Journal of Sport and Exercise Psychology, 31(3), 390-408. https://doi.org/10.1123/jsep.31.3.390

21. Felber Charbonneau, E., & Camiré, M. (2020). Parental involvement in sport and the satisfaction of basic psychological needs: Perspectives from parent–child dyads. International Journal of Sport and Exercise Psychology, 18(5), 655–671. https://doi.org/10.1080/1612197X.2019.1570533

22. Filho, E., Dobersek, U., Gershgoren, L., Becker, B., & Tenenbaum, G. (2014). The cohesion–performance relationship in sport: A 10-year retrospective meta-analysis. Sport Sciences for Health, 10(3), 165–177. https://doi.org/10.1007/s11332-014-0188-7

23. Greenberg, M. T., Weissberg, R. P., O’Brien, M. U., Zins, J. E., Fredericks, L., Resnik, H., & Elias, M. J. (2003). Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning. American Psychologist, 58(6-7), 466. https://doi.org/10.1037/0003-066X.58.6-7.466

24. Hampson, R., & Jowett, S. (2014). Effects of coach leadership and coach–athlete relationship on collective efficacy. Scandinavian Journal of Medicine & Science in Sports, 24(2), 454-460. https://doi.org/10.1111/j.1600-0838.2012.01527.x

25. Holt, N. L., Neely, K. C., Slater, L. G., Camiré, M., Côté, J., Fraser-Thomas, J., MacDonald, D., Strachan, L., & Tamminen, K. A. (2017). A grounded theory of positive youth development through sport based on results from a qualitative meta-study. International Review of Sport and Exercise Psychology, 10(1), 1-49. https://doi.org/10.1080/1750984X.2016.1180704

26. Jones, M. I., Dunn, J. G., Holt, N. L., Sullivan, P. J., & Bloom, G. A. (2011). Exploring the ‘5Cs’ of positive youth development in sport. Journal of Sport Behavior, 34(3). https://psycnet.apa.org/record/2011-17095-003

27. Jowett, S. (2017). Coaching effectiveness: The coach–athlete relationship at its heart. Current Opinion in Psychology, 16, 154–158. https://doi.org/10.1016/j.copsyc.2017.05.006

28. Jowett, S., & Cockerill, I. M. (2003). Olympic medalists’ perspective of the athlete–coach relationship. Psychology of Sport and Exercise, 4(4), 313–331. https://doi.org/10.1016/S1469-0292(02)00011-0

29. Jowett, S., & Ntoumanis, N. (2004). The Coach–Athlete Relationship Questionnaire (CART‐Q): Development and initial validation. Scandinavian Journal of Medicine & Science in Sports, 14(4), 245-257. https://doi.org/10.1111/j.1600-0838.2003.00338.x

30. Kipp, L. E., & Bolter, N. D. (2020). Motivational climate, psychological needs, and personal and social responsibility in youth soccer: Comparisons by age group and competitive level. Psychology of Sport and Exercise, 51. https://doi.org/10.1016/j.psychsport.2020.101756

31. Knight, C. J., Neely, K. C., & Holt, N. L. (2011). Parental behaviors in team sports: How do female athletes want parents to behave? Journal of Applied Sport Psychology, 23(1), 76-92. https://doi.org/10.1080/10413200.2010.525589

32. Larson, R. W. (2000). Toward a psychology of positive youth development. American Psychologist, 55(1), 170–183. https://doi.org/10.1037/0003-066X.55.1.170

33. Lee, M., & MacLean, S. (1997). Sources of parental pressure among age group swimmers. European Journal of Physical Education, 2(2), 167-177. https://doi.org/10.1080/1740898970020204

34. Lerner, R. M. (2005). Promoting positive youth development: Theoretical and empirical bases. Washington, DC: National Academies of Sciences.

35. Lerner, R. M., Almerigi, J. B., Theokas, C., & Lerner, J. V. (2005). Positive youth development A view of the issues. The Journal of Early Adolescence, 25(1), 10–16. https://doi.org/10.1177/0272431604273211

36. Lerner, R. M., Lerner, J. V., Almerigi, J. B., Theokas, C., Phelps, E., Gestsdottir, S., Naudeau, S., Jelicic, H., Alberts, A., Ma, L., Smith, L. M., Bobek, D. L., Richman-Raphael, D., Simpson, I., 37.

37. Lerner, R. M., Lerner, J. V., Lewin-Bizan, S., Bowers, E. P., Boyd, M. J., Mueller, M. K., Schmid, K. L., & Napolitano, C. M. (2011). Positive youth development: Processes, orograms, and problematics. Journal of Youth Development, 6(3), 38–62. https://doi.org/10.5195/JYD.2011.174

38. MacDonald, D. J., Côté, J., Eys, M., & Deakin, J. (2012). Psychometric properties of the youth experience survey with young athletes. Psychology of Sport and Exercise, 13(3), 332-340. https://doi.org/10.1016/j.psychsport.2011.09.001

39. Massey, W. V., Veliz, P. T., Zarrett, N., & Farello, A. (2024). Thriving through sport: The transformative impact on girls’ mental health. Women’s Sport Foundation.

40. McBride, A. M., Johnson, E., Olate, R., & O’Hara, K. (2011). Youth volunteer service as positive youth development in Latin America and the Caribbean. Children and Youth Services Review, 33(1), 34–41. https://doi.org/10.1016/j.childyouth.2010.08.009

41. Nikolina, K., & Đorić, V. (2023). Relations of coach-athlete relationship quality and athlete psychosocial functioning: A systematic review. SportLogia, 19(1). https://doi.org/10.7251/SGIA2319002K

42. Riggio, R. E., Throckmorton, B., & Depaola, S. (1990). Social skills and self-esteem. Personality and Individual Differences, 11(8), 799-804. https://doi.org/10.1016/0191-8869(90)90188-W

43. Salavera, C., Usán, P., & Quilez-Robres, A. (2022). Exploring the effect of parental styles on social skills: The mediating role of affects. International Journal of Environmental Research and Public Health, 19(6), 3295. https://doi.org/10.3390/ijerph19063295

44. Sancassiani, F., Pintus, E., Holte, A., Paulus, P., Moro, M. F., Cossu, G., Angermeyer, M. C., Carta, M. G., & Lindert, J. (2015). Enhancing the emotional and social skills of the youth to promote their wellbeing and positive development: A systematic review of universal school-based randomized controlled trials [Special issue]. Clinical Practice and Epidemiology in Mental Health, 11, 21-40. https://doi.org/10.2174/1745017901511010021

45. Senécal, J., Loughead, T. M., & Bloom, G. A. (2008). A season-long team-building intervention: Examining the effect of team goal setting on cohesion. Journal of Sport and Exercise Psychology, 30(2), 186-199. https://doi.org/10.1123/jsep.30.2.186

46. Sheridan, D., Coffee, P., & Lavallee, D. (2014). A systematic review of social support in youth sport. International Review of Sport and Exercise Psychology, 7(1), 198-228. https://doi.org/10.1080/1750984X.2014.931999

47. Sibthorp, J., & Morgan, C. (2011). Adventure‐based programming: Exemplary youth development practice. New Directions for Youth Development, (130), 105-119. https://doi.org/10.1002/yd.400

48. Smith, A. L., & Ullrich-French, S. (2020). Peers and the sport experience. In G. Tenenbaum & R. C. Eklund (Eds.) Handbook of sport psychology (pp. 410–428). John Wiley & Sons, Inc. https://doi.org/10.1002/9781119568124.ch19

49. Storm, M. K., Williams, K. N., Shetter, E. M., Kaminsky, J., Lowery, C. M., Caldas, S. V., & Winch, P. J. (2017). Sink or swim: Promoting youth development through aquatics programs in Baltimore, Maryland. Journal of Park & Recreation Administration, 35(1), 66-79. https://doi.org/10.18666/JPRA-2017-V35-I1-7295

50. Sullivan, P. J., LaForge-MacKenzie, K., & Marini, M. (2015). Confirmatory factor analysis of the Youth Experiences Survey for Sport (YES-S). Open Journal of Statistics, 5(05), 421. https://doi.org/10.4236/ojs.2015.55044

51. Sung, Y. Y., & Chang, M. (2010). Which social skills predict academic performance of elementary school students. Journal on Educational Psychology, 3(3), 23-34. https://doi.org/10.26634/jpsy.3.3.1078

52. Turman, P. D. (2003). Coaches and cohesion: The impact of coaching techniques on team cohesion in the small group sport setting. Journal of Sport Behaviour, 26(1), 86-104. https://psycnet.apa.org/record/2003-01497-007

53. White, R. L., & Bennie, A. (2015). Resilience in youth sport: A qualitative investigation of gymnastics coach and athlete perceptions. International Journal of Sports Science & Coaching, 10(2-3), 379-393. https://doi.org/10.1260/1747-9541.10.2-3.379

54. Whitley, M. A., Massey, W. V., Camiré, M., Boutet, M., & Borbee, A. (2019). Sport-based youth development interventions in the United States: A systematic review. BMC Public Health, 19, 1-20. https://doi.org/10.1186/s12889-019-6387-z

55. Worker, S. M., Iaccopucci, A. M., Bird, M., & Horowitz, M. (2019). Promoting positive youth development through teenagers-as-teachers programs. Journal of Adolescent Research, 34(1), 30-54. https://doi.org/10.1177/0743558418764089

Appendix A
Supplemental Materials

Table 4

Correlation Matrix of Study Variables

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

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

Table 5

Regression Results for Perceptions of Goal Setting Skills Gained by Athletes

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

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

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

Table 6

Regression Results for Perceptions of Initiative Gained by Athletes

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

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

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

2025-10-01T13:40:31-05:00March 18th, 2026|Leadership, Research, Sport Education, Sport Training, Sports Coaching, Sports Studies and Sports Psychology|Comments Off on The Role of Sport Relationships in Positive Youth Development
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Positional Differences and Workload Requirements of a 3-5-2 Formation in Women’s Soccer

Authors: Asher L. Flynn1, Joanne Spalding2, and Sellena Dixon3

1Department of Sport & Exercise Science, Lincoln Memorial University, Harrogate, TN, USA

2Department of Health Sciences, Georgia College & State University, Milledgeville, GA, USA

3Athletics Department, Lincoln Memorial University, Harrogate, TN, USA

 

Corresponding Author:

Asher L. Flynn, PhD, CSCS

6965 Cumberland Gap Parkway

[email protected]

423.869.6828

Asher L Flynn, PhD, CSCS is an Assistant Professor of Sport and Exercise Science at Lincoln Memorial University, TN. His research interests focus on fatigue and athlete monitoring in colligate athletes, and aspects of women’s soccer performance.

Joanne Spalding, PhD, is an Assistant Professor in Exercise Science at Georgia College & State University, GA. Her research interests include athlete monitoring and long term athlete development in female athletes.

Sellena Dixon, BS, is the graduate assistant for the women’s soccer team at Lincoln Memorial University, TN.

ABSTRACT 

The purpose of this research was to investigated the match demands of 24 female soccer players over one season (15 conference matches) playing in a 3-5-2 formation to determine the match demands and work rates of each position. In order to determine formation specific workload, positions were grouped as center-backs (CB), wingbacks (WB), midfielders (MF), and forwards (FW). Velocity bands used to compare distances and work rates were total distance (TD), 2-3 m/s, 3-4 m/s, 4-5 m/s, 5-6 m/s, and >6 m/s. Results revealed that MF covered significantly more total distance (TD; 10743 ±1520 m) and distance between 2-3 m/s (3444 ± 423 m) than all other positions (CB TD: 8549 ± 1106 m, 2-3: 2497 ± 276 m; WB TD: 8860 ± 1216 m, 2-3: 2324 ± 344 m; FW TD: 8069 ± 1286 m) and greater distance between 3-4 m/s (2515 ± 382m) compared to CB (1574 ± 214 m) and FW (1270 ± 281 m), but not WB (1814 ± 258 m). There were no significant differences between any of the higher velocity bands (>4 m/s). These results can be useful for the coaching staff as descriptive data for the expected distances covered and work rates of Division 2 Women’s soccer teams playing a 3-5-2 formation. These data can be used to determine practice plans in season and for off-season training plans, to prepare athletes for reporting for preseason in appropriate condition.

KEYWORDS: GPS, Sport Science, Athlete Monitoring, Fitness, NCAA

Abbreviations: CB: Center-back, WB: Wingback, MF: Midfield, FW: Forward, GPS: Global Positioning System, MD: Match Day, vVO2max: Velocity at VO2max

INTRODUCTION 

There is a growing interest in the physical demands of women’s soccer, but the majority of research has focused on the top levels of competition (International and Professional; 23). Although this information is important, it is likely to have limited use for lower levels of competition. It has been reported that “top class” females perform more high intensity running compared to “high-level” females (16, 17, 19). Currently, there is a lack of research investigating match demands at other levels of women’s soccer, especially at lower levels of collegiate soccer in the USA.

The majority of the literature investigating the demands of lower levels of women’s soccer has focused on the match demands of NCAA Division I (2, 5, 12, 20, 21), two articles focused on NCAA Division II athletes (9, 11), and one article focused on NCAA DIII (22), with no studies investigating the match demands of women’s NAIA soccer. Gentles et al (2018) observed that NCAA DII female players covered approximately 5480 m in 45 minutes of match play, while NCAA Division I players covered between 9486m and 9930 m in 90 minutes (20, 22). With little evidence exploring the activity profiles of the lower divisions of collegiate women’s soccer, further investigation is needed.

Many of the studies to date, at any level, have been conducted using only a few matches (3, 17), which, due to the high standard deviations (approximately 10 – 40%; 3, 11, 13, 20, 21-23), raise the question of whether a small number of matches truly reflects the average match demands. Previous studies have also shown differences in match demands based on position (20, 21). On average, defenders covered less distance than midfielders and attackers (forwards), and forwards covered more distance than midfielders (20, 21). Another complication when extrapolating information from current research is that the formation type may also alter the match demands (4, 6, 7). These limitations can lead to complications when inferring information from current research for use in a practical setting with different teams.

Factors that alter expected match demands, level of play and formation used, imply that it would be improper for the coaching staff to use data from a different level of play and unknown formation to determine the expected demands for their specific situation; that is, a high school coach should not be implementing a training plan based on data from college, professional, or international level teams. As such, the purpose of this research was to observe the match demands (distances and work rates) of an NCAA Division II women’s soccer team playing in a 3-5-2 formation, and to determine if there are significant differences in distances covered and/or work rates between positions.

METHODS 

Participants  

Retrospective data from 24 field players (age: 19.90 ± 1.56 years old; height: 163.43 ± 6.18 cm, weight: 59.35 ± 7.20 kg,) on the same team were included in this study. A total of 194 observations were included in the analysis (center back (CB), n = 49; wingback (WB), n = 36; midfield (MF), n = 61; and forward (FW), n = 48). This study was approved by the institutional review board of Lincoln Memorial University.

Procedures

Match data from an NCAA division II women’s soccer team playing a 3-5-2 formation were collected during a single competitive season. Only conference regular season matches, conference tournament, and national tournament matches were included, with 15 matches used for analysis.

Global Positioning System (GPS) devices (TITAN sports, Houston, TX, USA), sampling at 10 Hz, were used to track player movement duringcompetition. GPS units were activated at least 20-minutes prior to kick-off and were worn in a provided chest halter that secured the device between the shoulder blades under their game uniform. GPS devices were provided to all athletes (starters and substitutes) during the 10-minute window after the team warm-up and before the start of the match, and were collected again after the final whistle. All data from the duration of the match (substitute warm-up and half-time warm-up) were included in the analysis.

For positional analysis, distances accumulated by all athletes who played in a specific position for a match were summed and then divided by the number of positions in the formation. For example, in the forward position, if one athlete was substituted for a portion of the match, the total distance covered by all three athletes playing in the forward position was summed and then divided by two for the two forward positions in the 3-5-2 system. For the work rate analysis, meters covered per minute at each threshold (distances divided by total match time) were averaged for all players in that position (20). Due to the different substitution rules in college soccer, this analysis was deemed optimal to determine the distances and work rate of each position, instead of specific players. Variables of interest included total distance and distances covered in velocity thresholds; 2 – 3 m/s (7.2 – 10.8 km·h-1), 3 – 4 m/s (10.8 – 14.4 km·h-1), 4 – 5 m/s (14.4 – 18.0 km·h-1), 5 – 6 m/s (18 – 21.6 km·h-1), and >6 m/s (>21.6 km·h-1).

All matches analyzed were official NCAA matches consisting of two 45-minute halves with a 15-minute half-time period, with a maximum of two 10-minute extra-time periods with a 2-minute intermission, with extra time being stopped in the event of a goal if the competition was tied at the end of the normal 90-minute match.

Data Analyses

Two Repeated Measures ANOVA analyses with Bonferroni corrections were performed to determine whether there was a significant difference in distances and work rates for each position at each threshold (TD, 2 – 3 m/s, 3 – 4 m/s, 4 – 5 m/s, 5 – 6 m/s, and >6 m/s). Statistical analyses were performed using JASP (Version 0.16.2). The alpha level was set at 0.05.

RESULTS 

The first RM-ANOVA revealed significantly higher distances (F(3.37, 62.82) = 11.96, p < 0.001) covered in the MF position compared to all other positions for TD and 2 – 3 m/s. Midfielders also covered significantly more distances than CBs and FWs, but not WBs, at 3 – 4 m/s. The only other significant difference observed was between the WB and FW positions in TD covered. There were no significant differences between any other positions at any other threshold. Descriptive statistics are provided in Table 1.

Table 1

Mean distance covered by position in each velocity zone (meters).

 CBWBMFFW
TD8549 ± 1106 (6467 – 11066)8860 ± 1216# (5443 – 10854)10743 ± 1520* (7060 – 12864)8069 ± 1286# (6204 – 10537)
7.2 – 10.8 km·h-12497 ± 276 (1827 – 2972)2324 ± 344 (1389 – 2842)3444 ± 423* (2180 – 3916)2301 ± 400 (1668 – 2942)
10.8 – 14.4 km·h-11574 ± 214 ǂ (1191 – 1959)1814 ± 258 (1068 – 2218)2515 ± 382# ǂ (1771 – 3129)1270 ± 281# (759 – 1688)
14.4 – 18.0 km·h-1607 ± 113 (449 – 769)878 ± 135 (639 – 1124)1092 ± 211 (792 – 1453)587 ± 109 (395 – 734)
18.0 – 21.6 km·h-1246 ± 58 (155 – 342)404 ± 86 (252 – 552)381 ± 79 (241 – 533)272 ± 60 (149 – 357)
>21.6 km·h-196 ± 35 (45 – 175)200 ± 102 (68 – 426)120 ± 55 (68 – 295)180 ± 112 (45 – 530)

Note: Data are presented as mean ± Standard Deviation (Range).
TD: Total distance, CB: Center back, WB: Wingback, MF: Midfield, FW: Forward
* = p < 0.05 compared to all other positions in that velocity range
# = p < 0.05 compared to the other indicated positions in that velocity range
ǂ = p < 0.05 compared to the other indicated positions in that velocity range

The RM-ANOVA performed to determine differences in work rates at each threshold revealed significantly higher work rates (F(3.47, 64.82) = 6.05, p < 0.001) over the entire match (TD) for the MF position compared to all other positions, and a significantly higher work rate from the MF position in the 3 – 4 m/s velocity range compared to the FW position. There were no other significant differences between any of the other positions at any other threshold. The results are presented in Table 2.

Table 2

Mean work rate by position in each velocity zone (meters per minute).

 CBWBMFFW
TD96 ± 8 (88 – 123)101 ± 7 (85 – 117)119 ± 20 * (88 – 167)101 ± 24 (72 – 156)
7.2 – 10.8 km·h-128 ± 2 (25 – 33)26 ± 3 (21 – 35)36 ± 3 (29 – 40)27 ± 5 (19 – 35)
10.8 – 14.4 km·h-118 ± 2 (14 – 21)21 ± 2 (18 – 26)27 ± 3 # (21 – 32)16 ± 4 # (8 – 21)
14.4 – 18.0 km·h-17 ± 1 (5 – 10)10 ± 1 (8 – 12)12 ± 3 (9 – 19)8 ± 3 (4 – 14)
18.0 – 21.6 km·h-13 ± 1 (2 – 4)5 ± 1 (4 – 6)4 ± 1 (2 – 7)4 ± 2 (2 – 9)
>21.6 km·h-11.3 ± 1 (1 – 3)2.3 ± 1 (1 – 4)1.3 ± 1 (1 – 3)3.4 ± 4 (1 – 13)

Note. Data are presented as mean ± Standard Deviation (Range).
TD: Total distance, CB: Center back, WB: Wingback, MF: Midfield, FW: Forward

* = p < 0.05 compared to all other positions in that velocity range
# = p < 0.05 compared to the other indicated positions in that velocity range

DISCUSSION 

The purpose of this study was to examine the external demands of NCAA DII women’s college soccer playing in a 3-5-2 formation. The most interesting finding from these data was that the WB position was only significantly higher in total distance covered compared to the FW position (p = 0.044), but there were no other significant differences in distances or work rates compared to other positions. This was interesting, given that the WB position is commonly accepted as the most demanding position. Another interesting result was that the MF position had significantly higher distances at lower speeds (2-3 m/s, p < 0.001; 3-4 m/s, p < 0.001) only when compared to FW position. Other studies have reported that different positions have significantly different total distances covered in a match (1, 14). Abbot et al.(2018) reported that central midfielders covered the greatest distance (11,570 ± 469 m), followed by wide attackers (10,918 ± 353 m), wide defenders (10,747 ± 420 m), strikers (10,320 ± 420 m), and central defenders covering the least amount of distance (9,830 ± 428 m), while Lago-Peñas (2009) reported significant differences between nearly every position for each threshold, except for total distance (11.1 – 14 km·h-1, 14.1 – 19 km·h-1, 19.1 – 23 km·h-1, > 23 km·h-1). Both these studies observed high-level male soccer teams (U23 English Premier League, Professional Spanish Premier League) and as such they would not be expected to mimic the results of this study and highlight the importance of sex- and level-specific research.

In a more direct comparison with other women’s college soccer research, Sausaman et al(2019) and Alaxander et al (2014) reported significant differences in distances covered by position, whereas Corrales (2020) reported no differences, regardless of position. Sausaman et al (2019) reported that attackers covered more high-speed (> 15 km·h-1) and sprint (> 18 km·h-1) distances than midfielders and defenders, with no difference between midfielders and defenders. Alexander et al (2104) reported that central defenders covered the least amount of total distance (8041.2 ± 371.0 m), followed by central attacking midfielders (9236.1 ± 491.3 m), fullbacks (9306.2 ± 367.8 m), and wide midfielders (9500.4 ± 847.0 m), with central defensive midfielders (9947.4 ± 577.9 m) covering the greatest amount of total distance. Fullbacks (1321.5 ± 173.7 m) and wide midfielders (1208.2 ± 314.1 m) covered significantly more distance at high speed (> 15 km·h-1) compared to central defensive midfielders (847.7 ± 234.9 m), central attacking midfielders (747.64 ± 196.5 m), and central defenders (614.1 ± 98.9 m). The difference in results between these studies and the current investigation could be due to a different level of competition (NCAA Division 1), a possible difference in formation (not reported), or due to the current studies banding velocity zones (i.e. 4 -5 m/s, 5 – 6 m/s) instead of summing distances above thresholds (> 15 km·h-1).

CONCLUSION 

This present study provides information on the expected work and work rates of division 2 women’s soccer. Data analysis revealed minimal differences based on position, with the midfield position being the only position with significant differences and only at low intensity thresholds (TD, 2-3 m/s). All other positions and intensities were not statistically different, highlighting the possibility that training for these positions likely does not need to be modified to fit each position but rather each athlete.

APPLICATIONS IN SPORT

The primary application of this research is to allow the coaching staff to determine the appropriate fitness, conditioning, and practice workloads for their team with respect to their level of competition, formation, and style of play. Using typical tactical periodization plans for match-day preparation (Match day (MD) +1, MD -2, MD -1, etc.), position-specific workloads can be determined and monitored to ensure optimal loading during each practice session. This information can also be used to determine fitness testing requirements. Since there was a significant drop (43.7%) in distance covered and work rate (approximately 43% decrease) at intensities above 4 m/s (14.4 km·h-1) observed from this study, minimum criteria for aerobic fitness tests could be set at 15.5 km·h-1, which would allow players to do the majority of the work expected below their estimated lactate threshold (85% vVO2max; 8, 18)

In addition, these data can be used to determine the appropriate time requirements for different conditioning drills. For example, making a time criterion of 3:40 for an 800 m run would meet the Long Interval definition for an individual with a vVO2max of 15.5 km·h-1, but if a team had higher/lower requirements, adjusting time cut offs would be suggested (15). These data can also be used to create game-specific conditioning drills, such as creating an interval training exercise (100m active running, 100m recovery jog; Table 3) that would provide about 30 – 35% of game distances at the higher end of expected game work rates.

Table 3

Interval conditioning exercise.

Speed LevelRepsTimeWork RateAccumulated Distance
8.0 km·h-12245s89 m/min2200
12.0 km·h-11030s36 m/min1000
15.0 km·h-1524s20 m/min500
20.0 km·h-1218s8 m/min200
>21.6 km·h-11<16s4 m/min100

Note: Work Rate was calculated as the total distance covered in each velocity threshold divided by the total exercise time (24.5 min).

When retroactively analyzing team distances and work rates to create a training plan, it is important to note that since the majority of the total distance covered during a match is at low intensities (<3 m/s; 11), focusing on “running” the observed TD is likely unnecessary. Lower speed distances (<4 m/s) would be expected to be accumulated through daily technical drills and exercises. Higher speed distances (>4 m/s) could be accumulated in any manner chosen by the coach, such as a mixture of soccer technical drills and/or conditioning drills.

When using workload data in this manner, this would allow the coaching staff to create training plans that develop physical characteristics in a manner appropriate to the athletes level and expected match play requirements instead of arbitrarily spending time and effort developing a specific characteristic beyond projected usefulness. For example, since the majority of work is performed below 14.4 kph, spending the time and training effort for a vVO2max above 16 kph would be counterproductive. The extra focus could be better spent on improving other training targets to improve performance (technical, tactical, sprint, acceleration, change of direction)

ACKNOWLEDGMENTS

The authors declare no conflicts of interest, and no funding was received for this research.

REFERENCES 

1.   Abbott, W., Brickley, G., & Smeeton, N. J. (2018). Positional differences in GPS outputs and perceived exertion during soccer training games and competition. The Journal of Strength & Conditioning Research, 32(11), 3222-3231.

2.   Alexander, RP. (2014) Physical and Technical Demands of Women’s Collegiate Soccer (Publication No. 2421). [Doctoral dissertation, East Tennessee State University], Digital Commons.

3.   Andersson, H. Å., 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. The Journal of Strength & Conditioning Research, 24(4), 912-919.

4.   Aquino, R., Vieira, L. H. P., Carling, C., Martins, G. H., Alves, I. S., & Puggina, E. F. (2017). Effects of competitive standard, team formation and playing position on match running performance of Brazilian professional soccer players. International Journal of Performance Analysis in Sport, 17(5), 695-705.

5.   Bozzini, B. N., McFadden, B. A., Walker, A. J., & Arent, S. M. (2020). Varying demands and quality of play between in-conference and out-of-conference games in division i collegiate women’s soccer. The Journal of Strength & Conditioning Research, 34(12), 3364-3368.

6.   Bradley, P. S., Carling, C., Archer, D., Roberts, J., Dodds, A., Di Mascio, M., Paul, D., Gomez Diaz, A., Peart D., & Krustrup, P. (2011). The effect of playing formation on high-intensity running and technical profiles in English FA Premier League soccer matches. Journal of sports sciences, 29(8), 821-830.

7.   Carling, C., Espié, V., Le Gall, F., Bloomfield, J., & Jullien, H. (2010). Work-rate of substitutes in elite soccer: A preliminary study. Journal of science and medicine in sport, 13(2), 253-255.

8.   Cerezuela-Espejo, V., Courel-Ibáñez, J., Morán-Navarro, R., Martínez-Cava, A., & Pallarés, J. G. (2018). The relationship between lactate and ventilatory thresholds in runners: validity and reliability of exercise test performance parameters. Frontiers in physiology, 9, 1320.

9.   Choice, E. E., Tufano, J. J., Jagger, K. L., & Cochrane-Snyman, K. C. (2022). Match-play external load and internal load in NCAA division II women’s soccer. The Journal of Strength & Conditioning Research, 10-1519.

10. Corrales, I. (2020). The Physical Demands of Women’s Collegiate Soccer Matches Assessed Using GPS Devices [master’s thesis, California State University, Fullerton], https://scholarworks.calstate.edu/.

11. Gentles, J. A., Coniglio, C. L., Besemer, M. M., Morgan, J. M., & Mahnken, M. T. (2018). The demands of a women’s college soccer season. Sports, 6(1), 16.

12. Ishida, A., Travis, S. K., Draper, G., White, J. B., & Stone, M. H. (2022). Player position affects relationship between internal and external training loads during Division I collegiate female soccer season. The Journal of Strength & Conditioning Research, 36(2), 513-517.

13. Jagim, A. R., Murphy, J., Schaefer, A. Q., Askow, A. T., Luedke, J. A., Erickson, J. L., & Jones, M. T. (2020). Match demands of women’s collegiate soccer. Sports, 8(6), 87.

14. Lago-Peñas, C., Rey, E., Lago-Ballesteros, J., Casais, L., & Dominguez, E. (2009). Analysis of work-rate in soccer according to playing positions. International Journal of Performance Analysis in Sport, 9(2), 218-227.

15. Laursen, P., & Buchheit, M. (2019). Science and application of high-intensity interval training. Human kinetics.

16. Martínez-Lagunas, V., Niessen, M., & Hartmann, U. (2014). Women’s football: Player characteristics and demands of the game. Journal of Sport and Health Science, 3(4), 258-272.

17. Mohr, M., Krustrup, P., Andersson, H., Kirkendal, D., & Bangsbo, J. (2008). Match activities of elite women soccer players at different performance levels. The Journal of Strength & Conditioning Research, 22(2), 341-349..

18. Parpa, K., & Michaelides, M. A. (2025). Ventilatory thresholds in professional female soccer players. International Journal of Sports Medicine, 46(02), 97-103.

19. Ramos, G. P., Nakamura, F. Y., Penna, E. M., Wilke, C. F., Pereira, L. A., Loturco, I., Capelli, L., Mahseredjian F., Silami-Garcia E., & Coimbra, C. C. (2019). Activity profiles in U17, U20, and senior women’s Brazilian national soccer teams during international competitions: are there meaningful differences?. The Journal of Strength & Conditioning Research, 33(12), 3414-3422.

20. 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.

21. Vescovi, J. D. (2014). Motion characteristics of youth women soccer matches: female athletes in motion (FAiM) study. International journal of sports medicine, 35(02), 110-117.

22. Vescovi, J. D. (2015). Physical demands of regular season and playoff matches in professional women’s soccer: a pilot from the Female Athletes in Motion (FAiM) study. In International research in science and soccer II (pp. 95-106). Routledge.

23. Vescovi, J. D., & Falenchuk, O. (2019). Contextual factors on physical demands in professional women’s soccer: female athletes in motion study. European journal of sport science, 19(2), 141-146.

2025-09-30T16:08:05-05:00March 4th, 2026|Research, Sport Education, Sport Training, Sports Coaching, Women and Sports|Comments Off on Positional Differences and Workload Requirements of a 3-5-2 Formation in Women’s Soccer
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Supplemental lessons to the Peak Health and Performance curriculum: Nutritional considerations for injury, energy management, and gastrointestinal issues

Authors: Tyler B. Becker12, Ronald L. Gibbs, Jr2

1Department of Food Science and Human Nutrition, Michigan State University, East Lansing, MI, USA

2Michigan State University Extension, Health and Nutrition Institute, Michigan State University, East Lansing, Michigan, USA

 

Corresponding Author:

Tyler B. Becker, PhD, CSCS

469 Wilson Road, Room 125

East Lansing, MI 48824

[email protected]

517-353-3338

Tyler B. Becker, PhD, CSCS is an Associate Professor of Nutritional Sciences at Michigan State University in East Lansing, MI. His research interests focus on sports nutrition practices and strategies in youth athletes and higher education andragogy.

Ronald L. Gibbs, Jr, PhD, MCHES is currently a Program Evaluation Specialist for Michigan State University in East Lansing, MI. His research interests focus on coach and athlete education, long-term athlete development (LTAD), psychosocial aspects of sports and physical activity, adolescent nutrition and physical activity behavior change through sport participation, sports performance, and reducing childhood obesity.

ABSTRACT 

Youth sports injuries are quite common in sport and have several negative consequences, including healthcare costs, loss of playing time, and producing mental stress. Nutritional strategies have been suggested to improve recovery from sports-related injuries. The Peak Health and Performance (PHP) youth-sports curriculum was developed to use sport to promote healthy eating behaviors. Six additional lessons on nutrition for recovery from injury, energy management, and gastrointestinal issues have been added as addendums to PHP. Lesson A discusses the importance of key nutrients (eg., protein, complex carbohydrates, unsaturated fatty acids, water) for promoting tissue healing following an injury. Lesson B describes several micronutrients and the possible role of nitrates for aiding in injury recovery. Lesson C discusses the implications of low energy availability, including how to identify and prevent it. In Lesson D, several nutritional strategies for addressing mild traumatic brain injuries are explored. Lesson E discusses the importance of sleep for injury recovery and describes nutritional strategies for improving sleep quality. The final lesson (Lesson F) describes various gastrointestinal issues encountered in sport and how to prevent them. Future research will examine youth athlete knowledge of nutritional strategies for recovering from a sports-related injury following these lessons.

KEYWORDS: adolescent sports; sports nutrition; injury management

INTRODUCTION 

Sports-related injuries are a significant concern among adolescent athletes, with prevalence rates ranging from 34.1% to 65% (2). Certain groups, including female athletes, obese athletes, and those participating in contact sports, are at particularly high risk.  In the US, the rate of injuries in sports, recreation, and leisure activities is 117.1 per 1000 children and adolescents aged 12-17 years of age (57). These injuries impose substantial financial burdens; for example, over a 5-year period in Florida, inpatient care costs for pediatric sports injuries totaled $24.55 million, while emergency care expenses reached $87 million (61). Beyond the economic impact, sports-related injuries also incur both physical and mental challenges to the athlete, including lost playing time, with female athletes averaging 10 days of missed competition per injury (5). This contributes to social isolation and depressive symptoms in adolescent athletes during recovery (63). In addition, gastrointestinal (GI) problems, including diarrhea, vomiting, and abdominal injuries are common place among athletes (12,73), and can contribute to decreases in performance and a loss of playing time (39). Given these multifaceted challenges, there is a critical need to optimize injury prevention and rehabilitation strategies to support young athletes’ physical and psychological well-being.

Proper nutritional intake plays a critical role in injury prevention and rehabilitation among youth athletes (3). In addition to supporting overall health and well-being, adequate nutrition is essential during adolescence–a period marked by rapid growth and development–and contributes to athletic performance and post-workout recovery (15, 66). Despite its importance, many adolescent athletes demonstrate a lack of knowledge regarding both general and sport-specific nutritional practices (6).  A recent systematic review by Hulland et al. (2023) revealed that adolescent athletes are more familiar with general than sport-specific dietary strategies (32), while Gibbs and Becker (2025) found that both male and female adolescent athletes scored below 50% on assessments covering both areas (24). These findings underscore a significant gap in nutritional literacy among youth athletes, indicating the need for targeted education to optimize their health development and athletic outcomes.

Several nutritional strategies have emerged highlighting the importance of it for injury rehabilitation primarily in adult athletes (26, 54, 65). For example, kilocaloric and protein needs often increase following injury due to a need for recovery and maintenance of lean body mass resulting from disuse (58, 65). However, the application of nutritional strategies for recovering from injury for youth athletes remains understudied. Alcock et al. (2024) offered a comprehensive overview of injury rehabilitation strategies for youth, indicating practical applications; however there remains a critical gap in understanding how adolescent athletes perceive and apply nutrition during recovery (3). To date, no research has directly examined youth athletes’ knowledge of nutrition for injury rehabilitation, but existing evidence suggests they are likely deficient in this area as well (46). Research suggests that poor food literacy and nutrition knowledge could theoretically contribute to increased injury risk (3, 15). This reinforces the urgency of developing age-appropriate interventions that address both performance and recovery nutrition, particularly in the context of injury.

The Peak Health and Performance (PHP) curriculum was designed from a collaboration by faculty and staff at Michigan State University, Division of Sports and Cardiovascular Nutrition, College of Osteopathic Medicine, East Lansing, MI and Spartan Performance Training, East Lansing, MI (25). This curriculum incorporates various sports nutrition best practices from several areas of literature providing sports nutrition recommendations (17, 66, 69). Fruit and vegetable intake significantly increased in 290 children and adolescents who completed the PHP curriculum (4). Due to the success of the program in modifying nutrition behaviors, additional lessons were created to educate youth on nutritional strategies for injury recovery, energy management, and managing GI issues. These topics include nutritional strategies for musculoskeletal and mild traumatic brain injury (mTBI) recovery, and other nutritional considerations around injury risk and recovery including sleep, low energy availability (LEA), and GI issues. This manuscript describes the rationality and creation of these addendum lessons for the PHP curriculum.

LESSON CONTENT

The original PHP curriculum consists of six lessons labeled as: Lesson 1- Nutrition Basics; Lesson 2- Athletes Performance Plates; Lesson 3- Timing of Intake; Lesson 4- Hydration, Energy Drinks, and Sugary Beverages; Lesson 5- Convenience Foods; and Lesson 6- More Than a Game (25). Further information on PHP learning objectives and topics inclusion can be found in Gibbs & Becker (25). The new additional lessons and their learning objectives can be found in Table 1.  These additional lessons are meant to serve as their own lesson series, a single lesson session, or as supplemental lessons to the original PHP lessons.

Table 1.
Additional Lessons for the Peak Health and Performance Curriculum: Learning Objectives
LessonLearning Objectives
A: Macronutrients for Injury Rehabilitation • Explain the four phases of an injury
• Understand the importance of consuming enough calories following an injury
• Explain why protein is needed during the health process and recall amounts needed
• Explain the role that carbohydrates have during the healing process
• Describe what role unsaturated fatty acids, such as omega-3s, have while healing an injury.
• Understand the importance of water during the healing process
B: Micronutrients for Injury Rehabilitation• Explain the importance of choosing food sources of vitamins and minerals over dietary supplements
• List and understand the roles that vitamins A, C, D, and E have in injury healing
• Identify good food sources of vitamins A, C, D, and E
• List and understand the roles that calcium, zinc, and iron have in injury healing
• Identify good food sources of calcium, zine, and iron
• Explain why foods high in nitrates may promote injury healing and identify good food sources of them
C: Low Energy Availability• Explain what low energy availability is
• Identify what causes low energy availability
• Understand how low energy availability negatively impacts performance and recovery
• Explain how low energy availability may lead to other negative health outcomes
• Recognize the symptoms of low energy availability
• Describe prevention and treatment strategies for low energy availability
D: Nutrition for Head Injuries• Explain what happens during a head injury in sport
• List the different phases of concussion recovery
• Explain the benefits of creatine, magnesium, and flavonoids for head injury recovery
• Identify good food sources of creatine, magnesium, and flavonoids
• Identify other nutritional considerations to have when recovering from a head injury
E: Nutrition and Sleep for Injury Reduction and Recovery• Explain why sleep is important for performance and reducing and healing injuries
• Identify how much sleep an athlete should be getting each night
• Explain the benefits of melatonin and serotonin rich foods for improving sleep quality
• Identify other nutrients of interest that are related to sleep quality
• Identify foods to avoid prior to sleep
• List strategies to set up an ideal bedtime routine
F: Gastrointestinal Issues and Sport• Understand how vomiting and nausea symptoms may appear during practice and sport
• Provide strategies to reduce vomiting and nausea symptoms during practice and sport
• Explain how diarrhea can happen during practice and sport
• Identify strategies to prevent diarrhea during practice and sport
• Explain how probiotics and prebiotics are important for gut health

Each of these six lessons will be discussed in detail in the next section. These supplemental lessons were created in a manner to instruct participants to refer back to the original lessons for further information.

Lesson A: Macronutrients for Injury Prevention

This lesson begins by describing how musculoskeletal injuries heal and the importance of proper caloric intake and macronutrients during recovery from sports-related injuries. Each macronutrient is then highlighted to show its main role in providing both energy and nutritional needs to promote recovery. Macronutrient roles and responsibilities are described in detail in PHP Lesson 1 of the original curriculum (25).

Caloric Intake: The following section of the lesson describes the importance of meeting kilocalorie (kcal) needs to help heal an injury. Research on adult athletes suggest increasing kcal consumption by 10-15% during injury and recovery (58). Additionally, to offset sarcopenia in adults resulting from injury and disuse, energy intake should be between 25-40 kcal/kg of bodyweight per day (54). Independent of injury status, growth and development demands of children aged 9 and up typically require 60-65 kcal/kg of bodyweight per day (21). Taking energy needs during injury into account, coupled with normal demands for growth and development (21), an injured adolescent would need slightly more than the recommended 60-65 kcal/kg of bodyweight per day.

Protein: Following injury, protein requirements are significantly elevated to offset bodily stress incurred from the injury (65). Additionally, protein intake helps offset muscle atrophy due to disuse (47). Protein requirements for adult athletes and recreationally active adults is between 1.2 to 2.0 g/kg of bodyweight per day (69), with protein recommendations for adolescent athletes being in a similar range (15, 41). Following injury, it is suggested to increase daily intake of protein to 2.0 to 3.0 g/kg of bodyweight in athletic adults (65), which likely suffices for protein requirements for adolescent athletes.

Carbohydrates: Carbohydrates can provide a  source of energy while healing through an injury (65), and aid in muscle adaptations and recovery (69). Due to a decrease in the amount of high-intensity exercise that can be performed while injured, carbohydrate needs are not as large as what is needed in an uninjured athlete (65). Thus, to meet demand while recovering from an injury, up to 60% of daily kcals should come from carbohydrates (65), with an emphasis on complex carbohydrates.  Additionally, fatty acids are important in the recovery process as they synthesize several hormones and aid in the absorption of several vitamins (3,  27). Unsaturated fatty acids, such as omega-3 fatty acids may reduce inflammation, thereby making their need instrumental during the recovery process (27). It is recommended to consume good sources of omega-3 fatty acids including fatty fish, walnuts, flaxseed, and avocado, which this lessons includes as suggested food sources (27, 65).

Fluid Intake: Hydration for performance is covered in Lesson 4 of the original PHP, but in this lesson, it is explored in more detail pertaining to injury risk and recovery. Over half of US children are inadequately hydrated (37), and being in this state can increase risk of injury and prolong recovery (10). Muscles on average are 75% water with bones comprising 25% of it, suggesting that a lowered consumption of it could further exacerbate healing of injuries to these structures (27). Males aged 9 to 13 years need at least 8 cups of fluid per day, while females of the same age need at least 7 cups per day (34). Adolescent males aged 14 to 18 years of age, need at least 11 cups of fluid per day, while females of the same age need 8 cups. Thus, it could be hypothesized that an injured youth athlete should strive to meet and exceed these recommendations for fluid consumption.

Lesson B: Micronutrients for Injury Rehabilitation

Lesson B highlights the importance of specific micronutrients that provide a key role in injury rehabilitation (3, 26). Consuming adequate nutrients, including micronutrients, from whole food sources, is a major goal of the PHP curriculum (21). This lesson begins with a discussion on the concerns with the use of dietary supplements to meet micronutrient recommendations such as issues with regulation (20), and possible contamination (40). Each section of the lesson describes how the micronutrient of interest is implicated in the recovery process, how much is needed, other important functions it provides in the body, and suggested foods that are good sources for the micronutrient of interest.

Vitamin D and Calcium: As summarized in Alcock et al. (2024) micronutrients of interest for bone injury rehabilitation include vitamin D and calcium (3). Calcium is needed to increase bone mineral density and bone remodeling such as when following an injury (27). Vitamin D is needed for calcium absorption and maintenance. Children and adolescents between 9 and 18 years old, need 1,300 mg of calcium every day (23). Adolescents between 14- and 18-years old need at least 15 mcg (600 IUs) of vitamin D daily. Food sources of calcium listed in the lesson include milk, yogurt, salmon, fortified fruit juice, and collard greens (27). Good food sources of vitamin D suggested in the lesson includes salmon, fortified milk, tuna, and cashews.

Zinc and Iron: Other micronutrients of interest for muscle injury also include zinc and iron (3). Zinc and iron are both trace minerals that have several important functions in the human body (27). Zinc is involved in hundreds of functions in the body, such as involvement in DNA synthesis and wound healing, and immune system function (27). Zinc is needed for protein synthesis and iron is needed for the transport of oxygen to several tissues in the body which would increase healing (27). Youth aged 9 to 13 years, need 8 mg of zinc per day (23). Male adolescents aged 14-18 years of age need 11 mg of zinc per day, while females of the same age require 9 mg each day.  Children aged 9-13 years of age need 8 mg of iron per day (23). Males aged 14-18 years of age need 11 mg of iron per day, and females of the same age need 15 mg per day. Good sources of zinc include dark meat, legumes, shrimp, and nuts (27). Good food sources of iron includes dark meat, and also spinach and cashews.

Vitamins A, C, and E: Vitamin C plays a pivotal role in the synthesis of collagen (3). Similar to vitamin C, vitamin A aids in collagen formation, specifically the laying down of new collagen (65). Vitamin E can reduce muscle breakdown and promote muscle repair (27). Each of these vitamins can reduce oxidative stress and inflammation and improve tissue healing (27). Children aged 9 to 13 years old need 1,200 mg of vitamin C every day, and adolescents aged 14 to 18 years old, need 1,800 mg per day (23). Good food sources of vitamin C include kiwis, green peppers, strawberries, and cantaloupe (27). Youth aged 9-13 years need 600 mcg of retinol activity equivalents (vitamin A) per day, while adolescents aged 14-18 years need 600 mcg of retinol activity equivalents each day (23). Youth aged 9-13 years of age need 11 mg of vitamin E per day, while adolescents over the age of 14 need 15 mg per day (23). Dietary sources of vitamin A include sweet potatoes, pumpkins, spinach, and squash, while good sources of vitamin E include sunflower seeds, apricots, avocados, and almonds (65).

Although not a micronutrient, eating foods high in nitrates, like beets, could theoretically help heal an injury (76). About 20% of the nitrates consumed in food is converted to nitrite by bacteria found in the oral cavity (76). In turn, the stomach transforms this nitrite into nitrous oxide which can cause vasodilation. Thus, more oxygen and nutrients are transported to the injured area, supporting the healing process. A recent systematic review examined nine studies and concluded that short-term consumption of beetroot may accelerate the recovery of muscle soreness and various functional markers due to its antioxidant and inflammatory properties likely exerted by its nitrate content and several phenolic compounds (60). Therefore, it could be assumed that consuming foods high in nitrates and phenolic compounds could expedite the injury healing process. Aside from beets, good food sources of nitrates include spinach, radishes, celery, and rhubarb (36).

Lesson C: Low Energy Availability

Energy availability is the amount of energy available after energy expenditure, that is used for bodily functions (9). Thus, LEA is the state of inadequate energy intake relative to energy expenditure (9) and the prevalence for LEA in athletes ranges from 22% to 58% in a given sport (44). LEA can lead to several negative impacts on performance including decreased muscular strength, decreased endurance performance, and decreased responses to training responses and adaptations (50, 70). Additionally, there is an increased injury risk with LEA (29,  56).

The next section of this lesson discusses how LEA can negatively impact the growth and development of a child or adolescent, potentially resulting in poor bone health, delayed puberty, short stature, and menstrual irregularities (15). It also highlights several signs and symptoms felt by an athlete that could indicate LEA (9, 70). 

LEA, with or without the presence of an eating disorder, is a characteristic of the Female Athlete Triad, which is a condition that also includes decreased bone mineral density, and menstrual dysfunction (53, 59). The concept of Relative energy deficiency in sport (RED-S) expands upon the Female Athlete Triad by recognizing a broader range of health consequences including disruptions to the endocrine system, immune system, and cardiovascular health (9). Raising awareness of these signs and symptoms is essential, especially given that knowledge of LEA remains low among both athletes and coaches (44). The lesson concludes with evidence-based strategies to prevent LEA, as well as treatment options to address its underlying causes (9).

Lesson D: Nutrition for Head Injuries

This lesson discusses various nutritional considerations to assist in the healing process for someone who has had a concussion, or other types of mTBI (22, 62). Current concussion rates in youth sports are 4.17 cases per 10,000 athlete exposures (38). There are several nutritional aspects that may support brain health among those recovering from mTBIs (22, 62). Although several macronutrients are considered nutrients of interest during this process (22, 62), this lesson discusses other nutrients and micronutrients (aside from those discussed in previous lessons) that may have a place while recovering from a mTBI, including creatine, magnesium, and flavonoids.

Creatine: Creatine is a compound that is formed in protein metabolism and works to recycle adenosine triphosphate (ATP) for energy metabolism (42). It has been shown that creatine content in the brain is diminished after a mTBI, and increasing its intake could maintain ATP levels in the brain (1, 65). This could help offset injury sustained from the mTBI, such as decreasing protease activation that degrades axon structures (1). Good food sources of creatine listed in this lesson includes lean red meats, fatty fish, pork, and wild game (72). 

Magnesium: Magnesium is a trace mineral that has several functions within the body (27). In the brain, magnesium is involved in efficient nerve signaling and maintaining the blood brain barrier (45). Following a mTBI, magnesium levels decrease in the brain (67), and low magnesium levels have been associated with neuroinflammation and neurodegeneration, including several diseases such as Alzheimer’s and Parkinson’s diseases (67). Research has revealed that magnesium supplementation can reduce concussion symptoms in adolescents following injury (67). Youth aged 9 to 13 years of age need 240 mg of magnesium per day (23). Older adolescent, males aged 14-18 years of age need 410 mg or magnesium per day while their female counterparts need 360 mg per day. Good food sources of magnesium include almonds, cashews, peanut butter, and spinach (27).

Flavonoids: Lastly, flavonoids are phytochemicals found in many fruits and vegetables, that have anti-inflammatory and antioxidant effects, which may reduce swelling after a mTBI (28). Blueberries contain high amounts of flavonoids including anthocyanins, which contribute to the blueberry’s dark color (11). Anthocyanins could lower brain inflammation and stress caused by mTBI (30). Laboratory studies have shown beneficial effects from blueberry supplementation on various cognitive performance outcomes and symptoms following a mTBI (43, 68). Therefore, consuming foods high in flavonoids, including blueberries, could offer a benefit for healing from a head injury.

This lesson concludes with additional nutritional considerations for those recovering from a mTBI. For example, it is suggested to eliminate the consumption of caffeine following a mTBI (65). Other suggestions include taking note of any foods or drinks that cause vomiting or feelings of nausea, and reducing their consumption for a period of time while mTBI symptoms decrease (72).

Lesson E: Nutrition and Sleep for Injury Reduction and Recovery

This lesson highlights the importance of sleep for performance and injury recovery (19, 49). Youth athletes not getting enough sleep are 1.7 times more likely to get injured (52). School-aged children need 9-11 hours of sleep each night, while teenagers need 8-10 hours of sleep per night (31). It is likely an injured athlete should aim for the upper amount of sleep needed per day. Currently, adolescents aged 13-18 years of age are getting on average 7.7 hours of sleep per night, slightly less than the minimum amount needed (48). Several nutrients have been identified that can naturally aid in hormone regulation associated with sleep (55).

Melatonin: Melatonin is a hormone secreted by the pineal gland that is involved in circadian rhythm and increases total sleep time and may reduce time to fall asleep (13, 55). It is found naturally in several foods including tart cherries (18, 51). In addition, tart cherries include other constituents that have anti-inflammatory and antioxidant effects, which may aid in sleep and recovery (8). Other foods with a high melatonin content include milk, pineapples, oranges, and bananas (18, 55).

Serotonin: Serotonin is another hormone involved in sleep by synthesizing hypogenic substances that influence sleep quality (7, 55). Kiwi fruits are a good source of serotonin and contain several minerals, dietary fiber, and phytochemicals that also may aid in sleep (18, 55).

This section of the lesson also includes other nutritional considerations for quality sleep. For example, some foods that contain caffeine, can make it difficult to fall asleep and the recommendation is to reduce or eliminate its intake closer to bedtime (33). This lesson concludes with tips on how to establish an effective sleep routine such as minimizing screen time before it (33).

Lesson F: Gastrointestinal Issues and Sport

This lesson addresses common GI issues encountered in sport and concludes with practical applications for maintaining gut health.

Nausea and Vomiting: Nausea and vomiting are frequent complaints among athletes across various disciplines (77). These symptoms may result from elevated levels of norepinephrine reducing splanchnic blood flow to the gut, delayed gastric emptying, or increased production of gastric bile acids (77). This lesson outlines several risk factors that may contribute to these symptoms along with simple strategies to help prevent them.

Diarrhea: Diarrhea is a common condition experienced by athletes, particularly among endurance athletes (77). Proposed mechanisms include the secretion of vasoactive intestinal peptide which relaxes smooth muscle in the digestive system (35), and changes in gut motility (77). Many of the risk factors associated with diarrhea overlap with those linked to nausea and vomiting. This section concludes with evidence-informed approaches for minimizing the risk of diarrhea during training and competition.

Heartburn: Heartburn is another GI issue sometimes encountered by athletes during exercise and sport and can be caused by increased abdominal pressure, changes in posture, and changes in exercise intensity (74). Additionally, consuming large meals prior to exercise, not being properly hydrated, and having high levels of stress or anxiety can also trigger heartburn. Chronic heartburn could be caused by gastroesophageal reflux disease or GERD (74). This section provides strategies to prevent heartburn during practice or a game, with an emphasis on taking note of such foods that sometimes cause heartburn in an individual.

This lesson concludes by discussing several strategies to maintain gut health and gut microbiota which may impact immunological function and thus injury risk and recovery from them (75). Rationale for its inclusion within this lesson is from the US Olympic & Paralympic Committee sports nutrition handout on nutrients for GI injury (71). Consuming foods high in probiotics may maintain digestion and absorption while also preventing several GI issues described in this lesson (71). Prebiotic fibers are a type of fermentable fiber that stimulates intestinal bacteria growth and activity (64). In addition, prebiotic fiber consumption is associated with several other benefits including increasing the absorption of calcium, improving cognitive health, and reducing risk of some diseases (14). Therefore, it is important to incorporate prebiotic fibers into one’s diet.

CONCLUSIONS

Nutrition is a cornerstone of health and performance for adolescent athletes not only supporting their growth and development but also their ability to train, compete, and recover effectively (15). Integrating sound nutrition practices into youth athlete development programs is essential for promoting lifelong well-being and optimal athletic potential (16). In addition to enhancing performance, proper nutrition can play a key role in preventing injuries and accelerating recovery when injuries occur (3). To emphasize these critical areas, several new lesson have been added as targeted addendums to the PHP curriculum (25). When combined with the original PHP content, these additions aim to strengthen both general and sport-specific nutrition behaviors, equipping young athletes with the knowledge and habits needed to thrive on and off the field.

Following an injury, it is important to consume adequate kcals from protein, carbohydrates, and unsaturated fatty acids, along with being properly hydrated to facilitate recovery (3). Emphasizing certain micronutrients from food may also improve recovery from injury (3). Additionally, nutritional support is needed for athletes recovering from an mTBI (65). LEA is a common problem in youth sports and understanding its consequences and how to prevent it are important for reducing injury risk (9). Getting adequate sleep is important not only for athletic performance, but also injury prevention and healing from an injury (19, 49). Although not a direct injury caused by sport, GI issues can occur during it, and can be prevented using evidence-based nutritional strategies (77). Next steps are to examine adolescent knowledge of nutritional best practices for recovering from sports-induced injuries.

APPLICATIONS IN SPORT
These supplemental lessons are to serve as adjunct lessons to the PHP curriculum and to provide youth athletes with knowledge on injury management and other sports nutrition topics not otherwise discussed in athletic circles. Additionally, the hope is to encourage further research in this understudied area and add to the growing body of literature examining nutrition practices for injury management in youth athletes.

REFERENCES 

1. Ainsley Dean, P. J., Arikan, G., Opitz, B., & Sterr, A. (2017). Potential for use of creatine supplementation following mild traumatic brain injury. Concussion, 2(2), Cnc34. https://doi.org/10.2217/cnc-2016-0016

2. Al-Qahtani, M. A., Allajhar, M. A., Alzahrani, A. A., Asiri, M. A., Alsalem, A. F., Alshahrani, S. A., & Alqahtani, N. M. (2023). Sports-related injuries in adolescent athletes: A systematic review. Cureus, 15(11), e49392. https://doi.org/10.7759/cureus.49392

3. Alcock, R., Hislop, M., Vidgen, H. A., & Desbrow, B. (2024). Youth and adolescent athlete musculoskeletal health: Dietary and nutritional strategies to optimise injury prevention and support recovery. Journal of Functional Morphology and Kinesiology, 9(4). https://doi.org/10.3390/jfmk9040221

4. Becker, T. B., & Gibbs, R. L. (2024). Changes in nutrition, food safety, and physical activity behaviors: A comparison between the Peak Health and Performance and Teen Cuisine Curricula. Journal of Human Sciences and Extension, 12(3), 10.

5. Beech, J., Jones, B., Hughes, T., & Emmonds, S. (2024). Injury profile in youth female athletes: A systematic review and meta-analysis. Sports Medicine, 54(5), 1207-1230. https://doi.org/10.1007/s40279-023-01988-w

6. Bird, S. P., & Rushton, B. D. (2020). Nutritional knowledge of youth academy athletes. BMC Nutrition, 6, 35. https://doi.org/10.1186/s40795-020-00360-9

7. Cespuglio, R. (2018). Serotonin: its place today in sleep preparation, triggering or maintenance. Sleep Medicine, 49, 31-39. https://doi.org/https://doi.org/10.1016/j.sleep.2018.05.034

8. Chai, S. C., Davis, K., Zhang, Z., Zha, L., & Kirschner, K. F. (2019). Effects of tart cherry juice on biomarkers of inflammation and oxidative stress in older adults. Nutrients, 11(2). https://doi.org/10.3390/nu11020228

9. Charlton, B. T., Forsyth, S., & Clarke, D. C. (2022). Low energy availability and relative energy deficiency in sport: What coaches should know. International Journal of Sports Science & Coaching, 17(2), 445-460. https://doi.org/10.1177/17479541211054458

10. Chodkowski, J. (2024). The role of nutrition and hydration in injury prevention and recovery: A review. Journal of Educcation Health and Sport, 71, 56117.

11. Conti, F., McCue, J. J., DiTuro, P., Galpin, A. J., & Wood, T. R. (2024). Mitigating traumatic brain injury: A narrative review of supplementation and dietary protocols. Nutrients, 16(15), 2430. https://www.mdpi.com/2072-6643/16/15/2430

12. Costa, R. J. S., Snipe, R. M. J., Kitic, C. M., & Gibson, P. R. (2017). Systematic review: exercise-induced gastrointestinal syndrome-implications for health and intestinal disease. Aliment Pharmacolology and Therapeutics, 46(3), 246-265. https://doi.org/10.1111/apt.14157

13. Costello, R. B., Lentino, C. V., Boyd, C. C., O’Connell, M. L., Crawford, C. C., Sprengel, M. L., & Deuster, P. A. (2014). The effectiveness of melatonin for promoting healthy sleep: a rapid evidence assessment of the literature. Nutrition Journal, 13, 106. https://doi.org/10.1186/1475-2891-13-106

14. Davani-Davari, D., Negahdaripour, M., Karimzadeh, I., Seifan, M., Mohkam, M., Masoumi, S. J., Berenjian, A., & Ghasemi, Y. (2019). Prebiotics: Definition, types, sources, mechanisms, and clinical applications. Foods, 8(3). https://doi.org/10.3390/foods8030092

15. Desbrow, B. (2021). Youth athlete development and nutrition. Sports Medicine, 51(Suppl 1), 3-12. https://doi.org/10.1007/s40279-021-01534-6

16. Desbrow, B., Burd, N. A., Tarnopolsky, M., Moore, D. R., & Elliott-Sale, K. J. (2019). Nutrition for special populations: Young, female, and masters athletes. International Journal of Sport Nutrition and Exercise Metabolism, 29(2), 220-227. https://doi.org/10.1123/ijsnem.2018-0269

17. Desbrow, B., McCormack, J., Burke, L. M., Cox, G. R., Fallon, K., Hislop, M., Logan, R., Marino, N., Sawyer, S. M., Shaw, G., Star, A., Vidgen, H., & Leveritt, M. (2014). Sports Dietitians Australia position statement: sports nutrition for the adolescent athlete. International Journal of Sport Nutrition and Exercise Metabolism, 24(5), 570-584. https://doi.org/10.1123/ijsnem.2014-0031

18. Doherty, R., Madigan, S., Warrington, G., & Ellis, J. (2019). Sleep and nutrition interactions: Implications for athletes. Nutrients, 11(4). https://doi.org/10.3390/nu11040822

19. Dwivedi, S., Boduch, A., Gao, B., Milewski, M. D., & Cruz, A. I. J. (2019). Sleep and injury in the young athlete. The Journal of Bone and Joint Surgery Reviews, 7(9), e1. https://doi.org/10.2106/jbjs.Rvw.18.00197

20. Dwyer, J. T., Coates, P. M., & Smith, M. J. (2018). Dietary supplements: Regulatory challenges and research resources. Nutrients, 10(1). https://doi.org/10.3390/nu10010041

21. Faizan, U., & Rouster, A. S. (2024). Nutrition and hydration requirements in children and adults. In StatPearls. StatPearls Publishing. Copyright © 2024, StatPearls Publishing LLC.

22. Finnegan, E., Daly, E., Pearce, A. J., & Ryan, L. (2022). Nutritional interventions to support acute mTBI recovery. Frontiers in Nutrition, 9, 977728. https://doi.org/10.3389/fnut.2022.977728

23. Food and Nutrition Board, & Institute of Medicine. (2006). Dietary Reference Intakes: The essential guide to nutrient requirements. National Academies Press.

24. Gibbs, R. L., & Becker, T. B. (2025). General and sport-specific nutrition knowledge and behaviors of adolescent athletes. Journal of the Internation Society of Sports Nutrition, 22(1), 2477060. https://doi.org/10.1080/15502783.2025.2477060

25. Gibbs, R. L., Jr., & Becker, T. B. (2021). An evidence-based sports nutrition curriculum for youth. The Sport Journal, 24(6), 6-unpaginated. <Go to ISI>://CABI:20210342440

26. Giraldo-Vallejo, J. E., Cardona-Guzmán, M., Rodríguez-Alcivar, E. J., Kočí, J., Petro, J. L., Kreider, R. B., Cannataro, R., & Bonilla, D. A. (2023). Nutritional strategies in the rehabilitation of musculoskeletal injuries in athletes: A systematic integrative review. Nutrients, 15(4). https://doi.org/10.3390/nu15040819

27. Gropper, S. S., Smith, J. L., & Carr, T. P. (2018). Advanced nutrition and human metabolism. (7 ed.). Cengage Learning.

28. Hasan, S., Khatri, N., Rahman, Z. N., Menezes, A. A., Martini, J., Shehjar, F., Mujeeb, N., & Shah, Z. A. (2023). Neuroprotective potential of flavonoids in brain disorders. Brain Sciences, 13(9). https://doi.org/10.3390/brainsci13091258

29. Heikura, I. A., Uusitalo, A. L. T., Stellingwerff, T., Bergland, D., Mero, A. A., & Burke, L. M. (2018). Low energy availability is difficult to assess but outcomes have large impact on bone injury rates in elite distance athletes. International Journal of Sport Nutrition and Exercise Metabolism, 28(4), 403-411. https://doi.org/10.1123/ijsnem.2017-0313

30. Henriques, J. F., Serra, D., Dinis, T. C. P., & Almeida, L. M. (2020). The anti-neuroinflammatory role of anthocyanins and their metabolites for the prevention and treatment of brain disorders. International Journal of Molecular Sciences, 21(22). https://doi.org/10.3390/ijms21228653

31. Hirshkowitz, M., Whiton, K., Albert, S. M., Alessi, C., Bruni, O., DonCarlos, L., Hazen, N., Herman, J., Adams Hillard, P. J., Katz, E. S., Kheirandish-Gozal, L., Neubauer, D. N., O’Donnell, A. E., Ohayon, M., Peever, J., Rawding, R., Sachdeva, R. C., Setters, B., Vitiello, M. V., & Ware, J. C. (2015). National Sleep Foundation’s updated sleep duration recommendations: Final report. Sleep Health, 1(4), 233-243. https://doi.org/10.1016/j.sleh.2015.10.004

32. Hulland, S. C., Trakman, G. L., & Alcock, R. D. (2023). Adolescent athletes have better general than sports nutrition knowledge and lack awareness of supplement recommendations: a systematic literature review. British Journal of Nutrition, 1-15. https://doi.org/10.1017/s0007114523002799

33. Imbergamo, C. M., Patankar, A. G., & Milewski, M. D. (2021). Sleep optimization in the young athlete. Journal of the Pediatric Orthopaedic Society of North America, 3(3), 306. https://doi.org/https://doi.org/10.55275/JPOSNA-2021-306

34. Institute of Medicine of the National Academies. (2006). Dietary Reference Intakes.  The essential guide to nutrient requirements. The National Academies Press.

35. Iwasaki, M., Akiba, Y., & Kaunitz, J. D. (2019). Recent advances in vasoactive intestinal peptide physiology and pathophysiology: focus on the gastrointestinal system. F1000Research, 8. https://doi.org/10.12688/f1000research.18039.1

36. Karwowska, M., & Kononiuk, A. (2020). Nitrates/nitrites in food-risk for nitrosative stress and benefits. Antioxidants (Basel), 9(3). https://doi.org/10.3390/antiox9030241

37. Kenney, E. L., Long, M. W., Cradock, A. L., & Gortmaker, S. L. (2015). Prevalence of inadequate hydration among US children and disparities by gender and race/ethnicity: National Health and Nutrition Examination Survey, 2009-2012. American Journal of Public Health, 105(8), e113-118. https://doi.org/10.2105/ajph.2015.302572

38. Kerr, Z. Y., Chandran, A., Nedimyer, A. K., Arakkal, A., Pierpoint, L. A., & Zuckerman, S. L. (2019). Concussion Incidence and Trends in 20 High School Sports. Pediatrics, 144(5). https://doi.org/10.1542/peds.2019-2180

39. Koon, G., Atay, O., & Lapsia, S. (2017). Gastrointestinal considerations related to youth sports and the young athlete. Translational Pediatrics, 6(3), 129-136. https://doi.org/10.21037/tp.2017.03.10

40. Kozhuharov, V. R., Ivanov, K., & Ivanova, S. (2022). Dietary supplements as source of unintentional doping. BioMed Research International, 2022, 8387271. https://doi.org/10.1155/2022/8387271

41. Krabak, B. J., Roberts, W. O., Tenforde, A. S., Ackerman, K. E., Adami, P. E., Baggish, A. L., Barrack, M., Cianca, J., Davis, I., D’Hemecourt, P., Fredericson, M., Goldman, J. T., Harrast, M. A., Heiderscheit, B. C., Hollander, K., Kraus, E., Luke, A., Miller, E., Moyer, M.,…Wasfy, M. M. (2021). Youth running consensus statement: minimising risk of injury and illness in youth runners. British Journal of Sports Medicine, 55(6), 305-318. https://doi.org/10.1136/bjsports-2020-102518

42. Kreider, R. B., Kalman, D. S., Antonio, J., Ziegenfuss, T. N., Wildman, R., Collins, R., Candow, D. G., Kleiner, S. M., Almada, A. L., & Lopez, H. L. (2017). International Society of Sports Nutrition position stand: safety and efficacy of creatine supplementation in exercise, sport, and medicine. Journal of the International Society of Sports Nutrition, 14, 18. https://doi.org/10.1186/s12970-017-0173-z

43. Krishna, G., Ying, Z., & Gomez-Pinilla, F. (2019). Blueberry supplementation mitigates altered brain plasticity and behavior after traumatic brain injury in fats. Molecular Nutrition & Food Research, 63(15), e1801055. https://doi.org/10.1002/mnfr.201801055

44. Logue, D. M., Madigan, S. M., Melin, A., Delahunt, E., Heinen, M., Donnell, S. M., & Corish, C. A. (2020). Low energy availability in athletes 2020: An updated narrative review of prevalence, risk, within-day energy balance, knowledge, and impact on sports performance. Nutrients, 12(3). https://doi.org/10.3390/nu12030835

45. Maier, J. A. M., Locatelli, L., Fedele, G., Cazzaniga, A., & Mazur, A. (2022). Magnesium and the brain: A focus on neuroinflammation and neurodegeneration. International Journal of Molecular Sciences, 24(1). https://doi.org/10.3390/ijms24010223

46. Manore, M. M., Patton-Lopez, M. M., Meng, Y., & Wong, S. S. (2017). Sport nutrition knowledge, behaviors and beliefs of high school soccer players. Nutrients, 9(4). https://doi.org/10.3390/nu9040350

47. Marshall, R. N., Smeuninx, B., Morgan, P. T., & Breen, L. (2020). Nutritional strategies to offset disuse-induced skeletal muscle atrophy and anabolic resistance in older adults: From whole-foods to isolated ingredients. Nutrients, 12(5). https://doi.org/10.3390/nu12051533

48. Maslowsky, J., & Ozer, E. J. (2014). Developmental trends in sleep duration in adolescence and young adulthood: evidence from a national United States sample. Journal of Adolescent Health, 54(6), 691-697. https://doi.org/10.1016/j.jadohealth.2013.10.201

49. Mason, L., Connolly, J., Devenney, L. E., Lacey, K., O’Donovan, J., & Doherty, R. (2023). Sleep, nutrition, and injury risk in adolescent athletes: A narrative review. Nutrients, 15(24). https://doi.org/10.3390/nu15245101

50. Melin, A. K., Areta, J. L., Heikura, I. A., Stellingwerff, T., Torstveit, M. K., & Hackney, A. C. (2024). Direct and indirect impact of low energy availability on sports performance. Scandinavian Journal of Medicine & Science in Sports, 34(1), e14327. https://doi.org/10.1111/sms.14327

51. Meng, X., Li, Y., Li, S., Zhou, Y., Gan, R. Y., Xu, D. P., & Li, H. B. (2017). Dietary sources and bioactivities of melatonin. Nutrients, 9(4). https://doi.org/10.3390/nu9040367

52. Milewski, M. D., Skaggs, D. L., Bishop, G. A., Pace, J. L., Ibrahim, D. A., Wren, T. A., & Barzdukas, A. (2014). Chronic lack of sleep is associated with increased sports injuries in adolescent athletes. Journal of Pediatric Orthopaedics, 34(2), 129-133. https://doi.org/10.1097/bpo.0000000000000151

53. Nazem, T. G., & Ackerman, K. E. (2012). The female athlete triad. Sports Health, 4(4), 302-311. https://doi.org/10.1177/1941738112439685

54. Papadopoulou, S. K. (2020). Rehabilitation nutrition for injury recovery of athletes: The Role of macronutrient intake. Nutrients, 12(8). https://doi.org/10.3390/nu12082449

55. Pattnaik, H., Mir, M., Boike, S., Kashyap, R., Khan, S. A., & Surani, S. (2022). Nutritional elements in sleep. Cureus, 14(12), e32803. https://doi.org/10.7759/cureus.32803

56. Prus, D., Mijatovic, D., Hadzic, V., Ostojic, D., Versic, S., Zenic, N., Jezdimirovic, T., Drid, P., & Zaletel, P. (2022). (Low) energy availability and its association with injury occurrence in competitive dance: Cross-sectional analysis in female dancers. Medicina (Kaunas), 58(7). https://doi.org/10.3390/medicina58070853

57. QuickStats: Rates of injury from sports, recreation, and leisure activities among children and adolescents aged 1–17 years, by age group — National Health Interview Survey, United States, 2015–2017. (2019). Morbidity and Mortality Weekly Report, 68, 466.

58. Quintero, K. J., Resende, A. d. S., Leite, G. S. F., & Lancha Junior, A. H. (2018). An overview of nutritional strategies for recovery process in sports-related muscle injuries. Nutrire, 43(1), 27. https://doi.org/10.1186/s41110-018-0084-z

59. Raj, M., Creech, J., & Rogol, A. (2024). Female athlete triad. StatPearls Publishing. Retrieved February 3 from https://www.ncbi.nlm.nih.gov/books/NBK430787/

60. Rojano-Ortega, D., Peña Amaro, J., Berral-Aguilar, A. J., & Berral-de la Rosa, F. J. (2022). Effects of beetroot supplementation on recovery after exercise-induced muscle damage: A systematic review. Sports Health, 14(4), 556-565. https://doi.org/10.1177/19417381211036412

61. Ryan, J. L., Pracht, E. E., & Orban, B. L. (2019). Inpatient and emergency department costs from sports injuries among youth aged 5-18 years. BMJ Open Sport & Exercise Medicine, 5(1), e000491. https://doi.org/10.1136/bmjsem-2018-000491

62. Ryan, T., Nagle, S., Daly, E., Pearce, A. J., & Ryan, L. (2023). A potential role exists for nutritional interventions in the chronic phase of mild traumatic brain injury, concussion and sports-related concussion: A systematic review. Nutrients, 15(17). https://doi.org/10.3390/nu15173726

63. Sheehan, N., Summersby, R., Bleakley, C., Caulfield, B., Matthews, M., Klempel, N., & Holden, S. (2024). Adolescents’ experience with sports-related pain and injury: A systematic review of qualitative research. Physical Therapy in Sport, 68, 7-21. https://doi.org/https://doi.org/10.1016/j.ptsp.2024.05.003

64. Slavin, J. (2013). Fiber and prebiotics: mechanisms and health benefits. Nutrients, 5(4), 1417-1435. https://doi.org/10.3390/nu5041417

65. Smith-Ryan, A. E., Hirsch, K. R., Saylor, H. E., Gould, L. M., & Blue, M. N. M. (2020). Nutritional considerations and strategies to facilitate injury recovery and rehabilitation. Journal of Athletic Training, 55(9), 918-930. https://doi.org/10.4085/1062-6050-550-19

66. Smith, J. W., Holmes, M. E., & McAllister, M. J. (2015). Nutritional considerations for performance in young athletes. Journal of Sports Medicine (Hindawi Publ Corp), 2015, 734649. https://doi.org/10.1155/2015/734649

67. Standiford, L., O’Daniel, M., Hysell, M., & Trigger, C. (2021). A randomized cohort study of the efficacy of PO magnesium in the treatment of acute concussions in adolescents. The American Journal of Emergency Medicine, 44, 419-422. https://doi.org/https://doi.org/10.1016/j.ajem.2020.05.010

68. Sweeney, M. I., Kalt, W., MacKinnon, S. L., Ashby, J., & Gottschall-Pass, K. T. (2002). Feeding rats diets enriched in lowbush blueberries for six weeks decreases ischemia-induced brain damage. Nutritional Neuroscience, 5(6), 427-431. https://doi.org/10.1080/1028415021000055970

69. Thomas, D. T., Erdman, K. A., & Burke, L. M. (2016). Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and athletic performance. Journal of the Academy of Nutrition and Dietetics, 116(3), 501-528. https://doi.org/10.1016/j.jand.2015.12.006

70. United States Olypmic & Paralymipic Committee Sport Nutrition Team. (2020a). Low energy availability. Retrieved December 17 from chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://assets.contentstack.io/v3/assets/blt9e58afd92a18a0fc/bltaa7a0653bc475a00/6470c42af0d737510db117f0/LEA_Fact_Sheet.pdf

71. United States Olypmic & Paralymipic Committee Sport Nutrition Team. (2020b). Nutrients for GI injury. Retrieved December 17 from https://assets.contentstack.io/v3/assets/blt9e58afd92a18a0fc/bltea94cc2ce765e25a/6470c42b20efde585ba537d8/NutrientsforGIInjury2020.pdf

72. United States Olypmic & Paralymipic Committee Sport Nutrition Team. (2020c). Nutrients for head injury recovery. Retrieved December 17 from https://assets.contentstack.io/v3/assets/blt9e58afd92a18a0fc/blta749d96eff2de64b/6470c42baf1a6843564609ee/NutrientsforHeadInjury2020.pdf

73. Wardenaar, F. C., Schott, K. D., Mohr, A. E., Ortega-Santos, C. P., & Connolly, J. E. (2023). An exploratory study investigating the prevalence of gastrointestinal symptoms in collegiate division I American football athletes. International Journal of Environ Research and Public Health, 20(15). https://doi.org/10.3390/ijerph20156453

74. Waterman, J. J., & Kapur, R. (2012). Upper gastrointestinal issues in athletes. Current Sports Medicine Reports, 11(2), 99-104. https://doi.org/10.1249/JSR.0b013e318249c311

75. Wiertsema, S. P., van Bergenhenegouwen, J., Garssen, J., & Knippels, L. M. J. (2021). The interplay between the gut microbiome and the immune system in the context of infectious diseases throughout life and the role of nutrition in optimizing treatment strategies. Nutrients, 13(3). https://doi.org/10.3390/nu13030886

76. Zamani, H., de Joode, M. E. J. R., Hossein, I. J., Henckens, N. F. T., Guggeis, M. A., Berends, J. E., de Kok, T. M. C. M., & van Breda, S. G. J. (2021). The benefits and risks of beetroot juice consumption: a systematic review. Critical Reviews in Food Science and Nutrition, 61(5), 788-804. https://doi.org/10.1080/10408398.2020.1746629

77. Zaslow, T., & Mitrovich, C. (2023). Chapter 16 – Gatrointestinal and abdominal issues. In B. J. Krabak & A. Brooks (Eds.), The Youth Athlete (pp. 159-172). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-323-99992-2.00090-6

2025-09-25T16:05:07-05:00February 18th, 2026|Research, Sport Education, Sport Training, Sports Medicine, Sports Nutrition|Comments Off on Supplemental lessons to the Peak Health and Performance curriculum: Nutritional considerations for injury, energy management, and gastrointestinal issues
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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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