General – Page 2 – The Sport Journal

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

Alencar, M. (2024, Mar 20). Higuita: Scorpion kick changed football forever: Rene Higuita’s acrobatic clearance helped usher in new era of ball-playing goalkeepers, he tells Mauricio Alencar. City A.M. https://proxy.library.georgetown.edu/login?url=https://www.proquest.com/newspapers/higuita-scorpion-kick-changed-football-forever/docview/2968727496/se-2?accountid=11091
Bate, A. (2021, May 26). Stanley Menzo interview: Goalkeeping pioneer who changed the game under Johan Cruyff at Ajax. Sky Sports. https://www.skysports.com/football/story-telling/11946/12311915
Bugda, G., & Swann, S. (2024, July 9). Who is the “goalkeeper” for your organization? Medium. https://twodummies.medium.com/who-is-the-goalkeeper-for-your-organization-aa6b53838f9f
Carmichael-Brown, S. [Hashtag United]. (2024, January 9). CHELSEA GOALKEEPER TED CURD RECALLED! [Video]. Youtube. https://www.youtube.com/watch?v=BLCTnNQfWPY
Diamos, J. (2005, September 16). Hockey; new rule will take a weapon away from Brodeur. HOCKEY – New Rule Will Take a Weapon Away From Brodeur – NYTimes.com. http://web.archive.org/web/20131101025522/https://select.nytimes.com/gst/abstract.html?res=F10616F835550C758DDDA00894DD404482
FBRef. (2025). Premier League goalkeeper stats. FBref.com. https://fbref.com/en/comps/9/keepersadv/Premier-League-Stats#all_stats_keeper_adv
FBRef. (2025). Premier League stats. FBRef.com https://fbref.com/en/comps/9/Premier-League-Stats
FBRef. (2019). XG explained. FBref.com. https://fbref.com/en/expected-goals-model-explained/
FIFA. (2025, January 7). Goalkeeping: Dealing with crosses from corners. FIFA Training Centre. https://www.fifatrainingcentre.com/en/game/tournaments/fu17wwcfu17wwc-24/2024/goalkeeping-dealing-with-crosses.php
Garrick, O. (2024, May 19). Arsenal’s David Raya wins 2023-24 premier league golden glove award. The New York Times .https://www.nytimes.com/athletic/5495358/2024/05/19/david-raya-arsenal-golden-glove/
Jamil, M., Phatak, A., Mehta, S., Beato, M., Memmert, D., & Connor, M. (2021, November 22). Using multiple machine learning algorithms to classify elite and sub-elite goalkeepers in professional men’s football. Nature News. https://www.nature.com/articles/s41598-021-01187-5#Sec7
Marcolongo, D., & Miller, T. (2025, March 27). Interview with Tyler Miller. personal.
Obetko, M., Peráček, P., Mikulič, M., & Babic, M. (2022). Technical–tactical profile of an elite soccer goalkeeper. Journal of Physical Education and Sport, 22(1), 38-46. https://doi.org/10.7752/jpes.2022.01005
Patrikarakos, D. (2009). Defining Moment: The back-pass rule livens up football, 1992. FT.Com, https://proxy.library.georgetown.edu/login?url=https://www.proquest.com/trade-journals/defining-moment-back-pass-rule-livens-up-football/docview/229164255/se-2?accountid=11091
Premier League. (2025). Most punches – premier league goalkeeper stats. https://www.premierleague.com/stats/top/players/punches
Rose, G. (2025, January 8). Manchester City News: How Pep Guardiola has influenced goalkeeper evolution in English football. BBC Sport. https://www.bbc.com/sport/football/articles/clyvw8xlv4lo
Schmidt, C., & Hopkins, O. (2021, April 7). Manuel Neuer: Record-chaser and revolutionary. Opta Analyst. https://theanalyst.com/2021/04/manuel-neuer-record-chaser-and-revolutionary
Smith, A. (2023, August 15). Best goalkeeper in the Premier League revealed: Andre Onana, David Raya and Robert Sanchez transfers analysed. Sky Sports. https://www.skysports.com/football/news/11661/12933578/best-goalkeeper-in-the-premier-league-revealed-andre-onana-david-raya-and-robert-sanchez-transfers-analysed
Yam, D. (2019). A data driven goalkeeper evaluation framework. MIT Sloan Sports Analytics Conference. https://www.sloansportsconference.com/research-papers/a-data-driven-goalkeeper-evaluation-framework

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.

REFERENCES

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

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

A Manual Therapy Treatment for Headache Pain

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

Corresponding Author:

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

1845 Fairmount Street,

Box 16,

Wichita, KS 67260

[email protected]

(316) 978-5440

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

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

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

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

ABSTRACT

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

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

INTRODUCTION

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

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

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

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

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

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

TABLE 1

The ‘Yes/No’ Cervical Nerve Root Assessment Chart

Nerve Root	Location of Pain	Special Characteristics
C1	Anywhere on the head, this is determined when we do the ‘yes/no’ test.	If their head is ‘rotated only,’ it is C1.
C2	Top of the head, sides of the head, front and back of the head.	No lower than the occiput.
C3	In the eyes, between the eyes, behind the eyes, into the jaw or cheek area, top of the neck.

Case Report

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

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

METHODS

Assessment

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

Table 2

Inclusion and Exclusion Criteria.

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

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

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

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

Instrumentation

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

The Headache Impact Test Questionnaire

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

The Numerical Pain Rating Scale

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

The Neck Disability Index

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

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

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

RESULTS

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

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

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

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

DISCUSSION

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

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

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

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

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

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

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

Limitations

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

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

CONCLUSIONS

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

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

APPLICATIONS IN SPORT

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

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

Acknowledgments

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

REFERENCES

Bogduk, N. (2001). Cervicogenic headache: Anatomic basis and pathophysiologic mechanisms. Current Pain and Headache Reports, 5(5), 382–386. https://doi.org/10.1007/s11916-001-0025-y
Bogduk, N. (2004). The neck and headaches. Neurologic Clinics, 22(1), 151–171. https://doi.org/10.1016/j.ncl.2003.11.006
Bogduk, N., & Govind, J. (2009). Cervicogenic headache: An assessment of the evidence on clinical diagnosis, invasive tests, and treatment. The Lancet Neurology, 8(10), 959–968. https://doi.org/10.1016/S1474-4422(09)70209-9
Sjaastad, O., Fredriksen, T. A., & Pfaffenrath, V. (1998). Cervicogenic headache: Diagnostic criteria. Headache: The Journal of Head and Face Pain, 38(6), 442–445. https://doi.org/10.1046/j.1526-4610.1998.3806442.x
The International Classification of Headache Disorders, 3rd edition. (2018). Cephalalgia, 38(1), 1–211. https://doi.org/10.1177/0333102417738202
Yang, M., Rendas-Baum, R., Varon, S. F., et al. (2010). Validation of the Headache Impact Test (HIT-6™) across episodic and chronic migraine. Cephalalgia, 31(3), 357–367. https://doi.org/10.1177/0333102410379890
Quinn, C., Chandler, C., & Moraska, A. (2002). Massage therapy and frequency of chronic tension headaches. American Journal of Public Health, 92(10), 1657–1661. https://doi.org/10.2105/AJPH.92.10.1657
Haldeman, S., & Dagenais, S. (2001). Cervicogenic headaches. The Spine Journal, 1(1), 31–46. https://doi.org/10.1016/S1529-9430(01)00017-2
Schoensee, S. K., Jensen, G., Nicholson, G., et al. (1995). The effect of mobilization on cervical headaches. Journal of Orthopaedic & Sports Physical Therapy, 21(4), 184–196. https://doi.org/10.2519/jospt.1995.21.4.184
Youdas, J. W., Garrett, T. R., Suman, V. J., et al. (1992). Normal range of motion of the cervical spine: An initial goniometric study. Physical Therapy, 72(11), 770–780. https://doi.org/10.1093/ptj/72.11.770
Hall, T. M., Robinson, K. W., Fujinawa, O., et al. (2008). Intertester reliability and diagnostic validity of the cervical flexion-rotation test. Journal of Manipulative and Physiological Therapeutics, 31(4), 293–300. https://doi.org/10.1016/j.jmpt.2008.03.007
Uriarte, M. (2004). MyoKinesthetic system upper body training manual. MyoKinesthetic Institute.
Moy, B. (2015). Case study detail – The MyoKinesthetic Institute (MYK). MyoKinesthetic Institute. Retrieved August 18, 2021, from https://www.myokinesthetic.com/case-studies/the-treatment-of-c8-with-manual-therapy
Houts, C. R., Wirth, R. J., McGinley, J. S., et al. (2020). Determining thresholds for meaningful change for the Headache Impact Test (HIT‐6) total and item‐specific scores in chronic migraine. Headache: The Journal of Head and Face Pain, 60(10), 2003–2013. https://doi.org/10.1111/head.13950
Nachit-Ouinekh, F., Dartigues, J. F., Henry, P., et al. (2005). Use of the Headache Impact Test (HIT-6) in general practice: Relationship with quality of life and severity. European Journal of Neurology, 12(3), 189–193. https://doi.org/10.1111/j.1468-1331.2004.00929.x
Farrar, J. T., Young, J. P., LaMoreaux, L., et al. (2001). Clinical importance of changes in chronic pain intensity measured on an 11-point numerical pain rating scale. Pain, 94(2), 149–158. https://doi.org/10.1016/S0304-3959(01)00349-9
Chan Ci En, M., Clair, D. A., & Edmondston, S. J. (2009). Validity of the Neck Disability Index and Neck Pain and Disability Scale for measuring disability associated with chronic, non-traumatic neck pain. Manual Therapy, 14(4), 433–438. https://doi.org/10.1016/j.math.2008.07.005
Smith, J., & Bolton, P. S. (2013). What are the clinical criteria justifying spinal manipulative therapy for neck pain? A systematic review of randomized controlled trials. Pain Medicine, 14(4), 460–468. https://doi.org/10.1111/pme.12081
Norkin, C. C., White, D. J., Torres, J., et al. (2016). Measurement of joint motion: A guide to goniometry (5th ed.). F.A. Davis Company.

APPENDIX

Table 3

MYK Postural Assessment (pre/post)

Table 4

Patient Reported Outcomes

	NDI		HIT-6		NPRS
ASSESSMENT	Score	Ranking	Score	Ranking	Pre- Score	Post- Score	Mean of Raw
Initial	14/50	Mild	58	Substantial	4	3	3.75
Discharge	15/50	Moderate	54	Some	7	5	4
Mean	_	_	57.6	Substantial	–	–	–
30-Day	8	Mild	46	Little to no impact	–	–	0
60-Day	5	Mild	38	Little to no impact	–	–	.666

Table 5

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

Cervical Range of Motion
Movement	Normative Data	Pre-treatment	Post-treatment (change)	30-Day Follow Up	60-Day Follow Up
Flexion	40° ± 12	45°	40° (-5°)	47.3°	46°
Extension	50° ± 14	53°	60° (7°)	41.6°	37°
Left Rotation	49° ± 9	53°	54.6° (1.6°)	55.6°	51°
Right Rotation	51° ± 11	60°	61.6° (1.6°)	58.6°	62°

Troy Palmer2025-10-08T12:16:04-05:00April 15th, 2026|Concussions, General, Research, Sports Health & Fitness, Sports Medicine|Comments Off

Fundraising in Sports: A case study

Author: Francisco J. Quevedo¹

Corresponding Author:

Francisco J. Quevedo

72 Maple Street

Watchung, NJ, 07069

[email protected]

929-208-5289

¹Department of Marketing, Rutgers, The State University of New Jersey, Newark, NJ

Dr. Quevedo is an Assistant Professor of Marketing at Rutgers University. A UMass Amherst ’78 graduate, he got his doctorate, MBA, and CAGSB at Pace University. He taught there, and at NYU before joining Rutgers full-time in 2020. He worked corporate and developed his family’s businesses in insurance, tourism, sports, and agriculture for 33 years until returning to academia. He has taught college for 15 years and done consulting for Fortune 100 firms, NGOs, and governments in nine countries. He has worked with nonprofits for 20 years. He researches brand management and nonprofit marketing, publishing 12 articles and chapters since 2019. He received an Award for Teaching Innovation in 2023 and coordinates the CM3A consulting center at Rutgers.

ABSTRACT

Nonprofits in general long for fundraising guidance, market and donor research, and strategic planning support from academia. Within this sector, US amateur sports could represent a $60.5 billion segment, which receives but a small portion of total donations. To help close the gap, this paper presents a case study that can serve as a model to optimize nonprofit performance based on an amateur sports organization, which combines three related studies: a time-series analysis of nonprofits in the US showing that revenues depend largely on awareness and income, and points to the need to choose the right target and put the message out to raise funds; a donor survey which showed that, individually, decisions to give are based mostly on pride, pity, PR, personal interest, and pleasure, and points to the need to craft the right appeal; and a cross-sectional, six-country analysis of a proposed structure and processes that represents the underlying theory for this paper, which showed how networking, fiscal leveraging, and a coherent narrative, supported by the proper strategy and organization, generate external influence and revenues, thus emphasizing the need to follow proper procedure to achieve the desired results. A deep dive into the scientific literature sets the stage to analyze 17 years of experience in the WSKF Sports Foundation, part of a worldwide organization that spans over 110 countries and a million members, and raised up to $3.3 million at its peak in 2015, winning 266 world medals between 2007 and 2017, thereby providing a blueprint for fundraising in sports that can extend to most nonprofit organizations.

Key Words: sponsorship, strategy, process, model, medals, nonprofit, WSKF, foundation

INTRODUCTION

This paper points to the most pressing needs of nonprofit organizations. An unpublished survey of the Center for Marketing Advantage, Advancement, and Action of Rutgers University, working with the membership of the NJ Center for Nonprofits, pinpointed the demands of private foundations; fundraising, marketing and donor research stand out as the most urgent requirements of NGOs, followed by specifics like digital marketing and communications, market research, and strategic planning. Tracking 17 years of nonprofit research and amateur sports experience, we aim to present a tested and proven model to optimize nonprofit performance with the support of three specific research studies and a wide search of the literature.

The proposed model is supported by a cross-sectional test of Koschmann, Khun & Pfaerrer’s theory (23) done by Quevedo (33), a time series analysis of the US nonprofit sector by Quevedo & Quevedo-Prince (36), and a national survey that studied the driving motives to donate by Quevedo and Lee (35), which extended prior research by Quevedo and Gopalakrishna (34) on consumer preferences applying them in the nonprofit field.

The WSKF Venezuela Sports Foundation, part of a Japanese karate federation, the World Shotokan Karate-do Federation, that spans over 20,000 clubs and over a million members in more than 100 countries, served as the basis for a six-country analysis that showed how networking, leveraging, and a coherent narrative, deployed on the shoulders of the proper strategy, organization and processes, generate external influence (press coverage and lobbying power), and lead to substantially more revenues for the organization.

These studies and experiences showed that choosing the right target, designing the right appeal, and following the right approach, strategy and processes, will boost press coverage and drive fundraising. It is not just about saying and doing the right things, nonprofits must do the right things correctly.

A key paradox in amateur sports is whether funding follows medals or medals follow funding. In the case of the WSKF Sports Foundation, winning seemed to be the key to fundraising. Winning in one championship leveraged the next championship cycle. Looking at other causes, however, we must ask, should they generate social benefits to raise funds or raise funds to generate benefits? This chicken-and-the-egg paradox (Illustration 1) is paramount in sports, since medals increase media coverage and provide bragging rights to get more funds, but then funds, and training of course, are the means to get those medals, but it may not be necessarily true in other scenarios.

Illustration # 1: Medals and Funds – A Virtuous Circle in Amateur Sports

BACKGROUND

The youth and amateur sports industry is booming. The sector’s direct spending impact was valued at $39.7 billion in 2021, says a Sports ETA’s industry report signed by Clement (6). Wintergreen Research predicted that this market would grow at a compound annual growth rate of 8.9% until 2028. The NCAA generated a record $1.22 billion of revenue in 2022 from March Madness ticket sales, merchandise and television broadcast rights. Indeed, CBS and Turner Sports will pay the NCAA up to $19.6 billion over a 22-year contract term said Morones (31). These elements can add up to a $41 billion industry which depends in good part on fundraising to survive. However, sports are but a minuscule part of the philanthropic market and dynamics, so small that they do not make the charts. Certainly, more research support is needed to develop the sector. Unfortunately, marketing literature is unable to provide meaningful guidance because scant research attention has hampered a fuller understanding of why people help, as Bendapudi, Singh & Bendapudi found (2).

Chart 1: Nonprofit Revenues in the US

The professional sports market on the other hand is projected to reach close to $85 billion this year and that may not consider royalties for branded sports clothing and memorabilia according to Statista (39). Based on these figures, we could be looking at an umbrella sports market of $126 billion in the US alone, and perhaps as much as $500 billion worldwide by extrapolation (based on US vs. world GDP).

METHODS

Sargeant and Shang (2010) emphasized that the need for a comprehensive model for fundraising has never been greater (37). Accordingly, we aim to provide a blueprint for funding amateur sports based on both theory and practice, leaning on three specific research studies, a deep dive into the scientific literature, and 17 years of successful fundraising experience with the WSKF Venezuela Sports Foundation, and 20 years of foundational work overall. Furthermore, we aimed to answer the question “will the right target and message, the right appeal and the right approach drive fundraising success, or do we need credentials and credibility upfront to attract sponsors?”

Illustration # 2: Kushman’s et al (2012) Model for Nonprofits

The WSKF Venezuela Sports Foundation raised up to $3.3 million (at the official rate of exchange) in its peak year, 2015, when its national team won 66 world medals in Tokyo, and received 73 press mentions which reverberated throughout the web internationally. These results speak for themselves. Its model was in use since 2008, and was replicated in Japan, the US, Canada, Panama, Spain, Ireland and other countries where the organization is present. A cross-sectional study, covering six countries, tested how much a gap in the execution of the appropriate model will affect fundraising results.

Data Analyses

Statistical analyses were performed using SPSS version 29.0.2.0 (IBM). Multiple regression was combined with factor analysis in the time series modeling of the US nonprofit sector. Pearson correlation coefficients were calculated, as were the significance and p-values once the best fitting variables were identified. The donor decision model was determined through multinomial logistic regression, considering the extensive use of categorical variables. Cronbach’s alpha, Pseudo-R²coefficients, significance and Chi-square values were calculated as well. Compare means was used in the cross-sectional analysis of six countries represented in the WSKF Sports Foundation to validate variations in their results.

Prior Research Studies

Traditionally, the largest source of charitable giving in the US are individuals, not corporations, with $268.28 billion in donations which represent 71% of total giving, followed by foundations ($57.19 billion or 16%), bequests ($28.72 billion or 9%), and corporations ($18.46 billion or 5%). The average annual household contribution to nonprofits stood at $2,974, according to Statista (42). The majority of charitable dollars go to churches (32%), schools and colleges (15%), human services (12%), grant-making foundations (11%), and hospitals in general (8%). Sports does not make the Top 5 in this report.

List says that the nonprofit market revolves around three major players: (1) the donors, who provide the resources to charities. These can be corporations, public institutions, individuals, and non-government organizations (NGOs); (2) charitable organizations, which attract and allocate resources; and (3) the government, which decides on the fiscal framework for individual, corporate and NGO contributions, shapes the supply of grants to the various charities, and decides which public goods it will provide directly (28).

This proposal feeds from three research studies and 17 years of fundraising experience with the WSKF Sports Foundation. First, a predictive model of the US Nonprofit Sector based on time-series analysis showed that Nonprofit Revenues (NPR) depend largely on Public Awareness, as measured by TV coverage, and on Disposable Personal Income (DPI), specifically: NPR = – 4401.542 + 528.327(DPI) +23.121(TV Coverage) + Ɛ (36). Pearson’s R came up to 0.935, significance levels were at 0.001. Confirmatory Factor Analysis reaffirmed the fit of the equation, with an R² of 0.87. These findings indicate that nonprofits must first choose their targets well. Then fundraisers must put the message out, if they wish to get funds.

The question is “what should nonprofits say?” The second reference comes from a survey of 615 respondents, using their alma mater, the ASPCA, St. Jude’s Hospital for Children, a local homeless shelter, and their church as references; considering pride, pity, PR, personal interest, and pleasure as the driving motives, testing which appeal worked best to communicate a Nonprofit Organization’s message to generate funds. These were called “The 5-Ps of Fundraising” (35). Based on the pseudo-R²coefficients generated by Multinomial Logistic Regression, the model reflected a predictive ability of 49.7%. All criteria were statistically significant. The pleasure of giving was the strongest driver, coming out as an underlying motivator in the donating decision. Different social causes respond differently to alternate fundraising appeals, therefore, determining which appeal works best is key to success. Ignoring the key drivers in the decision to donate may lead to being both ineffective and inefficient. These findings tell fundraisers how to craft the right appeal.

The third study would show how to deliver the right appeal to the right target, and how to operate a nonprofit organization successfully. Looking into the literature, Curry, Rodin and Carlson proposed that organizations that operate on transformational approaches to fundraising have fared significantly better than those which operate on a more transactional basis, and that the greater physical proximity of the donor base of an organization would positively impact fundraising (7). Wallace said that predictive modeling has concentrated on big-donor analytics, largely aimed at the identification of potential donors (43). Nonetheless, Koschman et al. (23) presented a more detailed model for optimizing the performance of Nonprofit Organizations (Illustration 2), which in hindsight, was being used by the organization under study years before it was published. Their model became thereby the underlying theory for this case study.

Indeed, Harris says that case analysis is a valid learning tool for research in fundraising for sports (15). Accordingly, we tested the Koschman et al. (23) model on the WSKF Venezuela Sports Foundation, part of a Japanese federation that spans over 20,000 clubs and more than one million members in more than 100 countries throughout all the continents except Antarctica, using six countries (the US, Panama, Spain, Ireland, Canada, and Venezuela) to find cross sectional illustrations of how the “meaningful participation” of members, the “centripetal forces” generated by the organization and its environment, and the consolidation of an institutional image through a “coherent narrative,” worked on the basis of “authoritative texts,” to use the original labels (23), generated “external influences” and led to substantially more revenues for the organization (33). These findings in sum tell fundraisers to follow proper procedure, a solid strategy, detailed plans and professional processes to achieve the desired results, given the choice of the right target and an appropriate message and appeal.

Although a better understanding of nonprofit dynamics and of the factors that affect fundraising efficiency is essential to charity managers, policy makers, and private donors, research has focused more on the micro than the macro view, says Yi (46), and not quite on the “how to” of organizational performance. Guy and Patton say that nonprofit marketing should begin with a basic understanding of motivations and donor behavior rather than merely adopting prefabricated marketing techniques (14). Sure enough, to be competitive, charitable organizations must rely on carefully formulated promotional programs, but there is an urgent need for research to identify the prevalence and effectiveness of different messages, according to Leonhardt and Peterson (27), who add that more than 55% of all NGOs appeal to selfless consumer motives (i.e., altruism), which is appropriate. However, an important experiment revealed that appealing to more selfless vs. less selfless (i.e., reputation) motives results in consumers having a more favorable attitude toward the charitable organization. So, there is more to donating than just the desire to help, and there is more to fundraising than just asking for money to those who have it. Consumer involvement, for instance, is found to have an important effect on the decision to donate; selfless appeals promote a more positive attitude among consumers with low involvement, but not for those with high involvement with a charitable cause (e.g., animal welfare).

Furthermore, Cao found that psychological involvement with charities affects donation intentions; seeing a picture of a sad vs. a happy person increased intentions to give among participants with lower levels of psychological involvement, whereas the reverse was true for highly involved participants (3), hence the importance for NGOs and CSR executives to understand the nature and behavioral context of their operations. Huber, Van Boven, & McGraw combine what they call the internal and external influences on donor behavior (18), pointing in the direction of this paper and related research. Donor behavior has been disaggregated by researchers like Fajardo, Townsend, and Bolander into two components: donation choice and donation amount. Donor-related appeals have a greater effect on choice, while organization-related appeals have a greater effect on the amount pledged or donated. This could lead one to conclude that presenting both types of appeals in a solicitation is ideal (10).

On an individual level, the vast majority of donors are enthusiastic and positive about the organizations they give to, and about charities in general says Wooden (45). Leonhardt says that people give money to feel the “glow” associated with being the kind of person who helps a worthy cause (26). Kemp, Kennett-Hensel, and Kees studied emotions like pride and pity in charitable appeals, focusing on sex and gender as potential emotional collateral variables (21). Utility-based models that focus on the effects of lifetime, recency, seasonality, and appeals also show that fundraising attempts should emphasize commitment rather than amount, as stated by Kim, Gupta, and Lee, (22). Sectorial research by Kamatham, Pahwa, Jiang and Kumar focused on education’s 75% success rate studied how different appeals affect fundraising; sophistication of the appeal has a positive effect on fundraising and the amount donated. Providing information on the state of a project has a positive effect on donations, corroborating reinforcement models of donor behavior; individuals share a burden when supporting charitable causes and donate at least as much as the minimum donated (20). At the strategic level, Krug and Weinberg’s Merit Axis Model links the mission of the organization, the money raised, and merit as a standard for nonprofit management (24). Pride, pleasure, and personal interest were linked by Third to the legacy effect in the college and universities context, pointing to relational fundraising and the application of CRM to nonprofit marketing (41). A unified conceptual, behavioral, and econometric framework for optimal fundraising can combine approaches from Economics, Marketing, Psychology, and Sociology, said Haruvy, Popkowski, Leszczyc, Allenby, Belk, Eckel, Fisher, Li, Ma, Wang, and List (16), which is the intention of this paper, considering the need for developing a comprehensive model of giving behavior and nonprofit organization performance.

Although the marketization of nonprofit activities, given by the introduction of marketing practices like sales of POP and different goods and services, competing for consulting contracts, donor relations management (the philanthropic version of CRM), and social entrepreneurship has drawn criticism, according to Eikenberry and Drapal (8), fierce competition for funds and a tighter economy have given rise to innovative fundraising methods like web-based crowdfunding and what is called Cause Related Marketing or CRAM by Chaney and Dolli (5).

Little research has been published about the perhaps circular correlation between medals and funds raised. Slater’s study relates medals and press coverage (38) which in turn supports fundraising. A cross-sectional study covering Belgium, Finland, Japan, the Netherlands, and the United Kingdom by Funahashi, Shibli, Sotiriadou, Mäkinen, Dijk, and De Bosscher relates funding with sporting success (12), which seems logical. Funds allow athletes and teams to train and eat, even to rest properly, and of course to compete and classify, thereby increasing their chances of success in top-tier events. Another report by Hogan and Norton, published through the National Institutes of Health found a high direct correlation between medals and funds (17). Although correlation does not imply causation, definitely the more funds, the more medals (and vice-versa, we would add).

Fundraising will continue to be vital for sports programs and facilities to operate. However, the climate for fundraising has become more competitive as more organizations chase the same discretionary dollars, and donors become more demanding. In order to cope, fundraisers will need to readjust their strategies. Fundraisers must understand all fundraising-related elements such as the event’s purpose, target markets and donors, and methods and strategies to be employed, said a 1996 editorial in the Journal of Social Marketing. Indeed, Stier and Schneider claim that fundraising is one of the major responsibilities of sport managers in the 21st century (40).

The Case of the WSKF Sports Foundation

As mentioned, prior research showed that the secret to fundraising success lies on selecting the right target and getting the message out there (36), based on the right appeal (35), to set in motion the most effective model of nonprofit performance (33). Indeed, Koschmann et al. (23) suggested that a proper combination of networking, leveraging and communication, based on a clear strategy, and following well-targeted processes, will generate optimal press coverage and influence, and -of course- funds.

Illustration # 3: The Winning Strategy

At the WSKF Venezuela Sports Foundation, applying the Koschmann et al. (23) model, something it did four years before it was ever published, meant (1^st) leaning on the athletes and their parents to network and target corporations to gain access to their Corporate Social Responsibility (CSR) programs, (2^nd) leveraging fundraising efforts on the Law for the Development of Sports which created a 0.5% sports tax on profits and allowed corporations to channel half of that directly to projects accredited by the Ministry of Sports, and (3^rd) appealing to pride and PR interests, considering that Charity Sport Event (CSE) fundraisers are often confronted by the donors’ lack of interest, even though those events can provide participants with a meaningful experience, as stated by Filo, Fechner and Inoue (11). The message was carried by a top-of-the-line institutional DVD presentation, a quarterly newsletter, a website, direct and digital marketing efforts, and through an aggressive media management strategy that used timely press-releases, many of them sent from Tokyo, the common championship site, to gain immediate exposure.

This strategy, born out of a Shihan-kai meeting in Cyprus in 2010, blended well with Kaplan and Norton’s (19) map format, which kicks off from an organization that strove to muster the support of parents, athletes, and instructors to execute the fundraising process, by reaching out to the right target with the proper appeal and press support, and achieve the desired financial results, as seen on Illustration 3. The leading KPIs (Key Performance Indicators) were medals won and funds raised primarily, but press coverage was extremely important for fundraising, since it reinforced the pride and PR appeal, as were the dimensions of the donors’ database. Donor relationship management leaned on the newsletter, BUDOtips, and as many as 73 media mentions per championship cycle.

The fundraising process was detailed, starting with the identification of all possible sources of funds, since it is not all about sponsorship. Indeed, McKeever and Pettijohn stressed that nonprofit organizations derive half of their revenues quid-pro-quo (30), as Graph 1 shows; in terms of sports organizations, this 50% may come from ticket sales, broadcasting rights, advertising, memorabilia and fees charged, among other internal sources. Additional funding may come from government or NGO grants, private and corporate donors, even multilaterals; depending on a single source is myopic as Levitt (25) would most likely define it. Accordingly, the first question that nonprofit managers must ask themselves is “are we doing the things we need to do to get money, or should we be getting money for the things we do?” Some nonprofits miss this benchmarking and go straight to asking for donations without considering the monetization of things that they can do or sell to generate funds. In case of WSKF, this meant monthly fees, sales of sporting goods and memorabilia, special training sessions, and events like national and regional championships.

Chart 3: Structure of Nonprofit Revenues

Based on a clear understanding of nonprofit market dynamics and the supply of funds, and considering the Sports Law, corporate and government targets were identified, and a unique appeal was tailored for each segment. The operational planning began when all decisions had been made and defined, otherwise it could have turned into a map without destination. The organization would pursue its financial objectives through traditional fundraising means, grants, events, and crowdfunding. The technical arm, the WSKF organization, would be the one to charge fees and hold events, collecting money from attendance and participation, under foundational guidelines.

Illustration # 4: The WSKF Fundraising Process

A growing database of corporate donors was informed and nurtured with a newsletter called BUDOtips which circulated throughout the organization. A survey of athletes, parents, and instructors generated the structure of the magazine which was then tested against donors’ expectations. Four sections were created: “Budo,” dealing with principles, for the parents who sought discipline and principles for their children, and who represented over two-thirds of the membership; “Technique” for the athletes who wanted to improve their performance; “Management” for the instructors who wanted to run their clubs profitably; and “News” for the donors and for everyone; the Editorial was just an introduction and an invitation to read, as seen on the cover page below.

Illustration # 5: The WSKF Newsletter

The results of these concerted efforts were evident. Formal fundraising began after a lack of funding left the 2005 championship cycle dry. 14 medals were won in 2007. The WSKF Venezuela Sports Foundation was created in 2008, leading to 24 world medals in Tokyo the following year. As the organization learned and matured, the medal count skyrocketed to record-breaking numbers, 50 in 2011, 42 in 2013, a record-breaking 66 in 2015, and 60 in the following cycle, 2017. Eight medals were won by a small team in the World Cup held in Cyprus in 2010. Winning led to press coverage which peaked at 73 TV, newspaper, radio and digital mentions in 2015, which reverberated throughout the web, nationally and internationally.

Chart 4: The WSKF Venezuela Medal Count

rage of 158 days younger than those athletes who win bronze medals. Together, these results suggest that the results are generally consistent across males and females as well as Summer and Winter Games.

DISCUSSION

The predictive model points fundraising and communicational efforts toward deep pockets (36), which implies choosing the right target and putting out the most appropriate message; research into donor choice (35) leads to crafting the right appeal to carry that message; and testing Koschmann et al.’s communicative framework (23, 33) guides nonprofits to follow the right strategy and proper processes, supported on networking, leveraging on legal and fiscal incentives, and on the proper media strategy. Indeed, the strategy of the WSKF Sports Foundation, knowingly or not, and ahead of its time, blended these three theories and put them into practice, combining this theoretical framework with the Kaplan and Norton’s (19) strategy map format by adapting the organizational perspective to create a network of athletes and parents to reach out to corporate donors, crafting fundraising and sports operations to leverage on the Law for the Development of Sports, and fitting the customer perspective to the media strategy, and vice-versa. The financial perspective was led by the Balanced Score Card with metrics like revenues and average sponsorship level per athlete. The Strategy Map represented in and of itself a vital authoritative paper, along with the fundraising process flowchart. Moreover, it added an interesting twist, using world championship success and feedback to fuel fundraising, as medals triggered press coverage which in turn attracted sponsors, and then their sponsorship allowed the teams and athletes to train, compete and win more medals. This created a virtuous cycle. To feed the flame, the Foundation added reverberance by hosting a “Dinner with the Champs” upon returning from Tokyo, where the press and the donors would share photo-ops with the athletes in their colors and with their medals, while receiving plaques for their support, which added more press coverage and PR opportunities.

The Foundation continued to multiply its branding efforts by adding non-sports philanthropy to its credentials, networking with several organizations like Mayor’s Offices, corporate programs (CSR), and private foundations to help the needy, thereby positioning its brand at a national level and squeezing the most out of the athletes’ medals’ appeal (Illustration 6). Again, this added more press coverage. Indeed, the WSKF Venezuela Sports Foundation showed that theory, when put into practice, gets the most out of the strategy.

CONCLUSIONS

Theory says choose your target well, craft the right appeal, and execute the right strategy correctly, following proper procedure, through a well laid out fundraising process. Strategizing will require a detailed situational analysis and brainstorm, blending the theory and the best practices into your initiatives. Choose your KPIs well; funds, medals, or outside of sports, social impact, and press coverage should be the strongest drivers; medals add leverage, they lead to press coverage, press coverage attracts sponsors and triggers pride and PR opportunities; and sponsorship allows athletes to train and participate in world events, which leads to medals, as the virtuous cycle makes another rotation. Be relentless and thorough in the execution of the strategy; and whenever and wherever possible, widen your networking circles. The more, the merrier!

Limitations and Further Research

Although the Pearson coefficient of the first study is outstanding, the donor choice research could use additional criteria like peer influence and personal commitment with the social cause to increase its predictive ability. This would make it “The 7-Ps of Fundraising” and should raise the model’s pseudo-R². The cross-sectional study is pretty straightforward, but it also showed that not every country has such a favorable fiscal framework for sports as Venezuela, which enacted legislation that taxes corporate earnings to fund the development of sports. They finance the construction of sports complexes, sporting events, and national team competitions, both nationally and internationally. Corporate donors can channel one half of that tax directly to accredited projects; this benefits the leveraging aspect of Koschmann et al.’s model (23). Nonetheless, there are always tax incentives and breaks for donors and fundraisers in just about every country we analyzed; in the end, what donors are looking for are meaningful projects that are properly organized and well presented. Credibility is a must, and feelings and appearances matter.

It should be also mentioned that the Venezuelan socio-economic and political situation today may not be conducive to achieving the same 2007 ⎯ 2017 results that were analyzed here. Funding has been politicized, the economy has shrunk 80%, and the exchange rate has gone from Bs. 10 per US dollar, in August 2018, to Bs. 119,144,000,000,000 or 119.14 today, after the regime erased eleven zeroes from the currency to hide the mega-devaluation and hyper-inflation.

APPLICATIONS IN SPORT

Rarely has a combination of theory and practice been put together to recommend fundraisers how to balance strategy and operations; not one or two but three research studies support this paper; 20 years of foundational experience leverage them; raising up to $3.3 million a year in funds and winning 266 world medals in 10 years prove it right; an organization spanning over 110 countries and over one million members, make this a unique learning opportunity. The underlying theoretical model calls for networking among people and organizations, leveraging on legal and fiscal incentives, and communicating the right message to the right target, working on the shoulders of a clear strategy, a lean and mean organization, and a consistent fundraising process, to generate press coverage and lobbying power, and ⎯ultimately⎯ funds. The theory says choose wisely, and indeed strategy is all about choice: identify the right target, craft the right appeal, and do the right things correctly, which demands a fine-tuned organization and processes.

Now, to the question, “do we need to win medals to raise funds or raise funds to win medals?” Well, yes, credentials help fundraisers win support but in the absence of medals, the operational model and the right choices should cast a net that is wide enough to generate revenues and attract volunteers, but in the absence of results, in startup nonprofits, the founders’ accolades, and networks, can help. But appearances matter, that is why the WSKF Sports Foundation leaned on its website, a top-of-the-line DVD presentation, and its newsletter, all of which seemed bigger than life, to reach the target before the medal count skyrocketed and a virtuous cycle was created. Momentum did the rest.

It is important to remember that one half of nonprofit revenues are quid-pro-quo, coming from things nonprofit organizations do or sell (see Graph # 1). Hospitals recover medical costs, universities charge tuition, and the WSKF Sports Foundation collected fees from its membership. Income cannot depend solely on donations or grants. Nonprofits must make an effort to add to their revenue streams by monetizing their activities, something not always remembered, as our consulting efforts at Rutgers University have shown us. Private foundations struggle with lack of resources and specialized skills, but solutions are at an arm’s length.

Social Implications

The Nonprofit Sector in general, which represents 5.4% of the US economy, can benefit from strategies that are supported by data and research, plus decades of fundraising experience at the same time. Amateur sports fundraising in particular, a $60 billion industry, can surely profit from a fresh perspective.

Eather, Wade, Pankowiak, et al.’s research suggests that community sports programs, supported by fundraising, can significantly enhance social capital and promote social cohesion by increasing trust, improving social networks, and fostering a stronger sense of community amongst participants, providing opportunities for community members –athletes, coaches, volunteers, and supporters– to interact, build relationships, and develop a shared identity (8)

Supporting fundraising in amateur sports through scientific research goes beyond securing financial resources. It fosters community spirit, enhances social connections, and provides numerous positive social and psychological benefits for both participants and volunteers. These benefits contribute to stronger, healthier, and more cohesive communities says Wheatley (44). Ultimately, if the nonprofit sector does indeed pick up the slack of governmental failure, Matsunaga and Yamauchi’s theory (29), then anything that benefits philanthropy will benefit society as a whole.

REFERENCES

Author investigates why people give. Chronicle of Philanthropy, no. 18, 2005.
Bendapudi, N., Singh, S. N., & Bendapudi, V. (1996). Enhancing helping behavior: an integrative framework for promotion planning. Journal of marketing, 60(3), 33-49.
Cao, X. (2016) Framing charitable appeals: the effect of message framing and perceived susceptibility to the negative consequences of inaction on donation intention. International Journal of Nonprofit & Voluntary Sector Marketing. Feb2016, Vol. 21 Issue 1, p3-12. 10p.
Center for Marketing Advantage, Advancement, and Action (2022), Survey of nonprofit organizations’ needs, Rutgers University, New Jersey. Retrieved from https://www.business.rutgers.edu/cm3a
Chaney, I. and Dolli, N. (2001) Cause related marketing in New Zealand. International Journal of Nonprofit & Voluntary Sector Marketing. May2001, Vol. 6 Issue 2, p156. 8p.
Clement, J. (2024). Best practices for investors exploring the youth sports industry. Forbes Magazine. Jan 25, 2024. Retrieved from https://www.forbes.com/councils/forbesbusinesscouncil/2024/01/25/best-practices-for-investors-exploring-the-youth-sports-industry
Curry, J., Rodin, S. and Carlson, N. (2012) Fundraising in difficult economic times: best practices. Christian Higher Education, 11:4, 241-252, DOI: 10.1080/15363759.2011.559872 .
Eather, N., Wade, L., Pankowiak, A. et al. (2023) The impact of sports participation on mental health and social outcomes in adults: a systematic review and the ‘mental health through sport’ conceptual model. Syst Rev 12, 102. Retrieved from https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-023-02264-8
Eikenberry, A. M. and Drapal Kluver, J. (2004). The marketization of the nonprofit sector: civil society at risk? Public Administration Review. 64 2, pp. 132-140. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-6210.2004.00355.x
Fajardo, T., Townsend, C. & Bolander, W. (2018) Toward an optimal donation solicitation: evidence from the field of the differential influence of donor-related and organization-related information on donation choice and amount. Journal of Marketing. Mar2018, Vol. 82 Issue 2, p142-152. 11p. 1 Chart.
Filo, Kevin,^,Fechner, Davidand Inoue, Yuhei (2020). Charity sport event participants and fundraising: an examination of constraints and negotiation strategies. Sport Management Review. Jun2020, Vol. 23 Issue 3, p387-400. 14p. https://doi.org/10.1016/j.smr.2019.02.005
Funahashi, H., Shibli, S., Sotiriadou, P., Mäkinen, J., Dijk, B., & De Bosscher, V. (2020). Valuing elite sport success using the contingent valuation method: a transnational study. Sport Management Review, 23(3), 548–562.
Fundraising in Sports (1996). Journal of Sport Management. Apr1996, Vol. 10 Issue 2, p225-225. 1/3p.
Guy, B. S., & Patton, W. E. (1989). The marketing of altruistic causes: Understanding why people help. Journal of Consumer Marketing, 6(1), 19-30.
Harris, D. (2023). YMCA posts record-high grant, fundraising revenue. Journal of Business (1075-6124). 8/6/2023, Vol. 38 Issue 16, p19-24. 6p.
Haruvy, E., Popkowski Leszczyc, P., Allenby, G., Belk, R., Eckel, C., Fisher, R., Li, Sherry X., Ma, Y., Wang, Y. & List, J. (2020) Fundraising design: key issues, unifying framework, and open puzzles. Marketing Letters. 2020, Vol. 31 Issue 4, p371-380. 10p. 1 Chart.
Hogan, K and Norton, K. (2000). The ‘price’ of Olympic gold. Journal of Science Medical Sport. Jun;3(2):203-18. Retrieved from https://doi.org/10.1016/S1440-2440(00)80082-1
Huber, M., Van Boven, L., & McGraw, A. P. (2011). Donate different: external and internal influences on emotion-based donation decisions. The science of giving: Experimental approaches to the study of charity, 179-200.
Kaplan, R. S., & Norton, D. P. (2000). Having trouble with your strategy? Then map it. Focusing Your Organization on Strategy—with the Balanced Scorecard, 49(5), 167-176.
Kamatham, S., Pahwa, P., Jiang, J. & Kumar, N. (2021) Effect of appeal content on fundraising success and donor behavior. Journal of Business Research. Mar2021, Vol. 125, p827-839. 13p.
Kemp, E., Kennett-Hensel, P. & Kees, J. (2013) Pulling on the heartstrings: examining the effects of emotions and gender in persuasive appeals. Journal of Advertising. Spring2013, Vol. 42 Issue 1, p69-79. 11p. 5 Graphs.
Kim, S., Gupta, S. & Lee, C. (2021) Managing members, donors, and member-donors for effective nonprofit fundraising. Journal of Marketing. May2021, Vol. 85 Issue 3, p220-239. 20p. 3 Charts, 8 Graphs.
Koschmann, M. A., Kuhn, T. R., & Pfarrer, M. D. (2012). A communicative framework of value in cross-sector partnerships. Academy of management review, 37(3), 332-354.
Krug, K. and Weinberg, C. (2011). 9.4 Relating fund raising to the merit axis. Foundations & Trends in Marketing. 2011, Vol. 6 Issue 3/4, p296-303. 8p.
Levitt, T. (1960). Marketing myopia. Harvard Business Review, 38, 45–56
Leonhardt, D. (2008). What makes people give? New York Times Magazine, 44. ISSN: 0028-7822.
Leonhardt, J. and Peterson, R. (2019) Should charity promotions appeal to altruism? International Journal of Nonprofit & Voluntary Sector Marketing. Feb2019, Vol. 24 Issue 1, pN.PAG-N.PAG. 1p.
List, John A. The market for charitable giving. Journal of Economic Perspectives. Spring2011, Vol. 25 Issue 2, p157-180. 24p. 2 Charts, 3 Graphs. Retrieved from https://www.aeaweb.org/articles?id=10.1257/jep.25.2.157
Matsunaga, Y., & Yamauchi, N. (2004). Is the government failure theory still relevant? A panel analysis using US state level data. Annals of Public and Cooperative Economics, 75(2), 227-263.
McKeever, B. and Pettijohn, S. (2014) The nonprofit sector in brief: public charities, giving, and volunteering. The Urban Institute. October 2014. Retrieved from https://www.urban.org/sites/default/files/publication/33711/413277-The-Nonprofit-Sector-in-Brief–.PDF
Morones, S. (n.d.). Following the money in college sports. Morones Analytics. Retrieved from https://moronesanalytics.com/following-the-money-in-college-sports/
Ministerio del Poder Popular para Deporte y Recreación (2011). Ley orgánica del deporte, educación física y recreación, Gaceta Oficial No. 39.741, 23 de Agosto de 2011, República Bolivariana de Venezuela. Retrieved from http://www.ind.gob.ve/wp-content/uploads/2016/06/Ley-Organica-de-Deporte-y-Educacion-Fisica-2011.pdf
Quevedo, F. J. (2019). Testing Koschman, Khun & Pfaerrer’s (2012) communicative framework on a global NGO: the case of the WSKF Sports Foundation. International Journal of Recent Advances in Multidisciplinary Research, 6(10), 5248-5256.
Quevedo, F. J., & Gopalakrishna, P. (2021). Rationality is overrated: brand choice is largely intuitive. Rutgers Business Review, 6(3), 312-332.
Quevedo, F. J., & Lee, K. (2023). The 5-Ps of fundraising: lessons from consumer behavior to nonprofit marketing. Rutgers Business Review, 8(1), 28-38.
Quevedo, F. J., & Quevedo-Prince, A. K. (2019). A predictive model for the us nonprofit market: a macro to micro perspective. Advanced Journal of Social Science, 5(1), 1-9.
Sargeant, A., & Shang, J. (2010). Fundraising principles and practice (Vol. 17). John Wiley & Sons.
Slater, K. (2024). More medals, more press: African media coverage of the 2022 Commonwealth games. Howard Journal of Communications, 1–20.
Statista (2024) North American sports market revenues.
Stier Jr., W. F. and Schneider, R. (1999). Fundraising: an essential competency for the sport manager in the 21st century. Mid-Atlantic Journal of Business. Jun-Sep99, Vol. 35 Issue 2/3, p93. 11p.
Third, Rachel (2018). Act today, transform tomorrow: How a legacy appeal at Loughborough University had an unexpected legacy of its own. Journal of Education Advancement & Marketing. Autumn/Fall2018, Vol. 3 Issue 2, p182-187.6p.
Urban Institute; National Center for Charitable Statistics (2020). Revenues of reporting nonprofit organizations in the U.S. from 1998 to 2016. Statista. June2020.
Wallace, N. (2016). Data and the search for big donors. Chronicle of Philanthropy, 28(10), 7.
Wheatley, S, (2024). Building strong communities through amateur sports: Connecting athletes locally, February 8, 2024. Team Travel Source. Retrieved from https://www.teamtravelsource.com/2024/02/08/building-strong-communities-through-amateur-sports-connecting-athletes-locally/
Wooden, R. A. (2005). What makes donors give. Chronicle of Philanthropy, (05) December 2005. Retrieved from https://www.philanthropy.com/article/What-Makes-Donors-Give/171235
Yi, D. T. (2010), Determinants of fundraising efficiency of nonprofit organizations: evidence from US public charitable organizations. Managerial and Decision Economics, 31: 465-475. https://doi.org/10.1002/mde.1503

Troy Palmer2025-09-10T15:45:29-05:00January 21st, 2026|General, Olympics, Research, Sports Management, Sports Studies|Comments Off

Relationship Between the National Football League (NFL) Combine Measurables and Playing Time in the 2024 NFL Rookie Class

Authors: Greg A. Ryan, Kevin Harvey, Elijah Campbell, Mark Shoebridge, Landon Overby, Joshua Sauer, & Robert L. Herron

Corresponding Author:

Robert L. Herron, Ed.D., CSCS*D, ACSM-RCEP

75 College Drive

Montevallo, AL 35115

[email protected]

205-665-6118

Authors’ Affiliation: College of Health Professions, Department of Nursing & Health Sciences, University of Montevallo, Montevallo, AL, USA.

ABSTRACT

Purpose: This study investigated the relationship between anthropometric and performance measures collected at the 2024 National Football League (NFL) Combine and playing time (PT) during the 2024 NFL regular season. Methods: Data from four anthropometric (Body Mass Index; Hand Size; Arm Length; Wingspan) and seven performance tests (40-yard Dash; 10-yard Split; Vertical Jump; Broad Jump; 3-Cone Drill; 20-Yard Shuttle; 225lb Bench Press) of 315 players were standardized into average Anthropometric Z-Scores (AZ), Performance Z-Scores (PZ) and Total Z-Scores (TZ) for analyses. PT was calculated as a player’s total number of regular season snaps during their 2024 rookie season. Pearson correlations were used to investigate the relationships (α = 0.05) between AZ, PZ, and TZ to PT. Players were also analyzed for potential relationships within each position group. Results: A significant, weak, positive correlation existed between PZ and PT (r = 0.19, p < 0.01) and TZ and PT (r = 0.20, p < 0.01) for all players. No relationship existed for AZ and PT (r = 0.02; p = 0.73). Additionally, significant relationships existed among: Offensive Line – PZ and PT (r = 0.33, p = 0.01) and TZ and PT (r = 0.35, p < 0.01); Wide Receiver – PZ and PT (r = 0.39, p = 0.03) and TZ and PT (r = 0.46, p < 0.01); Linebacker – TZ and PT (r = 0.39, p = 0.05). Conclusions: NFL Combine performance metrics may provide insight on PT, but anthropometric measurables were not related to PT. The lack of relationship within position groups indicates the NFL Combine may not be valuable in evaluating a rookie’s success on the field. Applications in Sport: Professionals who work with prospects may choose to train Combine specific techniques to maximize a prospect’s chances of playing in the NFL. However, individualized training that focuses on position specific demands or weaknesses that are not directly measured by NFL Combine tests may be more useful in increasing PT. The NFL Combine may be a useful supplement to all factors that go into an NFL team’s decision to draft a player.

Key Words: performance testing, predictive analytics, scouting, correlational analysis, American football

INTRODUCTION

The National Football League (NFL) hosts an annual Scouting Combine in Indianapolis, Indiana of elite college football players. Only about 3% of college football players are invited to the NFL Combine and therefore represent those with the highest chance of being drafted into the NFL (4). The purpose of the NFL Combine is to allow coaches, scouts, and other team personnel representing the 32 NFL teams the opportunity to assess hundreds of players from all divisions of collegiate football.

Football has position-specific skills that are needed to excel at the highest level. However, there are similarities between each position. All positions need vertical and horizontal power, agility, and strength. During this weeklong event, players participate in a multitude of tests. These tests include anthropometric measurements (Height; Weight; Wingspan; Arm Length; Hand Size) and performance tests (40-yard dash; 10-yard split; Vertical Jump; Broad Jump; 3 Cone Drill; 20 Yard Shuttle; 225lb Bench Press). All the events in the NFL Combine have been shown to have face validity (4). NFL player personnel departments use the NFL Combine data as part of their criteria to determine whether to select a player in the upcoming NFL Draft.

While the NFL Combine tests are designed to determine that aptitude to play at the next level, research is conflicted on the ultimate usefulness of the NFL Combine in determining player performance and playing time (PT). Kuzmits and Adams (4) found no consistent significant relationship between NFL Combine tests and player performance during the years of 1999 to 2004. Research also noted that the NFL Combine from 2013 to 2015 lacked the ability to predict game performance when specifically analyzing first year game performance (3). Teramoto, Cross, and Willick (12) looked at whether the NFL Combine could predict future performance of Running Backs (RB) and Wide Receivers (WR). The results of this study were that the time on 10-yard split was the most important predictor of yards per attempt for RB while vertical jump was significantly associated with receiving yards per reception for WR. However, the measures cannot explain a large part of the variance in the future performance of RBs and WRs. Vincent et al. (15) looked at NFL Combine participants from 2005 to 2010 who then played in the NFL. Significant relationships were found between at least one NFL Combine measure and on-field success. Even though significant relationships were found the authors stated that the NFL Combine tests are only modest predictors of future performance. More recently, investigation of six physical skill tests at the NFL Combine to try and predict draft placement in the 2022 NFL Draft and showed no significant difference between drafted and nondrafted players in any of the six physical tests analyzed (14).

LaPlaca and McCullick (5) built on previous research looked at player performance from the years 2006 to 2018 and compared it to the NFL Combine from 2006 to 2016. They found that every position group, both offensive and defensive, had at least one NFL Combine test that was significantly correlated with player performance. The study made sure to disclose that even though they found significant correlations, the large sample size made it easier to find weaker correlations. A limitation that was discussed was that while the authors did use objective performance statistics such as Touchdowns scored, they also used a grading system through Pro Football Focus to determine player performance. This grading system was not purely objective because the grades are determined by multiple reviewers through the observation of game film. Therefore, the overall performance of each player was not entirely objective. Additionally, a robust study by Frank and colleagues (2) analyzed 20 years (2000-2018) of NFL Combine data and noted that for offensive positions, single measures often best predicted success, while various combinations of NFL Combine performance traits predicted success among defensive players. This study also suggested that NFL Combine data is best used in conjunction with scouting and personnel departments to supplement NFL draft decision making. Similarly, research was conducted looking at the impact of the NFL Combine on five-year performance data from the 2013-2017 NFL seasons and concluded that the NFL Combine lacked predictive ability during that timeframe (1). While historical research does exist in this field, each year provides another opportunity to determine the NFL Combine’s effectiveness in predicting success. Additionally, limited research exists discussing the relationship between NFL Combine Measurables and PT for first-year players. The primary purpose of this study was to determine if the anthropometric and performance measures of the athletes invited to the 2024 NFL Combine were related to PT during the 2024 NFL regular season.

METHODS

Participants

Participants for the data analysis in this study were college football players that participated in the 2024 NFL Combine (N = 315). Participants were also grouped by position for use of positional comparisons (Offensive Line [OL] (N = 70); Defensive Back [DB] (N = 67); Defensive Line [DL] (N = 50); Running Back [RB] (N = 29); Linebacker [LB] (N = 30); Quarterback [QB] (N = 14); Tight End [TE] (N = 16); Wide Receiver [WR] (N = 39)). All player positions were input based off their official designation at the time of the NFL Combine. Due to limited sample size (N = 6) and variations in specializations, NFL Combine athletes who were labeled Specialist (Kicker, Punter, Long Snapper) were excluded from analyses.

Procedures

Four anthropometric (Body Mass Index [BMI]; Hand Size; Arm Length; Wingspan) and seven performance measures (40-yard Dash; 10-yard Split; Vertical Jump; Broad Jump; 3-Cone Drill; 20-Yard Shuttle; 225lb Bench Press) were analyzed. BMI was calculated by the researchers using Height and Weight measurements taken at the NFL Combine. Full descriptions of the performance tests have been detailed previously by McShay (7).

The data from the NFL Combine was obtained from NFL.com/combine/tracker (8). Each participant’s scores were retrieved for every test that was completed. Standardization of data, via Z-scores, were created for every anthropometric and performance measure. The measures from the NFL Combine were standardized into averages for each player, taking each player’s combined Z-Score score and dividing by the number of NFL Combine events they participated in to account for players who did not complete every NFL Combine event. Standardized averages were created for Anthropometric Z-scores (AZ), consisting of the four anthropometric measures, Performance Z-scores (PZ), consisting of the seven performance measures, and Total Z-scores (TZ), consisting of all 11 NFL Combine measures, for analyses This method of standardization of NFL Combine data into Z-scores for analysis has previously been supported (1).

Once all NFL Combine data was standardized, researchers used Pro-football-reference.com (9) to retrieve offensive, defensive, and special teams snaps for each player during the 2024 NFL regular season. Each player’s total snap count was then combined to provide a single value to determine PT, which was used for analysis. Because of this study only requiring secondary analysis of data which is publicly available on web-based domains, which do not disclose individual’s health information, Institutional Review Board approval was not required, though the study was approved by the research institution.

Data Analyses

Pearson product moment correlations, using Statistical Product and Service Solutions (SPSS, v29.0, IBM Corporation, Armonk, NY), were used to determine the relationship (α = 0.05) between AZ, PZ, TZ to PT. Additionally, players were separated by position and Pearson product moment correlations (α = 0.05) were used to determine potential relationships within each group between AZ, PZ, and TZ, to PT. All data are presented as means ± standard deviation with 95% confidence intervals (95%CI) unless otherwise stated.

RESULTS

Descriptive Statistics

Of the 321 athletes whose data were collected, 315 were used for analysis. A total of six athletes were excluded from analysis due to their position of Specialist (punter, kicker, long snapper) because only anthropometric data was collected on this group. Of the 315 athletes used for analysis, 312 (99%) completed all anthropometric measurements. There was more variability in the performance testing, with 25 (8%) completing all seven performance events, and 263 (83.5%) completing at least one performance event. When broken down by event, 220 (69.8%) completed the 40yd (4.73 ± 0.31s) with a 10yd split (1.63 ± 0.11s), 227 (72.1%) completed the VJ (34.0 ± 4.3in), 220 (69.8%) completed the BJ (117.9 ± 9.0in), 78 (24.8%) completed the 3C (7.30 ± 0.40s), 89 (28.3%) completed the PRO (4.44 ± 0.28s), and 100 (31.8%) completed the BP (21.9 ± 5.6reps). When examining snaps played over the 2024 regular season, 239 (75.9%) players went on to play at least one snap, with 224 (71.1%) averaging more than one snap per game over the course of the season.

Anthropometric Correlation Analysis

The results of the correlation analysis for AZ and PT are presented in Figure 1. Pearson product moment correlation coefficients were calculated for the relationship between average AZ and PT for all players and separated by position group. No significant overall relationship existed for AZ and PT (n = 312; r = 0.02; p = 0.73).

Additionally, no significant relationships existed among position groups: OL (n = 70; r = 0.13; p = 0.29); RB (n = 29; r = 0.21; p = 0.29); WR (n = 37; r = 0.24; p = 0.16); TE (n = 16; r = 0.39; p = 0.14); QB (n = 13; r = 0.02; p = 0.95); DL (n = 50; r = -0.10; p = 0.52); LB (n = 30; r = 0.19; p = 0.33); DB (n = 67; r = -0.02; p = 0.89).

Performance Correlation Analysis

The results of the correlation analysis for PZ and PT are presented in Figure 2. Pearson product moment correlation coefficients were calculated for the relationship between average PZ and PT for all players and separated by position group. A significant, weak, positive correlation existed between PZ and PT (n = 263; r = 0.19, 95%CI [0.07, 0.31]; p < 0.01). The positive direction of this relationship indicates that players who performed better at the NFL Combine played more snaps during the 2024 NFL regular season.

When separated by position groups, significant, positive relationships existed for the following groups: OL (n = 61; r = 0.33, 95%CI [0.09, 0.54]; p = 0.01); WR (n = 34; r = 0.39, 95%CI [0.06, 0.65]; p = 0.03). The positive direction of these relationships indicates that OL and WR who performed better at the NFL Combine accumulated more snaps during the 2024 NFL Regular season. No significant correlations were noted for: RB (n = 25; r = 0.31; p = 0.14); TE (n = 12; r = 0.07; p = 0.15); QB (n = 7; r = -0.39; p = 0.40); DL (n = 43; r = 0.30; p = 0.06); LB (n = 26; r = 0.31; p = 0.13); DB (n = 55; r = -0.02; p = 0.89).

Total Correlation Analysis

The results of the correlation analysis for TZ and PT are presented in Figure 3. Pearson product moment correlation coefficients were calculated for the relationship between average TZ and PT for all players and separated by position group. A significant, weak, positive correlation existed between TZ and PT (r = 0.20, 95%CI [0.08, 0.31]; p < 0.01) for all players. The positive direction of this relationship indicates that players who had higher average TZ scores played more snaps in the 2024 NFL regular season.

When separated by position groups, significant, positive relationships existed for the following groups: OL (n = 61; r = 0.35, 95%CI [0.11, 0.56]; p < 0.01); WR (n = 34; r = 0.46, 95%CI [0.15, 0.69]; p < 0.01); LB (n = 26; r = 0.39, 95%CI [0.01, 0.68]; p = 0.05). The positive direction of these relationships indicates that players in these position groups who had higher average AZ scores played more snaps in the 2024 NFL Regular season. No significant correlations were noted for: RB (n = 25; r = 0.31; p = 0.14); TE (n = 12; r = 0.07; p = 0.85); QB (n = 7; r = -0.24; p = 0.61); DL (n = 43; r = 0.30; p = 0.06); DB (n = 55; r = 0.06; p = 0.70).

Discussion

The main finding of this study is that PZ and TZ may have a weak relationship to PT in a player’s first year in the NFL. There was no relationship between a player’s AZ and subsequent PT across all athletes nor when separated by position group. The study did find a significant weak positive correlation between average PZ and PT for all players. However, when separated by position groups significant, positive relationships existed for OL and WR. Finally, there was a significant weak positive correlation between TZ and PT for all players. When separated by position groups, significant, positive relationships existed for OL, WR, and LB.

There could be many reasons why these relationships exist for WR, LB, and OL. Previous movement analysis research for NFL players by position found that WR had highest in-game velocity and highest total running volume by an offensive position (6). Therefore, the 40-yard dash and 10-yard split may carry more importance among WR. The same study showed that LB had the most high-velocity efforts and high-velocity distance in game compared to all other positions. LB also showed the largest variability across player-games which is likely due to the roles that LB perform which include rushing the QB, play in space and cover offensive players, or primarily to tackle an opponent. Additionally, OL noted a positive relationship in the current study, with better NFL Combine performances leading to more PT. While previous research (11) has noted that OL have worse NFL Combine values compared to other positions, the nature of the OL position may lend itself to more direct relationships from NFL Combine performance, since these athletes require multidirectional power over limited space. The positional findings in the current study do support previous research that noted relationships between NFL Combine performance metrics and PT among WR (40-yard Dash, Vertical Jump), LB (40-yard Dash, 20-yd Shuttle) and OL (20-yard Shuttle, Vertical Jump) (1, 2).

The NFL is not the only sport that uses a combine to test and evaluate future players’ abilities. Teramoto et al. (13), investigated the National Basketball Association (NBA) scouting Combine to determine whether the NBA Combine could predict PT. The study showed that the NBA Combine metrics had minimal correlation with long-term performance. In the NBA, it was found that certain anthropometrics had slightly better predictive power than athletic tests, which contrasts with what researchers found about the 2024 NFL Combine. Both in the NFL and NBA Combine researchers have proposed that performance in college or in game is the biggest predictor of draft position and future performance (11, 13).

There are limitations associated with this study. As reported in the results only 25 (8%) of all prospects completed all seven performance events. Increasingly, players are opting out of some or all the NFL Combine process, due to injury concern, agent decision, recovering from an injury during the season, or to focus on performing well at individual workouts, where more variables can be controlled by that athlete. In the season being analyzed in this study, five of the first six picks in the NFL Draft did not participate in the NFL Combine process, which could impact these findings. A larger, more complete sample from all NFL Combine athletes would comprise a better representation of their athletic performance. Finally, players that played zero snaps their first year due to injury were included in analysis, due to limitations among researchers to determine the extent of every injury or whether a player was not on the field due to injury or coaching decisions. A player that may have had strong AZ, PZ, and TZ scores, but did not play during their rookie season because of injury, which would have impacted the relationship between those variables and PT.

CONCLUSIONS

Many studies have been conducted over the last 20 years to determine if and how NFL Combine measurables can predict performance in the NFL (1-6, 10, 12, 14, 15). These studies have found mostly found minimal relationships overall, though stronger relationships among certain position groups. Despite the general scientific consensus that the NFL Combine is not a strong predictor of future NFL success, a multitude of NFL Combine “prep courses” exist, with athletes paying for training specifically to improve in NFL Combine measurables. There has been scientific skepticism about these courses and their impact on performance at the NFL Combine and its translation to improved draft status or playing time. While these courses claim that they will improve an athlete’s chance of getting drafted, there is currently no scientific evidence to these claims (1, 4, 10). Training programs that focus on a prospect’s position specific demands or individual weaknesses that are not directly measured by NFL Combine tests may be more useful in increasing PT for that athlete. The results of the current study support the previous work in the literature, but do note that some position groups (OL, WR, LB) may benefit by improving NFL Combine-specific performance in the lead up to the NFL Combine and Draft.

APPLICATIONS IN SPORT

The results from the current study suggest PT among NFL rookies during the 2024 regular season could not be strongly predicted with data collected during the NFL Combine. However, due to the relationships that were found, specifically withing certain position groups, it may be important for athletes in those positions to train specifically for those performance tests to have a better chance at playing in their first year. The data can be important for NFL player personnel departments who may use data collected during the NFL Combine to influence drafting decisions. Due to the significant, but variable, nature of the relationships found in the current study, it appears that the NFL Combine may be a useful supplement to scouting, film analysis, interviews, and other factors that go into an NFL team’s decision to draft a player. However, it is apparent that there is more to determining PT during a rookie season than just superlative measurables collected during the NFL Combine.

REFERENCES

Cook, J., Ryan, G. A., Snarr, R. L., & Rossi, S. (2020). The relationship between the National Football League scouting combine and game performance over a 5-year period. Journal of Strength and Conditioning Research, 34(9), 2492–2499. https://doi.org/10.1519/JSC.0000000000003676
Frank, D., King, M., Dennard, C., & Macnamara, B. (2023). Discriminant function analysis reveals which combination of measures from the NFL scouting combine predict NFL performance. Journal of Expertise.
Hedlund, D. P. (2018). Performance of future elite players at the National Football League scouting combine. Journal of Strength and Conditioning Research, 32(11), 3112–3118. https://doi.org/10.1519/JSC.0000000000002252
Kuzmits, F. E., & Adams, A. J. (2008). The NFL combine: Does it predict performance in the National Football League? Journal of Strength and Conditioning Research, 22(6), 1721–1727. https://doi.org/10.1519/JSC.0b013e318185f09d
LaPlaca, D. A., & McCullick, B. A. (2020). National Football League scouting combine tests correlated to National Football League player performance. Journal of Strength and Conditioning Research, 34(5), 1317–1329. https://doi.org/10.1519/JSC.0000000000003479
Lyons, B., Hoffman, B., Michel, J., & Williams, K. (2011). On the predictive efficiency of past performance and physical ability: The case of the National Football League. Human Performance, 24(2), 158–172. https://doi.org/10.1080/08959285.2011.555218
McShay, T. (2016, February 27). Todd McShay’s guide to every combine drill. ESPN. http://www.espn.com/espn/feature/story/_/id/14837586/todd-mcshay-guide-every-combine-drill-nfl-draft
NFL.com. (2025). Combine tracker. https://www.nfl.com/combine/tracker
Pro-Football-Reference.com. (2025). Total snaps. https://www.pro-football-reference.com/
Robbins, D. W. (2010). The National Football League (NFL) combine: Does normalized data better predict performance in the NFL draft? Journal of Strength and Conditioning Research, 24(11), 2888–2899.
Sanchez, E., Weiss, L., Williams, T., Ward, P., Peterson, B., Wellman, A., & Crandall, J. (2023). Positional movement demands during NFL football games: A 3-year review. Applied Sciences, 13(16), 9278. https://doi.org/10.3390/app13169278
Teramoto, M., Cross, C. L., & Willick, S. E. (2016). Predictive value of National Football League scouting combine on future performance of running backs and wide receivers. Journal of Strength and Conditioning Research, 30(5), 1379–1390. https://doi.org/10.1519/JSC.0000000000001202
Teramoto, M., Cross, C. L., Rieger, R. H., Maak, T. G., & Willick, S. E. (2018). Predictive validity of National Basketball Association draft combine on future performance. Journal of Strength and Conditioning Research, 32(2), 396–408. https://doi.org/10.1519/JSC.0000000000001798
Tucker, R., Lee, C., & Black, W. J. (2024). The predictive ability of the physical skills used at the NFL combine to predict draft status. The Sport Journal, 24.
Vincent, L. M., Blissmer, B. J., & Hatfield, D. L. (2019). National scouting combine scores as performance predictors in the National Football League. Journal of Strength and Conditioning Research, 33(1), 104–111. https://doi.org/10.1519/JSC.0000000000002937

Troy Palmer2025-09-05T08:46:38-05:00January 7th, 2026|General, Research, Sports Management, Sports Studies|Comments Off