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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

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

Author: Barrett Snyder

Corresponding Author:

Barrett Snyder

[email protected]

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

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

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

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

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

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

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

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

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

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

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

Authors: Dennis M. Shaffer¹ and Ryanne E. Shaffer

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

Corresponding Author:

Dennis M. Shaffer, PhD

1760 University Drive

Mansfield, OH 44906

[email protected]

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

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

ABSTRACT

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

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

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

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

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

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

INTRODUCTION

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

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

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

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

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

STUDY 1

METHODS

Data Sets

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

Procedure

Evaluating a Player’s First Four Years in the NFL

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

Understanding the Pro Football Focus Grading System

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

Calculation and Coding of PFF Grades and Years in the League

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

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

Availability of Data and Material

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

RESULTS

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

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

If players drafted with picks 1-10 are better than players drafted with picks 10-20, and so on, then both the difference in trade chart values and rookie wage scale salaries from pick #1 to pick #2 and pick #2 to #3 and so on through the first five rounds of the draft should be highly correlated with PFF grades. Bayesian correlational analyses showed substantial to strong evidence that there was close to zero correlation between PFF grades and trade chart values, RH trade value chart: Bayes Factor in favor of the null hypothesis (BF₀₁)= 5.594, r = .086, JJ trade value chart: BF₀₁= 10.485, r = .014, and PFF grades and rookie wage scale salaries: BF₀₁= 9.047, r = .043. The Bayes factors may be interpreted that it is 5.594, 10.485, and 9.047 times as likely that there is no correlation between PFF grades and RH trade chart values, JJ trade chart values, and rookie wage scale salaries, respectively, than there is a correlation (12, 24). Values of BF₀₁ or BF₁₀of 0-1 = no evidence, 1-3 = anecdotal evidence, 3-10 = substantial evidence, 10-30 = strong evidence, 30-100 = very strong evidence, and >100 = decisive evidence in favor of whatever hypothesis is being tested (null (BF₀₁) or alternative (BF₁₀) (12, 24).

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

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

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

Coded Years in League

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

PFF Overall Mean Grade

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

Figure 1.

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

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

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

PFF Overall Mean Grade

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

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

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

Table 1.

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

	Round 1	Round 2	Round 3	Round 4	Round 5	Round 6	Round 7
Years in League	2.237	3.284	7.103	4.85	6.179	1.285	10.313
PFF Grades	8.543	18.625	6.603	2.52	19.475

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

Many times, draft experts will argue that some teams may “trade up” into the bottom third of the first round—that is, the last ~twelve to fifteen (~picks 18-32 or so)–from the top of the second round in order to draft a second player for whom they will have the 5^th year option (1^st round pick). Many teams have done this in the past. In fact, according to the values themselves, the NFL views the bottom 10-15 picks in round 1 as over 7008 times greater in value than the top 10-15 picks in round 2 , BF₁₀= 7008.114, Cauchy Prior with a scale of .707). When we analyzed PFF grades across ten years for the “bottom third” of the first round (12 picks—21-32) and compared them to PFF grades across ten years for the “top third” of the second round (12 picks—33-44), a Bayesian one-way ANOVA showed substantial evidence that there is no difference: BF₀₁= 6.854, Cauchy Prior with a scale of .707 (M_{Bottom10of1st} = 63.835; SD_{Bottom10of1st} = 10.642, M_Top10of2nd= 64.387, SD_Top10of2nd = 9.975).

STUDY 2

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

METHODS

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

Raters

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

Procedure

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

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

RESULTS

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

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

DISCUSSION

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

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

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

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

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

CONCLUSION

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

APPLICATIONS IN SPORT

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

ACKNOWLEDGEMENTS

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

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

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

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

REFERENCES

1. ALL-22 (2025). https://www.all-22.com/#/

2. All Access Football (2025). https://www.allaccessfootball.com/p/nfl-draft-pick-trade-value-chart

3. Deubert, C., Wong, G. M., & Howe, J. (2012). All four quarters: A retrospective and analysis of the 2011 collective bargaining process and agreement in the National

Football League. UCLA Entertainment Law Review, 19, 1-78. https://doi.org/10.5070/LR8191027149

4. Dienes, Z. (2024). Use one system for all results to avoid contradiction: Advice for using significance tests, equivalence tests, and Bayes factors. Journal of Experimental

Psychology: Human Perception and Performance. 50, 531-534. doi: 1 0.1037/xhp0001202.

5. Hadley, L., Poitras, M., Ruggiero, J., & Knowles, S. (2000). Performance evaluation of National Football League teams. Managerial And Decision Economics, 2, 63-70.

http://www.jstor.org/stable/3108334

6. Hartman, M. (2011). Competitive performance compared to combine performance as a valid predictor of NFL draft status. Journal of Strength & Conditioning Research, 25, S105-S106. DOI:10.1097/01.JSC.0000395746.03546.e8

7. Kruschke, J. K. (2021). Bayesian analysis reporting guidelines. Nature Human Behaviour, 5, 1282-1291. https://doi.org/10.1038/s41562-021-01177-7

8. Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8, 1-28. https://doi.org/10.1525/collabra.33267

9. Lapre, M., A., & Palazzolo, E. M. (2024). Does draft currency promote competitive balance? An empirical investigation of the National Football League 2002–2021

Journal of Sports Economics, 25, 779-801. https://doi.org/10.1177/15270025241264238

10. Lee, M. D., & Wagenmakers, E.-J. (2013). Bayesian cognitive modeling: A practical course. Cambridge University Press.

11. Lyons, B. D., Hoffman, B. J., Michel, J. W., & Williams, K. J. (2011). On the predictive efficiency of past performance and physical ability: The case of the National Football League. Human Performance, 24, 158-172. https://doi.org/10.1080/08959285.2011.555218

12. Massey, C., and Thaler, R. (2013). The loser’s curse: Decision making and market efficiency in the National Football League draft. Management Science, 59, 1479-1495. http://dx.doi.org/10.1287/mnsc.1120.1657

13. NFL (2025). https://www.nfl.com/news/2021-nfl-draft-trade-tracker-full-details-of-every-move

14. PFF football news and analysis (June, 2025). https://www.pff.com/.

15. Reynolds, Z., Bonds, T., Thompson, S., LeCrom, C. (2015). Deconstructing the draft: An evaluation of the NFL draft as a predictor of team success. Journal of Applied Sport Management, 7, https://doi.org/10.7290/jasm07bj7q.

16. Rishis, E., Johnston, K., & Baker, J. (2023). On the predictive validity of the National Football League combine: Does it forecast future success? Journal of Sports Sciences, 41,

217-231. https://doi.org/10.1080/02640414.2023.2207853

17. Simmons, R., & Berri, D. (2009). Gains from specialization and free Agency: The story from the gridiron. Review Of Industrial Organization, 34, 81-98. https://doi.org/10.1007/s11151-009-9200-9

20. Terry, N. (2007). Investing in NFL prospects: Factors influencing team winning percentage. International Advances In Economic Research, 13, 117. https://doi.org/10.1007/s11294-006-9071-x

21. 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. https://thesportjournal.org/article/the-predictive-ability-of-the-physical-skills-used-at-the-nfl-combine-to-predict-draft-status/

22. Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., & Wagenmakers, E. J. (2011). Statistical evidence in experimental psychology: An empirical comparison using 855 t tests. Perspectives on Psychological Science, 6, 291–298. http://dx.doi.org/10.1177/1745691611406923

Troy Palmer2025-10-13T10:22:32-05:00April 29th, 2026|Research, Sports Coaching, Sports Management|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