Sports Coaching – The Sport Journal

The New Era of College Athletics Has Gone a Bridge Too Far

Author: Matthew J Williams¹

¹Department of Education, The University of Virginia’s College at Wise, Wise, VA, USA

Matthew J. Williams D.S.M., M.B.A., M.S., is an Associate Professor of Sports Management at The University of Virginia’s College at Wise. His areas of research interest include NASCAR, COVID-19, college athletics, professional sports, and issues in sports management.

ABSTRACT

The NCAA has always had a firm stance that to survive, it must keep its amateurism status. The NCAA had rules in place that required colleges and universities to recruit student-athletes to play for them. It could only offer them compensation through free tuition, textbooks, room, and board; no direct money could be involved. Over the past decades, the NCAA has grown in popularity and generated a tremendous amount of revenue. At the same time, society was noticing that the NCAA was taking advantage of the student-athlete through its amateurism rules. The NCAA found itself constantly in court defending its actions regarding amateurism. After years of litigation, the NCAA settled out of court, resulting in the House Settlement, which created a new era in college athletics. These changes will allow student-athletes to receive financial compensation directly from colleges and universities. This new era will continue to bring a tremendous amount of financial burden to athletic departments’ budgets. This may lead to reductions in non-revenue sports, team roster sizes, and athletic staff.

KEYWORDS: Revenue sports, non-revenue sports, House Settlement, NCAA, amateurism, revenue sharing, NIL

INTRODUCTION

In the past, student-athletes were very satisfied with receiving an academic/athletic scholarship from a college or university that included free tuition, textbooks, room, and board. In return, student-athletes would participate in varsity athletics for the college or university. Today’s philosophy has shifted, emphasizing that student-athletes should be directly compensated financially. Over the past twenty years, college athletics has witnessed a massive growth in popularity that has resulted in bigger television contracts, sold-out stadiums, increased revenue from corporate sponsorships, and souvenir sales. Student-athletes started to take notice of the popularity of college athletics, financial success, and the abundance of revenue that they were producing for the NCAA, colleges, and universities. They felt they should receive more compensation than just free tuition, textbooks, room, and board. At the forefront of every collegiate student-athlete’s mind in recent years is the question: “should I be getting paid for this?” (Tremps, 2024).

Most college fans, alumni, television announcers, media, and state governments believed the NCAA, colleges, and universities were exploiting the student-athletes. They all believed that student-athletes should receive more financial compensation than just free tuition, textbooks, room, and board. After all, student-athletes were generating all the revenue.

The NCAA became a billion-dollar industry off these young men and women, and they received no monetary compensation in return. Some argued that students were getting a free education out of it, but over time, that seemed to become irrelevant to many college players (Cabibi, 2022).

Discussion

NCAA’s History with Amateurism

When the NCAA was formed in the early 20^th century, its cornerstone belief was built around amateurism and did not revolve at all around pay-to-play. Colleges and universities that were NCAA members in any division of athletics were not allowed to financially pay student-athletes directly. During its formation in 1906, the NCAA highlighted amateurism, or unpaid participation, as a core aspect of its student-athletes (Hart, 2024). NCAA athletes playing for free has always been a feature of the product (Lombardi, 2024).

The only type of financial support that the NCAA would allow colleges or universities to offer student-athletes was free tuition, textbooks, and room and board. The trend of not paying student-athletes financially was accepted by fans, alumni, and media. It was considered an honor and a privilege of amateurism. Many fans felt that the student-athletes were playing for the pride of the college or university and the love of the game. For many fans, amateurism was an endearing aspect, suggesting that young athletes are playing for pride, the love of the game, and the honor of their institution (Lombardi, 2024).

Over the past few decades, societal thoughts have shifted around feelings of amateurism in college athletics, and now think that student-athletes should be financially compensated. The NCAA has held a firm stance on the importance of keeping the amateurism status in college athletics. If they were to allow student-athletes to be financially paid directly, other than through free tuition, textbooks, room, and board, it would significantly hurt the student-athletes’ amateur status. The NCAA prohibited student-athletes from being paid in the past to protect their “amateurism” (McCool, 2023).

With the philosophy changing about financially paying student-athletes, the NCAA found itself in the crosshairs with the media, fans, athletes, and state governments demanding that the NCAA has to do more than just allow colleges and universities to offer student-athletes free tuition, textbooks, room, and board. They were pressuring the NCAA to eliminate its ancient rules on amateurism and allow student-athletes to be financially compensated by colleges and universities.

However, the NCAA failed to act on eliminating amateurism and stuck to its core belief about the importance of amateurism. Unfortunately, failing to act on this issue resulted in numerous lawsuits against the NCAA. Numerous lawsuits have challenged the amateur aspect of NCAA competitions (Hart, 2024).

Pressure from the media, sports broadcasters, and fans to allow student-athletes to profit from the use of their name, image, and likeness kept growing rapidly. Unfortunately, the NCAA continued to ignore the pressure to change bylaws that would allow student-athletes to do this. The failure to change its stance on this issue resulted in new state legislation.

The California State Legislature was the first to propose a bill to allow student-athletes to accept endorsement money for the use of their name, image, or likeness and not be punished by California universities. The state legislature passed this bill, and in 2019, California Governor Gavin Newsom signed the first bill to allow student-athletes to accept endorsement money. California Governor Gavin Newsom signed a bill in September 2019 stating that, starting in 2023, universities in the state couldn’t punish athletes for accepting endorsement money while in college (Moore, 2022).

The passage of California’s legislation created pressure on other states to do the same. Passing legislation to allow student-athletes to receive compensation for their endorsement deals concerning name, image, and likeness.

NCAA’s Litigation Battles

In 2014, the NCAA found itself in litigation with NCAA v. Alston. The lawsuit brought against the NCAA was that they were violating the Sherman Antitrust Act by not allowing student-athletes to profit from their Name, Image, and Likeness. The case went all the way to the U.S. Supreme Court, and in July 2021, the U.S. Supreme Court ruled in favor of Alston. In July 2021, the Supreme Court’s ruling on NCAA v. Alston allowed college athletes to receive money based on their Name, Image, and Likeness (Munn, 2023).

The Alston ruling was a tremendous blow to the NCAA’s stance on amateurism, forcing them to adopt new bylaws that would allow student-athletes to profit from their Name, Image, and Likeness. This was a complete turnaround from student-athletes being punished for receiving financial assistance. Members of the NCAA’s Board of Directors decided Wednesday to hop on this NIL train instead of getting crushed while trying to stand in front of it (Moore, 2022).

Litigation cases against the NCAA did not slow down after the Alston ruling. Instead, the lawsuits became bigger with more at stake financially for the NCAA, colleges, and universities. In 2020, House v. NCAA. Grant House, a student athlete who was a swimmer from Arizona State, and Sedona Prince, who was a women’s basketball player, and two other suits that were filed by college athletes. All three lawsuits against the NCAA were combined into one. A 2020 lawsuit by Arizona State swimmer Grant House and women’s college basketball player Sedona Prince, along with two separate suits by other college athletes, which were combined into one case (Jones, 2025).

The House lawsuit was based on the NCAA’s alleged violation of antitrust laws. The bylaws set by the NCAA prohibited the opportunity for student athletes to benefit financially from their Name, Image, and Likeness. Violated antitrust law by collectively agreeing to not provide benefits and compensation to student-athletes and denying student-athletes the opportunity to profit from the use of their name, image, and likeness (Jones, 2025).

The loss of previous antitrust lawsuits against the NCAA led to the realization that the current NCAA bylaws, allowing student-athletes only to receive free tuition, textbooks, room, and board, could no longer exist. They recognized there was no chance of winning the case and decided to settle out of court. On June 6^th, 2025, Judge Claudia Wilkens approved the House settlement. The settlement would now allow colleges and universities to directly pay student-athletes for their participation in college athletics. On June 6, 2025, the Northern District of California in House v. NCAA approved a landmark settlement deal allowing colleges and universities to pay their students directly for their participation in college athletics (Cernea & Pennesi, 2025).

The House settlement also eliminated three additional antitrust lawsuits against the NCAA, which was accused of not allowing student-athletes to profit off their Name, Image, and Likeness. The House v. NCAA settlement ends three separate federal antitrust lawsuits, all of which had claimed the NCAA was illegally limiting the earning power of college athletes (Murphy, D., 2025). The House settlement agreement also included continuation of the NIL along with back pay, roster limits, and revenue-sharing, which started July 1, 2025.

Financial Fallout from House Settlement

The most significant part of the House Settlement was the revenue-sharing agreement that required all Power Five Conferences to participate in. The agreement was put into place to allow student-athletes to be paid directly from colleges or universities. All other Division I Universities were not required to participate in the agreement, but each college or university could choose to either opt in or opt out. Schools are now free to begin paying their athletes directly, marking the dawn of a new era in college sports (Murphy, D., 2025).

The agreement now allows athletic departments to distribute directly to the sports programs of their choosing about a fourth of their annual revenue, roughly $20.5 million, this academic year. The annual percentage of revenue-sharing will increase each academic year after that. The athletic department’s revenue comes from ticket sales, corporate sponsorships, onsite advertising, concessions, auctions, donations, and, most importantly, media rights. Schools may distribute up to 22% of their revenue from ticket sales, sponsorship revenue, and media rights (Cernea, 2025). Schools will be allotted $20.5 million of revenue per school (Cernea, 2025).

Before the House settlement, colleges and universities’ athletic departments relied on a variety of revenue-generating streams, including ticket sales, corporate sponsorships, and, most importantly, media rights to finance their athletic programs. The implementation of revenue-sharing will create tremendous financial challenges for presidents and athletic directors to keep their athletic programs profitable.

Not all Division I athletic sports offered at colleges and universities are profitable at all. However, there are some sports that either break even or generate a profit: these include men’s basketball, women’s basketball, and football. Even more profitable programs are questioning how they will come up with the money. The Associated Press quoted Alabama Athletic Director Greg Byrne, who told Congress “Those are resources and revenues that don’t exist” (Jones, 2025).

Most colleges and universities’ athletic departments’ budgets at the Division I level across the country will either end in a deficit or break even every year; very few colleges or universities’ athletic departments make a profit. According to financial filings, Alabama reported a $28 million operating deficit during the last fiscal year (Peterson, 2025). All but a handful of Division I athletic departments operate as revenue-neutral (Schnable, 2025).

Each year, Division I conferences receive revenue distribution from the NCAA, which helps athletic departments fund all their sports. Because of the House settlement, along with the massive legal issues that the NCAA has gone through in the past few years. The NCAA was forced to distribute less money to all Division I conferences. It’s also expected to reduce the annual distributions all D-I conferences receive, as the NCAA covers damages (Christovich, 2025).

To survive the new era of college athletics and be competitive in athletics, presidents and athletic directors will have to redistribute monetary resources from non-revenue sports to their three revenue-generating sports. We have one team that makes a healthy profit in football. We have one that turns a profit in men’s basketball. We have 19 that don’t,” Byrne said (Peterson, 2025).

Texas Tech Red Raiders athletic director made a clear message to the public that the new revenue-sharing model would concentrate almost all revenue-generating sports, which are football, men’s basketball, and women’s basketball. Red Raiders will allocate $15.1 million to its football roster (74%), $3.6 million to men’s basketball (17.5%), $410,000 to women’s basketball (2%) (Dellenger, 2025).

A big challenge facing presidents and athletic directors is justifying non-revenue sports and why they should keep them. An argument can be made that non-revenue sports simply do not generate enough revenue to pay their bills. Athletic departments are under tremendous pressure to make a profit or at least break even every year. It gets harder to pay for sports that lose money, which is everything that’s not football or basketball (Talty, 2025).

Five years ago, when the COVID-19 pandemic hit college athletics, it decreased many revenue streams that colleges and universities’ athletic departments relied on. The pandemic forced presidents and athletic directors to find creative ways to trim their athletic budgets. They eliminated some non-revenue sports and laid off athletic support staff to balance their athletic budgets. To this day, some colleges and universities’ athletic departments still have not fully recovered financially from the pandemic.

No one would have ever thought that college and university athletic departments would have to do the same thing again. Because of the House settlement, athletic directors and presidents will continue to look and see where cuts can be made to fund the new era of college athletics. Just as they did during the COVID-19 pandemic, they will be forced once again to eliminate some athletic support staff and some non-revenue sports. “I didn’t think another year would be as tough as COVID [in 2020], but this year has done that,” Yurachek said (Murphy, T, 2025).

Restructuring of Athletics Programs

With the new era of college athletics now in place, athletic directors and presidents will be forced to devote more money to the three revenue-generated sports, which could inflict damage on non-revenue sports budgets. Some non-revenue sports, such as cross country, volleyball, tennis, wrestling, track and field, could either be eliminated or have their roster size reduced. Athletic budgets no longer have enough money to support all the non-revenue sports that generate zero revenue for the athletic departments. As more NIL money is dedicated to football over all other sports on campus, many teams are at risk to be disbanded when there is no money to support their program (Stankovich, 2025). The doomsday option is eliminating sports altogether, which some schools are already doing with sports like tennis that neither bring in revenue nor television exposure (Talty, 2025).

A big concern about athletic departments cutting non-revenue sports is the fact that they produced many of our current or future Olympic athletes. If this does happen, many of our future Olympic athletes could be in jeopardy. Schools have outright used the House v. NCAA settlement as justification to cut Olympic sports programs (Christovich, 2025). There are deep concerns about the potential impact on sports that feed the U.S. Olympic teams (Carey, 2025).

To help reduce financial increases, athletic departments are facing now and in the future. The NCAA has decided to move away from the standard rules of scholarship limits. Now they will impose roster sizes instead for all Division I competing sports. Unfortunately, by the NCAA implementing these new rules on roster sizes, it could effectively eliminate walk-ons. Roster limitations is expected to leave walk-ons, partial scholarship earners, nonrevenue sport athletes and high school recruits at risk (Carey, 2025).

One of the biggest revenue generators for athletic departments is NIL collectives. These collectives are organizations separate from colleges or universities’ athletic departments that generate revenue to help pay for the students’ athletic performance. Unfortunately, most collectives’ money is usually designated for the revenue-producing sports. Collectives generally pay for the athlete’s performance (Hart, 2024). NIL collectives generate revenue from fundraisers, local or national businesses, donations from boosters, alumni, and fans. Collectives are organizations that collect funds from businesses and boosters to facilitate NIL deals for athletes (Hart, 2024).

Financial Cost to Student Body and Fans

To generate more revenue for athletic departments, state legislators have gotten involved in paying student-athletes. Legislation has been passed that will allow institutional funds from colleges or universities to be given to athletic departments to help pay student-athletes. In Missouri, a state law has existed for more than a year permitting the school’s collective to receive institutional funds for distribution to athletes (Dellenger, 2024). Some colleges or universities either have or are in the process of raising student fees to help athletic departments pay their student-athletes. South Carolina announced a new annual $300 athletics auxiliary fee (Rumsey, 2025).

With the implementation of revenue sharing and the NIL athletic departments are now being forced to find creative ways to generate new revenue streams. The University of Tennessee’s athletic department has found an additional revenue source in its ticket sales. They now charge each ticket purchased a 10% talent fee to generate more revenue to pay their student-athletes. Tennessee fans for all sports will be charged a 10% “talent fee” on tickets to help pay athletes as part of the new revenue-sharing plan set to begin in 2025 (Low, 2024). During the Football bowl season, the NCAA has allowed bowl sponsorship patches to be placed on football jerseys. Now conferences and or individual schools are seeking approval from the NCAA to allow advertisement on their game day jerseys to generate additional revenue for the athletic departments. The NCAA’s expected and eventual approval of commercial jersey patches looms large (Dellenger, 2025).

CONCLUSIONS

Athletic department budgets had already been strained from the COVID-19 pandemic. There was a heavy financial toll on many college and university athletic budgets. The House Settlement created additional expenses for athletic departments’ budgets. Presidents and athletic directors know that to be competitive, they must allocate all the necessary resources that they have to their revenue-generating sports to survive financially.

The settlement has caused catastrophic destruction to college athletics. The settlement could seriously damage our U.S. Olympic stronghold; it will eliminate the walk-on dreams and take away the chance for many student-athletes’ opportunities to play college athletics. The settlement rewards only a minority of student-athletes, not the majority. It has created a new era of college athletics that is hurtful and not financially sustainable long term. Lastly, this settlement has created a new era of college athletics that has truly gone a bridge too far.

REFERENCES

1. Cabibi, S. R. (2022, March 15). How money, greed, and the nil destroyed college football… or did it?. Medium. https://medium.com/@seancabibi/how-money-greed-and-the-nil-destroyed-college-football-or-did-it-5e1ea268df4d

2. Carey, M. (2025, May 8). “hands tied”: Athletes left in dark as NCAA settlement leaves murky future for nonrevenue sports. AP News. https://apnews.com/article/ncaa-house-settlement-37ad7713f540c4597627116b1f219483

3. Cernea, E. H., & Pennesi, E. J. (2025, June 18). Long-awaited settlement agreement raises new challenges for Nil Licensing deals. Long-Awaited Settlement Agreement Raises New Challenges for NIL Licensing Deals –. https://www.morganlewis.com/blogs/sourcingatmorganlewis/2025/06/long-awaited-settlement-agreement-raises-new-challenges-for-nil-licensing-deals

4. Christovich, A. (2025, June 19). Olympic sports face cuts in wake of House v. NCAA settlement. Front Office Sports. https://frontofficesports.com/dozens-of-olympic-sports-have-been-cut-in-wake-of-house-v-ncaa-settlement/

5. Dellenger, R. (2024, May 28). The next evolution of Nil Collectives and the battles that await: “this is a big inflection point.” Yahoo! Sports. https://sports.yahoo.com/the-next-evolution-of-nil-collectives-and-the-battles-that-await-this-is-a-big-inflection-point-120051261.html

6. Dellenger, R. (2025, January 7). With nil era ending, college sports is on verge of seismic change. how will schools adapt with industry in upheaval?. Yahoo! Sports. https://sports.yahoo.com/with-nil-era-ending-college-sports-is-on-verge-of-seismic-change-how-will-schools-adapt-with-industry-in-upheaval-154722732.html

7. Hart, J. (2024, September 27). Is nil a good thing or a bad thing? sports industry expert weighs in. Temple Now . https://news.temple.edu/news/2024-06-10/nil-good-thing-or-bad-thing-sports-industry-expert-weighs

8. Jones, S. (2025, May 16). House v. NCAA settlement complicated–and still not yet settled. University Times. https://www.utimes.pitt.edu/news/house-v-ncaa-settlement

9. Lombardi, E. (2024, October 3). Right now, nil is bad for college football. Medium. https://spec.hamilton.edu/right-now-nil-is-bad-for-college-football-809c8af4b9ec

10. Low, C. (2024, September 17). Tennessee increases ticket prices by 10% to help pay athletes. ESPN. https://www.espn.com/college-football/story/_/id/41302985/tennessee-ups-season-ticket-prices-10-help-pay-athletes

11. McCool, J. (2023, November 9). Why name, image and likeness policies could ruin college sports. FSView. https://www.fsunews.com/story/opinion/2023/11/09/why-name-image-and-likeness-policies-could-ruin-college-sports/71508112007/

12. Moore, T. (2022, November 9). NCAA had no choice, but nil rule will damage college football and basketball. Forbes. https://www.forbes.com/sites/terencemoore/2021/07/06/the-ncaa-hadnt-a-choice-but-nil-rule-will-damage-college-football-and-basketball/

13. Munn, T. (2023). What is name, image, and likeness? explained by NCC News. NCC News. https://nccnews.newhouse.syr.edu/college-athletes-can-now-get-paid-but-how-name-image-and-likeness-explained-by-ncc-news/

14. Murphy, D. (2025, June 6). Judge OK’s $2.8B settlement, paving way for colleges to pay athletes. ESPN. https://www.espn.com/college-sports/story/_/id/45467505/judge-grants-final-approval-house-v-ncaa-settlement

15. Murphy, T. (2025, June 27). Arkansas Athletic Department makes staff cuts in preparation for “major changes” with revenue sharing. Whole Hog Sports. https://www.wholehogsports.com/news/2025/jun/27/arkansas-athletics-department-makes-staff-cuts-in-preparation-for-major-changes-with-revenue-sharing/

16. Peterson, D. (2025, June 20). Alabama athletic director comments on future of non-revenue tide teams. Saturday Down South. https://www.saturdaydownsouth.com/news/college-football/alabama-athletic-director-comments-on-future-of-non-revenue-tide-teams/

17. Rumsey, D. (2025, June 23). Colleges raising student fees to pay for athlete revenue-sharing. Front Office Sports. https://frontofficesports.com/colleges-are-raising-student-fees-to-pay-for-athlete-revenue-sharing/

18. Schnable, A., & Thompson, S. (2025, July 1). House Settlement FAQ: What will college sports look like after landmark legal case?. Post-Gazette. https://www.post-gazette.com/sports/pitt/2025/07/01/house-settlement-faq-ncaa-nil/stories/202506300051

19. Stankovich, C. (2025, March 19). College athletics at a crossroads: Nil, transfer portals, and eliminating non-revenue sports. The Sports Doc Chalk Talk with Dr. Chris Stankovich . https://drstankovich.com/college-athletics-at-a-crossroads-nil-transfer-portals-and-eliminating-non-revenue-sports/

20. Talty, J. (2025, June 7). The biggest winners and losers from House v. NCAA settlement: Amateurism is dead and the class divide grows. CBSSports.com. https://www.cbssports.com/college-football/news/the-biggest-winners-and-losers-from-house-v-ncaa-settlement-amateurism-is-dead-and-the-class-divide-grows/

21. Tremps, N. (2024, October 14). The memorandum heard around the college athletics world: Why student-athletes in non-revenue-generating sports should not enjoy the status of “employee” under the NLRA. Wake Forest Law Review. https://www.wakeforestlawreview.com/2024/04/the-memorandum-heard-around-the-college-athletics-world-why-student-athletes-in-non-revenue-generating-sports-should-not-enjoy-the-status-of-employee-under-the-nlra/

Troy Palmer2025-10-31T13:04:38-05:00July 7th, 2026|Contemporary Sports Issues, Research, Sports Coaching, Sports Management, Sports Studies|Comments Off

A Longitudinal Cross-Sectional Analysis of Physical Fitness and Motor Competency for Intermediate School Students

Authors: Moez Baklouti¹

¹Full Professor, Human Sciences Department, Institut Superieur de Sport et de l’Education Physique, University of Mannouba, Tunisia

Editor’s Note: This article uses the pseudonym Nm.Wr.Qs. The Sport Journal has discussed this with the author. The acronym represents a school in North America, and The Sport Journal has confirmed that the school and district exist. This note serves to assure readers that reasonable steps have been taken to confirm the legitimacy of the content presented.

Corresponding Author:

[email protected]

ABSTRACT

Background: The systematic assessment of physical fitness and motor skills, including fundamental coordination tasks like jump roping, is critical for monitoring health, development, and the foundational constructs of physical literacy in school-aged youth. Objective: This study aimed to conduct a cross-sectional analysis of fitness data across eight grade cohorts (Pre-K to Grade 8) to identify developmental and gender-related trends, with a specific focus on the diagnostic value of a 30-second jump rope test as a measure of coordination. Methods: A retrospective analysis was conducted on anonymized fitness test records from 146 students. Data included measures of flexibility, jump rope coordination, horizontal jump (H.J.), vertical jump (V.J.), 30-meter sprint, medicine ball throw (MB6), weight, and height. Descriptive statistics (Mean, SD), independent samples t-tests, and one-way ANOVA with post-hoc tests were used to analyze gender and grade-level differences. Results: Significant increases in performance were observed for power (H.J., V.J., MB6) and speed (30m) from early to later grades. Coordination, measured by jump rope skips in 30 seconds, showed a dramatic and variable increase, indicating it is a skill highly dependent on practice and instruction. Gender differences emerged prominently in middle school, with males generally demonstrating superior performance in power and speed tasks, while females showed more proficiency in coordination in several grade cohorts. Conclusion: The fitness test battery, particularly the jump rope coordination test, proved highly effective in tracing developmental trajectories and identifying skill-specific deficits. The results underscore the necessity of integrating regular, standardized motor assessment, including object-control coordination tasks, into the educational curriculum to foster physical literacy, promote lifelong physical activity, and identify at-risk students early.

Keywords: physical fitness, motor competency, physical literacy, jump rope, coordination, school-based assessment, developmental trajectories, gender differences

INTRODUCTION

The declining levels of physical activity and concomitant rise in childhood obesity and related metabolic conditions represent a significant global public health challenge of the 21^st century (Guthold et al., 2020). In response, there has been a renewed and urgent focus on the role of educational institutions as primary settings for promoting physical health and fostering the concept of ‘physical literacy’. Physical literacy is holistically defined as motivation, confidence, physical competence, knowledge, and understanding to value and take responsibility for engagement in physical activities for life (Whitehead, 2019). Central to this multifaceted concept is the robust development of fundamental motor skills (FMS) -categorized as locomotors (e.g., running, jumping) and object-control (e.g., throwing, catching, striking) skills- which are the foundational building blocks for participation in sports, games, and an active lifestyle across the lifespan (Robinson et al., 2015).

The assessment of physical fitness in school settings has a long history, traditionally utilized to evaluate overall health status and identify athletic talent. However, contemporary perspectives, particularly those emerging between 2020 and 2025, increasingly emphasize its diagnostic value in gauging a child’s journey toward physical literacy (Edwards et al., 2023). While tests of muscular strength, power, speed, and flexibility provide objective data on a student’s physical capacity, measures of coordination offer unique insight into neuromuscular control and skill proficiency. The jump rope test, a classic assessment of coordination, rhythm, and cardiovascular endurance, requires the integration of visual tracking, timing, and bilateral coordination. Its utility in school-based assessments has been highlighted in recent literature as a practical and valid measure of motor competence (Drenowatz et al., 2021). When analyzed collectively and longitudinally, these data can reveal critical information about both typical and atypical developmental pathways, the efficacy of physical education (PE) curricula, and can highlight specific neuromuscular or conditional areas where students may require additional support or intervention (Cattuzzo et al., 2016).

Recent literature has further cemented the link between early motor competence, including coordination, and a spectrum of broader educational and health outcomes. Studies indicate that children with higher levels of motor competence are more likely to be physically active, exhibit better cardiorespiratory fitness, and maintain a healthier weight status (López-Gil et al., 2023). Furthermore, emerging evidence suggests a positive correlation between physical fitness components -particularly executive function- and cognitive performance, academic achievement, and psychosocial well-being in youth (Donnelly et al., 2024). This positions physical fitness and coordination assessment not as an isolated measure of athleticism, but as a key indicator of holistic child development, integral to the educational mission.

Despite this robust understanding, many school systems lack a systematic, longitudinal approach to fitness assessment, often overlooking specific coordination skills like jump roping in favor of more general fitness metrics. Analyzing a comprehensive cross-sectional dataset that spans multiple developmental stages, from early childhood through adolescence, can provide a powerful illustration of these developmental trends and articulate the immense value of such a longitudinal perspective, particularly for skill-based assessments.

This study presents a scientific analysis of a cross-sectional dataset encompassing students from Pre-Kindergarten (PPK) through Grade 8 (S2), with a specific focus on the jump rope coordination test. The primary aims are:

To describe and quantify the physical fitness and motor competency levels, with a detailed analysis of jump rope proficiency, across different school grades.
To analyze gender differences in fitness components, including coordination, within and across grade levels.
To identify key developmental trends and critical periods for motor skill development, particularly for coordinated jumping.
To discuss the implications of these findings for the promotion of physical literacy and the implementation of evidence-based assessment practices in educational settings, integrating recent (2020-2025) scholarly work.

METHODS

Research Design and Data Source

This study employed a retrospective, cross-sectional analysis of existing anonymized physical fitness test records. The data were compiled from eight separate grade-level cohorts: PPK (Pre-K), K5 (Kindergarten), Grade 1, Grade 2/3, Grade 3/4, Grade 5/6, Grade 6, and Secondary (S1 & S2). The combined dataset included records for 146 students at Nm.Wr.Qs.

Participants

The sample consisted of 146 children and adolescents. A breakdown of the sample by grade and gender is presented in Table 1. Students’ gender distribution was relatively balanced across the entire sample, though some grade-level cohorts had small sample sizes, which is a noted limitation for sub-group analyses.

Grade Cohort	Male (n)	Female (n)	Total (n)
PPK	6	2	8
K5	9	5	14
Grade 1	7	5	12
Grade 2/3	5	6	11
Grade ¾	7	10	17
Grade 5/6	5	8	13
Grade 6	5	11	16
Secondary (S1/S2)	15	13	28
Total	59	60	119

Table 1: Sample Size and Gender Distribution by Grade Cohort

*Note: Gender was not reported for 27 participants in the original S2 dataset; these were excluded from gender-specific analyses, hence the total for this table is 119.*

Measures and Variables

The following fitness components were assessed using standardized field tests, as recorded in the original data tables:

Flexibility (Flex.): Measured in centimeters using a sit-and-reach test. Positive values indicate reach beyond the toes.
Coordination (Coor.): Number of successful jump rope skips in a 30-second interval.
Lower-Body Power (Horizontal Jump – H.J.): Standing broad jump distance measured in centimeters.
Lower-Body Power (Vertical Jump – V.J.): Vertical jump height measured in centimeters.
Speed (30m): Time to sprint 30 meters, measured in seconds. All times were converted to seconds (e.g., 8″ 36 became 8.36 seconds).
Upper-Body Power (MB6 Lb.): Distance thrown for a 6-pound medicine ball, measured in centimeters.
Anthropometrics: Body weight (in pounds) and height (in centimeters). These were used to calculate Body Mass Index (BMI).

Data Analysis

All statistical analyses were conducted using IBM SPSS Statistics (Version 29). Data from the original tables were cleaned and standardized. Descriptive statistics (means and standard deviations) were calculated for all variables by grade and gender. To examine gender differences, independent samples t-tests were conducted within each grade cohort where sample size permitted (n>5 per group). A one-way Analysis of Variance (ANOVA) was used to test for significant differences in mean performance across grade levels for each fitness variable. Where the ANOVA was significant (p < .05), Tukey’s HSD post-hoc test was applied to identify which specific grade levels differed from one another. Effect sizes were calculated using Cohen’s d for t-tests (small: d=0.2, medium: d=0.5, large: d=0.8) and eta-squared (η²) for ANOVA (small: 0.01, medium: 0.06, large: 0.14). The alpha level for statistical significance was set at p < .05.

RESULTS

The results are presented in four sections: an overview of developmental trends across grades, a detailed analysis of gender differences, an examination of body composition, and a focused analysis of jump rope coordination.

Developmental Trends Across Grade Levels

A clear and statistically significant developmental trend was observed for all performance-based measures. As expected, as children grew older, their performance in tasks requiring power, speed, and strength improved markedly. Descriptive statistics for key variables across grades are presented in Table 2.

*Grade*	n	H.J. (cm) M (SD)	V.J. (cm) M (SD)	30m (s) M (SD)	MB6 (cm) M (SD)	Flex. (cm) M (SD)	Coor. (Jumps) M (SD)
*PPK*	8	89.4 (8.5)	9.0 (2.1)	8.76 (1.45)	93.8 (18.9)	+5.5 (4.8)	1.0 (1.6)
K5	14	103.9 (13.7)	12.6 (3.5)	7.01 (0.76)	108.6 (18.1)	+5.8 (4.1)	0.2 (0.4)
*Gr 1*	12	125.8 (15.2)	11.0 (3.1)	6.50 (0.83)	136.8 (16.9)	+4.8 (4.9)	9.3 (7.5)
*Gr 2/3*	11	124.1 (21.2)	16.7 (4.9)	6.22 (0.95)	166.4 (37.1)	+3.6 (8.6)	16.3 (7.8)
*Gr 3/4*	17	141.2 (25.8)	17.9 (5.1)	6.28 (0.75)	228.5 (40.8)	–4.4 (8.2)	21.5 (10.2)
*Gr 5/6*	13	141.2 (22.7)	16.5 (5.3)	6.05 (0.62)	218.1 (38.4)	+2.8 (7.8)	67.2 (48.1)
*Gr 6*	16	162.8 (16.3)	21.9 (5.1)	5.62 (0.47)	289.7 (65.8)	+1.9 (8.5)	27.6 (11.2)
*Secondary*	28	181.8 (29.1)	25.9 (8.8)	5.66 (0.84)	372.9 (78.9)	–1.9 (11.3)	30.5 (12.8)

Table 2: Descriptive Statistics (Mean and Standard Deviation) for Key Fitness Variables by Grade Level

One-way ANOVA revealed significant main effects for grade level on all performance variables: H.J. (F(7, 111) = 32.15, p < .001, η² = 0.67), V.J. (F(7, 111) = 21.44, p < .001, η² = 0.58), 30m sprint (F(7, 111) = 16.02, p < .001, η² = 0.51), and MB6 throw (F(7, 111) = 71.89, p < .001, η² = 0.82). Post-hoc analyses indicated that the most significant jumps in performance occurred between early elementary (PPK, K5) and later elementary grades (Gr 2/3, 3/4), and again between late elementary and secondary school.

Figure 1. Mean Horizontal Jump Distance by Grade Level

For upper-body power (MB6), the progression was even more dramatic, increasing by nearly 400% from the PPK to the Secondary cohort, highlighting the significant development of muscular strength through adolescence, particularly in males.

Flexibility showed a distinct pattern, with positive mean scores (indicating reach beyond toes) in early grades that declined, becoming negative on average in the Grade 3/4 and Secondary cohorts. This suggests a relative decrease in hamstring and lower back flexibility as children age, a common finding associated with growth spurts and reduced activity.

The Development of Jump Rope Coordination

The jump rope coordination scores presented a unique and highly informative non-linear trend (F(7, 111) = 15.89, p < .001, η² = 0.50). Performance was minimal in PPK (M=1.0, SD=1.6) and K5 (M=0.2, SD=0.4), indicating a near-universal inability to perform the skill in early childhood. A significant jump occurred in Grade 1 (M=9.3, SD=7.5), suggesting this period is a critical window for initial skill acquisition. Scores then showed a steady, significant increase through Grade 3/4 (M=21.5, SD=10.2).

A remarkable outlier was observed in the Grade 5/6 cohort, where the mean score skyrocketed to 67.2 jumps, albeit with an enormous standard deviation (SD=48.1). This indicates extreme variability within this group; while some students were highly proficient, others remained at a beginner level. This suggests that by this age, jump rope proficiency becomes highly dependent on specific practice and exposure outside of general physical development. Scores then consolidated in Grade 6 (M=27.6, SD=11.2) and Secondary (M=30.5, SD=12.8), showing less variability and indicating a stabilization of skill among those who have acquired it.

Gender Differences in Physical Performance

Gender differences were minimal in the earliest grades (PPK, K5) but became increasingly pronounced throughout elementary and middle school. Detailed comparisons for selected cohorts are presented in Table 3.

Grade & Variable	Males M (SD)	Females M (SD)	p-value	Cohen’s d
*Grade 3/4 (n=7 / n=10)*
H.J. (cm)	151.4 (33.9)	133.9 (17.1)	0.17	0.66
MB6 (cm)	247.1 (40.1)	215.6 (37.2)	0.10	0.81
Coordination (Jumps)	18.1 (8.2)	23.9 (10.9)	0.23	-0.60
Flexibility (cm)	-11.0 (6.5)	+0.3 (6.9)	0.002	-1.69
*Grade 6 (n=5 / n=11)*
H.J. (cm)	170.0 (8.9)	159.5 (17.8)	0.25	0.75
MB6 (cm)	290.0 (67.1)	289.5 (68.3)	0.99	0.01
Coordination (Jumps)	31.2 (15.5)	26.5 (9.7)	0.49	0.36
*Secondary (n=15 / n=13)*
H.J. (cm)	194.7 (26.3)	167.1 (23.8)	0.005	1.11
30m (s)	5.38 (0.72)	5.98 (0.83)	0.04	-0.78
MB6 (cm)	422.7 (71.5)	316.9 (38.7)	<0.001	1.86
Coordination (Jumps)	28.7 (13.1)	32.5 (12.4)	0.42	-0.30

Table 3: Gender Comparisons (Mean, SD, and p-value) for Selected Grade Cohorts

As shown in Table 3, by the secondary school level, males significantly outperformed females in the Horizontal Jump (p = .005, d = 1.11), the 30m Sprint (p = .04, d = -0.78), and the Medicine Ball Throw (p < .001, d = 1.86), representing medium to very large effect sizes. While not always statistically significant in smaller cohorts, the trend of males demonstrating superior performance in strength and power tasks was consistent from Grade 3/4 onward.

In contrast, no significant gender differences were found in jump rope coordination at any grade level, though the effect sizes in Grade 3/4 (d = -0.60) and Secondary (d = -0.30) suggested a trend favoring females, while in Grade 6, the trend slightly favored males (d = 0.36). This indicates that coordination, as measured by this task, is not gender-dimorphic in the way strength and power are, and proficiency is likely more linked to opportunity and practice. Females maintained a significant advantage in flexibility in Grade 3/4 (p = .002, d = -1.69), though this difference was no longer significant by secondary school.

Body Composition Trends

Height and weight increased predictably with age. The Body Mass Index (BMI) was calculated and converted to kg/m² for analysis. Mean BMI percentiles, estimated based on CDC growth charts, generally fell within the healthy range for most cohorts. However, individual cases of very high BMI (>95th percentile) were present, particularly in the Grade 3/4 (e.g., Participant ZMP: BMI ~31) and Grade 6 (e.g., Participant KW: BMI ~33) cohorts, aligning with national concerns about childhood obesity. These outliers often corresponded with notably poor performance in weight-bearing fitness tasks like the 30m sprint and horizontal jump, as well as very low jump rope scores, demonstrating the impact of body composition on motor skill performance.

DISCUSSION

This cross-sectional analysis provides a compelling snapshot of the physical development of students from early childhood through late adolescence, with particular insight into the development of coordination through jump roping. The results largely align with established motor development literature and offer several key, actionable insights for promoting physical literacy in educational settings, viewed through the lens of recent research.

The Jump Rope as a Diagnostic Tool for Physical Literacy

The jump rope coordination data provide perhaps the most vivid illustration of the difference between physical growth and skill acquisition. The near-zero scores in PPK and K5 are expected, as jump roping is a complex skill requiring bilateral coordination, rhythm, and timing that typically emerges around age 6 or 7 (Haywood & Getchell, 2020). The significant jump in Grade 1 marks a critical sensitive period for introducing this skill. The dramatic spike and high variability in the Grade 5/6 cohort are highly informative. This pattern suggests that by ages 10-12, mere physical maturation is insufficient to develop proficiency. Instead, performance becomes heavily influenced by factors such as deliberate practice, participation in sports or activities that incorporate jump roping, and cultural or social exposure to the activity (Drenowatz et al., 2021). The subsequent consolidation of scores in later grades suggests a proficiency barrier (Stodden et al., 2008) has been crossed by some, while others may have disengaged from the skill entirely.

This has direct implications for physical literacy. A child who cannot jump rope may be excluded from playground games and certain physical activities, negatively impacting their confidence and motivation, key affective domains of physical literacy (Whitehead, 2019). Therefore, the jump rope test is not merely a measure of coordination; it is a powerful diagnostic for identifying students who are missing fundamental, culturally relevant movement skills that can facilitate social inclusion and ongoing participation.

Interpreting Broader Developmental Trajectories

The observed, statistically significant improvements in power, speed, and strength are consistent with normal physiological growth and maturation (Malina et al., 2004). The steep improvements in lower-body power (H.J., V.J.) and speed (30m) during the elementary years correspond to a critical period for developing fundamental movement skills (FMS). As Robinson et al. (2015) argue, proficiency in FMS is a primary mechanism underlying physical literacy. The dramatic increase in upper-body power (MB6), particularly in males during adolescence, can be attributed to the surge in testosterone and the development of greater muscle mass (Lloyd et al., 2014).

The significant decline in average flexibility is a concerning trend that has been documented elsewhere and is linked to increased sedentary behavior (e.g., screen time) and a lack of targeted stretching (Schranz et al., 2020). This highlights a specific, often overlooked, area for intervention within a physical literacy framework.

Addressing the Emergent Gender Gap and Skill Equity

The emergence of a significant gender gap in adolescence in strength and power tasks with large effect sizes is a well-established phenomenon (Thomas et al., 2022). While biological factors play a role, sociocultural factors are also at play. Research indicates that adolescent girls often experience a decline in physical self-perception and participation in strength-based activities (Barnett et al., 2022). Our findings suggest that the middle school years represent a critical window for implementing targeted, inclusive strength-building programs for girls (Behringer et al., 2024).

The lack of a significant gender gap in jump rope proficiency is a crucial finding. It demonstrates that when skills are equally practiced and valued for all children, performance gaps need not emerge. This reinforces the importance of a curriculum that explicitly teaches and provides ample practice for a wide range of motor skills to all students, regardless of gender.

CONCLUSION

This comprehensive analysis of multi-grade fitness data vividly illustrates the dynamic nature of physical development throughout the school years. The results confirm expected trends of improving power and speed, highlight a critical period of declining flexibility, and reveal a pronounced gender gap in strength-related tasks emerging in adolescence. The in-depth analysis of jump rope coordination provides a powerful testament to the role of practice and instruction in motor skill development, separate from mere physical maturation. This skill-based assessment proved to be a highly sensitive diagnostic tool for identifying variability in motor competence and potential gaps in physical literacy. By moving beyond mere data collection to data-informed action, educators and policymakers can create more effective, inclusive, and developmentally appropriate physical education programs. Such programs, which explicitly teach foundational skills like jump roping to all children, are fundamental to empowering them with the competence, confidence, and desire to lead active, healthy lives, thereby fulfilling the core promise of physical literacy.

LIMITATIONS AND FUTURE RESEARCH

This study has several limitations. Its cross-sectional design infers longitudinal trends from different individuals at single time points; a true longitudinal study would provide more robust data on individual developmental pathways. The sample sizes for some grade-level cohorts were small, limiting the statistical power of some gender comparisons. Future research should employ longitudinal designs with larger samples, track the relationship between early jump rope proficiency and later physical activity levels, and incorporate qualitative measures of students’ confidence and enjoyment in performing these skills.

APPLICATIONS IN SPORT

The data strongly supports the integration of systematic fitness and skill assessment as a core component of a physical literacy-informed curriculum. As Edwards et al. (2023) argue, assessment should not be for grading but for guiding. The results of such tests can:

Identify Skill Deficits Early: The jump rope test can flag students in Grade 1 who are not acquiring fundamental coordination skills, allowing for early intervention.
Inform Instruction: Physical educators can use these data to form small groups for targeted skill instruction (e.g., a jump rope clinic for the low-performing students in Grade 5/6) and to ensure their curriculum addresses flexibility and upper-body strength for girls.
Promote a Mastery Climate: By focusing on individual improvement in skills like jump roping, rather than solely on athletic performance, teachers can foster the confidence and motivation that are central to physical literacy (Robinson & Goodway, 2021).

REFERENCES

Barnett, L. M., Webster, E. K., Hulteen, R. M., et al. (2022). Through the looking glass: A systematic review of longitudinal evidence, providing new insight for motor competence and health. Sports Medicine, 52 (4), 875–920. https://doi.org/10.1007/s40279-021-01516-8
Behringer, M., Vom Heede, A., Matthews, M., & Mester, J. (2024). Effects of strength training in children and adolescents: A meta-analysis. Pediatrics, 153 (1), e2023062512. https://doi.org/10.1542/peds.2023-062512
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Donnelly, J. E., Hillman, C. H., Castelli, D., et al. (2024). Physical activity, fitness, cognitive function, and academic achievement in children: An update of the 2016 ISPAH International Consensus Statement. Journal of Sport and Health Science, 13(1), 1–10. https://doi.org/10.1016/j.jshs.2023.09.002
Drenowatz, C., Greier, K., Ruedl, G., & Kopp, M. (2021). Association between motor competence and physical activity and health-related fitness in children and adolescents. European Journal of Sport Science, 21 (10), 1450–1459. https://doi.org/10.1080/17461391.2020.1842512
Edwards, L. C., Bryant, A. S., Keegan, R. J., Morgan, K., & Jones, A. M. (2023). Definitions, foundations and associations of physical literacy: A systematic review. Sports Medicine, 53 (1), 1–21. https://doi.org/10.1007/s40279-022-01761-5
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Haywood, K. M., & Getchell, N. (2020). Life span motor development (7^th ed.). Human Kinetics.
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Malina, R. M., Bouchard, C., & Bar-Or, O. (2004). Growth, maturation, and physical activity (2^nd ed.). Human Kinetics.
Robinson, L. E., & Goodway, J. D. (2021). Instructional climates in preschool children who are at-risk. Part I: Object-control skill development. Research Quarterly for Exercise and Sport, 92 (1), 1–10. https://doi.org/10.1080/02701367.2020.1712316
Robinson, L. E., Stodden, D. F., Barnett, L. M., et al. (2015). Motor competence and its effect on positive developmental trajectories of health. Sports Medicine, 45 (9), 1273–1284. https://doi.org/10.1007/s40279-015-0351-6
Schranz, N., Tomkinson, G., Olds, T., & Dannecker, L. (2020). What is the effect of resistance training on the strength, body composition and psychosocial status of overweight and obese children and adolescents? A systematic review and meta-analysis. Sports Medicine, 50 (1), 1–10. https://doi.org/10.1007/s40279-019-01238-y
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Troy Palmer2025-12-12T09:51:04-06:00July 1st, 2026|General, Sport Training, Sports Coaching, Sports Exercise Science, Sports Health & Fitness, Sports Studies|Comments Off

Super Shoes: A Quantitative Analysis of Short-Term and Long-Term Performance Gains

Authors: Ryan Savitz¹, Divit Gupta², Jared Ward³, Andrew Bjorkelo¹

¹Neumann University

²Conestoga High School

³Brigham Young University

Corresponding Author:

Ryan Savitz

[email protected]

ABSTRACT

Purpose:

This paper analyzes the long-term effect of carbon plated running shoe technology (super shoes) on the performance of elite female and male marathoners.

Methods:

In order to do this, we collected data on the number of male sub-2:08 and female sub-2:26:50 marathons in years both prior to and after the introduction of such shoes. Regression models were then constructed to assess the yearly trend in these data both pre and post super shoe introduction (this was done separately for each gender).

Results:

We found a statistically significant increase in the slope following the introduction of super shoes, with the annual number of sub-2:08 performers increasing by approximately 11.8 more athletes per year for men and 22.2 for women. Additionally, we compared the change in men’s slope to the change in women’s slope, finding that women’s times responded significantly more to the introduction of super shoes than did the men’s times.

Conclusions:

In summary, super shoes not only provide an immediate boost to race day performance, but also appear to have ongoing time improvement effects over time.

Applications in Sport:

This research will allow runners to make informed decisions regarding their use of shoe technology in competition. These findings suggest that performances in elite marathoning are improving at a faster rate since the introduction of super shoes. This implies that athletes, coaches, and governing bodies must account for the ongoing effects of shoe technology in training, competition, and qualification standards.

Keywords: Marathon, carbon plated shoes, performance benefits from shoes

INTRODUCTION AND LITERATURE REVIEW

The marathon race traces its origins back to the legend of Pheidippides. We owe the standardization of the distance to the royal family at the 1908 London Olympics, who requested the race to pass the palace, and thus at 26 miles and 385 yards, and a tradition of long distance racing was born.

Following the running boom of the early 1970s, marathon running has become increasingly popular at both a recreational and elite level. Currently, the most competitive marathons are part of the Abbott World Marathon Majors.

One noteworthy thing about long-distance running is that it requires minimal equipment. Perhaps the greatest innovation in equipment technology was the introduction of carbon plated shoes by Nike in 2016. Initially, knowledge of their existence was rather limited, although the three male marathon medalists at the Rio De Janeiro Olympics all wore some prototype of these shoes (5). These shoes, however, did not become widely available until 2017 and, therefore, we use 2017 as their year of introduction for the purposes of the analyses we conduct in this paper. Over time, the use of these shoes has grown to encompass recreational runners as well, and they have become increasingly popular for use in training, due to their extensive cushioning.

Previous work by Bjorkelo et al. (2024) has shown that the use of these shoes has an immediate effect on performance. In particular, they found an immediate increase in the number of sub-2:08 marathons run per year by male marathoners. The goal of this paper, however, is to determine what, if any, long-term benefits these shoes offer. In other words, our goal is to see, if, in addition to the aforementioned immediate benefit, this shoe technology also affects the rate at which the number of sub-2:08 marathons per year is increasing. We assess the same relationship for the number of women’s marathons run under 2:26:50 each year. Mathematically, this study models elite marathon performance counts as a piecewise linear time series with a structural break corresponding to the introduction of new shoe technology. To provide background for these analyses, we now turn to a review of the literature.

A great deal of research into the various factors affecting long distance running performance has been conducted over the years. Running shoe technology has become an increasingly popular area of research following the introduction of super shoes. Much of this research has involved the effect of these shoes on running economy (RE). Morgan et al. (8) define RE as the volume of oxygen that must be consumed (per kg body weight) in order to support a particular running velocity.

While many factors affect RE, the one most relevant to this study is related to running mechanics. Specifically, this factor involves the force with which an athlete’s foot can hit and depart from the ground (3). Much of the research into the efficacy of super shoes in reducing marathon times has been lab research related to these ground forces. For example, Herbert-Losier and Pamment (5) found that while the Nike Zoom Streak 6 (a traditional racing shoe) had an energy return of 65.5%, the Nike Vaporfly (a super shoe) returned 87% of the expended mechanical energy. They found that this increase in energy return results in approximately a 4% increase in RE and a 2% increase in performance. Similarly, Hunter et al. (7) found runners’ oxygen consumption to be between 1.9% and 2.8% lower in carbon plated shoes, as opposed to traditional racing shoes.

The aforementioned laboratory gains in RE can, naturally, vary quite a bit from one individual to another. For instance, Paradisis et al. (9) found that, among recreational runners, the reduction in oxygen consumption attributable to carbon plated racing shoes can be up to 3.8%. It is also important to note that most of the studies on RE focused on male subjects or pooled male and female subjects (1). As we will shortly see, one of the analyses performed in this study attempts to discern any differences in separately averaged male and female response to super shoes.

Although much of the research into carbon plated shoe technology has been conducted in the laboratory, some work has been done outside of the laboratory. In particular, Bjorkelo et al. (2) found that the introduction of super shoes in 2017 was associated with a 91 second decrease in elite male marathoning times. Additionally, Robbin et al. (11) found that, since the introduction of super shoes, elite male and female marathoning times have improved according to the 3 following criteria: (1) the arithmetic mean of the medians of the 100 best performances per year was at least 0.3% faster than the reference value, (2) at least 50% of the years in the observation period were faster than the reference value, and (3) two years within the observation period were the fastest years analyzed. Most notably, they found that arithmetic mean of the medians decreased by 1.45% for the females and by 0.73% for the males. This corroborates the 1.174% decrease in elite male times found by Bjorkelo et al. (2).

In this paper, we will take the previous research several steps further. While the previous research looked at the one-time effect of super shoes on race times (e.g. a 1.45% decrease in marathon times for women and a 0.73% decrease for men) (11), we will address the question: on a yearly basis, are race times improving more rapidly than they used to for elite male and female marathon runners? Additionally, we will statistically quantify any differences that exist between this rate of improvement for men versus women.

METHODS

In order to address the questions posed above, we collected data on the number of male individuals running under 2:08 for the marathon for each year from 1985 through 2024, and similarly, collected data on the number of female individuals running under 2:26:50 for each year from 2002 through 2024 (note that we examine the number of unique individuals under these time standards, not the total number of performances under these standards). These data are publicly available, and were obtained from the World Athletics database (6). We then conducted several linear regression analyses. Due to the time-series nature of the data, we used Cochrane-Orcutt transformations on all continuous variables, in order to remediate the autocorrelation of the residuals (4). This transformation transforms the regression variables such that the correlation of model errors over time is dramatically reduced. After correcting for autocorrelation, no evidence of heteroskedasticity or non-normality of residuals was detected. Additionally, in order to minimize the multicollinearity in the models, we centered the year about 2017. Note that all hypotheses are tested at the 0.05 level of significance.

In each of the aforementioned regressions, the dependent variable is either the number of individuals who ran sub-2:08 marathon in a given calendar year (when dealing with men), or the number of individuals who ran sub-2:26:50 marathon times in a given calendar year (when dealing with women). The times of 2:08 and 2:26:50 were chosen for the following reasons: (1) they allowed us to find data dating back a few decades, (2) they would still be considered an elite marathon time today, and (3) the data for this particular set of times was readily available. Further, the choice of 2:08 allows for a nice comparison to work previously done by Bjorkelo et al. (2), and as 2:08 is near the 2024 Olympic standard for men, the Olympic standard for women seemed a compatible complement. That said, we note that there is nothing intrinsically special about the times of 2:08 and 2:26:50.

While we acknowledge that the use of counts of performances below a fixed threshold differs from directly modeling finishing times, this approach offers two advantages. First, it provides a consistent and interpretable measure of performance depth over time, allowing us to assess how many athletes are achieving historically high standards in any given year. Second, threshold-based measures such as ours are less sensitive to extreme outliers (e.g., world records) and instead capture overall changes in competitive field quality.

In each regression, the year (e.g. 2010) is used as an independent variable. As noted in the introduction, we consider 2017 to be the first year for which super shoes were widely available.

In practical terms, the approach outlined above allows us to compare how quickly elite-level performances were improving before and after the introduction of super shoes. Instead of focusing on individual race times, the model captures changes in the depth of elite performances over time.

RESULTS

We now address the first research question: has the annual rate of increase of the number of men running under 2:08 changed since the introduction of super shoes?

Men

To clarify the above statement, we assume (and can see from the data) that the number of men running under 2:08 each year has been increasing over time, independent of shoe technology. This may be attributable to such things as improved nutrition and better training methods. Our goal is to see if that rate of increase changed in 2017, upon the introduction of super shoes. In order to do this, we estimate the following equations:

Y = b₁₁ + b₂₁X, where Y = the number of men under 2:08, and X = year (for years 1985-2016)

Y = b₁₂ + b₂₂X, where Y = the number of men under 2:08, and X = year (for years 2017-2024).

In the first equation above, b₁₁ is the estimate y-intercept and b₂₁is the estimated slope. Similar notation is used throughout the remainder of this section for the remaining equations. In practice, b₂₁ is the pre-super shoe slope and b₂₂is the post-super shoe slope. b₂₁ tells us, on average, how many sub-2:08 performers were being added per year prior to the introduction of super shoes (presumably due to things like improved nutrition), while b₂₂tells us, on average, how many sub-2:08 performers have been added per year after the super shoes were widely available.

The estimated equations are presented below (with standard errors in parentheses below the parameter estimates):

(equation 1a) Y = -2022.04 + 2.595X

(0.516)

(equation 1b) Y = -39725.17 + 14.395X

(1.973)

Although it is not our primary topic of interest, we note that each of the slope parameter estimates above are statistically significant, and have p-values < 0.001.

After estimating both equations (using ordinary least squares regression), we test the following hypothesis:

H₀: b₂₁= b₂₂

H₁: b₂₁ ≠ b₂₂

Note that we use the approach above, as opposed to simply estimating one equation with an interaction term, because our attempts to do so were met with serious multicollinearity issues.

In order to test the hypothesis above, we utilized a modified Sattherthwaite approach (13) to estimating the degrees of freedom for the corresponding t-test. We utilize this approach because (1) some of our sample sizes are relatively small and (2) the variance of the parameter estimates we are comparing do not appear to be equal.

From equations 1a and 1b, we find a test statistic value of T = 5.78. Using the method of von Davier (13), we find an effective degrees of freedom of 8.23. This results in a p-value = 0.00042. Hence, we reject our null hypothesis of no difference between the slopes. Indeed, it appears that the annual rate of change (slope) in the number of sub-2:08’s during the super shoe era is significantly greater than the rate of change prior to the introduction of super shoes.

The difference between the slopes above is 11.8. This means that, upon the introduction of super shoes, the rate of increase in the number of sub-2:08 runners each year increased by 11.8. In other words, we are now adding nearly 12 more athletes per year to the sub-2:08 ranks than was the case prior to 2017. In a practical sense, this suggests that elite performance is not just improving, but improving at an accelerating rate since the introduction of super shoes.

Women

Similarly, we now address our second research question: has the annual rate of increase of the number of women running under 2:26:50 changed since the introduction of super shoes?

In order to answer this question, we estimate the following equations:

Y = a₁₁ + a₂₁X, where Y = the number of women under 2:26:50, and X = year (for years 2002-2016)

Y = a₁₂ + a₂₂X, where Y = the number of women under 2:26:50, and X = year (for years 2017-2024)

The estimated equations are presented below (with standard errors in parentheses below the parameter estimates):

(equation 2a) Y = -3780.75 + 3.58X

(1.127)

(equation 2b) Y = -55464.92 + 25.79X

(3.521)

As with the male marathoners, note that both of the slope parameter estimates above are statistically significant, and have p-values 0.008 and less than 0.001, respectively.

We now test the following hypothesis for the women:

H₀: a₂₁= a₂₂

H₁: a₂₁ ≠ a₂₂

From equations 2a and 2b, we calculate a test statistic value of T = 6.01. Using the method of von Davier (13), we find an effective degrees of freedom of 5.87. This results in a p-value = 0.0018. Hence, we again reject our null hypothesis of no difference between the slopes. It appears that, as with the male marathoners, the annual rate of change (slope) in the number of sub-2:26:50’s run by females during the super shoe era is significantly greater than the rate of change prior to the introduction of super shoes.

Finding the difference of the slopes above, we are now seeing a rate of increase in sub-2:26:50 runners that is 22.21 athletes per year more than it was previously.

Comparison of Men and Women

We have just seen that there is convincing statistical evidence to show that the rate of increase of both male and female fast (under 2:08 and 2:26:50, respectively) marathons has increased since the introduction of super shoes. Our final research question involves determining whether or not these two changes in rate of fast times is different between the genders. In order to do this, we estimate one regression equation for each gender. Each of these equations involves the entirety of the years available for that gender. The dependent variable is unchanged from before. We now use the 3 following independent variables: X₁ = year, X₂ is a 0-1 dummy variable which indicates whether or not super shoes were available that year, and the interaction term X₁ X₂. We can then test to see if there is a difference in the changes of the two genders’ slopes by testing to see if the parameter estimates for the two interaction terms are equal or not. Specifically, we test:

H₀: c_4m= c_4f

H₁: c_4m ≠ c_4f,

Where the c_i are the coefficients of the two equations’ parameter estimates, and m and f refer to the male and female equations, respectively. The coefficients in the hypotheses above are taken from equations 3a and 3b below, which represent the two regression equations we estimated:

(equation 3a) Y = 26.71 +1.008X_1m + 25.94X_2m + 2.73X_1mX_2m

(0.208) (8.54) (2.06)

(equation 3b) Y = 108.85 +3.194X_1f + 321.71X_2f + 20.27X_1fX_2f

(0.90) (48.64) (3.36)

Recall that Y is the estimated number of athletes under 2:08 or 2:26:50 (elite), and each equation above contains an intercept, an intercept additive “shift” for the super shoe era (X₂), a slope representing the estimated annual increase in number of elite marathons from 1985-2024 (X₁),and an additive increase in slope for the estimated additional number of elite marathons each year after the introduction of super shoes (X₁X₂₎).

From equations 3a and 3b, we used the same techniques as in the first two hypothesis tests, and calculate a test statistic value of T = 4.45 with an effective degrees of freedom of 5.67. This results in a p-value = 0.0067. Hence, we reject the null hypothesis and do, indeed, find evidence that the rate of change in the two genders’ slopes is different. Namely, the women’s slope appears to have changed more than did the men’s slope. We now discuss the aforementioned results in more detail.

DISCUSSION

The results from the previous section provide several interesting implications for the future of marathoning. The preceding findings are not only statistically important, but also have applications for coaches and athletes who want to understand how rapidly the competitive standard in elite marathoning is evolving. To our knowledge, this is the first study to provide statistical evidence that advanced shoe technology is associated not only with immediate performance improvements, but also with an increased rate of elite performance progression over time.

In order to put these new results in context, however, it is important to recall a prior result. Bjorkelo et al. (2) previously found that the widespread introduction of super shoes in 2017 resulted in an immediate increase in the number of sub-2:08 marathons run per year. Specifically, they found two things: (1) the introduction of super shoes was associated with an immediate increase in the number of sub-2:08 marathons by just over 23 per year and (2) after accounting for this shoe effect, there was a trend over time of an additional 2.56 sub-2:08 times per year. Their data set, however, only included times through 2021. Combining these results, we can look at number of sub-2:08 times per year as a linear function of time that took a one time jump in 2017.

Our results extend this past work in a significant way. Namely, we found that, in addition to this one time jump the number of fast (where, for purposes of this paper, we define fast as under 2:08 for men and under 2:26:50 for women) marathons, the number of fast marathons being added per year has also increased. In other words, the number of fast marathon times per year can no longer be viewed as a simple linear function. Rather, the number of fast times per year is a piecewise function of time, with the changepoint occurring in 2017. At that time, the slope of the function changed.

Regarding the specifics of this change in slope, we find that in 2017, for men, the number of additional sub-2:08 times per year increased from 2.595 to 14.395. Similarly, for women, the number of additional sub-2:26:50 times per year increased from 3.58 to 25.79. There are a few possible reasons for this increase. One likely reason involves the possibility that training in these highly cushioned shoes allows runners to train at higher volume and/or intensity. This ability to run hard sessions with less residual fatigue may allow marathoners to improve their times faster than before. While a thorough discussion of marathon training methods is beyond the scope of this paper, we do mention an example. First, Ruiz et al. (12) found that carbon plated marathon racing shoes allowed athletes to run faster later in hard track workouts. Similarly, it would be reasonable to expect that these shoes might allow athletes to recover more quickly following the completion of hard workouts. If this is true, it would allow marathoners to run more hard workouts during any given time period.

In addition to the recovery effect noted above, it is possible that there might be a psychological effect influencing the increasing rate of fast marathon times being seen each year. Pfister (10) found that a super shoe placebo effect might exist. Specifically, they found that, given 2 structurally identical shoes, runners perceived a reduction in running effort when they were told the shoes were super shoes.

Related to this is the potential for super shoes to have initiated a “Bannister effect” in marathon running. The Bannister effect refers to the flood of sub-4:00 miles run in the immediate aftermath of Roger Bannister breaking that long revered barrier in 1954 (14). It is possible that the physical effects of super shoes resulted in people running faster than before, which, in turn, led to people believing they could run faster than before. If a 2:08 marathon is no longer seen as especially fast for an elite male marathoner, this belief may result in more elite athletes going after this as a realistic goal, thus increasing the pool of people who may run under 2:08. It would seem reasonable for all of the aforementioned super shoe effects to hold for both men and women and, indeed, we found statistically significant evidence that the rate of increase in fast marathons did increase for both men and women.

Other possible explanations include that the “slope” and “intercept” considerations are being confounded by the effects of some early adopters and some later adopters. This is less likely for Olympic caliber athletes as those considered here.

Further, it seems that super shoe producers are continuing to innovate. Nike’s original super shoes were named “4%s,” a nod to the purported energy savings. As time goes by and technologies improve, this 4% number may grow.

Our next result of interest involves comparing the super shoe effect in men and women. As seen in our results section, the rate of increase in women’s fast (2:26:50) marathon times was statistically significantly greater than the rate of increase in men’s fast (2:08) times. This implies that, for some reason, super shoes may have a greater effect on women’s times than on men’s. Minimal work has been done comparing men’s and women’s responses to super shoes, so the reasons behind the difference we detected are speculative. One possible reason could be due to potential differences in male and female physiology and/or biomechanics. A second reason could be related to the possibility that there may simply be more room for improvement in women’s marathoning than in men’s marathoning (perhaps due to later access).

While this study focuses on elite-level performances, the findings may also have implications for non-elite runners. As improvements in shoe technology continue to influence performance at the highest levels, similar results have been found for recreational runners (9). This could affect pacing strategies, training approaches, and goal setting for individuals whose objectives are things like setting personal best times or qualifying for the Boston Marathon.

As can be seen, maintaining one’s competitive status may increasingly depend not only on talent and training, but also on access to and the use of advanced footwear technology.

This research also provides interesting avenues for future research. First, it would be valuable for more research to be done comparing the effect of carbon plated shoes on males versus females. Research comparing effects in both training, racing, and recovery would be valuable. Second, a repeat of the study contained herein in several years would be of interest. In particular, such a study could shed light on whether or not the change in slope we observed is permanent. Finally, extending the work done in this paper to track races would be most useful. The technology present in super shoes was, even more recently, introduced to spikes used for track races. It would be interesting to see how similar the effects of these spikes are to the effects we found in the marathon shoes.

There are some limitations to the research presented here. First, and most importantly, our sample sizes are relatively small. This is unavoidable, however, since super shoes have only been widely available for 8 years as of the writing of this paper. Additionally, we note that the results we found speak to the evolution of marathon racing as a whole, and do not offer predictions as to the effect of shoe technology on any given runner. Finally, it is certainly possible that factors such as changes in prize structures and advances in training may have contributed to the observed changes over time. That said, our inclusion of a time variable in each regression should account for incremental changes in performance over time. By comparing the change in the time variable’s slope upon the introduction of super shoes, we attempt to isolate this major change as best as reasonably possible.

CONCLUSION

In summary, we have found that the use of carbon plated shoe technology is significantly related to the rate of increase in the number of fast marathoners per year. In addition to the immediate performance effect of super shoes, the number of additional fast times being added each year has increased significantly for both men and women since the introduction of these shoes in 2017. In order to remain competitive in this environment, athletes are going to have to take advantage of every possible opportunity offered by equipment technology. This increase in competitiveness appears to be even greater in women’s marathoning than in men’s marathon racing. More broadly, these findings highlight how new technologies can alter the trajectory of performance progression in endurance sports.

APPLICATIONS IN SPORT

The results of this study have important implications for athletes, coaches, and sport governing bodies. First, the ongoing benefit of super shoe technology provides one important additional reason for competitive runners – both elite and non-elite – to consider the use of super shoes. As Paradisis et al. (9) showed, the lab effect of super shoes is quite significant, even among recreational competitors. While elite athletes generally have their shoes paid for by sponsors, recreational athletes must consider the costs and benefits of these shoes. With most super shoes costing at least $250, it is important to be aware of all of their benefits prior to making a purchasing decision. For competitive runners, this implies that, despite their cost, not using super shoes may place them at a growing disadvantage as performance standards continue to improve.

Second, as noted earlier, a portion of the ongoing benefit of super shoes appears to be due to their ability to allow runners to perform more frequent high intensity training sessions. Having empirically verified that this benefit is significant, athletes of all levels may now consider working with their coaches to modify past training regimens, due to the enhanced ability to recover that these shoes provide. Coaches may therefore consider revisiting traditional recovery assumptions when developing training micro and macrocycles. For example, coaches may consider modest increases in weekly training volume or intensity, while carefully monitoring recovery, in order to leverage the enhanced recovery capacity offered by super shoe technology.

Finally, there are applications for race directors and governing bodies. The people in charge of determining qualifying times for events such as the Olympics, Olympic Trials, and Boston Marathon often determine these standards years in advance with a rough idea of the field size they desire. Since we have now shown, and quantified, that the rate of increase in the number of fast times has increased, it may be useful to consider this information when setting qualifying standards, in order to optimize the number of competitors in a marathon. Failure to account for these trends may result in the use of qualifying standards that no longer reflect the intended level of selectivity.

REFERENCES

Batista, K., Peel, S., Healey, L., & Paquette, M. (2025). The effects of forefoot curvature in “super-shoes” on the biomechanics and metabolic cost of female runners. Footwear Science, 17(sup1), S181-S182.
Bjorkelo, A., Savitz, R., Ward, J., & Waggoner, B. (2024). Super shoes: How super are they? Journal of Sports Analytics, 10(1), 137-140.
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Herbert-Losier, K., & Pamment, M. (2022). Advancements in running shoe technology and their effects on running economy and performance– a current concepts overview. Sports Biomechanics, pp.1–16. doi:10.1080/14763141.2022. 2110512
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Hunter, I., McLeod, A., Valentine, D., Low, T., Ward, J., & Hager, R. (2019). Running economy, mechanics, and marathon racing shoes. Journal of Sports Sciences, 37(20), 2367-2373
Morgan, D.W., Martin, P.E. and Krahenbuhl, G.S. (1989). Factors affecting running economy. Sports Medicine, 7(5), 310–330. doi: 10.2165/00007256-198907050-00003
Paradisis, G. P., Zacharogiannis, E., Bissas, A., & Hanley, B. (2023). Recreational runners gain physiological and biomechanical benefits from super shoes at marathon paces. International journal of sports physiology and performance, 18(12), 1420-1426.
Pfister, A. (2024). The potential placebo effect of advanced footwear technology on running economy and comfort in female recreational runners (Doctoral dissertation, The University of Waikato).
Robbin, J., Mai, P., Helwig, J., and Willwacher, S. (2023) Does an analysis of the world top 100 track and road running performances provide an indication for the effects of super shoes and spikes?, Footwear Science, 15:sup1, S16-S17, doi: 10.1080/19424280.2023.2199262
Ruiz-Alias, S. A., Pérez-Castilla, A., Soto-Hermoso, V. M., & García-Pinillos, F. (2023). The effect of using marathon shoes or track spikes on neuromuscular fatigue caused by a long-distance track training session. International Journal of Sports Medicine, 44(13), 976-982.
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Troy Palmer2026-06-29T08:43:40-05:00June 26th, 2026|Contemporary Sports Issues, General, Olympics, Sport Training, Sports Coaching, Sports Exercise Science, Sports Marketing|Comments Off

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

Authors: Daniel J. Marcolongo¹, Bret R. Myers²

¹Graduate of Sports Industry Management Program, Georgetown University, Washington DC, USA

²Department of Management and Operations, Villanova University, Villanova, PA, USA

Corresponding Author:

Bret R, Myers, Ph.D.

Department of Management and Operations

Villanova School of Business

800 E Lancaster Avenue

Villanova, PA 19085

[email protected]

Daniel J. Marcolongo is a 2025 graduate of Georgetown University’s Sports Industry Management masters program. His focus is in soccer analytics, which he developed as a collegiate soccer player and lifelong student of the game.

Bret R. Myers, Ph.D. is a Professor of Practice in the Department of Management and Operations in the Villanova School of Business. His research interests focus on sports analytics, specifically, in the areas of team evaluation and managerial decision-making. He also is an active Analytics Consultant with 10+ years of experience working with professional teams and other sports organizations.

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.

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.

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.

REFERENCES

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

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

Authors: Stephen Cadoux¹, Kimberly Shaffer²

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

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

Corresponding Author:

Kimberly Shaffer

[email protected]

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

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

ABSTRACT

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

Keywords: coaching, NCAA athletics, peer assessment

INTRODUCTION

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

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

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

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

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

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

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

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

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

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

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

METHODS

Participants

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

Procedures

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

Instruments

Demographic Questionnaire

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

Multifactor Leadership Questionnaire

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

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

Data Analyses

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

RESULTS

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

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

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

Table 1

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


	IA (SD)	IB (SD)	IM* (SD)	IS* (SD)	IC (SD)	CR* (SD)	MBEA* (SD)	MBEP (SD)	LF (SD)	EE* (SD)	EFF* (SD)	SAT (SD)
Captain 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.

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