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

Authors: Michael J. Diacin1

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

 

Corresponding Author:

Michael J. Diacin, Ph.D.

1400 E. Hanna Ave.

Indianapolis, IN 46227

(317)791-5703

[email protected]

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

ABSTRACT 

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

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

KEYWORDS: management, incentives, employees

INTRODUCTION 

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

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

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

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

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

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

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

INITIATIVES TO REDUCE TURNOVER AND OFFSET STAFF SHORTAGES 

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

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

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

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

“Survive the Day” Initiatives

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

“Survive the Season” Initiatives

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

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

Recognition for Performance Initiatives

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

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

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

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

PROJECT DETAILS

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

Facility Setting and Description

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

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

Facility Operating Schedule

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

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

Full-Time Manager Descriptions

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

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

Part Time Staff Descriptions

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

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

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

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

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

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

Concessions

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

Skating staff

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

Pro shop staff

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

Fitness center workers

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

After hours reception desk

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

Building attendant/ice resurface machine driver

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

Custodial

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

APPLICATION TO SPORT MANAGEMENT

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

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

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

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

REFERENCES 

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

2025-12-05T10:58:04-06:00June 3rd, 2026|Contemporary Sports Issues, General, Leadership, Research, Sports Management, Sports Studies|Comments Off on Reducing absenteeism and turnover among part-time labor in community sport settings: A case study example and project guidelines for sport management students

An Analysis of Carbon Emissions from College Football Recruiting Visits

Authors: Jeffrey J. Fountain1, Thomas Wuerzer2, & Peter S. Finley1

1Department of Management, Nova Southeastern University, Fort Lauderdale, FL, USA

2Department of Public Administration and Real Estate Development, Nova Southeastern University, Fort Lauderdale, FL, USA

 

Corresponding Author:

Jeffrey J. Fountain, Ph.D.

3301 College Avenue

Fort Lauderdale, FL 33314

[email protected]

954-262-8129

Jeffrey Fountain, Ph.D., and Peter Finley, Ph.D., are Professors of Sport Management at the H. Wayne Huizenga College of Business and Entrepreneurship at Nova Southeastern University. Their research interests focus on issues in college athletics.

Thomas Wuerzer, Ph.D., is Professor in the Department of Public Administration & Real Estate Development at Nova Southeastern University. His research focus is on Geographic Information Systems.

ABSTRACT 

Recruiting college football players to come play for a National Collegiate Athletic Association (NCAA) Power-5 school is highly competitive, with each school inviting recruits nationwide on official campus visits. By estimating the carbon emissions generated, this study examined the environmental impact of official recruiting visits (n = 7,045) to Power-5 schools from 2013 to 2020. Using Geographic Information Systems (GIS) to geocode recruits’ hometowns and calculate travel distances, a Recruit Visit Carbon Footprint (RVCF) was calculated to approximate the CO2eq emissions for each visit. The analysis focused on the 23 Power-5 schools with over 250 reported official visits. The findings revealed substantial variability in RVCF among schools, with 15 of the 23 schools increasing their carbon footprint in the latter years of the study. Still, the higher-spending athletic departments tended to have lower RVCFs. The findings provide valuable insights into the environmental impact of recruiting activities and highlight the importance of addressing this overlooked aspect of college sports’ carbon emissions.

KEYWORDS: Carbon Footprint, Power-5, Recruiting, Official Visit, College Football

INTRODUCTION 

As societal awareness of the environmental impact of both mega sporting events and routine contests (regular season games) has increased, many sports organizations, teams, and sponsors have come to understand the need to assess the carbon footprint they create (10). As noted by Dolf et al. (13), several researchers have stressed that sports events are worth investigating to leverage broader change because of the high-profile nature of such events, because they are capable of creating real and meaningful action (11, 19). Several athletic departments have promoted their initiatives throughout the last decade and publicized their efforts to reduce and offset their environmental impact by tracking and reducing carbon dioxide-equivalent emissions (CO2eq). The typical path toward claiming to be carbon neutral for college athletic departments is to assess the environmental impact of the day-to-day operations, home game operations, and off-campus travel for official tournaments and games. However, it is important to recognize that the carbon footprint begins long before sporting events are played; for college sports, this goes back to the initial recruitment phase of the athletes, which typically requires them to travel as part of the recruiting process.

In 2020, the Power-5 conferences included the Atlantic Coast Conference (ACC), Big 12 Conference, Big Ten Conference, Pacific 12 Conference, and the Southeastern Conference (SEC). Over the years, the number of Power-5 schools increasing their investment in recruiting athletes has grown, with 38 of the 52 public Power-5 schools reporting a significant growth in overall athletic department recruiting expenditures (37). One extreme example was the University of Georgia’s athletic department, which increased its overall recruiting budget from $308,000 in 2005 to $4.5 million by 2022 (23).

Recruiting

Each recruit is permitted one official visit per school, extendable only if there is a change in the coaching staff, with each visit lasting no more than 48 hours or one weekend (29). Visits are classified by the funding source; when the host school covers expenses such as transportation, lodging, meals, and entertainment for the recruit and their parents or guardians, it is deemed an official visit (29). Historically, recruits were limited to five official visits; however, this cap was removed in 2023, allowing unlimited visits while maintaining the “only one visit per school” rule (30).

College football recruiting visits often feature expensive, extravagant events designed to attract recruits (12, 24, 36). The financial commitment to a recruiting weekend at Clemson University in the fall of 2019, during which the Tigers brought eleven prospects to campus (they would eventually sign ten of them), ended with a total bill of $85,000 (32). While the NCAA prohibits media from attending recruiting events or interacting with prospects while on campus, the expenditures from that weekend provided insight into the itinerary, which included travel by professional car service to and from local airports, flights to Greenville-Spartanburg, and transportation to the campus, about 40 miles away. In addition, two charter buses were used to transport prospects and their families to the finest restaurants in the area, including a steakhouse about 45 minutes from campus (32). Another example was the University of Texas spending over $280,000 during a single weekend in June 2022 to host nine recruits, including highly touted quarterback Arch Manning (20).

Carbon Footprinting

The concept and measurement of an “ecological footprint” was introduced by Wackernagel and Rees (34) and originally quantified the land and sea area necessary to support human populations. Subsequent adaptations of this concept have focused on the “carbon footprint,” which estimates the land required to sequester CO2 emissions attributable to human activities. This notion has evolved into broader assessments such as the “life cycle impact,” which evaluates the environmental impact of products and services throughout their life cycles (31).

Research by Čuček et al. (9) and Pandey et al. (31) expanded the scope of assessment to include calculating sustainability metrics and measuring energy, water, and ecological impacts. These studies defined a carbon footprint as “the quantity of Greenhouse Gases (GHGs), expressed in terms of CO2 equivalents, emitted by an individual, organization, process, product, or event within a specified boundary” (31) and as “a quantitative measurement describing the appropriation of natural resources by humans,” (9). This study adopted these definitions to evaluate the carbon footprint of prospective college football players while making their official recruiting visits to college campuses.

Attempts to measure carbon footprint related to sports have historically focused on major events and the travel of sports teams. Examples include the findings that approximately 560 tons of CO2eq was created during the 2004 Football Association (FA) Cup Final in the United Kingdom (one soccer game) (4), 1,260 tons of CO2eq for the 2004 Wales Rally (an Autosport’s event over four days) (5), and 144,120 tons of CO2eq for the stages of the Tour de France held in the United Kingdom in 2007 (the Prologue and Stage One) (6). Most studies focused solely on the carbon footprint of spectators, though a limited number of studies examined participants, such as teams and staff members.

The environmental impact of all college activities, including collegiate sports has garnered significant attention (28). However, there appears to be no available research that has explicitly focused on the environmental impact (carbon footprint) produced throughout the college football recruiting season. Therefore, the researchers sought to explore and determine the approximate carbon emissions produced during official college football recruiting visits from Power-5 schools. This study utilized the reported official recruiting visits between 2013 and 2020. Using Geographic Information Systems (GIS) to conduct spatial analysis of multimodal travel, including car and plane trips, the researchers were able to calculate the approximate travel distances and corresponding carbon footprint of each recruit.

The Recruit Visit Carbon Footprint (RVCF) was created as a proxy measure utilizing prior carbon footprinting research of sport tourism. This approach enabled a systematic exploration of three primary research questions.

RQ1: Which Power-5 schools generated the largest RVCF between 2013 and 2020?

RQ2: Did RVCF totals increase or decrease over time?

RQ3: Was there a correlation between each school’s financial, recruiting, and performance variables and their RVCF?

METHODS 

Data Collection

Data on official recruiting visits, published by 247sports.com, was collected for the years 2013 to 2020. Previous research has utilized data from 247sports.com, recognizing it as a well-established source of college football recruiting information (21, 27, 35). The dataset included dates of official school visits and recruits’ hometowns. Prior research also utilized GIS to geocode locations such as athletes’ hometowns or high school locations for analysis (1, 26, 38). GIS geocoding takes a specific location, such as addresses or towns, and references it as a mapped location. Therefore, this study geocoded each football recruit’s hometown, the location of each college visited, and the closest major airport to calculate the approximate travel distances for spatial analysis.

The study utilized ESRI ArcPRO 3.5 (Esri, Redlands, CA, USA) software with the Business Analyst extension to geocode the dataset. To focus on the highest-producing RVCF programs and to make the data set more manageable, a minimum threshold of 250 visits was established. Of the 64 Power-5 schools, 23 (35.9%) met the 250-visitor threshold, totalling 7,045 reported official visits. The travel routes for each visit were then calculated using GIS to determine the most efficient mode of travel. Driving directly to the school was the most efficient mode for 1,636 visits. Typically, these distances were around 200 miles or less to the campus. For recruits living over 200 miles from the visiting campus, if their distance from their home to an airport necessitated a long drive followed by a flight, driving was deemed more efficient due to the extensive travel time involved in flying to the campus. For the remaining 5,409 visits, air travel was deemed the most efficient mode. For these visits, three travel distances were calculated: 1) the drive from the recruits’ hometown to the nearest major airport, 2) the flight miles from that airport to the nearest major airport to the campus they visited, and 3) the drive from that airport to the campus. These distances were doubled to account for the return trip and integrated into a travel matrix to approximate CO2eq emissions from transportation.

Additionally, financial data for athletic departments (i.e., Football Revenue, Football Recruiting was sourced from the Knight-Newhouse College Athletics database (25), team performance was sourced from ESPN.com (16). The descriptions and summary statistics for these variables are provided in Table 1. Utilizing these variables allowed for additional analysis to explore potential correlations between an athletic department’s RVCF and financial data, performance data, and recruiting data.

Table 1 Descriptive Analysis of Variables: Mean and Standard Deviation
VariableDescriptionMeanSD
FB_TotalRevTotal Revenue from Football$66,518,526$25,205,244
Mens_TotalRevTotal Revenue from all Men’s Sports (including Football)$84,428,967$25,300,581
FB_MensRev%Football’s Revenue as a Percentage of all Men’s Sports Revenues77.40%11.17%
Dept_TotalRevTotal Revenue from the entire Athletic Department$125,143,966$31,108,327
FB_DeptRev%Football’s Revenue as a Percentage of the entire Athletic Department Revenues52.50%13.10%
Mens_RecruitExpTotal Recruiting Expenses from all Men’s Sports (including Football)$1,391,362$704,861
Dept_RecruitExpTotal Recruiting Expenses from the entire Athletic Department$1,878,962$855,080
FB_OpsExpTotal Operation Expenses for Football$5,683,499$2,558,649
Mens_OpsExpTotal Operation Expenses for all Men’s Sports (including Football)$8,800,193$4,035,500
Dept_OpsExpTotal Operating Expenses for the entire Athletic Department$12,787,529$5,068,156
FB_TotalExpTotal Expenses for the entire Football Program$33,846,192$11,218,516
Mens_TotalExpTotal Expenses for all Men’s Sports Programs (including Football)$53,035,310$13,927,935
FB_MensExp%Football Expenses as a Percentage of all Men’s Sports Expenses63.18%7.58%
Dept_TotalExpTotal Expenses for the entire Athletic Department$116,141,712$27,071,219
FB_DeptExp%Football Expenses as a Percentage of the entire Athletic Department Expenses63.18%7.58%
Win_PercentageFootball teams Win Percentage62.43%19.97%
    

Recruit Visit Carbon Footprint

Calculating CO2eq emissions from travel can vary depending on the methods and formulas used. In this study, the researchers approximated the RVCF utilizing established methods from prior sport tourism carbon footprint research. The framework by Franchetti and Apul (18) required three boundaries. 1) Temporal Boundary, which refers to the period used for analysis, which, in this study, included Power-5 official recruiting visits from 2013 to 2020. 2) Organizational Boundary, which defines the measured entity, ensuring that only emissions produced from the designated entity are included. Here, it refers to the travel for a single recruit’s official visit to a Power-5 school. 3) Operational Boundary, which is based on the scope of emissions, including direct emissions, indirect emissions, and indirect products. The operational boundary was set at direct emissions only for this study.

In order to operationalize the boundaries, calculations were used to approximate each recruit’s carbon footprint as they travelled from their hometown to their selected school for an official recruiting visit. Cooper’s (2020) approximation of the University of Tennessee’s football gameday tourism carbon footprint was used as a framework for this study. The method for approximating the carbon footprint of sport tourism was applied to the dataset to calculate the approximated total amount of CO2eq emissions produced by each recruiting visit. The total carbon footprint of each visit was calculated by considering direct emissions from transportation (car and flight miles), food consumption per day, waste per day, and hotel stays (8, 14). The EPA formula for the average gasoline-powered passenger vehicle (3.91 × 10^-4 metric tons CO2eq per mile) was applied and converted into kilograms (15). For air travel emissions, the formula (air miles × 0.24 × 1.891) combined the Blue Sky Model formula and the Carbon Fund’s radiative forcing factor (1.891) to provide a total CO2eq per person per pound figure, which was then converted to kilograms (2, 3). Hotel accommodation emissions were calculated using Filimonau’s (17) factor of 11.65 kg CO2eq per night, multiplied by two to account for the typical two-night stay during a recruiting visit. For food and waste emissions, factors from Cooper’s (7) study were used: 7.4 kg CO2eq per person per day for food and 1.1 kg CO2eq per day for waste, multiplied by two for the typical 48-hour visit. Utilizing these formulas allowed the researchers to approximate the RVCF for each reported recruiting visit.

RESULTS AND DISCUSSION

Over the eight years, the top 23 highly-visited schools collectively emitted 2.3 million kg of CO2eq, averaging 328 kg CO2eq per recruiting visit. For context, the global average annual CO2eq emission per person is approximately 4.7 tons (4,263 kg), according to the IEA (22). Thus, the CO2eq for a single 48-hour recruiting visit represents about 7.7% of the average person’s global annual CO2eq footprint.

Table 2 provides a breakdown of RVCF variables along with the means and totals for all 23 schools to address RQ1, “Which Power-5 schools generated the largest RVCF between 2013 and 2020?” Washington State (n = 276) reported the highest total RVCF at 171,489.84 kg CO2eq, and the highest mean RVCF at 621.34 kg CO2eq. In contrast, the University of South Carolina (n = 263) had the smallest carbon footprint, with a total RVCF of 55,621.71 kg CO2eq and an average RVCF per visit of 211.49 kg CO2eq. All official visits to Washington State and South Carolina are depicted using GIS maps in Figure 1, which shows Washington State attracted several recruits from the Midwest, Florida, and Texas. At the same time, South Carolina only invited a few recruits who required a long-distance flight to visit Columbia, South Carolina.

Table 2   RVCF by school for all reported official visits from 2013 to 2020  
Schooln% Drove (No Flight)Car (No Flight)Car  (To/From Airport)FlightHotelFoodWasteMSDTotal
Washington State2762.17%564.4821,782.22137,796.796,405.954,084.79855.60621.34362.98171,489.84
Oregon2813.20%667.817,322.00150,332.796,522.004,158.79871.10604.54323.99169,874.50
Nebraska3735.90%1,437.039,203.29131,283.868,657.325,520.391,156.30421.60171.96157,258.20
Alabama37817.99%6,138.0121,504.7481,842.828,773.375,594.391,171.80330.34219.30125,025.12
Minnesota32815.55%1,543.639,279.4494,019.657,612.874,854.391,016.80360.75190.85118,326.78
Louisville3437.87%1,814.239,356.0792,166.147,961.025,076.391,063.30342.38189.87117,437.16
Oklahoma31521.27%7,627.739,318.5385,483.067,311.144,661.99976.50364.79187.59115,378.96
Tennessee35614.89%5,866.3411,129.2172,691.178,262.755,268.791,103.60293.04213.71104,321.87
Texas A&M32746.18%15,370.2818,162.7656,491.357,589.664,839.591,013.70314.50223.19103,467.35
Washington25122.31%2,329.335,626.1481,958.425,825.713,714.80778.10399.33250.68100,232.49
Ohio State30126.58%7,676.345,596.1469,715.536,986.204,454.79933.10316.82223.4995,362.10
Arkansas32515.38%4,563.0713,222.0664,152.597,543.244,809.991,007.50293.23166.9795,298.46
Indiana27315.38%3,307.9013,293.6462,085.006,336.324,040.39846.30329.34169.1389,909.57
Florida33328.53%8,658.696,782.7656,932.067,728.924,928.391,032.30258.45179.7686,063.13
Miami30139.53%3,920.904,735.8663,566.686,986.204,454.79933.10280.01263.3084,597.54
Florida State31714.20%3,929.026,600.1159,945.347,357.564,691.59982.70262.44182.3083,506.32
Auburn31335.14%9,914.5514,971.1242,452.127,264.724,632.39970.30256.25161.7180,205.21
Georgia27033.70%6,709.3215,279.3141,800.056,266.693,995.99837.00276.78196.7074,888.37
Penn State25429.53%9,210.7316,663.5938,471.375,895.343,759.20787.40289.49192.6574,787.61
Mississippi State29356.31%17,224.2017,413.1326,745.766,800.524,336.39908.30250.61176.9073,428.32
Kentucky27419.71%4,331.997,066.4249,895.236,359.534,055.19849.40264.81146.5772,557.76
LSU30038.00%7,465.215,491.1341,783.316,962.994,439.99930.00222.53146.7367,072.63
South Carolina26332.70%7,526.485,361.7631,921.566,104.233,892.40815.30211.49128.8055,621.71
Total7,04523.57%137,797.27255,161.411,633,532.66163,514.32104,265.8721,839.48328.91203.012,316,111.00
Note: Car, Flight, Hotel, Food, Waste, Mean, Standard Deviation, and Total are in kg CO2eq

To explore the second research question, “Did RVCF totals increase or decrease over time?” the dataset needed to be segmented. During this time period college football programs did not get an entirely new roster of players each year; consequently, examining each year’s change would yield varying results based on how many recruits the school needed that year. Rosters typically turn over every 4 to 5 years. Therefore, with eight years of data available, the dataset was subdivided into two four-year periods (2013-2016 and 2017-2020) to better examine changes over a longer period of time.

Table 3 shows the schools with the largest changes in their mean RVCFs. Fifteen schools experienced an increase in mean RVCF between the two time periods. Ohio State had the largest increase in mean difference (MD = 74.77 kg CO2eq), with its mean RVCF rising from 280.80 kg CO2eq in 2013-2016 to 355.57 kg CO2eq in 2017-2020. Oregon saw the largest overall increase in total RVCF, increasing 29,617.65 kg CO2eq during the latter period. Figure 2 utilizes GIS maps to depict all recruiting visits to Ohio State for each period, highlighting an expanded recruiting range that targeted more players from Texas and the Western United States. Conversely, eight schools showed a reduction in mean RVCF between the two time periods, with the University of Miami experiencing the largest decrease in mean difference (MD = -61.96 kg CO2eq). Although Washington State’s mean reduction was not as considerable as the bottom three schools, it had the largest total reduction in RVCF, decreasing by 19,562.28 kg CO2eq between the two periods.

Table 3 Largest Mean Difference in RVCF between the two time periods
 2013-2016 2017-2020 
SchoolsnTotalM nTotalM DifferenceMD
Ohio State15643,804.86280.80 14551,557.24355.57 7,752.3974.77
Penn State10225,847.68253.41 15247,683.11313.70 21,835.4460.30
Oregon12270,128.43574.82 15999,746.07627.33 29,617.6552.51
Florida St.16546,656.85282.77 15236,535.52240.37 -10,121.32-42.40
Arkansas15349,311.35322.30 17245,987.11267.37 -3,324.24-54.93
Miami15146,944.88310.89 15037,339.37248.93 -9,605.51-61.96
Note: Totals and Means are in kg CO2eq

Wuerzer et al. (38) identified county-level geographical hotspots in the United States overproducing elite college football talent, necessitating migration to other states to find available roster spots on Power-5 football teams. Consequently, Power-5 schools in regions with minimal elite talent and far from these hotspots must expand their recruiting efforts, increasing their RVCF. Schools that rely heavily on air travel for recruiting will naturally have a larger carbon footprint, as air travel is the primary contributor to total RVCF. This is evident from the top three schools with the highest total RVCF also have the lowest percentages of recruits visiting within driving distance to their campuses (Washington State (2.17%), Oregon (3.20%), and Nebraska (5.90%)). Despite this, schools still make strategic choices in their recruiting practices. For example, as shown in Figure 1, Washington State invited several recruits from Florida, a state with prominent county-level recruiting hotspots, instead of focusing on nearby regions or closer recruiting hotspots in California and Texas.

A Pearson correlation coefficient analysis was conducted to address research question three: “Were there any correlations between schools’ financial, recruiting, and performance variables and their RVCF?” The analysis identified two significant correlations, both negative: Total RVCF and Athletic Department Total Annual Revenue [r(176) = -.202, p = .007] and Athletic Department Total Annual Expenses [r(176) = -.198, p = .008]. These findings suggest that athletic departments with higher revenues and expenses tend to have lower RVCFs. This could be attributed to the fact that Power-5 programs with substantial financial resources often have well-established and highly regarded football programs, enabling them to attract top recruits from within a closer geographical range. Consequently, these programs would be less dependent on long-distance recruiting, which typically requires greater air travel, the primary contributor to a school’s RVCF, thereby lowering their overall RVCF.

Overall, these findings highlight the multifaceted nature of college football recruiting, shaped by a complex interplay of positional needs, recruits’ availability, and recruits’ geographical location. The competitive nature of Power-5 college football recruiting requires substantial time and effort to build top-tier recruiting classes, prompting many schools to expand their recruiting reach over time, which subsequently increases their RVCF. The findings show that 15 of the 23 schools increased their RVCF over the two periods. Given the fierce competition for elite talent, it is unlikely that any football program would willingly reduce its recruiting-related carbon emissions if it jeopardizes on-field performance. This creates a significant challenge for universities wanting to adopt more sustainable operations.

CONCLUSION 

This study provides a substantial initial assessment of the carbon footprint associated with college football recruiting. By utilizing GIS for recruits’ hometowns, college locations, and nearest major airports to calculate travel distances, the researchers provided an approximation of each school’s RVCF Recruiting Visit Carbon Footprint (RVCF). The findings reveal substantial variability in RVCF among schools, highlighting the different levels of environmental impact of recruiting. The study also found that higher-spending athletic departments tended to have lower RVCFs, suggesting that successful programs may not need to extend their recruiting reach as widely.

However, several limitations must be acknowledged. The data for this study came from a third-party recruiting website, thus allowing for only an approximate carbon footprint for each official visit. Also, various models and formulas can be used to estimate CO2eq emissions from travel, but each carries assumptions and biases. Moreover, policy changes during the study period, such as the NCAA’s 2016 rule change allowing schools to cover travel costs for up to two parents or guardians accompanying a recruit, could result in a higher actual carbon footprint than the reported RVCF from this study (33). More detailed research is essential for a more accurate and comprehensive understanding of the carbon emissions associated with college football recruiting. Unfortunately, without a governing body mandating standardized reporting of recruiting carbon emissions using consistent formulas, it will remain difficult to fully assess and compare the carbon emissions of different athletic departments.

APPLICATIONS IN SPORT

For universities aiming to reduce their athletic department’s carbon footprint, including all recruiting activities in their calculations is crucial. A comprehensive approach would enable the development of effective strategies that promote sustainability without sacrificing athletic success. Athletic departments can better incorporate sustainability into their planning and decision-making processes by understanding the true carbon footprint generated by each sport, school, and conference.

ACKNOWLEDGMENTS
This research was supported by a college-level seed grant focused on sustainability issues from the Huizenga College of Business and Entrepreneurship’s Societal Impact Seed Grant program.

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2025-10-13T15:18:13-05:00May 27th, 2026|Contemporary Sports Issues, Research, Sports Studies, Sports Studies and Sports Psychology|Comments Off on An Analysis of Carbon Emissions from College Football Recruiting Visits

Over-promised, under-delivered: Does position in the National Football League draft matter?

Authors: Dennis M. Shaffer1 and Ryanne E. Shaffer

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

 

Corresponding Author:

Dennis M. Shaffer, PhD

1760 University Drive

Mansfield, OH 44906

[email protected]

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

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

ABSTRACT 

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

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

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

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

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

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

INTRODUCTION 

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

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

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

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

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

STUDY 1

METHODS

Data Sets

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

Procedure

Evaluating a Player’s First Four Years in the NFL

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

Understanding the Pro Football Focus Grading System

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

Calculation and Coding of PFF Grades and Years in the League

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

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

Availability of Data and Material

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

RESULTS

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

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

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

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

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

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

 Coded Years in League

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

 PFF Overall Mean Grade

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

Figure 1.

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

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

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

PFF Overall Mean Grade

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

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

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

Table 1.

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

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

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

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

STUDY 2

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

METHODS

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

Raters

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

Procedure     

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

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

RESULTS

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

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

DISCUSSION 

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

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

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

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

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

CONCLUSION 

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

APPLICATIONS IN SPORT

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

ACKNOWLEDGEMENTS

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

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

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

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

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

A Manual Therapy Treatment for Headache Pain

Authors: Lindsay C. Luinstra1, Dan Sigley1, Heidi A. VanRavenhorst-Bell1

Corresponding Author:

Dr. Lindsay Luinstra, DAT, MS, LAT, ATC

1845 Fairmount Street,

Box 16,

Wichita, KS 67260

[email protected]

(316) 978-5440


1Department of Human Performance Studies, Wichita State University, Wichita, KS, USA

Dr. Lindsay Luinstra, DAT, MS, LAT, ATC is an assistant professor of athletic training at Wichita State University in Wichita, KS. Her research interest is in sports medicine and manual therapy techniques to treat athletic-related injury.

Dr. Dan Sigley, DAT, LAT, ATC is an assistant professor of athletic training at Wichita State University in Wichita, KS. His research interest is in concussion education, evaluation, and treatment paradigms.

Dr. Heidi A. VanRavenhorst-Bell, PhD is Chair and Associate Professor in the Department of Human Performance Studies and Manager of the Human Performance Laboratory at Wichita State University. She has an established interdisciplinary line of research directed toward functional performance across exercise physiology and orofacial myology.

ABSTRACT

Cervicogenic headache (CEH) is caused by dysfunction in the cervical spine and surrounding muscles. It is typically characterized by unilateral or sometimes bilateral head pain, often accompanied by limited neck movement.  Postural and neuromuscular dysfunction in the cervical spine may contribute to the onset of headache-related pain. This study aims to address headache-related pain using the C2 evaluation and treatment protocol from the MyoKinesthetic System, a manual therapy method focused on evaluating and treating postural imbalances.  A female patient with self-reported chronic headache-related pain and neck discomfort underwent six treatments using the C2 cervical nerve root protocol over a two-week period, with 48-72 hours between each session. Each treatment lasted approximately 8 minutes. Subjective and objective outcome measures were collected throughout the treatment period, including clinician-assessed cervical range of motion, the Numerical Pain Rating Scale (NPRS), the Neck Disability Index (NDI), and the Headache Impact Test-6 (HIT-6). At the initial assessment, the patient reported an NPRS score of 4/10, an NDI score of 14/50, and a HIT-6 score of 58.  After the final treatment, the patient’s NPRS pain score was 5/10, with NDI and HIT-6 scores of 15/50 and 54, respectively. Cervical extension range of motion improved by 7 degrees post-treatment. However, the average NPRS pain reduction over the two weeks was only 0.25 points and not clinically significant. At the 30-day follow-up, NPRS results met the minimally clinically important difference (MCID), with a score of 0. Headache frequency decreased from daily to once every three days, with the duration reduced to around 15 minutes. The patient reported improved tolerance for physical activities and fewer work disruptions. Lasting improvements were observed in neck function, headache impact, pain, and range of motion.  These findings are promising, but more research is needed to confirm the MyoKinesthetic System’s effectiveness for CEH. Targeting the C2 cervical nerve root helped reduce the patient’s chronic headache frequency and neck discomfort, suggesting potential for addressing neuromuscular imbalances. However, since this is a single case study, further research with larger samples and comparisons to other treatments is needed to assess its broader efficacy and long-term effects.

Key Words: MyoKinesthetic System; cervical nerve root; head-related discomfort

INTRODUCTION

Cervicogenic headache (CEH) is characterized by pain in the head associated with the cervical spine and cervical musculature (Bogduk, 2001; Bogduk & Govind, 2009; Haldeman & Dagenais, 2001). Sjaastad et al. (1998), along with the International Headache Society (The International Classification of Headache Disorders, 2018), define CEH as a unilateral headache that may also present bilaterally, associated with the cervical spine and muscles. Identifying signs and symptoms, including a reduced active and passive range of motion in the cervical spine leading to mechanical dysfunction, is critical in diagnosing CEH (Sjaastad et al., 1998). Accompanying symptoms may include nausea, vomiting, flushing, dizziness, phonophobia, photophobia, blurred vision, and dysphagia (Sjaastad et al., 1998). The burden of a headache is measured by the degree of pain and suffering experienced by the patient.

Treatment options are available across multiple healthcare specialties (Yang et al., 2010), including athletic training, and treatment choice appears to depend on the specialty of the healthcare provider treating the patient (Smith & Bolton, 2013). Various treatment methods have been studied, both invasive (e.g., surgery and injections) and non-invasive (e.g., massage, cervical mobilizations, trigger point therapy, and acupressure) in nature (Bogduk & Govind, 2009; Haldeman & Dagenais, 2001; Quinn et al., 2002; Schoensee et al., 1995). The goal of clinicians using non-invasive manual therapy techniques is to resolve patient complaints by treating the cervical spine as the primary source of CEH symptoms (Bogduk, 2004).

Non-invasive therapeutic techniques for CEH include cervical spine mobilization, massage, trigger point therapy, and acupressure (Bogduk & Govind, 2009; Haldeman & Dagenais, 2001; Quinn et al., 2002; Schoensee et al., 1995; Youdas et al., 1992). Researchers have demonstrated clinically significant reductions in headache intensity, frequency, and duration among patients treated with non-invasive techniques over at least a six-week treatment protocol (Bogduk & Govind, 2009; Haldeman & Dagenais, 2001; Quinn et al., 2002; Schoensee et al., 1995; Youdas et al., 1992). Although manual therapy techniques have been reviewed as effective management tools for CEH (Bogduk & Govind, 2009; Haldeman & Dagenais, 2001; Quinn et al., 2002; Youdas et al., 1992), no studies have specifically evaluated the effects of pain intensity changes and cervical range of motion after shorter treatment durations, such as a two-week treatment protocol. Conservative treatments that require extended durations to achieve significant results may motivate patients to seek faster remedies (e.g., medication) that perpetuate their condition by altering symptoms without addressing the underlying cause.

The MyoKinesthetic (MYK) System is an evaluation and treatment paradigm used to restore the central nervous system’s (CNS) communication with the musculoskeletal system to achieve allostasis. The MYK evaluation is designed to identify abnormalities in a patient’s static posture and connect those abnormalities to specific nerve root(s) via the associated myotome(s). The clinician then treats at the level of the identified myotome by using active and passive patient movements with a simultaneous external stimulus, similar to massage, to stimulate the communication pathways of the CNS.

The MYK System is theorized to decrease nociceptive firing that may cause or occur due to joint and tissue movement restriction (Smith & Bolton, 2013). The MYK system aims to create postural balance by treating the bilateral neuromuscular system along a specific nerve root. Specifically, for headaches, the MYK System utilizes additional classification beyond postural evaluation, including assessing headache pain and location. The MYK system, which helps the clinician determine the nerve root to be treated, offers a headache assessment table designed by Dr. Mike Uriarte (Uriarte, 2004). The location of headache-related symptoms in one or multiple areas (e.g., top of the head, sides of the head, front or back of the head, front of the head above the eyes, and back of the head no lower than the occiput) is used to determine which cervical nerve root may be affected. Currently, limited published research examines the effectiveness of the MYK headache treatment on headache-related pain (Moy, 2015).

The purpose of this case study was to examine the effects of the MYK system over two weeks when treating a patient classified with chronic CEH (i.e., occurring 15 days or more per month for longer than three months).

TABLE 1

The ‘Yes/No’ Cervical Nerve Root Assessment Chart

Nerve RootLocation of PainSpecial Characteristics
C1Anywhere on the head, this is determined when we do the ‘yes/no’ test.If their head is ‘rotated only,’ it is C1.  
C2Top of the head, sides of the head, front and back of the head. No lower than the occiput.  
C3In the eyes, between the eyes, behind the eyes, into the jaw or cheek area, top of the neck. 

Case Report

The patient, a thirty-three-year-old female, reported her main complaints were headache pain and neck discomfort off and on for over ten years, starting while she was in middle school.  A signed HIPAA and informed consent form were obtained before the initial evaluation and treatment. The patient’s prior history of significant injury included rotator cuff lesion and finger, foot, and toe fractures. The patient underwent shoulder arthroscopy to repair the rotator cuff three years prior. Still, since the headaches were present before and after the surgery, it was not believed to be a primary contributing factor. The patient’s contributing factors that coincided with her headache symptoms included sinusitis and bilateral numbness in her hands.  The patient also reported that she had missed significant events in her life because of her chronic headache pain. Her work-life was frequently disturbed; she required breaks often and was unable to stay focused on her tasks. In her own words, her ‘everyday active lifestyle was disrupted frequently’. 

The patient pursued multiple treatments and techniques over several years to relieve her headaches and neck discomfort but found little to no success. Some treatments positively impacted her condition for a short period but had not changed her condition long-term. These treatments and techniques included chiropractic care, medication, injections, essential oils, and physical therapy. Prescription pain medication and muscle relaxers were used as a last resort.  Over-the-counter medicines were used by the patient weekly as needed.

METHODS

Assessment

After obtaining a complete history and satisfying the inclusion/exclusion criteria (see Table 2), a physical examination was performed, consisting of cranial nerve and vertebral artery insufficiency testing, before the MYK ‘yes/no’ test and the MyoKinesthetic (MYK) full-body postural assessment.  Cranial nerve function tested normal, as did the vertebral artery performance.

Table 2

 Inclusion and Exclusion Criteria.

Inclusion CriteriaExclusion Criteria
-Pain projected to the forehead, orbital region, temples, ears, neck, or occipital region; -Pain with specific neck movements or sustained postures; -Complaints of palpable pain or discomfort/limitation of active or passive ROM.-Participants > 50 years old; -Positive Vertebral Artery Test; if positive, refer out  -If any analgesic or non-steroidal anti-inflammatory drugs (NSAIDs) were taken within the last 24 hours; -If the participant has an acute diagnosis of concussion or has not been released by a physician for full activity with no restriction from a concussion diagnosis

The MYK ‘yes/no’ test is used within the MYK System to determine resting head position. The patient stands with eyes closed and nods and shakes his/her head several times before coming to a comfortable resting position. The position of the head at rest is noted. Assessing cervical posture/imbalance with eyes closed may help to remove the visual input that the body uses to level itself with the horizon. In conjunction with the location of symptoms as outlined in Table 1, the ‘Yes/No’ Test is used to determine the cervical nerve root associated with the patient’s posture and symptoms. In this case, the patient’s cervical posture was visibly laterally flexed to the right. 

The MYK full-body postural assessment consists of the clinician evaluating the patient’s posture and stance, noting any imbalances when compared bilaterally and against postural norms (e.g., neutral).  In this case, clinical evaluation utilizing the MYK full-body postural assessment (Table 3) and clinician expertise demonstrated a C1-T1 dysfunction, with considerable postural imbalances associated with C6. The patient’s primary complaint was headache pain on the top of the head and temples with general neck discomfort. As outlined in Table 1, the C2 nerve root was identified as the affected nerve root using the headache treatment guidelines.

Pain-free active cervical ranges of motion (extension, flexion, and right/left rotation) were assessed using a goniometer with the patient’s eyes closed. At the initial examination, the patient had 53 degrees of pain-free active cervical extension and 45 degrees of pain-free active cervical flexion.  Pain-free active cervical rotation to the left and right was 60 degrees and 67 degrees, respectively.

Instrumentation

For patient-reported instruments to be most helpful in clinical practice and research, those with good psychometric properties and clinical applicability were utilized (Houts et al., 2020; Farrar et al., 2001). Instruments that were well-established in the literature and validated were selected to measure the impact of headaches in this case study.

The Headache Impact Test Questionnaire

The Headache Impact Test (HIT-6) is designed to assess the global impact of headaches on patients, measuring content areas such as pain, social-role limitations, cognitive functioning, psychological distress, and vitality (Houts et al., 2020). Nachit-Ouinekh et al. (2005) evaluated the global impact of episodic headaches in patients consulting general practitioners using the HIT-6 questionnaire and compared headache severity and quality of life. A comparison of the HIT-6 scores was conducted for each of the four sub-scores (i.e., functional, psychological, social, and therapeutic indices) against the French Qualité de Vie et Migraine (QVM) questionnaire (Nachit-Ouinekh et al., 2005). Scores range from “60 or more—headache has a severe impact on your life” to “49 or less—headache has little to no impact on your life” (Nachit-Ouinekh et al., 2005).

The Numerical Pain Rating Scale

The Numerical Pain Rating Scale (NPRS) is an 11-point numerical scale in which the clinician asks the patient to rate their pain verbally on a scale from 0 (no pain) to 10 (worst pain imaginable) (Farrar et al., 2001). In this study, average scores were calculated using the patient’s “current,” “best,” and “worst” pain scores, which were then compared to the post-treatment “current” pain score.

The Neck Disability Index

The Neck Disability Index (NDI) is a patient-reported, condition-specific functional status questionnaire that includes items related to pain, personal care, lifting, reading, headaches, concentration, work, driving, sleeping, and recreation. Out of a possible 50 points, a higher score indicates greater patient-perceived neck disability. A 5-point change on the index is considered a clinically important difference (Chan Ci En et al., 2009).

At the initial assessment, the patient reported an NPRS of 4/10, a HIT-6 score of 58, and an NDI score of 14/50. Measurements and outcomes were also collected at 30- and 60-day follow-ups.

The treatment of the C2 nerve root was determined based on the MyoKinesthetic (MYK) System’s “yes/no” test results. Treatment was performed following MYK System guidelines with the patient in a seated position. The clinician administered treatment using the MYK System parameters: passive movements were completed first, with the clinician passively moving the participant through each muscle’s range of motion (five times) while applying manual stimulus similar to massage to the muscles of the C2 myotome. Then, the participant actively moved (seven times) through the same range of motion while the clinician applied the same stimulus to the muscles. Once all muscles innervated by the C2 nerve root were treated bilaterally, treatment was complete. Treatments lasted approximately eight minutes on average and were conducted six times over two weeks, with 48 to 72 hours between each treatment.

RESULTS

After the final treatment, the pain reported on the NPRS was 5/10. The patient also completed the NDI and HIT-6, with scores of 15/50 and 54 points, respectively (see Table 4). Cervical range of motion (ROM) measurements were recorded in degrees and evaluated pre- and post-treatment. There were significant improvements in cervical extension ROM, with an increase of 7 degrees post-final treatment. A summary of ROM measurements is presented in Table 5.

The mean pain scores across the two weeks of treatment were not clinically significant compared to the NPRS minimally clinically important difference (MCID), which is defined as an average decrease of 2 points. In this case, the average decrease was only 0.25 points (Chan Ci En et al., 2009). However, daily NPRS results met the minimally clinically significant difference at the 30-day follow-up, with an average of 0 (Chan Ci En et al., 2009). Lastly, the patient’s postural examination changed between intake and discharge, as many imbalances were corrected within normal limits (see Table 3; Uriarte, 2004).

The patient reported a dramatic decrease in headache frequency over the two-week period, from experiencing a headache daily to only one every three days. By the end of the two-week treatment period, the patient noted that headache duration significantly decreased, lasting approximately 15 minutes compared to several hours or days before treatment. The patient also reported improved tolerance for physical activities she had previously been unable to perform, such as walking for extended periods, lifting weights, completing household tasks, and playing with her child. Disruptions at work were also greatly diminished, and the patient reported improved ability to focus on tasks with greater ease.

While the patient reported notable improvements, it is essential to analyze the raw data to form a proper conclusion. When evaluating follow-up scores, the findings suggest lasting improvements in multiple aspects of the patient’s life, including but not limited to neck function, perceived headache impact, pain levels, and range of motion. The follow-up scores are illustrated in Table 4.

DISCUSSION

The MyoKinesthetic (MYK) System elicited positive and lasting changes in this patient with frequent and intense cervicogenic headaches (CEH) over just two weeks of treatment. By the 60-day follow-up, the patient’s pain was nearly eliminated, and headache frequency had become rare. The patient also reported no headache-related pain or discomfort between treatments, which were spaced 48 to 72 hours apart. Improvements were observed in cervical flexion and right rotation, and the patient reported a significant enhancement in functional activities, allowing her to enjoy a more comfortable home life and a less painful work environment. The MYK System may be beneficial for other patients with CEH; however, research on its effectiveness remains limited, as is the case with other manual therapy techniques. Further studies are needed to determine why MYK may have been effective in treating this patient.

Manual therapy has been shown to decrease pain, improve function, and enhance quality of life in patients with musculoskeletal conditions, though its effectiveness varies among individuals (Uriarte, 2004). For example, massage therapy is commonly used to treat general pain complaints, yet some patients experience substantial relief while others show little to no improvement. Similarly, alternative treatment approaches, such as mobilizations with movement, may have been more or less effective in addressing the patient’s primary complaint. Treating patients with pain is inherently subjective, as each patient’s response is influenced by a combination of mental, physical, and emotional factors.

The MYK technique may extend its effects beyond conventional treatment boundaries. Patients may perceive MyoKinesthetic treatment as similar to joint mobilization and massage (e.g., pressure, squeezing, trigger point therapy). Neural mobilization may also occur as all tissues move through various ranges of motion. Some patients report a stretching or traction effect, while others describe experiencing a “pop” sensation, suggesting a possible manipulative effect. The MYK System is designed to be quick and efficient, requiring minimal space and exertion from the clinician (Moy, 2015).

Although limited research has explored manual therapy as a viable treatment for headaches, Smith and Bolton (2013) provided a compelling argument supporting its use. While acknowledging study limitations, their evaluation considered both postural and pain-related factors. Headaches related to stress, nerve irritation, or muscle spasms were subjectively identified, and chronic pain in the neck and upper trapezius region was also noted. MYK was used in this case to address the patient’s symptoms, and the treatment was beneficial. The systematic evaluation process within the MYK System highlighted neuromuscular imbalances, targeted their treatment, and raised the question of whether MYK could serve as an effective intervention for headaches (Uriarte, 2004).

A study by Moy (2015) applied the MYK System to a patient with complaints of neck pain, shoulder pain, hip pain, and headaches. Through a comprehensive assessment, the C8 nerve root was identified as the source of the patient’s symptoms. Following targeted MYK treatment, the patient experienced a significant reduction in pain, improved cervical range of motion, and enhanced quality of life after nine treatment sessions.

At the conception of the MYK System, a review of research addressing neuromuscular function and dysfunction was conducted. Understanding the neuromuscular system was fundamental to its development. Dr. Uriarte (2004) conceptualized the neuromuscular system as a “two-sided story,” emphasizing the necessity of bilateral treatment to address the root cause of pain rather than merely targeting the symptomatic area.

Furthermore, during MYK treatment, the body may perceive movement as normal and recognize the applied stimulus as non-threatening. This process allows patients to transition from painful to non-painful motion. A unique aspect of the MYK System is how treatment concludes. According to Dr. Uriarte (2004), posture serves as an external reflection of the neurological system. Before treatment, compensatory patterns may develop due to dysfunction and gravitational forces. Following treatment, the body and neurological system are expected to feel more balanced and better equipped to adapt to movement and gravity naturally.

Limitations

As with any attempted case study, limitations were present. Limitations included the following: 1) The treatment pressure may vary among treatments over the two weeks.  While the type of stimulus (stroking, tapping, massaging) may not matter, varying pressure has not been studied; therefore, the effects of pressure have not been determined.  This may be viewed as a limitation of the technique rather than a limitation of this study.  2) Reliability of goniometric measurement was not established before data collection, which may have created a limitation on reporting significant cervical ROM changes.  However, all measurements were taken in the same setting, patient position, and by the same clinician.  Validity and reliability of goniometric measures are usually established amongst clinicians, with multiple ROM measurements collected blindly over some time with the same subjects.  With there only being one patient and one clinician in this study, inter- and intra-reliability are lacking.  3) Although the patient was instructed not to take medication or have other treatments for headaches, the clinician cannot control what happens outside the clinic.  The patient did not report any other treatments or taking medication during the time of the study.

Further research should be conducted, exploring whether the muscles’ stimulation affects multiple participants with suspected cervicogenic headache during the acute stages of a CEH.  Other research should be conducted utilizing the MYK manual therapy treatment technique on different body regions to determine treatment effectiveness.  Another viable research topic would be comparing the specific nerve root treatment based on the location of headache pain (C1, C2, C3) compared to the location of dysfunction according to the MYK Upper Body assessment findings (C1-T1).    

CONCLUSIONS

MYK manual therapy helped this patient improve in their complaint of headache pain and frequency.  This study demonstrates that the MYK System headache treatment may be a practical treatment choice to reduce the intensity of patient-reported pain in patients with suspected cervicogenic headaches.  The treatment of cervical nerve root C2 from the MYK System created a clinically significant change in the participant’s perceived pain, including some results found after the 30-day and 60-day follow-ups.   

The question arises: Is MYK the most viable option for patients suffering from headache-related pain?  MYK is quick, easy, and presents as effective.  The treatment needs more research and discussion to support the idea that MYK is effective and helps validate more manual therapy techniques.  While MYK is not the only manual therapy technique available, it appears viable when assessing and treating patients. Overall, the changes in pain, intensity, and frequency observed in this study support the MyoKinesthetic System headache treatment along cervical nerve root C2 as a successful form of a non-invasive technique when treating cervicogenic headaches.

APPLICATIONS IN SPORT

For coaches, athletic trainers, and parents, understanding cervicogenic headaches (CEH) and their potential impact on athletes is crucial. Athletes, especially those involved in contact sports or repetitive motions, are at a higher risk for neck injuries that could lead to headaches. These headaches can affect an athlete’s performance and overall well-being, causing discomfort, limiting movement, and sometimes sidelining them from practice or competition.

As a coach or athletic trainer, recognizing the signs of CEH and addressing them early can make a significant difference in an athlete’s recovery and performance. Techniques such as cervical mobilizations, myofascial release, and other manual therapies can relieve, improve range of motion, and prevent long-term issues. By being proactive and incorporating strategies to address CEH, you can help athletes stay on track, reduce downtime, and support their physical function, ultimately enhancing their athletic experience and success. Parents, too, can play an important role by being aware of the symptoms and encouraging their athletes to seek timely treatment.

Acknowledgments

The authors declare no conflict of interest and did not receive payment for this study.

REFERENCES 

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APPENDIX

Table 3

MYK Postural Assessment (pre/post)

Table 4

Patient Reported Outcomes

 NDIHIT-6NPRS
ASSESSMENTScoreRankingScoreRankingPre- ScorePost- ScoreMean of  Raw
Initial14/50Mild58Substantial433.75
Discharge15/50Moderate54Some754
Mean__57.6Substantial
30-Day8Mild46Little to no impact0
60-Day5Mild38Little to  no impact.666

Table 5

Goniometric measurement mean normative data for cervical range of motion taken from Norkin et al.

Cervical Range of Motion
MovementNormative DataPre-treatmentPost-treatment (change)30-Day Follow Up60-Day Follow Up
Flexion40° ± 1245°40° (-5°)47.3°46°
Extension50° ± 1453°60° (7°)41.6°37°
Left Rotation49° ± 953°54.6° (1.6°)55.6°51°
Right Rotation51° ± 1160°61.6° (1.6°)58.6°62°
2025-10-08T12:16:04-05:00April 15th, 2026|Concussions, General, Research, Sports Health & Fitness, Sports Medicine|Comments Off on A Manual Therapy Treatment for Headache Pain

Accreditation, Curriculum, and Competition: An Explanatory Case Study of Sport Sales Education in Undergraduate Sport Management Programs

Authors: Joshua S. Greer1, Nicholas Zoroya2, and Tim Wilson3

1Cumberland University

2Wayne State University

3Middle Tennessee State University

 

Corresponding Author:

Joshua S. Greer

[email protected]

Joshua S. Greer. https://orcid.org/0009-0005-2890-1673

We have no known conflict of interest to disclose.

ABSTRACT 

This explanatory mixed-methods case study explored the relationship between accreditation, curriculum design, and student performance in sport sales education within undergraduate sport management programs. Using archival data from the 2024–2025 National Collegiate Sports Sales Championship (NCSSC), the study compared outcomes among 25 institutions, including COSMA- and non-accredited programs. Quantitative analysis found no significant relationship between accreditation status and Top-10 finishes in either the Ticket Sales or Corporate Partnerships divisions (p > .05). Qualitative findings indicated that student performance was more closely associated with experiential learning depth, faculty expertise, and the integration of customer relationship management and analytics tools. Grounded in Experiential Learning Theory, Competency-Based Education, Human Capital Theory, and Communities of Practice, the study concludes that accreditation provides useful structure but does not independently predict competitive success. Program-level factors such as applied pedagogy, simulation-based learning, and industry partnerships appear to be stronger indicators of professional readiness and employability in sport sales.

KEYWORDS: Experiential Learning Theory, Competency-Based Education, Human Capital Theory, Communities of Practice

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).

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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.

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