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Troy Palmer2025-12-05T10:58:04-06:00

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

Troy Palmer2025-12-05T10:58:04-06:00June 3rd, 2026|Contemporary Sports Issues, General, Leadership, Research, Sports Management, Sports Studies|

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.

Troy Palmer2025-10-13T15:18:13-05:00

An Analysis of Carbon Emissions from College Football Recruiting Visits

Troy Palmer2025-10-13T15:18:13-05:00May 27th, 2026|Contemporary Sports Issues, Research, Sports Studies, Sports Studies and Sports Psychology|

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 CO_2eq 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 (CO_2eq). 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 CO_2eq was created during the 2004 Football Association (FA) Cup Final in the United Kingdom (one soccer game) (4), 1,260 tons of CO_2eq for the 2004 Wales Rally (an Autosport’s event over four days) (5), and 144,120 tons of CO_2eq 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 CO_2eq 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
Variable	Description	Mean	SD
FB_TotalRev	Total Revenue from Football	$66,518,526	$25,205,244
Mens_TotalRev	Total 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 Revenues	77.40%	11.17%
Dept_TotalRev	Total 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 Revenues	52.50%	13.10%
Mens_RecruitExp	Total Recruiting Expenses from all Men’s Sports (including Football)	$1,391,362	$704,861
Dept_RecruitExp	Total Recruiting Expenses from the entire Athletic Department	$1,878,962	$855,080
FB_OpsExp	Total Operation Expenses for Football	$5,683,499	$2,558,649
Mens_OpsExp	Total Operation Expenses for all Men’s Sports (including Football)	$8,800,193	$4,035,500
Dept_OpsExp	Total Operating Expenses for the entire Athletic Department	$12,787,529	$5,068,156
FB_TotalExp	Total Expenses for the entire Football Program	$33,846,192	$11,218,516
Mens_TotalExp	Total 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 Expenses	63.18%	7.58%
Dept_TotalExp	Total Expenses for the entire Athletic Department	$116,141,712	$27,071,219
FB_DeptExp%	Football Expenses as a Percentage of the entire Athletic Department Expenses	63.18%	7.58%
Win_Percentage	Football teams Win Percentage	62.43%	19.97%

Recruit Visit Carbon Footprint

Calculating CO_2eq 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 CO_2eq 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 CO_2eq 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 CO_2eq 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 CO_2eq 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 CO_2eq per person per day for food and 1.1 kg CO_2eq 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 CO_2eq, averaging 328 kg CO_2eq per recruiting visit. For context, the global average annual CO_2eq emission per person is approximately 4.7 tons (4,263 kg), according to the IEA (22). Thus, the CO_2eq for a single 48-hour recruiting visit represents about 7.7% of the average person’s global annual CO_2eq 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 CO_2eq, and the highest mean RVCF at 621.34 kg CO_2eq. 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
School	n	% Drove (No Flight)	Car (No Flight)	Car  (To/From Airport)	Flight	Hotel	Food	Waste	M	SD	Total
Washington State	276	2.17%	564.48	21,782.22	137,796.79	6,405.95	4,084.79	855.60	621.34	362.98	171,489.84
Oregon	281	3.20%	667.81	7,322.00	150,332.79	6,522.00	4,158.79	871.10	604.54	323.99	169,874.50
Nebraska	373	5.90%	1,437.03	9,203.29	131,283.86	8,657.32	5,520.39	1,156.30	421.60	171.96	157,258.20
Alabama	378	17.99%	6,138.01	21,504.74	81,842.82	8,773.37	5,594.39	1,171.80	330.34	219.30	125,025.12
Minnesota	328	15.55%	1,543.63	9,279.44	94,019.65	7,612.87	4,854.39	1,016.80	360.75	190.85	118,326.78
Louisville	343	7.87%	1,814.23	9,356.07	92,166.14	7,961.02	5,076.39	1,063.30	342.38	189.87	117,437.16
Oklahoma	315	21.27%	7,627.73	9,318.53	85,483.06	7,311.14	4,661.99	976.50	364.79	187.59	115,378.96
Tennessee	356	14.89%	5,866.34	11,129.21	72,691.17	8,262.75	5,268.79	1,103.60	293.04	213.71	104,321.87
Texas A&M	327	46.18%	15,370.28	18,162.76	56,491.35	7,589.66	4,839.59	1,013.70	314.50	223.19	103,467.35
Washington	251	22.31%	2,329.33	5,626.14	81,958.42	5,825.71	3,714.80	778.10	399.33	250.68	100,232.49
Ohio State	301	26.58%	7,676.34	5,596.14	69,715.53	6,986.20	4,454.79	933.10	316.82	223.49	95,362.10
Arkansas	325	15.38%	4,563.07	13,222.06	64,152.59	7,543.24	4,809.99	1,007.50	293.23	166.97	95,298.46
Indiana	273	15.38%	3,307.90	13,293.64	62,085.00	6,336.32	4,040.39	846.30	329.34	169.13	89,909.57
Florida	333	28.53%	8,658.69	6,782.76	56,932.06	7,728.92	4,928.39	1,032.30	258.45	179.76	86,063.13
Miami	301	39.53%	3,920.90	4,735.86	63,566.68	6,986.20	4,454.79	933.10	280.01	263.30	84,597.54
Florida State	317	14.20%	3,929.02	6,600.11	59,945.34	7,357.56	4,691.59	982.70	262.44	182.30	83,506.32
Auburn	313	35.14%	9,914.55	14,971.12	42,452.12	7,264.72	4,632.39	970.30	256.25	161.71	80,205.21
Georgia	270	33.70%	6,709.32	15,279.31	41,800.05	6,266.69	3,995.99	837.00	276.78	196.70	74,888.37
Penn State	254	29.53%	9,210.73	16,663.59	38,471.37	5,895.34	3,759.20	787.40	289.49	192.65	74,787.61
Mississippi State	293	56.31%	17,224.20	17,413.13	26,745.76	6,800.52	4,336.39	908.30	250.61	176.90	73,428.32
Kentucky	274	19.71%	4,331.99	7,066.42	49,895.23	6,359.53	4,055.19	849.40	264.81	146.57	72,557.76
LSU	300	38.00%	7,465.21	5,491.13	41,783.31	6,962.99	4,439.99	930.00	222.53	146.73	67,072.63
South Carolina	263	32.70%	7,526.48	5,361.76	31,921.56	6,104.23	3,892.40	815.30	211.49	128.80	55,621.71
Total	7,045	23.57%	137,797.27	255,161.41	1,633,532.66	163,514.32	104,265.87	21,839.48	328.91	203.01	2,316,111.00
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 CO_2eq), with its mean RVCF rising from 280.80 kg CO_2eq in 2013-2016 to 355.57 kg CO_2eq in 2017-2020. Oregon saw the largest overall increase in total RVCF, increasing 29,617.65 kg CO_2eq 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 CO_2eq). 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 CO_2eq between the two periods.

Table 3 Largest Mean Difference in RVCF between the two time periods
		2013-2016								2017-2020
Schools			n	Total		M			n		Total	M			Difference	MD
Ohio State			156	43,804.86		280.80			145		51,557.24	355.57			7,752.39	74.77
Penn State			102	25,847.68		253.41			152		47,683.11	313.70			21,835.44	60.30
Oregon			122	70,128.43		574.82			159		99,746.07	627.33			29,617.65	52.51
Florida St.			165	46,656.85		282.77			152		36,535.52	240.37			-10,121.32	-42.40
Arkansas			153	49,311.35		322.30			172		45,987.11	267.37			-3,324.24	-54.93
Miami			151	46,944.88		310.89			150		37,339.37	248.93			-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 CO_2eq 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.

Troy Palmer2025-12-03T09:26:11-06:00

Total Goalkeeper Performance (TGP): A Comprehensive Metric for Evaluating Modern Soccer Goalkeepers

Troy Palmer2025-12-03T09:26:11-06:00May 20th, 2026|General, Sport Training, Sports Coaching, Sports Exercise Science|

ABSTRACT 

The purpose of this study was to develop and validate a comprehensive metric for evaluating modern soccer goalkeepers that accounts for both defensive and offensive responsibilities. Total Goalkeeper Performance (TGP) was constructed using publicly available data from the English Premier League, incorporating shot-stopping, cross-stopping, sweeping, and distribution metrics. Analysis of 70 observations of goalkeeper performance revealed a moderate positive correlation between TGP and team success (r = 0.474, p < 0.001), with TGP explaining 22.5% of variance in expected team points per game (3 points for win/1 point for draw/0 points for loss). A one-unit increase in TGP corresponded to 1.75-4.64 additional expected points over a 38-match season. Year-over-year analysis showed moderate consistency in goalkeeper performance as measured by TGP. These findings suggest TGP effectively captures goalkeeper contribution to team success while accounting for the evolving multidimensional nature of the position. TGP provides a data-driven framework for recruitment, talent identification, and tactical planning that aligns with the demands of modern soccer.

Key Words: soccer analytics, goalkeeper metrics, player evaluation, player development

INTRODUCTION 

The goalkeeper is a unique position in sports. The player is often isolated from the rest of the team as the last line of defense. They even have different equipment from the rest of the team. In hockey, soccer, and more, one can immediately distinguish a goalkeeper from their teammates (3, 5). It leads to a feeling that the goalkeeper is isolated from the rest of the team. But no one is an island. There have been times when goalkeepers have done more than just protect their goal, which has been seen prominently in soccer (13).

This has been supported by several studies into the position. A study using machine learning algorithms showed that the difference between what they called elite and sub-elite goalkeepers was their ability with their feet (11). This suggests that the position has evolved so much that shot stopping is not even a goalkeeper’s main priority at the world’s best clubs. This was far from the only study to suggest that goalkeepers have seen an increase in their responsibilities. Goalkeepers are asked to do a lot more than just save shots in today’s game (13, 19). Soccer is not the only sport where this has occurred.

From roughly the mid-1990s to the mid-2000s, certain hockey goaltenders had a similar task. The most notable was Martin Brodeur. The New Jersey Devils, Brodeur’s team, employed a strategy called a neutral zone trap. The trap utilized a goalkeeper’s ability outside of the net to limit their opponent’s scoring chances. The Devils won three Stanley Cup titles from 1994 to 2003 with this strategy before the NHL introduced a new rule which severely limited what a goalkeeper could do outside of making saves (5).

In soccer, a goalkeeper is the only player on the team who can touch the ball with their hands and the player can only do this inside the 18-yard box (2). Historically, this led to goalkeepers not playing with their feet at all. But as the game evolved, this began to change, helped along by a rule change after the 1990 World Cup. The 1990 World Cup is considered one of the worst World Cups of all time due to the boring play. Part of the reason for the dullness was the goalkeepers who would waste time by holding the ball as long as legally allowed (14).

To combat this, the back-pass rule was introduced. This established the rule that goalkeepers could not pick up the ball if a player on their team passed it to them (14). With the change introduced to the game, it made goalkeepers’ ability to play with their feet more important (1). Following the success of goalkeepers like Manuel Neuer and Ederson, a goalkeeper’s distribution has become an essential skill (16-17). It is to the point that in some development teams, such as Chelsea, goalkeepers are judged more for their passing than their saves (4). Goalkeepers need to do so much more than just stop shots. However, that idea still hasn’t taken hold.

There is no way to rank soccer goalkeepers in a way that accounts for what they do with the ball. There isn’t even a statistic to accurately rank a goalkeeper defensively. Some statistics individually look at saves, cross-stopping, and sweeping, but no statistics takes all those aspects into account (6). In fact, some awards recognize players for a single performance statistic. For example, the Premier League Golden Glove award is given to the goalkeeper who has had the most ‘clean sheets’ (i.e., a game where they did not allow a goal) (10). The way goalkeepers are ranked has not kept up with the times. There should be a new statistic that accurately rates a goalkeeper based on everything they have to do, their Total Goalkeeper Performance.

The purpose of this study is to develop and advance a comprehensive metric for evaluating modern soccer goalkeepers that captures both their defensive and offensive responsibilities. First, the study outlines the methodology, including the acquisition of player performance data from the English Premier League for men’s professional soccer. Second, the offensive and defensive statistics used to construct the Total Goalkeeper Performance (TGP) metric are defined, and the procedures for calculating these statistics are detailed. Third, the data analysis plan—featuring correlation analysis and regression modeling—is described. The results are then presented and interpreted, followed by the study’s conclusions. Finally, the practical applications of this metric within sports analytics, particularly in organized soccer, are discussed.

METHODS 

Dataset and Sampling

The data used in the paper spans eight seasons from the Premier League (2017-2018 through 2024-2025), a period where modern goalkeeper statistics are publicly available. All of the data comes from FBRef.com, with the exception of the data on punches which came from the Premier League’s website. In all, 70 goalkeepers were examined.

TGP features several different parts of a goalkeeper’s responsibilities. These can be divided into defensive and offensive statistics. Based on the data available the following performance statistics are used to construct TGP.

Defensive Statistics

Defensively, the main responsibility a goalkeeper has is shot stopping, making saves to prevent goals, but that’s not their only task. Goalkeepers also must defend the goal when balls come into their area. This can be from either crosses that a goalkeeper must deal with inside the 18-yard box or passes that force a goalkeeper to leave the box (18). These skills will be called cross-stopping and sweeping.

1. Shot Stopping

Shot stopping will be measured with expected goals (xG). xG tracks how likely a goal is to be scored from the moment it is struck on a scale of 0-1 with a better shot being ranked closer to 1 (8). To make this stat useful for a goalkeeper, one must track how much xG a goalkeeper faces and then subtract the total number of goals allowed to get the post-shot (PS) expected goals minus goals allowed (GA). In order to standardize this for all goalkeepers, the statistic will be converted into a per 90 minutes through using the minutes played by each goalkeeper (PSxG-GA/90).

2. Cross Stopping

Cross Stopping is another skill needed to be quantified. Goalkeepers typically face several crosses being put into their box during a game. The best way to stop a cross is to claim it, catching the cross before the opposition can get to it. Punching a cross away can also be beneficial but is not preferable to catching as the ball could go back to the opposition but it is still preferable to leaving the cross to the opposition (9). When making cross-stopping into a statistic, one must factor in both crosses claimed (CC), crosses punched (CP), and total crosses faced (TC), but claiming and punching crosses are not equal.. Because of that, TGP weighs a punch as half of a claim when measuring cross-stopping. The final stat to measure cross stopping for TGP is: Cross Stopping = (CC+.5xCP)/TC.

3. Sweeping

Sweeping is the easiest of the three defensive skills to quantify. The statistic – defensive actions outside of the penalty area – measures sweeping well. This tracks how often a goalkeeper comes outside of his goal to help his team (6). The more often a goalkeeper does this, the better they are at sweeping. To fairly measure these statistics in comparison, it must be looked at on a per-90-minute basis as well. TGP will use defensive actions outside the penalty area per 90 (DAOP/90) minutes to measure sweeping.

From an interview with US International goalkeeper Tyler Miller, shot-stopping is the most important, followed by cross-stopping, then sweeping (12). TGP will weigh the skills 3:2:1 in that order. The stats will then be added together to make a defensive score.

Offensive Statistics

Days 1-4 focused on primary lift progression (Front Squat, Bench Press, Deadlift, Overhead Press) with integrated plyometric, conditioning, and movement quality components. Day 5 emphasized pulling strength and unilateral work. Day 6 focused on coordination, explosive power, and metabolic conditioning. All days included Tabata rowing (20 seconds work/10 seconds rest × 8 rounds) for conditioning stimulus and mental toughness development.

1. Pass Completion Percentage (in buildup)

Offensive skills are more difficult to track due to the limitations of data on goalkeepers’ offensive abilities. One skill to track is a goalkeeper’s ability in buildup, making short passes to help his team up the field. In addition, a goalkeeper’s ability to make decisive passes that can start an attack on his team is important as well. The best widely available statistic to track a goalkeeper’s ability in buildup is completion percentage (PC) , how accurate they are as a passer.

2. Long Pass Completion Percentage

Similarly, long pass completion percentage (LP) shows how effective a goalkeeper is with long passes that are more likely to lead to an attack (6). Both statistics, completion percentage and long pass completion percentage, will be weighed equally to make an offensive score.

Component Weighting and Possession-Based Adjustments

The TGP metric is a weighted average of offensive and defensive scores.  However, weights are conditionally applied based on possession characteristics of the team. Teams with more possession tend to take more advantage of the offensive skills of the goalkeeper through more time with the ball. Meanwhile, teams with less of the ball have much less of a use for an offensively minded goalkeeper. (18). Because of that, possession, the statistic for how much of the ball a team has per game, is a way to weigh how important a goalkeeper’s offensive skills are for a team (6).

To ensure that the statistic is applicable across different seasons, a player’s score in different statistics will be weighed against the league’s average score. This includes the league average scores on shot-stopping (μPSxG – GA/90), cross stopping (μ(CC + .5CP)/TC)), sweeping (μDAOP/90), pass completion percentage(μPC), and long pass completion percentage (μLP).

For a team with 62.5 percent possession (P) or more, a number chosen for ease of calculations though it is a number only the most ball dominant teams can reach, the offensive and defensive scores will be weighted equally. For a team with 37.5 percent possession or below, a number chosen for the same reasons but for the least ball dominant teams, it will be weighted 3:1 defensive score to the offensive score (7). For example a player on a team with 62.5% possession or more his defensive and offensive scores would remain the same for calculations. In a team with 37.5% possession or below his defensive score would be multiplied by 1.5 and his offensive score multiplied by 0.5. For a team with between 37.5 and 62.5 percent possession the weight would slide between those ratios. For example, in a team with .531 percent possession a goalkeeper would have his defensive score multiplied by 1.188 and his offensive score multiplied by .812.

The overall TGP formula for a goalkeeper per match can be expressed as follows:

 TGP = (DS*(2 – (2P – 0.25))) + (OS*(2P – 0.25))) / 2

where:
 DS = 1.47*(((PSxG – GA/90 + 0.52)/(μPSxG – GA/90 + 0.52)*5.09) + 0.97*(((CC + .5CP)/TC)/μ(CC + .5CP)/TC)*5.15) + 0.62*((DAOP/90)/μ(DAOP/90)*4.02) OS = (0.90*((PC*/μPC*)/10.12) + 1.01*((LP*/μLP*)/9.92)/(4/3)

*μ represented the mean levels of the performance metric by league.

Here is a sample calculation for a high performing goalkeeper with the following measures:

Nick Pope 2023-24: TGP=(19.37*1.206 + 16.05*0.794) / 2 = 18.03

DS=1.47*(.58/0.44)*5.09 + (0.97*0.98)/0.83)*5.15) + 0.62*(1.87/1.29)*4.02=19.37

OS=(0.99(76.9/72.9)*10.12+1.01*(47.9/44.27)*9.92)/(4/3)=16.05

Here is a sample calculation for a low performing goalkeeper with the following measures:

James Trafford 2023-24: TGP (14*1.302 + 12.09*.698) / 2 = 13.36

DS=1.47*(0.31/0.44)*5.09 + 0.97*(0.95/0.83)*5.15) + 0.62*(1.54/1.29)*4.02=14.00

OS=(0.99(65.5/72.9)*10.12+1.01*(32/44.27)*9.92)/(4/3)=12.16

The formula is created with a score of 15 to be the league average for every season. The numbers each individual statistic is multiplied by is there to ensure that no stat is weighted more than any other.

Analyses and Visualizations

Three key areas in this study are explored: 1) Relationship between TGP and Team Success, 2) Individual TGP Rankings and Year-Over-Year Repeatability, 3) TGP vs. Player Market Value. In order to evaluate Team Success, Team EPL Points per Game (PPG) will be used (3 points for team 1, 1 point for team draw, 0 points for team loss). Python is used to carry out correlation and regression analyses exploring the relationship between TGP and Team Success based on n = 70 qualifying goalkeepers from the EPL. Specifically, the scipy library is used for correlation analysis and statsmodels library is used for regression analysis. Furthermore, data visualization is carried out using Python’s matplotlib library. Correlation analysis and data visualization (also from Python) are also used to explore year-over-year repeatability of TGP scores based on n = 10 qualifying goalkeepers, Similar methods are also used to help examine the relationship between TGP and Player Market value.

RESULTS

Relationship between TGP and Team Success

In order to assess the relationship between TGP and team success, a Pearson correlation analysis was performed comparing TGP to PPG for 70 observations across the 2022-2023, 2023-2024, and 2024-2025 English Premier league seasons. The data set is representative of 39 distinct goalkeepers that qualify by having played at least 10 matches.

The analysis revealed a moderative positive correlation between TGP and PPG (r = 0.474 and p < 0.001). This indicates that goalkeepers with higher TGP scores tend to play for teams that earn more points per match. Figure 1 displays the scatterplot with a fitted regression line which demonstrates the positive, linear trend between TGP and team performance. While correlation does not imply causation, the statistically significant relationship suggests that the multidimensional TGP metric captures aspects of goalkeeper performance that contributes directly to winning.

Figure 1

Note. Scatterplot depicting relationship Points per Game and TGP across 2022-2023, 2023-2024, and 2024-2025 seasons in the English Premier League.

Furthermore, a simple linear regression was performed to help understand the magnitude of the contribution to team success. The analysis was performed using the statsmodels library in Python and the results are included in Figure 2.

Figure 2

Note. Ordinary Least Squared Regression Results for TGP vs. Team Performance

The model was statistically significant, F(1,68)=19.74, p<0.001, and explained 22.5% of the variance in PPG (R² = 0.225). The resulting regression equation was:

PPG=0.143+0.084×TGP

The TGP coefficient was positive and significant (β=0.084, t=4.44, p1<0.001), with a 95% confidence interval ranging from 0.046 to 0.122. To put it more in practical terms, every 1 unit increase in TGP is expected to increase points per game from 0.046 to 0.122. In the context of a 38 match EPL season, a 1 unit increase in TGP exhibited by GKs would lead to 1.75 to 4.64 additional points.

Individual TGP Rankings and Year-over-Year Analysis

The Total Goalkeeper Performance (TGP) results for the 2024–2025 Premier League season are summarized in Table 2 below. The top-performing goalkeeper was Guglielmo Vicario of Tottenham Hotspur, who achieved a TGP score of 19.93 across 24 league appearances. Based on the established regression model, this corresponds to an expected points-per-game (PPG) value of approximately 1.81. In contrast, Alphonse Areola of West Ham recorded the lowest TGP score of 11.50 over 26 matches, translating to an expected PPG of roughly 1.10. When extrapolated over a full 38-match season, the difference in expected point contribution between a high-performing and low-performing goalkeeper equates to 26.98 points (68.78 vs. 41.80). While overall team success depends on multiple factors—including defensive structure and attacking capabilities—this analysis demonstrates that goalkeeper performance, as captured by TGP, is a significant independent driver of team outcomes.

Table 2

2024-2025 TGP Rankings in the EPL

Ranking	Player	Club	Effective Matches played (per 90)	TGP
1	Guglielmo Vicario	Tottenham	20	19.93
2	Ederson	Man City	21.8	18.95
3	Nick Pope	Newcastle	23	18.28
4	Robert Sánchez	Chelsea	27	17.92
5	Arijanet Muric	Ipswich	18	16.93
6	David Raya	Arsenal	33	16.78
7	Alisson	Liverpool	22.9	16
8	Mark Flekken	Brentford	31.4	15.87
9	Kepa Arrizabalaga	Bournemouth	26	15.7
10	Emiliano Martínez	Aston Villa	31	15.57
11	Jordan Pickford	Everton	33	14.98
12	Łukasz Fabiański	West Ham	11.9	14.54
13	Mads Hermansen	Leicester	25.5	14.43
14	Stefan Ortega	Man City	11.2	14.12
15	Bart Verbruggen	Brighton	31	14.1
16	André Onana	Man United	32	13.53
17	Bernd Leno	Fulham	33	13.45
18	Dean Henderson	Crystal Palace	33	13.38
19	José Sá	Wolves	25	12.37
20	Aaron Ramsdale	Southampton	25	12
21	Matz Sels	Nottingham Forest	32	11.68
22	Alphonse Areola	West Ham	21.1	11.5

There is also good evidence of the repeatability of TGP ratings year over year. That is – there is slight to moderate positive correlation between seasons. Table 3 represents the TGP performance of 10 GK who had qualifying minutes in the 2022-2023, 2023-2024, and 2024-2025 seasons.

Table 3

Year-over year TGP performances of qualifying Goalkeepers

Player	24-25 TGP	23-24 TGP	22-23 TGP
Emiliano Martínez	15.57	20.98	19.42
Ederson	18.95	19.53	16.57
Nick Pope	18.28	18.03	17.41
Alisson	16.00	16.06	20.72
David Raya	16.78	16.67	17.72
Robert Sánchez	17.92	17.33	13.96
Bernd Leno	13.45	14.47	18.67
Jordan Pickford	14.98	16.55	14.88
José Sá	12.37	17.43	12.96
Dean Henderson	13.38	11.70	12.90

To accompany this table, Figure 3 below shows a correlation matrix that summarizes the strength of the pairwise association between each of the last three seasons in terms of TGP performance.

Figure 3

Note. Correlation matrix of TGP performance for 2022-2023, 2023-2024, and 2024-2025 seasons

TGP vs. Player Market Value

Player evaluators and scouts need to be in tune with the market value of players. One common method is to use Transfermkt (https://www.transfermarkt.com/), a highly reputable site used to estimate player market value based on performance, potential, age, and other market trends. Accordingly, the player market values from the recent 2024-2025 season were collected and paired against TGP values. The relationship between the two variables is depicted in Figure 4.

Figure 4

Note. TGP vs. Player Market Value for the 2024-2025 season.

As you can see, there is a baseline positive relationship between TGP and player market value. The scatterplot also labels the player with a color-coding system such that players above the expectation of performance by salary are in green, while those at expectation are in yellow, and those below expectation in red. Given the typical club operates on player budgets for wages, it is a common goal to try to acquire players that deliver at or above expectations in terms of performance.

DISCUSSION

Interpretation of Findings

The results of this study provide compelling evidence for the utility of the Total Goalkeeper Performance (TGP) metric as a comprehensive evaluation tool for modern soccer goalkeepers. The correlation (r = 0.474, p < 0.001) between TGP and PPG is evidence of a moderate, positive association between goalkeeping performance (as measured by TGP) and team performance. Furthermore, it can be said that 22.5% of the variation in PPG can be explained by the TGP metric. Given that there are 11 players on the field that contribute to team performance, 22.5% in the goalkeeping position signifies how critical the position is to team success.

The regression model also indicates that a single unit increase in TGP corresponds to an additional 1.75 to 4.64 points over a 38-match season. This finding quantifies the tangible impact a high-performing goalkeeper can have on a team’s league position. The substantial 26.98-point difference in expected contribution between the highest and lowest TGP scores in our sample (Vicario at 19.93 vs. Areola at 11.50) underscores the potential competitive advantage teams can gain through goalkeeper selection and development.

The year-over-year analysis reveals moderate consistency in goalkeeper performance as measured by TGP, suggesting that while the metric captures some stable aspects of goalkeeper ability, performance also fluctuates due to contextual factors such as team defensive structure, managerial approach, and opposition quality. This temporal stability adds credibility to TGP as a metric that identifies genuine skill rather than merely capturing random variation.

Tactical or Practical Implications

The TGP metric offers several practical applications for soccer professionals. First, it provides a data-driven framework for recruitment and talent identification that aligns with the multidimensional demands of the modern goalkeeper position. Clubs can use TGP to identify goalkeepers whose specific skill profiles match their tactical approach, rather than relying on traditional metrics that may not capture relevant abilities.

For teams with high possession percentages, our findings suggest that investing in goalkeepers with strong distribution skills yields tangible benefits. Conversely, teams that typically have less possession might prioritize shot-stopping and cross-claiming abilities. This contextual approach to goalkeeper evaluation enables more nuanced decision-making in the transfer market. Our analysis shows how TGP can be paired with player valuations, which can enable front offices to make smarter decisions.

The year-over-year analysis provides insights for player development specialists. The moderate temporal stability of TGP scores suggests that while goalkeeper performance has a skill component that persists across seasons, there is also room for improvement through targeted training. Development programs could use TGP component scores to identify specific areas for improvement in young goalkeepers.

From a tactical perspective, managers can use TGP to inform game strategy. Understanding the relative strengths of opposition goalkeepers across different dimensions could influence pressing approaches, crossing strategies, and shot selection. Similarly, awareness of one’s own goalkeeper’s TGP profile might influence defensive organization and build-up patterns.

Limitations

Several limitations must be acknowledged when interpreting these results. First, while our dataset includes three seasons of Premier League data, it represents only one league.. Goalkeeper requirements may differ substantially across leagues with different tactical tendencies, and the TGP weightings established here may not generalize perfectly to other contexts.

Second, our reliance on publicly available data limits the granularity of our analysis. More sophisticated tracking data could provide additional insights into goalkeeper positioning, command of area, and communication—aspects that are difficult to quantify with event data alone. The offensive component of TGP is particularly constrained by data availability, as metrics like pass completion percentage do not fully capture the quality and tactical significance of goalkeeper distribution.

Third, while we adjusted for team possession, other contextual factors like defensive structure, opposition quality, and score state may influence goalkeeper performance in ways not fully accounted for in the TGP metric. A goalkeeper playing behind a well-organized defense may face fewer high-quality shots, potentially affecting their PSxG-GA/90 component.

Finally, our weighting system, while informed by intuitive insight, introduces a subjective element to the metric. Different experts might propose alternative weightings based on their philosophical approach to the position. Future research could explore the sensitivity of TGP to different weighting schemes or develop data-driven approaches to component weighting. Despite these limitations, TGP represents a significant advancement in goalkeeper evaluation methodology and provides a foundation for future refinements as data availability and analytical techniques continue to evolve.

CONCLUSION 

This study demonstrates that the Total Goalkeeper Performance (TGP) metric is a robust and comprehensive tool for evaluating modern goalkeepers. By integrating both defensive and offensive contributions into a single, possession-adjusted framework, TGP captures the multidimensional nature of the position more effectively than existing measures. The results show a clear and statistically meaningful relationship between TGP and team success, as well as moderate year-over-year consistency, establishing TGP as a credible and practical benchmark for goalkeeper performance.

TGP should be recognized as a new standard for goalkeeper evaluation. It provides clubs, coaches, and analysts with a powerful framework for recruitment, player development, and tactical decision-making. The metric moves beyond traditional, outdated statistics such as clean sheets and instead delivers a data-driven, holistic assessment that reflects the modern demands of the position.

While future refinements—particularly improved offensive data, expanded league coverage, and longitudinal tracking—will further strengthen its utility, the evidence presented here is clear: TGP represents a decisive advancement in goalkeeper analytics. By adopting this framework, the soccer industry can better align evaluation practices with the realities of today’s game and gain a competitive edge in identifying and developing top goalkeepers.

APPLICATIONS IN SPORT

TGP provides practical value for multiple stakeholders in professional soccer. For technical directors and recruitment teams, it offers a multidimensional framework for goalkeeper evaluation that aligns with modern tactical demands, enabling more informed transfer decisions by identifying goalkeepers whose specific skill profiles match a team’s playing style. For coaches and tactical analysts, TGP components can inform game strategy by highlighting opposition goalkeeper weaknesses across different dimensions. Teams might adjust pressing approaches against goalkeepers with poor distribution or increase crossing volume against those who struggle with aerial control. Player development specialists can utilize TGP component scores to create targeted training programs addressing specific goalkeeper weaknesses, allowing youth academies to track development progress across all relevant goalkeeper skills rather than focusing exclusively on traditional shot-stopping metrics.

This type of expanded analysis has proven transformative in other sports. In American football, quarterback evaluation has evolved far beyond simple counting statistics such as touchdowns or interceptions. Advanced metrics like Expected Points Added (EPA), Completion Percentage Over Expectation (CPOE), and QBR now provide a multidimensional assessment of quarterback decision-making, efficiency, and contextual performance. In baseball, the introduction of Wins Above Replacement (WAR) revolutionized how players are valued, combining offensive, defensive, and baserunning contributions into a single comprehensive number. These examples illustrate the power of moving past one-dimensional measures to holistic frameworks that better reflect player impact.

Soccer goalkeepers are a natural candidate for this type of approach, but they are not alone. Other sports positions that blend defensive and offensive responsibilities—such as catchers in baseball, liberos in volleyball, or goaltenders in lacrosse and hockey—could benefit from similar metrics that capture their multifaceted roles. Expanding evaluation frameworks in this way allows teams across sports to more accurately quantify player value, align talent acquisition with tactical systems, and design targeted development programs that reflect the true demands of the position.

Troy Palmer2025-10-10T11:45:06-05:00

BOOK REVIEW: Moawad, T (2020). It takes what it takes: How to think neutrally and gain control of your life. HarperOne.

Troy Palmer2025-10-10T11:45:06-05:00May 13th, 2026|Book Reveiws, Contemporary Sports Issues, Leadership|

Author: Barrett Snyder

Corresponding Author:

Barrett Snyder

[email protected]

EDITOR’S NOTE: This article was written while the author was a student. The author has since graduated. The author holds an M.S. Exercise Science degree from West Chester University of Pennsylvania

Before his name appeared on a bestselling book, Trevor Moawad was already shaping champions behind the scenes. Dubbed “The World’s Best Brain Trainer” by Sports Illustrated in 2017, he spent years redefining mental conditioning at the highest levels of sport. From IMG Academy to a nine-season run with Nick Saban at Alabama, his résumé spanned elite college programs, pro teams like the Memphis Grizzlies and Miami Dolphins, and military units such as the U.S. Navy SEALs.

Moawad gained wider recognition through his work with NFL quarterback Russell Wilson, whom he met in 2012. He soon became a core member of Wilson’s performance team, and their relationship evolved into a close friendship and business partnership. In 2018, they co-founded Limitless Minds, a company focused on building sustainable mindset habits.

Despite years of working with world-class athletes, Moawad didn’t publish his first book until 2020: It Takes What It Takes: How to Think Neutrally and Gain Control of Your Life—a work I consider essential reading for anyone interested in mental performance, both in sports and in life. The book is divided into twelve chapters, covering topics such as planning, visualization, self-awareness, handling pressure, and leadership. While every chapter offers valuable insight, this review highlights the three that best capture Moawad’s message and resonated most with me: Chapter 1, “It Takes Neutral Thinking”; Chapter 3, “It Takes Hard Choices”; and Chapter 4, “It Takes a Verbal Governor.”

Chapter 1 introduces the cornerstone of Moawad’s philosophy: neutral thinking. Rather than leaning into overly positive or negative mindsets, it centers on the present and what can be controlled in the moment. Neutral thinking accepts that the past is irrevocable—it can’t be changed with mantras or platitudes. Moawad warns of a common bias in performance: “We elevate the past. We give it too much importance. We serve the past when we should be giving it berth.” That line stayed with me. Like many, I often overanalyze past decisions and dwell on mistakes. Moawad’s perspective challenged me to let go of that habit. Neutral thinking encourages us to move forward without being anchored by what came before. The past may be real, but it’s not predictive. In a culture often drawn to blind optimism, Moawad’s approach felt both grounding and liberating.

In Chapter 3, Moawad poses a powerful question: is choice an illusion? Often, he argues, it is. Success isn’t about what you feel like doing—it’s about what must be done. “A lot of times we feel as if we have choices to make about where we want to go and what it takes to get there. The reality is that what it takes to succeed is not really a choice,” he writes. He illustrates this idea with everyday decisions: sleep or binge Breaking Bad? Jack and Coke or water? Time with your kids or video games? These moments reveal how easily we confuse comfort with choice—and how small, daily decisions shape long-term outcomes. To illustrate this further, Moawad offers one of the book’s most memorable lines: “When I started working with the Alabama football team, I would hold a bag of Doritos in one hand and an apple in the other. ‘Do you really need a nutritionist to tell you which of these things is better for you?’” What first sounds like a joke lands as one of the book’s most honest truths: we usually know the better option—we just don’t always choose it.

Chapter 4 offers the most immediately actionable advice: “What if we could get people to just stop saying stupid sh— out loud?” The brain absorbs negativity more easily than positivity, and voicing our struggles makes them more harmful than merely thinking them. As someone prone to verbalizing self-doubt, I found Moawad’s message powerful—what we say out loud can reinforce the very negativity we’re trying to overcome. Moawad draws on research to show how negative self-talk can undermine performance, citing the infamous error by Red Sox first baseman Bill Buckner in the 1986 World Series. Nineteen days earlier, Buckner had said aloud, “The dreams are that you’re gonna have a great series and win. The nightmares are that you’re gonna let the winning run score on a ground ball through your legs.” Whether or not it affected the outcome, Moawad argues the fear was already present—and that’s the point: stop saying stupid sh— out loud.

As someone who values academic literature, I’ll note the book includes little scholarly research beyond a few selected studies. Rooted mainly in Moawad’s anecdotal experience, it isn’t meant as an academic work—and while it may lack empirical depth, that feels secondary to its purpose. In terms of content and accessibility, the book’s heavy use of sports examples may be a barrier for some. Readers less interested in athletics might find the frequent game and athlete references less relatable. While Moawad includes a few business and everyday examples, the book is firmly rooted in sports. Still, its core lessons extend well beyond the field, which is why I ultimately recommend it to all audiences.

Moawad concludes the book by returning to his core principle of neutral thinking, offering a memorable metaphor: “The idea of living neutral is putting a comma at the end of the event…and knowing that the next words will determine how the sentence continues.” That image reshaped how I view setbacks—a reminder that the story isn’t over unless I decide it is. Moawad’s message is clear: we hold the pen, and with it, the power to shape what comes next.

Troy Palmer2026-04-15T11:28:29-05:00

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

Troy Palmer2026-04-15T11:28:29-05:00May 6th, 2026|General, Sport Education, Sports Coaching, Sports Studies|

ABSTRACT 

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

Keywords: coaching, NCAA athletics, peer assessment

INTRODUCTION 

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

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

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

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

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

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

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

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

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

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

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

METHODS 

Participants

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

Procedures

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

 Instruments

Demographic Questionnaire

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

Multifactor Leadership Questionnaire

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

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

Data Analyses

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

RESULTS

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

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

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

Table 1

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

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

Table 2

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

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

Figure 1

Captain X 360-Degree Feedback Data

Figure 2

Captain Y 360-Degree Feedback Data

Captain Y

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

DISCUSSION

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

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

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

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

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

Limitations & Future Directions

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

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

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

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

CONCLUSION 

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

APPLICATIONS IN SPORT

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