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.

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

A Comparison of Perfectionism and Time of Sport Specialization of Division-1 Athletes

Authors: Jason N. Hughes¹,Colby B. Jubenville²,Mitchell T. Woltring^3,and Helen J. Gray

¹Department of Business, Accounting and Sport Management, Elizabeth City State University, Elizabeth City, NC, USA

²Department of Health and Human Performance, Middle Tennessee State University, Murfreesboro, TN, USA

³Department of Health, Kinesiology, and Sport, University of South Alabama, Mobile, AL, USA

⁴Associate Dean of Academic Affairs, North Carolina Agricultural and Technical State University, Greensboro, NC, USA

Corresponding Author:

Jason Hughes, Ph.D., M.S.,

1704 Weeksville Rd.

Elizabeth City, NC 27909

[email protected]

252-335-3488

Jason N. Hughes, Ph.D., is an Assistant Professor of Sport Management at Elizabeth City State University in Elizabeth City, NC. His research interests include sport specialization, perfectionism, and athletic burnout.

Colby B. Jubenville, PhD., is a Professor of Sport Management at Middle Tennessee State University. His research interests include student success, leadership, and emotional intelligence in business.

Mitchell T. Woltring, Ph.D., is an Associate Professor at the University of South Alabama. His research interests include student-athlete success and service learning.

Helen J. Gray, Ph.D., is the Associate Dean of Academic Affairs at North Carolina Agricultural and Technical State University. Her research interests include sport management, youth sport, and pedagogy in sport, leisure, and tourism.

ABSTRACT

Sport specialization has become increasingly popular among athletes aiming to gain a competitive edge. Despite its prevalence, there is a notable lack of research exploring the psychological impacts of sport specialization. One area that remains insufficiently studied in relation to sport specialization is perfectionism—a psychological trait known to influence both positive and negative outcomes in sports. The primary purpose of this study was to examine the previously unexplored relationship between the time in which an athlete specializes in sport with perfectionism concerns and strivings. A series of one-way ANOVAs were conducted to investigate the relationship between time of sport specialization based on the Developmental Model of Sport Participation and perfectionistic strivings and concerns. The results of the analyses showed that there was not a relationship between sport diversification and perfectionism. However, participants did score high on perfectionistic concerns despite adhering to proper diversification, participants showed higher scores in perfectionistic concerns than strivings. This suggests that athletes, parents, and coaches need to be aware that sport diversification may not be a buffer against negative psychological consequences. The results suggest that sport specialization’s psychological repercussions are confined to whether the athlete is concurrently engaged in sport specialization

Key Words: perfectionistic concerns, perfectionistic strivings, athletes, sport diversification, athletic development

INTRODUCTION

Early sport specialization among young athletes has surged, drawing increased scholarly attention. Research suggests that youth athletes are engaging in sport specialization at rates from 17% to as high as 41% (4, 30). In response, researchers have emphasized the need to examine both motives and the consequences of. Sport specialization refers to rigorous, year-round training focused on a single sport to the exclusion of others (21). Motivations for why athletes choose to specialize include improving specific skills, securing financial reward, and aiming for professional success (37). Ironically, researchers argue that this approach might hinder rather than help these goals. The consensus among experts is that well-rounded athletic development is better achieved through sport diversification, which involves engaging in multiple sports (37).

Advocates of sport specialization assert it plays a vital role in developing elite-level skills through deliberate practice. They argue that athletes who concentrate on one sport can attain greater proficiency than those who play multiple sports (37). Supporting this claim, one study found that both current and former elite soccer players dedicated more time to deliberate, soccer-specific training than non-elite athletes who were sport-diversified (14). This study suggested that deliberate practice during sport specialization significantly contributed to elite athlete status (14). Moreover, research on elite soccer players suggests that specialization enhances motivation, dedication, and enjoyment, leading to increased focus and commitment to improvement (36).

Critics of early sport specialization challenge its effectiveness, arguing that intense skill development at a young age may yield ambiguous results. A study on Russian swimmers found no performance advantage for early specializers compared to those who specialized later; in fact, those who specialized later showed greater progress (2). This suggests that early specialization may not be universally beneficial. Instead, it might be more appropriate in certain sports such as women’s gymnastics, diving, women’s basketball, figure skating, and dance, where early peak performance occurs before full body maturation (22). Furthermore, a 2023 meta-analysis found that world-class athletes engaged in multi-sport diversification, started their main sport later, and accumulated less main sport deliberate practice (19).

The pursuit of athletic scholarships and professional contracts remains a major motivator for sport specialization among young athletes. (24). Yet, the actual probability of attaining such rewards is notably low. Studies show that only 2% of high school athletes received a college scholarship, with an even lower percentage (1.2 % for females and 1.1% for males) obtaining full scholarships. The prospect of reaching professional levels is even less likely. The NCAA reports that only 0.9% – 5.1% of collegiate athletes make the professional ranks, depending on the sport. In high-profile sports like college football and basketball, only 1.34% of athletes advance to play professionally (29). Despite these sobering statistics, many athletes continue to specialize with the hope of achieving collegiate and professional success.

Another key criticism of sport specialization revolves around the potential harmful and unintended consequences, particularly of physical and psychological health. The most cited concern of sport specialization is the prevalence of injuries. Sport specialization may expose athletes to increased risk of overuse injuries due to the frequency of repetitive motions, higher training volumes, and voluminous competitions (26, 31, 22, 12, 11). While physical injuries are often the focus, there is limited comprehensive epidemiological data on the emotional and psychological impacts of sport specialization (32). Previous research suggests that specialization can contribute to an increase in social isolation, overdependence, athletic burnout, reduced enjoyment, heightened dropout rates, and a decline in motivation (25, 27, 33, 28).

A compelling psychological construct within the context of sport specialization is perfectionism. Perfectionism is defined as having “a commitment to exceedingly high standards combined with a tendency to critically appraise performance accomplishments” (15, 20). It is conceived as a multidimensional personality disposition construct capturing an individual’s pursuit of flawlessness in achievement and their concerns about failing to meet these high standards (13). Contemporary researchers posit that perfectionism overlaps a wide domain of ranges that fall in line with two higher-order dimensions: perfectionistic concerns and perfectionistic strivings (33). Perfectionistic concerns reflect the extent to which individuals are concerned about failing to achieve the standards that are placed on them by themselves or others, leading them to engage in harsh self-evaluation, which can negatively affect athletic performance (25). Moreover, perfectionistic concerns were positively correlated with burnout, rumination, fear of failure, amotivation, and performance-avoidance (21). The higher order of perfectionistic strivings is linked with self-oriented striving, where one places high goals on oneself intrinsically, and the setting of very high personal performance standards (18).

Overall, research suggests that athletes who engaged in diversification were more likely to achieve sporting success. One survey of 376 Division-1 intercollegiate athletes revealed that, apart from the sport of swimming, 83% of college athletes reported participating in various sports, and many had different initial sporting experiences from their current sport (26). Diversification offers opportunities to cultivate a more versatile skill set essential for athletic success. Among elite athletes, those who participated in multiple sports during their formative years (ages 0-12) required less specialized training to acquire high-level skills in their chosen sport (1). Experts opine that early diversification, followed by specialization in later adolescence, leads to increased enjoyment, fewer injuries, and prolonged participation (2, 16, 35), which ultimately contributes to overall sport success (2).

A framework for understanding sport involvement can be found in the Developmental Model of Sport Participation (DMSP). The DMSP is a framework that outlines pathways for youth sport involvement, emphasizing how participation can lead to different outcomes such as lifelong engagement, elite performance, or dropout. It integrates developmental, psychological, and social factors to guide sport programming and coaching practices. By outlining various pathways of sport participation, the DMSP provides insights into how individuals’ involvement in sports can potentially unfold over time. Young athletes enter the model in one of two ways: the sampling pathway or the early specialization pathway. In the early sport specialization pathway, athletes starting from age six to adulthood specialize in one sport characterized by a high deliberate amount of practice, a low deliberate amount of play, and focus on one sport. The other pathway, the sampling pathway, involves a high amount of deliberate play, a low amount of deliberate practice, and involvement in multiple sports in the initial stage (7).

According to the DMSP, athletes who enter the sampling pathway, there are four main stages of development that align with specific ages and developmental needs. In the first stage, called the “sampling years”, there is an emphasis on deliberate play and sport diversification by participating in the sampling of multiple sports. The goal of the sampling years is that during this stage, youth athletes can either participate in sport sampling, meaning they play multiple sports, or they intensively participate in only one sport. This occurs approximately at the ages of six to twelve years old. Proceeding this stage, at approximately age thirteen, serious athletes transition into the “specializing years”. The second stage of progression is called the “specializing years”, which happens around adolescence, during the ages of thirteen to fifteen years old, when youth athletes begin to focus on a smaller number of sports. While fun and enjoyment are still crucial features of their participation, sport-specific specialization starts in this phase, characterized by deliberate play, balanced practice, and a reduction in the involvement of other sports. During this stage, youth athletes can take three routes: continue participating in sport as a recreational activity, they can progress to the investment stage or opt to discontinue altogether (7). The final stage, known as the” investment phase”, occurs at 16+ years of age. This stage is characterized by a high amount of deliberate practice, a low amount of deliberate play, and an increased focus on one sport (7). During this stage, the athlete becomes committed to high-performance goals in a specific sport where strategic, competitive, and skill development are the primary focus (22).

To date, there has been insufficient research that has investigated the effects that specializing in sport might have on perfectionism. Thus, this study sought to investigate if there was a difference between athletes who specialized early or later in their athletic careers using the DMSP as a framework to construct our study (7, 8, 9). For this study, two research questions are being assessed. Research question I hypothesized that there is a significant difference between the time in which an athlete specialized in a sport during the sampling years (ages 6-11), specializing years (ages 12-14), investment years (ages 15-17), or post-investment years (ages 18+) with perfectionistic concerns. Research question II hypothesized that there is a significant difference between the time in which an athlete specialized in a sport during the sampling years, specializing years, investment years, and post-investment years. A series of one-way ANOVAs were conducted, one for each research question.

METHODS

Participants

A total of 416 student-athletes (156 males, 260 females) from Division-1 colleges and universities participated in this study. Participants ranged in age of 18-25 years (M = 20.24, SD = 1.36), and competed in 15 overall sports. Participants were recruited following approval from the primary researcher’s institutional review board. Recruitment was conducted through an online survey administered via SurveyMonkey.com. Inclusion criteria stipulated that respondents must concurrently compete or be a member of an intercollegiate athletics team at a Division-1 NCAA institution. Participants were recruited from various Division-1 NCAA schools representing all the Power Five and Group of Five conferences. Data collection from participants took place over a period of years beginning in 2018 and ending in 2024.

Measures

Participants completed a demographic questionnaire, a self-perceived sport specialization questionnaire, a questionnaire of subscales of perfectionistic concerns and strivings, and a questionnaire asking when athletes specialized in sports.

Perfectionism

Multiple measures were employed to assess the higher-order constructs of perfectionistic striving and perfectionistic concerns, following recommendations from previous studies (33, 34). The foundation for this study was provided by Hewitt and Flett’s Multidimensional Perfectionism Scale (H-MPS) (20) and Gotwals and Dunn’s Sport Multidimensional Perfectionism Scale (Sport-MPS-2) (17). Components from both inventories were amalgamated to form a 7-point Likert scale. The combined measures exhibited strong reliability (α = .892), consistent with previous findings (20, 17).

Perfectionistic Concerns. To assess perfectionistic concerns accurately, three subscales were employed in the study. Two subscales from the Sport Multidimensional Perfectionism Scale-2 (Sport-MPS-2) (17) were utilized. The first subscale, titled “concerns over mistakes,” comprised eight items and assessed participants’ reactions to failure in competition, such as feeling like a failure as a person. The second subscale, “doubts about actions,” consisted of six items aimed at capturing participants’ uncertainties about the adequacy of their pre-competition practices. Additionally, a segment of Hewitt and Flett’s Multidimensional Perfectionism Scale (H-MPS) (20) was integrated to gauge fear of negative social evaluations. This segment, extracted from the “socially prescribed” perfectionism subscale, encompassed 15 items probing participants’ perceptions of others’ expectations of perfectionism from them, such as “People expect nothing less than perfectionism from me.”

Perfectionistic Strivings: Perfectionistic strivings encompass self-oriented striving and the establishment of high personal performance standards. To assess this higher-order construct, two subscales were employed from both the Sport Multidimensional Perfectionism Scale (Sport-MPS-2) (17) and the Hewitt & Flett Multidimensional Perfectionism Scale (H-MPS) (20). To measure self-oriented perfectionism, the five-item self-oriented perfectionism subscale from the H-MPS was utilized. This subscale includes items such as “One of my goals is to be perfect in everything I do.” For the assessment of high personal performance standards, the seven-item personal standards subscale from the Sport-MPS-2 was employed. Example items from this subscale include “I hate being less than the best at things in my sport.” (17). Evidence supporting the internal consistency of these subscales has been provided, with reliability coefficients (α) exceeding .74 for both the H-MPS and the Sport-MPS-2 (10, 17)

Sport Specialization

In line with established methodologies (4, 22), a self-perceived questionnaire was utilized for this study. The questionnaire consisted of a three-point scale classification method, whereby respondents classified themselves as high, moderate, or low in terms of sport specialization. The questionnaire’s questions included: “Have you quit other sports to focus on one sport?”, “Do you train more than eight months out of the year in one sport?”, and “Do you consider your primary sport more important than others?” Respondents indicated their responses to these questions using a categorical classification system, where “yes” responses were assigned a value of 1 and “no” responses were assigned a value of 0. Based on the cumulative score from these questions, individuals were classified into different levels of specialization: a score of 3 denoted high specialization, a score of 2 indicated moderate specialization, and a score of 0 or 1 signified low specialization.

Time of Sport Specialization

To align with the Developmental Model of Sport Specialization, participants were asked three questions aimed at determining when they specialized in their current sport. Specifically, athletes were asked if they engaged in any other sport besides their current primary sport during their sampling years (ages 6-11), specializing years (ages 12-15), investment years (ages 15-17), and post-investment years (ages 18+).

Data Analysis

All data were assessed with IBM SPSS Statistics. A series of one-way ANOVAs were employed for this study.

RESULTS

Results for Perfectionistic Concerns

For research question I, the research sought to investigate the hypothesis that there is a significant difference between the time in which an athlete specializes in a sport during elementary/primary school, middle school, high school, or college with perfectionistic concerns. Descriptive results from the participants for perfectionistic concerns and time of sport specialization can be found in Table 1.

A one-way between-subjects ANOVA was conducted to compare the effect of when an athlete specializes in sport on perfectionistic concerns in elementary/primary school, middle school, high school, or college as conditions. There was not a significant effect on perfectionistic concerns for the four specialization time frames [F (3, 413) = .996], p > .05. Therefore, concerning the first research question, it was determined that the timing of specialization in sport did not exhibit any association with perfectionistic concerns among the participants. Regardless of whether athletes specialized during their sampling years, specializing years, investment years, or post-investment years, there was no discernible correlation with perfectionistic concerns, despite the athletes exhibiting high scores on this measure.

Results for Perfectionistic Strivings

For research question II, the research sought to investigate the hypothesis that there is a significant difference between the time in which an athlete specializes in a sport during sampling years, specializing years, investment years, and post-investment years with perfectionistic strivings. Descriptive results from the participants for perfectionistic strivings and the time of sport specialization can be found in Table 3.

A one-way between-subjects ANOVA was conducted to compare the effect of when an athlete specializes in sport on perfectionistic strivings in the sampling years, specializing years, investment years, post-investment years. There was not a significant effect on perfectionistic strivings for the four specialization time frames [F (3, 413) = .805], p > .05. As it pertains to research question II, it was found that the time in which the participants specialized in sport was not a significant predictor of perfectionistic strivings. The analysis revealed that regardless of whether participants specialized in their primary sport during sampling years, specializing years, investment years, and post-investment years, there was no observable association with perfectionistic strivings.

DISCUSSION

The primary aim of these analyses was to investigate the relationship between the timing of sport specialization and perfectionism. Contrary to our hypotheses, the results indicated that regardless of the stage of sport specialization, there was no significant association observed with either perfectionistic concerns or perfectionistic strivings. Although this was not the primary focus, participants in the study displayed elevated scores on perfectionistic concerns overall.

One potential explanation for the lack of differentiation between groups, despite athletes scoring high on perfectionistic concerns, could be attributed to the similarity in experiences among athletes. It is hypothesized that athletes may have had comparable sporting experiences, particularly since a significant portion of participants specialized during college (N = 235, ≈ 56%). This similarity in experiences might have led to the development of perfectionistic concerns in a uniform manner across the sample.

Another potential reason for the absence of variation is due to the smaller number of participants who experienced early specialization in sampling and specialization years (N= 85, ≈ 20%) as compared to the high number of athletes who specialized later in investment and post-investment stages (N= 331, ≈ 80%). Our sample, however, parallels previous studies about when athletes tend to specialize, suggesting that sport diversification might not be a buffer or contributor to psychological constructs, either negative or positive ones. For example, a study found that athletes who engaged in sport diversification had no discernible difference in the measurement of mental toughness (5). It might be that psychological constructs develop over time and have a myriad of factors that contribute to their development, and that sport specialization and diversification play a small role, if any.

The athletes in our study exhibited elevated levels of perfectionistic concerns but not perfectionistic strivings. According to the Development Model of Sport Participation, the ages of 13-15, yet even athletes who engaged in sport diversification prior to this stage still reported elevated perfectionistic concerns. These findings may contradict arguments that support sport diversification as a safeguard against negative psychological outcomes. However, it is important to consider that the participants in our study were current Division-1 NCAA athletes who were actively specializing in sport and no longer engaged in diversification. This suggests that concurrent sport specialization is more important than the stage of specialization.

Given these findings, further longitudinal research on sport specialization and the timing of specialization is warranted. Understanding how specialization impacts athletes’ psychological well-being over time, particularly in comparison to those who engage in sport diversification, could provide valuable insights into the potential risks and benefits associated with different approaches to sport participation.

These findings collectively suggest that the timing of sport specialization may not be a critical factor in determining psychological outcomes such as mental toughness or perfectionism among athletes. Instead, other variables such as individual personality traits, coaching styles, and environmental influences may play a more substantial role in shaping these psychological characteristics.

Since our sample was limited to Division-1 college athletes and contained few individuals who specialized early, future research should examine athletes in sports where early specialization is the norm, such as gymnastics and figure skating, to explore differences between early and later specializers. Additionally, our findings imply that sport diversification may not act as a preventive measure against future psychological issues. Any psychological effects of sport specialization appear more closely tied to the current intensity and environment of specialization than to the specific age at which specialization began.

LIMITATIONS

While the present study contributes to the overall knowledge regarding athletes’ perceptions regarding sport specialization and perfectionism, this study is not without limitations. The sample included only Division-1 NCAA college athletes, a population considered “elite” due to their high level of athletic achievement. This homogeneity may have limited the variability of responses and reduced generalizability to broader athletic populations, such as youth, high school, or recreational athletes. Given their success, these athletes may also be more resilient to the negative effects of sport specialization and perfectionism, which may not be the case in less experienced or less accomplished athlete groups.

Secondly, the classification of athletes into low, medium, or high levels of specialization relied on the widely used Jayanthi scale, which includes only three items. While this scale is prominent in the literature, its brevity may limit the depth and accuracy with which an athlete’s specialization history is captured. It may overlook key dimensions such as training intensity, emotional investment, or motivational drivers behind specialization, potentially leading to overly simplistic classifications.

Third, the study utilized a cross-sectional and retrospective design based on self-report surveys. Participants were asked to recall past experiences and report on them at a single point in time, introducing potential recall bias and limiting the ability to draw causal inferences. A longitudinal design, tracking athletes’ specialization and perfectionism over time, would likely yield more robust and temporally sensitive data.

Finally, purposive-homogeneous sampling was used, selecting participants from a distinct and specific subpopulation. While this method allows for targeted recruitment and can yield insights from a well-defined group, it may introduce researcher selection bias and limit generalizability. That said, this study was not designed to generalize to the broader population but rather to provide insight into a specific group of athletes who have achieved a high level of competitive success.

CONCLUSION

While the results of the study were contrary to our research hypothesis, the results of this study are not without merit. Findings from the current study add to the literature but also provide areas to be further studied. Athletes are continuing to specialize in sport at an increasing rate, despite current research showing that sport specialization is a non-adaptive behavior that yields very little benefit while carrying many potential negative consequences. Sport management professionals, coaches, parents, and athletes should be fully aware of the consequences of sport specialization, both physically and psychologically, before having athletes become specialized. The results of the present study indicate that even if an athlete follows the Development Model of Sport Participation by practicing proper sport diversification by the recommended age, it might not be enough to blunt the effects of maladaptive perfectionism, even if they reach the highest levels of competition, such as Division-1 athletics. Our results suggested that there was no difference between the athletes who specialized early or later in their athletic career.

APPLICATIONS IN SPORT AND FUTURE RESEARCH

Sport specialization continues to provoke debate among scholars, coaches, and parents, particularly regarding its efficacy and developmental impact. Similarly, perfectionism remains a focal point in sport psychology research, with ongoing research surrounding its adaptive and maladaptive dimensions. The current study aimed to add to the current body of knowledge for the sport community regarding both perfectionism and sport specialization.

The Development Model of Sport Participation Model serves as a guiding framework for

for coaches, athletes, and researchers to examine the implications of sport specialization and diversification. This study aimed to enhance understanding of how DMSP related to perfectionism in sport. The results of the analysis indicated that there was not a significant relationship between when an athlete specializes in sport, whether in their sampling, specialization, investment or post-investment years with perfectionistic strivings and perfectionistic concerns. While the null hypothesis was accepted, the finding still offer valuable insight for scholars, coaches and parents. Notably, even among elite Division-1 athletes are prone to maladaptive perfectionism, despite engaging in sport diversification properly. The lack of differentiation based on specializing timing raises concerns, given perfectionism association with negative psychological outcomes. Although these athletes achieved the highest levels of success, suggesting resilience, it remains uncertain whether similar patterns, or more severe psychological consequences, would manifest in less accomplished or younger athletes lacking the same resilience or comparable coping mechanisms. The need to further investigate this issue is clear.

The physical consequences of sport specialization remain well documented, but its psychological ramifications warrant more research. Our findings support earlier research that the timing of sport specialization may be less impactful than concurrent sport specialization. Coaches and parents may benefit from using this information to better support athletes’ mental health, particularly while engaging in sport diversification. Despite an overwhelming percentage of participants adhering to DMSP principles, nearly all were engaged in specialization at the time of data collection and still reported elevated perfectionistic concerns. In a similar study also involving college athletes, there was no discernible difference found in mental toughness between early sport specializers and those who diversified (5). Similarly, our current study indicates that the stage of sport specialization, whether early or late in an athlete’s career, does not predict perfectionism tendencies.

Athletes are continuing to specialize in sport at an increasing rate, despite current research showing that sport specialization is a non-adaptive behavior that yields very little benefit while carrying many potential negative consequences. Furthermore, one can surmise that Name, Image, and Likeness in college athletics, with increased financial incentives and opportunities, may exacerbate the rate of sport specialization in the future, since athletes no longer need to reach the professional levels to reap financial reward. Sport management professionals, coaches, parents, and athletes should be fully aware of the consequences of sport specialization, both physically and psychologically, before having athletes become specialized.

The study sets a foundation for future research on sport specialization, albeit with limitations. Participants retrospectively reflected on past experiences, and the study’s cross-sectional design may have drawbacks. A longitudinal approach, tracking athletes during active participation, could yield more precise insights. Additionally, the exclusive focus on Division-1 NCAA athletes may limit generalizability; exploring athletes across various levels and ages is imperative. Furthermore, investigating specialization dynamics in different sports, particularly those requiring early specialization like gymnastics, versus those promoting diversification, is crucial. Moreover, exploring how team sports compare to individual sports regarding specialization and perfectionism would add depth to understanding these phenomena. This study sought to explore an emerging area of research in sport specialization. Overall, this study provides a basis for further research as well as provides future suggestions by offering additional opportunities to further investigate the effects of sport specialization on perfectionism.

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Troy Palmer2025-05-22T15:03:47-05:00October 31st, 2025|Research, Sport Education, Sport Training, Sports Coaching, Sports Exercise Science|Comments Off

Extending the curve: A Closer Look at High-Velocity Measures in the Power Clean.

Wei Qian Lim1, MS, David Smith2, Eric D. Magrum2, PhD,
The George Washington University1, Washington, DC
James Madison University2, Harrisonburg, Virginia

Editor’s Note: Table 1 was incorrectly published. This has been corrected. Tables 2 and 3 were reformatted during the revision process.

Corresponding Author:

Eric D. Magrum

261 Bluestone Dr.

Harrisonburg, VA

22807

540-568-6957

[email protected] :

Abstract
Purpose: This study examines the validity and reliability of a commercially available velocity-based training device, GymAware, when measuring barbell velocity during submaximal power cleans. While GymAware has been validated for slower movements, limited research has assessed its accuracy at higher velocities, particularly in Olympic weightlifting derivatives.
Methods: Ten resistance-trained participants completed two sets of five repetitions at 40%, 50%, and 60% of their perceived one-repetition maximum in the power clean. Mean and peak barbell velocity were recorded using GymAware and compared to a motion capture system as the criterion measure. Data were analyzed for reliability using intraclass correlation coefficients and validity through correlation and regression analysis.

Results: Mean velocity measurements from GymAware demonstrated strong agreement with motion capture across all loads, with correlations exceeding 0.85 and an intraclass correlation coefficient of 0.85, indicating good reliability. However, peak velocity measurements exhibited greater variability, with a systematic overestimation of 0.37 m/s and a lower reliability coefficient (0.31). Linear regression models confirmed that GymAware accounted for 88% of the variance in mean velocity but only 44% in peak velocity, suggesting less precision in high-velocity movements.

Conclusion: GymAware provides reliable and valid measurements of mean barbell velocity but has limitations in accurately assessing peak velocity during rapid weightlifting movements. Coaches and practitioners should prioritize mean velocity when utilizing velocity-based training for performance monitoring.

Application in Sports: Velocity-based training offers an efficient method for tracking performance and adjusting training loads. GymAware’s ability to measure mean velocity reliably makes it a useful tool for monitoring training adaptations and providing immediate feedback to athletes. However, practitioners should be cautious when interpreting peak velocity data, particularly in high-velocity Olympic weightlifting derivatives, and consider alternative methods for precise assessment.

Introduction
Resistance training is a well-documented modality for improving force production, power, lean body mass, and overall athletic performance (10-11,13,20,27). For these reasons resistance training has become synonymous with athlete preparation. Before the technological renaissance, tracking athletes’ progress and assessing program effectiveness was almost entirely comprised of assessing progressive overload via number of repetitions completed or through the manipulation of external load lifted (15,19,22). However, these more traditional methods come with several challenges, making it difficult to assess program effectiveness. Specifically, athlete’s perceived exertion, range of motion, and different pacing strategies can confound practitioners’ ability to assess meaningful changes as it relates to physiological adaptations resultant resulting from training (12,18,19,22). Because of this, numerous efforts have been made to leverage technological tools to enhance the assessment of training efficacy.

Recent technological advancements have popularized the tracking of barbell velocity, termed velocity-based training (VBT), and highlighted its usefulness in gauging training efficacy. VBT is utilized for a multitude of reasons, including but not limited to predicting 1 repetition maximum (1RM) without the accumulation of excessive fatigue and increased risk of injury, monitoring training performance and neuromuscular fatigue, and providing immediate kinematic feedback potentially leading to enhanced training outcomes (1,3,-4,7,-8,10-11,18,23,24,26,28). As with any technological tool, measures of validity and reliability are paramount to assess the meaningfulness of the data provided. Providing reliable data is important for coaches and athletes alike, to accurately assess the physiological changes associated with training programs, as well as make appropriate alterations when needed.

For over 20 years, GymAware (GYM) has been considered the gold standard of linear positional transducers (LPT). LPT’s function by measuring displacement of a barbell as well as the time taken to complete said displacement. By using this data, the LPT computes several variations of barbell velocity and power (average, peak, etc.) (17). Previous research suggests that the GYM is both highly valid and reliable at slow velocities (0.3-0.7 m/s) . (3-5,7-9,14,15,21). However, few studies have examined the reliability and validity of the GYM during low load, high velocity weightlifting or plyometric movements (0.7+ m/s). Studies that have investigated GYM at these velocities report that the GYM system typically underreports peak velocity and power outputs at lower loads and higher velocity (2,6,14).

Askow et al. (2) examined the reliability and validity of GYM software at both 60 and 80% of 1RM back squats. They found that GYM tends to underestimate peak velocity by 11.6% and software is not the most accurate measure of barbell velocity during high velocity movements. Despite this, Askow and his team of researchers still reported high levels of reliability at high velocities (2). Orange et al. (17) reported excellent reliability for both peak and mean velocity measurements at a range of different percentages of 1RM in the back squat and bench press with interclass correlations (ICCs) ranging from 0.96 to 0.99. Lorenzetti et al. (14) found that GYM was both reliable and valid at tracking bar velocity at 70% of 1RM and during a ballistic jump squats; however, they found much higher reliability and validity at lower velocities when compared to the high velocity jump squat plyometrics. A systematic review of LPTs and linear velocity transducers (LVT) corroborated these findings and reported that LPTs, including the GYM, were valid and reliable in measuring velocity during powerlifting and weightlifting movements . (25).
Another review on the subject highlights the need for independent investigations of velocity-based sensors to examine higher velocity lifts such as Olympic weightlifting derivatives (1.2-1.6 m/s) (16). Due to their unique utility and force-velocity characteristics, weightlifting movements , such as the snatch, clean and jerk, are routinely utilized in sport performance settings around the globe. An essential element of these lifts is how fast the weight moves. Few studies have compared such devices to a criterion measure, namely motion capture (25). However, existing research on devices like the GYM Power Tool suggests high validity and reliability when measuring velocity during high-velocity barbell movements. Orange et al. (17) reported excellent reliability of GYM for back squats and bench presses, with ICCs ranging from 0.96 to 0.99 for velocity, suggesting that it could similarly perform well in more dynamic lifts. There is limited research on the reliability and validity of LPDT when measuring velocity during Olympic lift derivatives. Thus, the current study will address the gap in the literature and extend our understanding of the validity and reliability of VBT devices at higher velocities. Specifically, the purpose of this study is to examine the reliability and validity of GYM compared to Qualisys Motion Capture during the power clean.

Methods
The study was carried out with 10 participants (Table 1). Participants had at least one year of prior experience strength training, defined as an average of two training sessions per week. Subjects were between the ages of 18-40, technically proficient in the clean, not pregnant, free of known cardiovascular, metabolic, or renal disease, and free of injuries. After giving written consent, technical proficiency in the clean was determined during a familiarization session prior to data collection.

Table 1. Participant Characteristics

Sex	Age (years) (mean ± SD)	Height (m) (mean ± SD)	Weight (kg) (mean ± SD)	Predicted 1RM (kg) (mean ± SD)
Male (n=5)	23.4 ± 4.4	1.74 ± 0.06	83.3 ± 9.8	106.6 ± 24.5
Female (n=5)	22.0 ± 0.7	1.62 ± 0.06	72.6 ± 22.6	61.2 ± 17.0
Total (n=10)	22.7 ± 3.1	1.68 ± 0.09	78.0 ± 17.4	83.9 ± 31.1

For a clean, participants had to lift the barbell in one smooth move from the floor, catching the barbell in a front rack position. Feet were to be shoulder width apart or just outside shoulder width at the catch. The participants were cued to move the weight as quickly as possible while staying under control. Participants with working weights lighter than what could be provided with bumper plates, the lift began from a hang at mid-shin height.

During the familiarization session participants were asked to complete a health history questionnaire before height and weight were taken. After a general warm up that consisted of 50 jumping jacks, 10 bodyweight squats, 5 jump squats and 5 cleans with the empty barbell, the participants provided a perceived 1RM (ex. 200 lbs.). 50% of the participants’ perceived 1RM was loaded onto the barbell (ex. 50% of 200 lbs. = 100 lbs.). The participant was then asked to perform 1 set of 5 repetitions, at which point the research team determined if technical proficiency was sufficient (binary yes or no).

Participants who met the inclusion criteria and demonstrated proficiency in the clean were invited back for a lifting session. The session began with the same general warm-up detailed above. Participants whose schedules permitted both sessions to be completed consecutively (familiarization + lifting) were not asked to perform the warmup prior to the lifting session. In total, participants completed six sets: two sets of five repetitions at 40%, 50%, and 60% of perceived 1RM (ex. 200lbs 1RM: 40% = 80lbs, 50% = 100 lbs., and 60% = 120lbs). Each set began with the signal “You may begin your lift.” Participants were instructed to fully stop and/or set down the bar at the end of each repetition for at least a one count to prevent the use of momentum and allow for a distinct ending to each repetition. This was reinforced with a count of “one” between each repetition. Participants were given three minutes to rest between each set.
Qualisys motion capture system was used as a gold standard/criterion reference. The motion capture set-up consisted of six cameras: three from the Miqus M3 series and three from the Oqus series. Six reflective markers were attached to the barbell. Two markers were attached to either end of the bar, while four markers were attached in square configuration on the collar of the barbell (Figures 1 and 2). The data were recorded with the software QTM 2020-2 Build 5710, with a frequency of 100 Hz. The limits for standard deviation for wand length calibration were 0.3 and 0.5 mm.

The GYM RS, placed on the ground between the pad and platform, was tethered to the shaft of the barbell close to the four reflective markers (see Figures 1 and 3). The GYM RS device was connected via Bluetooth to the free version of the GYM iOS application (Version 4.0.1). GYM RS records at 50 Hz. Peak and mean velocity (m/s) for each repetition were hand recorded from the application into a Microsoft Excel spreadsheet.

Velocity data were exported from the Qualisys Track Manager (QTM) software to Microsoft Excel. The beginning of the lift was determined by the inflection of barbell velocity denoted by an increase of 0.01 m/s for three consecutive frames. The end of the concentric portion of the lift was determined by the first maximum velocity value or crest of velocity curve. Corresponding with GYM, mean concentric velocity (m/s) was determined by averaging marker velocities over the entire concentric portion of the lift. Peak concentric velocity (m/s) was calculated by averaging the individual velocities of each marker over a sample period of 20 milliseconds immediately preceding peak velocity.

Participants stood on a wooden platform with the barbell resting on black foam pads on either side of the platform. Unless the participant’s working weight utilized change plates or the empty bar, the clean started from the black foam pads. If not, the clean started from a hang at mid-shin height. The materials were a 20 kg bar, Rouge change plates between 0.5 and 5 kg, 2.5 and 5 lb. plates, as well as 25 and 45 lb. bumper plates. Working weights for each participant were calculated to get as close to 40%, 50%, and 60% of perceived 1RM.

Results
Data was collected for 10 participants during a single data collection session. Subjects completed six sets: two sets of five repetitions at 40%, 50%, and 60% of perceived 1RM. Mean and peak velocity was recorded using GYM and Qualisys motion capture software for each repetition. There was a total of 60 data points per participant, resulting in 600 total data points.
3.1 Validity

Figure 4. Scatter plots expressing the peak and mean bar velocities at 40, 50, and 60% of one repetition maximum as measured by GYM and Qualisys motion capture systems. Error is defined as the difference between the GYM measurements and Qualisys measurements, with cooler colors representing less error and hotter colors representing more error. Dashed line represents a perfect linear fit that assumes no variance between the two devices. All correlations were statistically significant with a p<0.05

Scatter plots for peak velocity at each percentage of 1RM showed varied levels of correlation between GYM and Qualisys. At 40% of 1RM r=0.706, at 50% r=0.512, and at 60% r=0.703. Each of the aforementioned correlations reached statistical significance at the 0.05 level and indicate a moderate correlation between the GYM and Qualisys measurements of bar velocity. 50% of 1RM demonstrated the highest variability (Figure 4).

The mean velocity measurements between the two systems demonstrated stronger correlations across all load percentages. At 40% r=0.958, at 50% r=0.938, and at 60% r=0.871. All correlations were statistically significant (p<0.05) and indicate a consistent, strong relationship between GYM and Qualisys when assessing mean bar velocity (Figure 4).
GYM software tended to overpredict peak barbell velocities at all intensities by 0.37 m/s on average, while only over predicting mean barbell velocity by 0.09 m/s (Figure 5).

Table 2. Comparison of Linear Regression Model Results for GYM and Qualisys Motion Capture System at Different Percentages of Perceived One Repetition Max

Load (%1RM)	R²		F-statistic
Load (%1RM)	Mean Velocity (MV)	Peak Velocity (PV)	Mean Velocity (MV)	Peak Velocity (PV)
40%	0.92	0.50	1073.66	97.15
50%	0.88	0.26	723.49	34.83
60%	0.76	0.51	301.48	101.32
All data	0.88	0.44	2086.1	234.72

*All data was significant with a p-value<0.001.

A linear regression model indicated a significant relationship between mean and peak bar velocity as reported by the GYM when compared to Qualisys tracking software. Mean velocity linear regression: F (1,293) = 2086.61, p<0.001, R2 = 0.88. Peak velocity linear regression: F (1,293) = 97.15, p < 0.001, R2 = 0.44. This model indicates that across all percentages of 1RM tested, GYM software was able to account for 88% of the variance in mean bar velocity and only 44% of peak bar velocity.
When parsed out and compared by loads, the data highlights a closer relationship between mean velocity measures as compared to peak velocity measures (Table 2.) At 40% 1RM: Mean velocity: F (1,293) = 1073.66, p < 0.001, R² = 0.92; Peak velocity: F (1,293) = 97.15, p < 0.001, R² = 0.50. At 50% 1RM: Mean velocity: F (1,293) = 723.49, p < 0.001, R² = 0.88; Peak velocity: F (1,293) = 34.83, p < 0.001, R² = 0.26. At 60% 1RM: Mean velocity: F (1,293) = 301.48, p < 0.001, R² = 0.76; Peak velocity: F (1,293) = 101.32, p < 0.001, R² = 0.51.
3.2 Reliability

Table 3. Intraclass Correlation Coefficients for mean and peak barbell velocity measurements.

	Mean Barbell Velocity	Peak Barbell Velocity
ICC (95% CI)	0.848 (0.341-0.941)	0.306 (-0.092-0.632)
F-statistic	23.6	4.8
p-value	0.00261	0.128

The ICCs were calculated to assess the reliability of mean and peak barbell velocity measurements. A two-way random-effects model with absolute agreement (ICC (A,1)) was used for both metrics. Mean barbell velocity had an ICC of 0.848 (0.341–0.941), with an associated F-test indicating statistical significance (F (296, 4.22) = 23.6, p = 0.00261). These calculations indicate good reliability. Peak barbell velocity had an ICC of 0.306 (-0.092–0.632), with a non-significant F-test (F (299, 2.69) = 4.8, p = 0.128). This ICC value indicates poor reliability.
The coefficients of variation (CV) were calculated to assess the relative variability in mean and peak values for both GYM and Qualisys datasets. For the mean values, the CV was 17.06% for GYM and 20.46% for Qualisys. For the peak values, the CV was 10.75% for GYM and 15.37% for Qualisys, with GYM showing the lowest relative variability among all measures.

Discussion
The findings of this study offer valuable insight into the reliability and validity of GYM as a VBT tool. While GYM demonstrated strong validity in tracking mean barbell velocity across all intensities, it was substantially less accurate when assessing peak barbell velocity. These results highlight important considerations for practitioners when using GYM as a training tool.
There was a strong correlation observed between GYM and Qualisys for mean velocity measurements, highlighting the reliability of GYM. The ICC for mean velocity (0.848) reflects good reliability, supporting its use by coaches and athletes where consistent data is essential for assessing training adaptations and adjusting programs accordingly. This finding demonstrates that GYM’s mean velocity measure is capable of providing practitioners with insightful data that can reliably indicate changes in athletes’ performance capabilities. For example, this means that a positive change of 0.15 m/s in an athletes mean clean velocity at a given load is likely due to changes in the athletes’ performance capabilities, as opposed to the measurement error associated with the VBT tool. This is rather important when competitive success has such slim margins and even more important when resistance training programs are dictated by real time data collected by VBT tools. These findings are consistent with prior research that has identified GYM as a reliable tool for monitoring barbell velocity during traditional resistance training exercises (17). Importantly, this examination focused on high velocity movements, hence the loads of 40-60%, and extended the range of velocities studied within the literature.
Despite this, GYM had a moderate correlation and systematically overestimated barbell velocity limiting its application. GYM had a mean bias of +0.37 m/s when assessing peak velocity suggesting that GYM may not offer the precision required for accurately evaluating peak velocity during rapid, explosive movements. What is perhaps more concerning is the poor ICC for peak velocity (0.306), indicating low reliability for this metric.. For example, if an athlete were to improve peak barbell velocity by 0.15 m/s, the same amount as with their mean velocity, we wouldn’t be able to confidently attribute this change to a performance improvement due to the low reliability.

These findings agree with previous research that has identified similar discrepancies in GYM’s accuracy. In Lorenzetti et al. (14), the GYM device showed a higher root mean square error (RMSE) of 0.06 m/s when assessing peak barbell velocity during ballistic jump squats compared to slower squat movements. This higher RMSE suggests that the device was less accurate in measuring peak velocity during higher velocity, explosive jumps. The study found the mean difference between GYM and the reference method (motion capture) to be -0.05 m/s, further indicating potential measurement errors in high-velocity movements. These results highlight that peak velocity measurements may be prone to greater variability in ballistic exercises. Additionally in Askow et al. (2), the GYM device consistently underestimated peak barbell velocities by 11.6% (or -0.13 m/s) when compared to a more accurate criterion measure. This bias was particularly evident during high-velocity movements, indicating that the device may not be as precise for measuring peak velocity in such contexts. The underestimation suggests a systematic error that could limit the utility of GYM for tracking performance improvements in peak velocity during explosive lifts. These values along with our data showcase that GYM may not be an effective tool at assessing peak barbell velocity at lower loads/higher barbell velocities.

This study also reinforces the importance of context when interpreting data from VBT devices. Contrary to our ICC data, the coefficients of variation (CV) highlight the consistency of GYM for both mean velocity (17.06%) and peak velocity (10.75%). Interestingly, this statistic suggests that peak velocity is more reliable when compared to mean velocity; however, this is likely due to the systematic overestimation of both peak and mean barbell velocity by GYM. Utilizing both ICC and CV’s the data supports the notion that GYM has strong reliability for mean velocity, however peak velocity measures capture by GYM leave something to be desired. These data suggest that practitioners should use mean barbell velocity measurements to achieve the best results, especially when utilizing VBT to monitor fatigue, track progress, and adjust training intensity in real time. Should practitioners have a penchant for peak velocity measures, the authors strongly encourage practitioners to run in-house statistics to understand what constitutes a meaningful change as compared to a change within the VBT’s measurement error.
Findings align with the broader literature discussing VBT devices and explore a gap in the literature by examining high-velocity movements while highlighting aspects that have practical significance. Future investigations should explore GYM’s performance with other high velocity movements such as the snatch or jerk, to better understand its broader applications. Importantly, while these results contribute to the growing body of evidence, it is important to situate the use of VBT within the broader training context and provide guidance to practitioners.

Application in Sport
The authors contend that reliable VBT tools can be leveraged by practitioners. First, VBT tools provide a cost-effective and time efficient avenue to collect data and highlight changes as a result of the training prescription. VBT data may be leveraged as biofeedback and a load modulation technique but only in synchrony with more traditional loading prescription (% of 1RM/% of set/rep best). Important to note, these strategies utilize VBT tools as a secondary data stream to inform when load changes may be needed and not as a primary load prescriber. Coaches must retain load prescription responsibilities, while utilizing their eyes and ears (in addition to VBT tools) to skillfully make load adjustments when needed. Practitioners must also bear in mind that VBT tools are inaccurate when estimating 1RM, therefore other methods for estimating are necessary. Perhaps the most compelling reason for utilizing VBT tools resides in their ability to potentiate participant performance. The presence of VBT devices may improve athlete motivation and training intent, which is paramount for optimal training. While VBT tools generally provide a positive return on investment, the practitioners’ eyes and ears should remain the primary data source which guide training decisions while VBT tools serve a supportive role. Based on available data, it would be shortsighted to rely solely on VBT tools to make real-time training decisions.

In conclusion, this study demonstrates that GYM provides reliable and valid measurements for mean barbell velocity during submaximal power cleans. As a result, practitioners may leverage GYM’s strengths, particularly its ability to provide immediate feedback and monitor mean velocity, while remaining cognizant of its limitations for high-velocity movements. This approach may allow for the effective integration of VBT tools to enhance training decisions, outcomes and athletic performance.

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George Minoso2025-10-28T09:21:32-05:00October 17th, 2025|Research, Sport Training, Sports Exercise Science, Sports Studies|Comments Off

Efficacy of 12-Week Handgrip Strength Training Program Amongst Older Adults: A Pilot Study

Author’s: Abbey Keller¹, David Cason¹, Shannon Hardy², Madison Norris², Angila Berni¹, Michel Heijnen¹, Alexander McDaniel¹, Lindsey Schroeder¹, Tiago Barriera³, Wayland Tseh¹

¹School of Health and Applied Human Sciences, University of North Carolina Wilmington, Wilmington, North Carolina, United States of America

²Carolina Bay at Autumn Hall, 630 Carolina Bay Drive., Wilmington, North Carolina, United States of America

³ School of Education, Syracuse University, Syracuse, New York, United States of America

Corresponding Author:

Lindsey H. Schroeder, Ed.D., LAT, ATC, CES

University of North Carolina Wilmington
School of Health & Applied Human Sciences

601 South College Road
Wilmington, NC 28403-5956
O: (910) 962-7188

F: (910) 962-7073

ABSTRACT

Handgrip strength is indicative of overall health and longevity. The significance of a strong grip increases with age as it relates to lower mortality rates and improved functional capacity.

PURPOSE: To evaluate the effectiveness of a 12-week handgrip strength training program amongst older adults. METHODS: A total of 12 participants (mean age = 82.7 ± 4.8 years; height = 160.7 ± 7.4 cm; body mass = 64.2 ± 13.9 kg; 2 males; 10 females) completed the 12-week exercise intervention. The participants engaged in a twice-weekly, 45-minute suspension training regimen that incorporated a range of exercises targeting upper body strength and stability. Handgrip strength was assessed via a handgrip dynamometer at baseline and post-intervention. A paired samples t-test was employed to assess differences between pre-and post-intervention grip strength. A Bonferroni correction was applied to mitigate the risk of Type I error due to multiple comparisons, setting the adjusted alpha level at p = 0.025. Effect sizes were calculated using Cohen’s d to assess the practical significance of the findings. RESULTS: The analysis revealed a statistically significant improvement in right-handgrip strength, with values increasing from 21.5 ± 1.3 kg in Week 1 to 23.0 ± 1.4 kg in Week 12 (p = 0.006). No significant improvement was observed in left-handgrip strength (20.2 ± 1.2 kg to 21.1 ± 1.5 kg; p = 0.12). The right handgrip strength demonstrated a large effect (d = 0.99), whereas the left handgrip strength exhibited a moderate effect (d = 0.48). CONCLUSION: Findings from this study suggest that the 12-week suspension training and handgrip strength exercise regimen was both statistically and practically effective in increasing HGS in older adults. PRACTICAL APPLICATIONS: Allied healthcare professionals should educate older adults on the importance of HGS and incorporate targeted exercises into their regimens to mitigate age-related functional decline and promote better outcomes.

KEYWORDS: Suspension Training, Longevity, Handgrip Strength

INTRODUCTION

By the year 2050, the global population of older adults is projected to reach 2.1 billion (10). As this demographic shift occurs, various risks associated with aging, including falls, cognitive decline, and impaired longevity and quality of life, become increasingly concerning (8, 14, 45). A crucial yet frequently underappreciated factor contributing to falls and other age-related risks is diminished handgrip strength (HGS), which impairs an individual’s capacity to stabilize themselves and prevent injuries (16, 19). Research suggests that HGS is representative of overall body strength (1). Handgrip strength is defined as the maximum amount of force the hand generates when gripping an object. Thresholds for HGS required to perform functional tasks in older adults are estimated at greater than 18.5 kg for females and 28.5 kg for males (2). Beyond serving as a measure of physical strength, HGS is also a strong predictor of longevity and overall quality of life, making it especially relevant in the context of aging (1). Comprehending the relationship between HGS and other fitness components is essential for devising effective strategies to preserve functional independence and enhance quality of life, particularly as the global population experiences unprecedented aging trends.

According to the Centers for Disease Control and Prevention (CDC), falls represent the leading cause of mortality among individuals aged 65 years and older. Annually, approximately 36 million older adults experience falls, with 32,000 cases resulting in fatal outcomes (4). Falls impact the quality of life by jeopardizing health, mobility, and independence. Although multiple factors influence fall risk, prioritizing interventions to improve HGS may offer a practical and impactful approach to reducing the incidence of falls among older adults (24).

In 2016, Szulc and colleagues examined 890 men aged 50 and older, assessing appendicular skeletal muscle mass (ASM), physical function, and HGS (42). Over a 5-year follow-up period, 813 participants aged 60 and above were monitored, of whom 144 experienced multiple falls. Findings from this research investigation revealed that those who sustained Grade 2 or Grade 3 vertebral fractures and multiple fractures had reduced HGS, decreased physical function, and an increased risk of multiple falls (42).

The number of global dementia cases is expected to almost triple from 57.4 million cases in 2019 to 152.8 million in 2050 (17). That said, aging significantly elevates the risk of cognitive decline, potentially leading to a loss of independence and other adverse outcomes. Although many factors are involved in preventing and treating cognitive decline and related illnesses, HGS may play a key role in determining who is at risk for these diseases. Physical impairments, such as diminished HGS, can interact with other factors to amplify the risk of age-related cognitive decline (7, 18). Consequently, investigating the relationship between HGS and cognitive function is essential for addressing the challenges of an aging global population.

In 2022, Orchard et al. evaluated both gait speed and HGS as predictors of cognitive decline and dementia (36). The participants were community-dwelling older adults who were cognitively intact at the onset of the study. Researchers assessed each participant’s 3-meter walk time and measured their HGS. A 4.7-year median follow-up was used to gather data on the prevalence of cognitive decline and dementia among participants. Slower walking gait and low HGS were independently related to an increased incident risk of dementia and cognitive decline. When these variables were combined, slow walking gait and low HGS were associated with a 79% increase in the risk of dementia development and a 43% increased risk of cognitive decline (36).

Precursory research has revealed that a culmination of exercise methods, including resistance training, Vitality Acupunch training program, multi-modal training, and suspension training (ST), can impact the HGS of older adults (2, 3, 10, 21, 23, 25, 26, 44). Among these, ST programs, such as total resistance exercise (TRX), stand out as accessible and adaptable methods. Due to the nature of ST, users possess the unique opportunity to train in several different facets of fitness at differing scalable resistances in a single bout of exercise (27). The suspension training system enables individuals to perform strength exercises adapted to their unique capabilities, offering progressive resistance to facilitate individualized strength development (15, 27).

In 2018, Campa, Silva, and Toselli conducted a study to determine the effects of a 12-week ST intervention on the phase angle and HGS of female older adults. Thirty older women were randomly assigned to either a control or training group. Participants in the control group continued their usual activities throughout the study, while those in the training group underwent a 12-week ST program. Both groups were assessed on various fitness parameters, including HGS. At the conclusion of the study, researchers found that ST promoted improvements in HGS in older women (3).

In 2022, Pierle and associates conducted a study to examine the efficacy of a 6-week ST program on a sample of 11 older individuals (37). The fitness parameters of interest were functional reach, overall balance, body fat, body mass, and HGS. While this study demonstrated improvements in functional reach and overall balance, body fat, body mass, and HGS showed no significant changes. These findings suggest that ST may be an effective exercise modality for enhancing certain aspects of fitness in older adults. However, further investigation is crucial to understand its impact on HGS better and determine whether ST can optimize strength outcomes in this population (37).

Against this backdrop, given the dearth of research examining the effects of ST protocols on HGS and the relationship between HGS and fall prevention, further investigation is imperative to elucidate the potential benefits of ST, especially amongst the older adult population. Therefore, the primary purpose of this study is to fill this critical gap by evaluating the efficacy of a 12-week ST and HGS exercise program in enhancing handgrip strength in this population. The apriori hypothesis posits that significant improvements in HGS will be observed between pre- and post-assessment measurements, underscoring the potential of ST and HGS as a targeted intervention to improve strength and reduce fall risk among older adults.

METHODS

Participants

Prior to participating in this study, participants were screened using inclusionary and exclusionary criteria. The inclusion requirements included participants who currently exercise, are older than 55 years of age, and are independent of assistive walking devices (e.g., walker, rollator, wheelchair, etc.). The exclusionary criteria included participants not having a medical release form on record, being overwhelmed by the exercise routine, specifically, mild increases in heart rate and blood pressure during exercise, or possessing a pacemaker or other internally implanted device. All participants, therefore, were required to have a medical release to participate. This study was approved by the university’s institutional review board and adhered to the practice of ethical research standards.

All participants were recruited from a local retirement community and were required to report to the Wellness Center onsite for 24 sessions over 12 weeks. Flyers were posted, and those interested were instructed to sign up for an appointment with the principal investigator (PI) to complete the protocol requirements. Participants were encouraged to contact the PI or co-PI by phone or email if any question(s) arose or if any of the requirements remained unclear.

Upon arrival for the pre-assessment session, participants read/signed/dated an informed consent form approved by the University’s Institutional Review Board (IRB) for human subject use (IRB#: H24-0565). Ten females and 2 males (Age = 82.7 ± 4.8 years; Height = 160.7 ± 7.4 cm; Body Mass = 64.2 ± 13.9 kg), completed the 12-week exercise intervention.

Protocol

Once the informed consent was obtained, pre-assessment data was collected. All participants were instructed to remove footwear, socks, and stockings before stepping onto the scale. Height (cm) and body mass (kg) were assessed via Seca 217 Mobile Stadiometer (Model Number 2171821009, USA). The participant’s height and body mass results were displayed and recorded via a data collection sheet. Grip strength was assessed via the Smedley Creative Health Products III Analog Grip Strength Dynamometer (T.K.K. 5001, Japan). Participants were instructed to maintain the standard bipedal position during the entire test with the arm in complete extension and to avoid touching any part of the body with the handgrip dynamometer except the hand being measured. Participants comfortably grasped the handgrip dynamometer and were encouraged to exert maximal grip.

Three trials, with brief pauses, were allowed for each hand alternately. The sum of the highest left and right values was recorded on the data collection sheet. The PI was the lead exercise instructor of the 12-week exercise intervention. The PI took attendance, organized, and provided corrective feedback/instructions during each exercise session. A team of fitness instructors at the retirement community and a research assistant also led these classes by providing feedback to participants and keeping each session organized. The exercise intervention required participants to attend two sessions per week for 12 weeks, with each class being 45 minutes. Attendance was recorded at the start of each class to keep track of the adherence rate. Every session consisted of seven strength training exercises in a circuit style (Table 1), followed by a grip strength series consisting of four exercises (Table 2).

Strength training exercises were advanced every 4 weeks, specifically, progressing from 30-second intervals (first micro-cycle) to 35 seconds (second micro-cycle) to 40 seconds (final micro-cycle). The Farmer’s Carry exercise specifically intensified each micro-cycle, starting with holding one dumbbell each set, then holding one dumbbell each set vertically upright by the head of the weight, and finally holding the head of a dumbbell in each hand. The grip strength series progressed throughout the 12-week intervention, starting with one set of each exercise for 15 seconds per hand in the first 4 weeks and followed by 8 weeks of performing each exercise for two sets of 15 seconds. Each session started with a 5-minute warm-up, followed by 35 minutes of exercise, and concluded with a 5-minute cooldown. The 12-week exercise training intervention took place as a group fitness class in the fitness center of a local retirement community, giving participants the advantage of working with partners for each exercise, increasing accountability and motivation. The TRX suspension training (ST) allowed users to exercise in a customizable and scalable capacity that fits their personal specifications, comfort, and intensity levels (27). Additionally, the PI used a timed-circuit style class versus measuring each exercise based on repetition, allowing participants to perform at their own intensified pace.

Statistical Analysis

A paired samples t-test was employed to assess differences between pre-and post-intervention grip strength. To mitigate the risk of Type I error due to multiple comparisons, a Bonferroni correction was applied, setting the adjusted alpha level at p = 0.025. Effect sizes were calculated using Cohen’s d to assess the practical significance of the findings.

RESULTS

The primary objective of this study was to evaluate the efficacy of a 12-week exercise intervention on handgrip strength (HGS) in a population of community-dwelling older adults. Sixteen participants were initially recruited; however, four withdrew during the study, resulting in a final sample size of 12 participants (Age = 82.7 ± 4.8 years; Height = 160.7 ± 7.4 cm; Body Mass = 64.2 ± 13.9 kg; 2 males and ten females). Attendance was monitored at each session, yielding an average adherence rate of 83%. The adherence rate remained consistent throughout this study.

A paired-sample t-test was conducted to assess differences between pre- and post-intervention measurements. A Bonferroni correction was applied to mitigate the risk of Type I errors due to multiple comparisons, resulting in an adjusted alpha level of p = 0.025. Effect sizes were quantified using Cohen’s d, with thresholds of 0.2, 0.5, and >0.8 representing small, medium, and large effects, respectively.

The analysis revealed a statistically significant improvement in right-hand grip strength, which increased from 21.5 ± 1.3 kg at baseline (Week 1) to 23.0 ± 1.4 kg post-intervention (Week 12, p = 0.006). In contrast, no statistical improvement was observed for left-hand grip strength (20.2 ± 1.2 kg to 21.1 ± 1.5 kg, p = 0.12). The effect size for right-hand grip strength was large (d = 0.99), whereas the left-hand grip strength demonstrated a moderate effect (d = 0.48). Detailed results are presented in Table 3.

DISCUSSION

Limited research exists with respect to investigating sustained strength training (ST) programs and handgrip strength (HGS) in older adults (12, 23). Therefore, the primary purpose of this study was to determine the efficacy of a 12-week ST and HGS exercise program in a community-dwelling older adult population. The researchers hypothesized a statistically significant improvement in HGS between pre- and post-assessment data. At the conclusion of the 12-week ST and HGS exercise program, right-HGS improved significantly and demonstrated a large effect size, while the left hand showed a moderate but non-significant change. These findings suggest that a 12-week suspension training exercise program may enhance grip strength and potentially improve functional independence and reduce fall risk in older adults. However, additional research is needed to fully understand these effects and any differences between dominant and non-dominant hands.

In 2018, a research study was conducted by Campa and colleagues in which the participants were divided into two groups: 1) 12-week ST exercise group and 2) control group that maintained their usual daily activity (3). Both groups of participants underwent pre-and post-tests, evaluating several fitness components, including HGS. Findings from the current research study and the study by Campa et al. (3) revealed both shared and contrasting results in how structured exercise interventions affect HGS in older adults. More precisely, both studies reported statistically significant HGS improvements following their 12-week interventions. The current research study observed an increase in right-hand grip strength from 21.5 ± 1.3 kg to 23.0 ± 1.4 kg, equating to an approximate 7.0% improvement. Similarly, Campa et al. (3) reported an increase in dominant-hand HGS from 38.2 ± 9.7 kg to 40.1 ± 9.0 kg, reflecting a significant 4.97% improvement. Both findings confirm the efficacy of a 12-week exercise program in promoting upper-body strength among older adults. Notably, both studies targeted older adults, with the current study involving a mixed-gender cohort (mean age 82.7 years) and Campa et al. (3) focusing on men with a mean age of 67.4 years. Despite this approximate 15-year age difference, the consistency in outcomes underscores the adaptability of exercise interventions across different subsets of older adults. Both research studies spanned 12 weeks, suggesting that this time frame is sufficient to elicit measurable improvements in muscular strength. Given these similarities, improvements in HGS in both studies align with broader health and functional benefits. Because HGS is a well-established predictor of overall physical health (29, 35), these findings highlight the role of resistance-based interventions in enhancing the quality of life and functional independence among older adults.

While both studies displayed shared findings, it was noted that the baseline mean HGS of the current study was strikingly lower (21.5 ± 1.3 kg) compared to Campa et al.’s (3) sample group (38.2 ± 9.7 kg). This discrepancy may be due to the age difference of about 15 years, which more than likely contributed to variations in baseline physical fitness and adaptive capacity. Older adults often experience diminished neuromuscular responsiveness and muscle plasticity (7, 32).

To summarize, the current research study and Campa et al.’s (3) study demonstrate significant improvements in HGS following 12-week exercise programs, reinforcing the utility of structured ST in mitigating age-related strength decline. Both studies provide compelling evidence that targeted interventions can yield functional strength gains in older populations regardless of modality. However, the differences in participant demographics highlight the influence of baseline fitness levels and age on HGS outcomes.

The results from a study by Gaedtke and Morat (16) also revealed results like those of the current study. Eleven older adults (Mean Age = 66.0 ± 4.0 yrs) participated in a 12-week TRX-OldAge training program, composed of seven exercises progressing through multiple stages of difficulty. The intervention method utilized TRX equipment, shared by Gaedtke and Morat (16) and the current study. Both studies also had similar sample sizes and durations, spanning 12 weeks. The results displayed within Gaedtke and Morat’s (16) research study share thematic similarities with the current research in demonstrating improvements in HGS. Both studies emphasize the potential of targeted programs to enhance functional strength, which is critical for maintaining independence and reducing the risk of falls in aging populations. Specifically, the current research reported a 7.0% increase in right-hand grip strength, showcasing the tangible benefits of a 12-week intervention. Similarly, participants in Gaedtke and Morat’s (16) study subjectively reported strength gains as the most notable improvement following the TRX-OldAge program. However, Gaedtke and Morat (16) did not provide quantifiable pre- and post-assessment metrics for HGS, which limits direct comparisons. While participant feedback highlights strength improvements, the lack of quantifiable data undermines the ability to assess the efficacy of the intervention, specifically on grip strength. This limitation in Gaedtke and Morat’s (16) study underscores the importance of incorporating quantifiable assessments in future investigations to validate self-reported outcomes and to draw more substantial comparisons with similar studies. Regardless, given the vast similarities between the two research studies, it is evident that a TRX-related exercise regime conducted for 12 weeks does enhance muscular strength in older individuals.

In a study conducted by Skelton et al. (41), a 12-week progressive ST intervention was implemented to assess its effects on the strength, power, and functionality of women aged 75 and older (41). The intervention included three exercise sessions per week, with two sessions conducted at home and one in a group setting. The additional day of exercise, as well as the inclusion of home exercise sessions, differs from the current study, which took place twice a week in a group fitness class setting. While the exercises did not mimic the functional tests entirely, each session was tailored to work the specific muscles relevant for functional tasks. Exercises were performed in three sets of four to eight repetitions, using rice bags and elastic bands for resistance. An assortment of pre- and post-assessments were conducted, including a HGS test, resembling the current study.

Despite these methodological differences, Skelton and colleagues (41) demonstrated increases in HGS, which aligns with the improvements observed in the current research study. In Skelton et al.’s (41) 12-week progressive resistance training program, participants experienced a significant 4% increase in HGS, from a pre-training mean of 21.6 ± 3.4 kg to a post-training mean of 22.3 ± 3.9 kg. This outcome parallels findings from the current research study, whereby a significant 7% improvement in HGS was observed. This supports the notion that 12 weeks of functional resistance training may improve HGS amongst a sample of older individuals.

A potential explanation for the greater improvement in HGS observed in the current study may be the focused, grip-specific training regimen utilized. Skelton et al.’s (41) training program, while progressive and resistance-based, did not include exercises that mimicked or directly engaged the musculature required for grip strength improvement. Instead, the program targeted broader functional movements, such as knee extensors, elbow flexors, and other large muscle groups. This specificity likely contributed to the larger improvement in grip-related performance observed in the current study.

Because the current study partially mimicked and addressed some of the limitations of Pierle and colleagues (37), detailed comparative results will be described. Pierle et al. (37) evaluated the efficacy of a 6-week ST intervention on multiple fitness components of older adults (37). This intervention consisted of 1-2 sets of 8 ST exercises performed twice a week. At the conclusion of this study, participants showed improvements in several fitness and functional areas. In contrast to the current study, Pierle et al. (37) did not observe improvements in HGS.

In the current study, participants demonstrated a statistically significant improvement in right-HGS following a 12-week intervention. Pre-assessment HGS for the right hand was 21.5 ± 1.3 kg, which increased to 23.0 ± 1.4 kg, reflecting a 7.0% improvement and a large effect size (d = 0.99). Conversely, left-hand HGS exhibited a smaller, non-significant increase from 20.2 ± 1.2 kg to 21.1 ± 1.5 kg (4.5% improvement, d = 0.48). Comparatively, Pierle et al. (37) observed no statistically significant changes in HGS, with pre-assessment values averaging 22.4 ± 1.9 kg and post-assessment values averaging 22.8 ± 1.8 kg. The effect size (d = 0.03) was minimal, indicating negligible gains in grip strength.

The differences in duration and intervention may explain this disparity in findings. For instance, the intervention in Pierle et al.’s (37) study lasted for 6 weeks, with two sessions per week, totaling 12 training sessions. This short duration may have limited the time available for participants to experience significant neuromuscular adaptations, such as improved motor unit recruitment and muscle hypertrophy, which are crucial for strength gains (6, 33). In contrast, the current study required participants to exercise for 12 weeks, providing twice the intervention time, therefore allowing for a more progressive overload and adaptation. The longer program likely facilitated more robust changes in muscle strength, particularly in the dominant hand. Previous research documents that strength improvements, particularly in older adults, rely on consistent and prolonged exposure to resistance-based stimuli to elicit meaningful neuromuscular adaptations (9, 20).

Another potential reason for the difference in findings is the modality and specificity of exercises. Pierle and colleagues’ study (37) focused on general ST, which emphasized functional movements, overall balance, core stability, and flexibility but did not prioritize grip-intensive exercises. In contrast, the current study employed targeted resistance and isometric exercises specifically designed to enhance HGS, ensuring a more direct focus on grip-related adaptations. Previous research has shown that exercise modality plays a critical role in the specificity of adaptations (15, 21). The lack of direct HGS training in Pierle et al.’s (37) protocol likely limited the magnitude of HGS improvements compared to the current research study.

The current study displayed a statistically significant improvement in right-HGS. While no statistically significant improvement was observed in left-HGS. While said findings were unanticipated, previous research investigations have displayed similar asymmetrical findings (22, 30, 43). In 2008, Thomas & Sahlberg recruited 41 college-aged males and females to complete an 8-week resistance training protocol with the aim of enhancing HGS. Data revealed by Thomas and Sahlberg (2008) align closely with the current investigation in demonstrating significant improvements in right-hand HGS, while no significant changes were observed in the left-hand HGS. In Thomas and Sahlberg’s (43) study, participants in the training group exhibited a statistically significant increase in right-hand HGS (32.9 ± 8.6 kg to 35.5 ± 7.6 kg) over an 8-week general resistance training intervention. However, the left-hand HGS showed no significant changes (30.7 ± 8.4 kg and 30.2 ± 6.0 kg). Similarly, the current research reported a statistically significant improvement in right-hand HGS (21.5 ± 1.3 kg to 23.0 ± 1.4 kg) but observed no significant change in left-hand HGS, which increased only marginally from 20.2 ± 1.2 kg to 21.1 ± 1.5 kg.

The consistency between these studies highlights the tendency for dominant-hand HGS to exhibit greater responsiveness to resistance training interventions. Both studies emphasize the role of hand dominance in determining training outcomes, with dominant hands showing significant strength gains due to frequent daily use and greater neuromuscular efficiency (5, 39). Conversely, the non-dominant hand may require more targeted stimuli to achieve comparable improvements, as evidenced by the lack of significant HGS gains in the left hand in both studies (13, 40). These findings emphasize the importance of tailoring training programs to address asymmetries and maximize bilateral strength development.

In 2019, Labott and colleagues conducted a comprehensive meta-analytical review to evaluate the effects of various exercise interventions on HGS in older adults. The review analyzed 24 research articles involving 3,018 participants with a mean age of 73.3 years (22), focusing on interventions ranging from resistance training to multimodal programs. While the findings revealed small but statistically significant improvements in HGS overall, the results emphasized a common trend across studies to the extent that greater responsiveness in right-hand HGS compared to the left-hand HGS. These authors concluded that task-specific and multimodal training interventions often yielded measurable gains in dominant hand strength, as this hand benefits from more frequent use and neuromuscular efficiency in daily activities. In contrast, left-hand HGS frequently displayed minimal or no significant change, reflecting the need for targeted stimuli to elicit comparable adaptations in the non-dominant hand. The review highlights this asymmetry as a recurring observation in HGS research, reinforcing the importance of tailored interventions to address disparities between dominant and non-dominant hand strength (5,22).

Although no statistically significant improvement was observed in left-hand HGS among participants in the current study, the practical implications of the findings should not be overlooked. A mean increase of 1.1 kg (4%) represents a meaningful real-world difference, particularly within aging populations. For older adults, even modest improvements in HGS can translate into enhanced functional capacity, better mobility, fall mitigation, greater independence in activities of daily living, and improved overall quality of life (11, 22, 28, 31, 38, 46). Moreover, from an applied perspective, a 4% increase in left-hand HGS may provide critical support in scenarios requiring quick reflexive actions, such as maintaining balance or catching oneself during a fall (28, 34). This seemingly minor improvement could make a significant difference in preventing injury and maintaining mobility, highlighting the value of targeted interventions to enhance HGS, even in cases where statistical significance is not achieved.

There were several limitations to this study that may have impacted the results. The small sample size (n = 12) and the low male participation in this study may have stifled the results from reaching their full expression. Future studies would benefit from a larger and more gender-balanced sample to enhance the generalizability of findings. Additionally, an increased sample size would allow for a control group to be utilized, bolstering the findings of future studies. Adherence to the 12-week intervention proved difficult as it slowly declined by 17% throughout the study, as many participants had busy schedules and prior commitments that interfered with consistent session attendance. Future studies may consider methods to improve adherence, such as scheduling flexibility or at-home modifications. Longer intervention durations may yield more robust findings, as 12 weeks might not have allowed the intervention to reach its full potential. Confounding variables, such as diet, sleep, and baseline activity levels, were not accounted for and may have influenced the results. Tracking these variables in future studies could provide additional insights into their potential impact.

As individuals age, their priorities often shift toward improving quality of life, extending longevity, and maintaining functional independence. Because HGS directly impacts these aspects of healthy aging, its maintenance, or better yet, improvement, should remain a priority in interventions targeting older adults. The intention of this study was to discover the efficacy of a 12-week ST exercise intervention on the HGS of older adults and underscore its importance for healthy aging. The current study revealed a statistically significant improvement in right-HGS, whereas no significant improvement was observed in left-HGS. Future research should evaluate asymmetrical HGS, as this was not an anticipated finding. Additionally, further research should investigate ST in older adult populations, addressing the limited existing evidence on its efficacy in this demographic.

CONCLUSION

Findings from this study suggest that the 12-week ST and HGS exercise regime was statistically and practically effective in increasing overall HGS in older adults. These findings may serve as valuable guidance for fitness instructors, physical therapists, and other allied healthcare professionals working with older adults. Integrating ST exercises and HGS-specific exercises results in improved HGS, an essential component of maintaining functional independence as individuals age. Utilizing the TRX system for this intervention provided unique advantages, as the exercises were simple to perform and customizable to each participant.

PRACTICAL APPLICATIONS

Implementing an exercise program focusing on HGS has broader implications, as HGS correlates with improved quality of life, longevity, and reduced risk of falls. Allied healthcare professionals working with older adult populations should educate their patients on the importance of HGS and adopt intentional HGS-focused exercises into their regimens. In doing so, they can help mitigate age-related functional decline and promote better outcomes for aging individuals.

ACKNOWLEDGMENTS

The author would like to personally thank the health and wellness team at Carolina Bay at Autumn Hall: Shannon Hardy and Madison Norris.

The author would also like to thank the Center for the Support of Undergraduate Research and Fellowships for their generous contributions.

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Troy Palmer2025-05-23T11:26:33-05:00June 13th, 2025|Research, Sport Education, Sport Training, Sports Coaching, Sports Exercise Science, Sports Health & Fitness, Sports Medicine|Comments Off

The Globalization of Professional Basketball: Context and Competition Matters in the NBA, WNBA, and Olympics

Authors: Howard Bartee, Jr., Ed.D.¹

1School of Public and Allied Health, Division of Kinesiology and Physical Education, Prairie View A & M University, Prairie View, TX, USA

Corresponding Author:

Corresponding Author:
Howard Bartee, Jr., Ed.D.
Prairie View A & M University
700 University Drive
Prairie View, TX 77446
[email protected]
770-314-4415

Howard Bartee, Jr., Ed.D. is an Assistant Professor of Health and Kinesiology-Sport Management at Prairie View A & M University in Prairie View, TX. His research interests include sports management and communication, sports analytics, and organizational behavior within the context of health and kinesiology. With nearly twenty-five years in higher education, Dr. Bartee has served in administrative capacities and previously taught sports management and sports administration courses at Houston Christian University in Houston, TX and Belhaven University in Jackson, MS. Dr. Bartee has further spearheaded initiatives related to sports career services, student advisement, and program and curriculum development.

ABSTRACT
The role of professional basketball has evolved through the years given socio-historic and current perspectives involving the NBA, WNBA, and Olympics. Such perspectives have shaped the context and competition for globalization and the subsequent impact and implications for the broader basketball industry.

Key Words: athletic competition, sports history, international ambassadors

INTRODUCTION

Professional basketball for both men and women, as a globalized sport, has grown tremendously from the days of the peach basket on the basketball court to now being played in a virtual environment of NBA 2K video games. Globalization refers to global, international merging of diverse national economic, socio-cultural, political, and technological forces into a single and coalesced society (14). Internal and external forces have influenced the expansion of the game and which, in effect, draw attention to professional basketball leagues and the Olympics in understanding how they have impacted these outcomes.

From a practical viewpoint, while the careers of LeBron James (NBA), Kevin Durant (NBA), Steph Curry (NBA), Tina Charles (WNBA) and Diana Taurasi (WNBA) may have reached a twilight stage, when considering their careers in totality, their contributions to professional basketball arena and the broader public of media and related markets informed globalization given their appeal across the world stage. When considering the emerging careers of Jaylen Brown (NBA), Victor Wembanyama (NBA), Caitlin Clark (WNBA), A’ja Wilson (WNBA), and Angel Reese (WNBA) launch, their emerging careers offer a unique opportunity for the professional game of basketball within the United States to (re)define a model for how to expand globally within the current state of professional basketball and the role of the Olympics.

Thus, using sociohistorical and current perspectives and demographical information, the following questions guide this exploration:

What is the impact of the WNBA and NBA, post-1992 Olympics to the present, for the globalization of the game of basketball?
What implications do the globalization of professional basketball hold for WNBA, NBA, and the broader Olympics?

These questions provide the context for understanding how the game of basketball and some marketing aspects has evolved given expanding technological aspects and the unique comparisons between the different eras of growth since 1904.(13) These questions show how competition within the NBA and WNBA contributes to overall globalization and marketing outcomes. (1). Using the implications of both context and competition, these questions offer a broader understanding of the impact of the globalization of basketball and how it informs the future state of the game, the players and related marketing components (9).

Context Matters for the NBA and WNBA and Olympics Demographics as Globalization Impacts

A View on the 1992 to the 2024 Olympics on Men’s Basketball for Globalization

Context matters for globalization of men’s basketball, particularly given how the 1992 Olympics for men brought forth a new playing field of competition. The competition that became apparent was focused on the United States closing the gaps between amateurism, professionalism, and international competition. With the convergence of these three concepts came the entrance of NBA players into the Olympics Games as well as the first steps toward globalization. According to Olympic history, “in 1992, for the first time, NBA players were allowed by FIBA to represent the USA and all other countries in national team competition” (7). At the time, the 1992 U.S. team was considered the greatest team ever assembled as they dominated the 1992 Olympic tournament, led by Michael Jordan, Magic Johnson and Larry Bird, on their way to winning the gold medal. Photo #1 features this team of NBA professional players competing on the international scene changed the game of basketball forever. (2)

Photo Credit: Bill Bender The Sporting News) Inside the ‘Dream Team’: A complete roster & history of USA’s 1992 Olympic men’s basketball team | Sporting News

And so, from the 1992 Olympics to the 2024 Olympics, globalization of basketball has increased on various levels, both domestically and internationally. The resulting impact of these changes has resulted in different responses from different nations. It is important to note that not all countries are excited to release their valuable athletic resources for the capitalistic society of the NBA in the United States, yet there are many countries that do support the globalization movement to a more diverse marketplace of professional basketball.

To that end, when it comes to the global sports marketplace, professional basketball has grown as indicated by the countries represented. This has allowed new players and fans to enter the game. One of the most important entrances into the NBA was that of Yao Ming from China being drafted by the Houston Rockets in 2002 as the #1 pick and later a global ambassador for the 2008 Olympic Games. During these years, following the Beijing Olympics until 2012, basketball competition highlighted the effect of how global inclusion started affecting the outcome of games as the European league players were competing more closely with NBA players. The progression of basketball globalization moved to whole new levels not only based upon player competition in the Olympic Games, but also, based upon player entrance into the professional ranks of the NBA. Over the last sixteen years, the team has won gold in 2012, 2016, 2021 (during the pandemic years, following postponement in 2020), and most recently, in 2024. With the influx of new players, fans, and corporate sponsors, especially since the 1992 Barcelona Olympics until the 2024 France Olympics, consideration of different aspects of this globalization are provided.

As a result, what is of interest to note for the NBA teams is that the countries now performing well on the Olympic stage are also sending players to the NBA through the draft. The impact of this new wave of draftees is not only influencing the Olympics, but it is also influencing the draft classes, as history shows us. For example, the NBA and the Olympic Games have both seen shifts in roster makeups and globalization efforts over the last 32 years, since the 1992 Dream Team played in Barcelona, Spain. In the following Figure 1, there is a state-by-state visualization of the birthplace of U.S. born NBA and ABA Players. Figure 1 is as follows:

From countries abroad to the United States, a basketball “rite of passage” is being seen in the total number of draft picks being selected between U.S. Born NBA and ABA Players in comparison to those non-U.S. Born basketball players. Figure 1 shows the top 5 states are as follows: California (443), New York (440), Illinois (302), Pennsylvania (250), and Texas (211).

As a result, Figure 1 provides the foundation for understanding how opportunities could be provided through the NBA draft on a worldwide scale, particularly given the relationships or networks that can be established within each of these countries. These contacts help to create a context for toward globalizing efforts. And while these networks or relationships do not guarantee NBA stardom or a roster spot, they do provide a glimmer of hope and expanded area for recruitment. This hope extends for not only the individual players, but for their countries, communities, families, and friend, which, in effect, is an upside trend of a new global basketball marketplace is emerging. Table 1 particularly identifies the birthplace of non-US born NBA and ABA Players. Table 1 indicates the following:

Table 1, according to (16), shows most of the non-US born NBA and ABA players are born in the top three (3) countries of Canada (n=54, France (n=38), and Germany (n=27). Table 1 also shows the gap existing between the birthplaces of those coming from larger countries compared to those coming from smaller countries. What can be surmised from Table 1 is that while the competition gap has gotten smaller, the challenge to enhance greater roster structures has become increasingly important. Owners, general managers, and coaches are feeling the need to scout not only the colleges of America, but they must also scout the high schools and the international leagues of the world. The increased attention on these different talent pools is not only affecting NBA business locally, but it is also affecting NBA business globally. Particularly within this structure, global scouting is being shown through current NBA rosters. The NBA is experiencing expanded growth internationally. Table 2 particularly identifies the countries of those players from the different countries. Table 2 is as follows:

Table 2, according to (11), shows that the majority of the players come from the country of Canada with the next highest number of players coming from the country of France. A number of countries have only one player that comes from there. Table 2 identifies the frequency in which foreign players (N=125) were on opening day NBA rosters during the 20232024 season. The table reveals that 20.8% of the players were from Canada, while 79.2% of the players were from 39 other countries. In effect, it can be surmised that over a period of one season, Canada had more players on 2023-2024 Opening Day NBA rosters as compared to the other 39 countries represented on the 2023-2024 rosters. Table 3 shows the nationalities of the

NBA All Star players. Table 3 is as follows:

Table 3, according to (11), identifies the frequency in which foreign players (N=7) were on the NBA All-Star rosters during the 2023-2024 season. The table reveals that 27% of the player appearances were from seven countries, while 73% of the player appearances were from the United States during this same period. As a result of these findings, it can be assumed that over a period of the most recent NBA All-Star Game, players with a primary United States nationality had more All-Star game appearance in the 2023-2024 season as compared to the other7 foreign countries and 7 foreign players represented during this same period inclusive of the Eastern and Western Conferences. Context matters.

A View on the 1976 Olympics on Women’s Basketball for Globalization

Context matters, too, with regards to women’s basketball. Starting in 1976 at the Olympics and continuing in 2024, there has been tremendous growth in the sport of women’s basketball. During these past forty-eight years, the United States has led the world in the number of gold medals received during Women’s Basketball Olympics competition. With this level of dominance, the United States and women’s basketball players have evolved since winning a silver medal in 1976. Their first year of competition included players Luisa Harris, Nancy Lieberman, Ann Meyers, Cindy Brogdon, Susan Rojcewicz, Nancy Dunkle, Charlotte Lewis, Gail Marquis, Patricia Roberts, Mary Anne O’Connor, Patricia Head and Juliene Simpson and Photo #2 features this Women’s Basketball Olympic Team. (5)

Photo Credit: Bill Bender The Sporting News) Inside the ‘Dream Team’: A complete roster & history of USA’s 1992 Olympic men’s basketball team | Sporting News

These players were coached by Cal State Fullerton Head Coach Billie Moore and assisted by Stephen F. Austin Head Coach Sue Gunter in the first year of Olympics competition to their current eight Olympics gold medal winning streak in 2024. Photo #3 highlights the women’s basketball team winning in 2024. (6)

Photo Credit: Mark J. Terrill/AP (2024 USA Women’s Basketball Team) US women win eighth straight Olympic basketball gold medal – CSMonitor.com

Table 4 highlights the 2024 Olympics Team comprised of players from across the country and is shown as follows:

Source: Kyle Irving (The Sporting News) USA women’s Olympic basketball roster: A’ja Wilson, Breanna Stewart headline 2024 U.S. team for Paris | Sporting News

Table 4 shows that the majority of the women’s basketball players came from the Las Vegas Aces. Only one player came from the Connecticut Sun and the Seattle Sun. Table 5 highlights the coaching staff for this Olympic Team and is shown as follows:

Table 5 shows a diversity of coaches that was inclusive of both university and professional areas. This integrated approach certainly allowed for a broadened perspective on coaching to be enacted. Notwithstanding, with the passage of Title IX in 1972 and the growth of women’s basketball in the United States between 1972 and the bicentennial year of our nation’s founding in 1976, a team was able to be fielded for the Montreal Olympic games in Canada. Though the team from the Soviet Union would win the gold medal in 1976, there was stiff competition as the United States finished with the silver medal and the team from Bulgaria would win the bronze. Consequently, the evolution of women in basketball emerged in various ways within the country and beyond. Context matters.

Competition Matters for NBA and WNBA and Olympics Demographics as Globalization Impacts

A View on The Team and Medals Received in Men’s Basketball for Globalization

Competition matters as part of globalization and impact for the NBA. History shows that since 1936, the United States has led the world in the number of gold medals received during Men’s Basketball Olympics competition. As Table 6, Table 7, and Table 8 show, excluding, 1940 and 1944, in which Olympic Games were not held and noted as N/A, the United States has won 81% of the gold medals, three countries, the old Soviet Union (17.3%), Yugoslavia (17.3%) and France (17.3% )have won 52% of the silver medals, and two countries, Brazil (13%) and

Lithuania (13%), have won 26% of the bronze medal. With this level of dominance, the United States and its’ basketball players are a cut above the rest in terms of Olympic basketball and international participation in both men’s and women’s basketball. More specifically, Table 6 indicates that the men received a substantial number of gold medals. Table 6 indicates the following:

Men’s Olympic Gold Medals Since 1936 (N=21)

Table 6, according to (10), shows how the United States has won substantially more gold medals than any of the other competing countries. No other country has come close to the United States in receiving gold medals in basketball. Table 7 highlights the silver medals received by the United States since 1936. Table 7 is as follows:

Table 7, according to (10), shows that a three-way tie existed between France, the Soviet Union, and Yugoslavia with having four (4) medals. The United States has received one (1) silver medal along with the countries of Canda, Croatia, and Serbia. Table 8 highlights the number of bronze medals received since 1936 by different countries. Table 8 shows the following:

Table 8, according to (10), shows that the countries of Brazil and Lithuania have received three (3) bronze medals. The United States has received two bronze medals along with the countries of the Soviet Union, Uruguay, Yugoslavia, and the one listed as N/A. Thus, the composition of the medals received by the United States is clearly at the gold level with less medals being received at the silver and bronze levels. Table 9, however, provides insights into the competition experienced by those who were part of the NBA finals. Table 9 is as follows:

Table 9, according to (4), identifies the frequency in which players with foreign nationalities (N=6) were on NBA Finals rosters during the 55 years of NBA Finals MVP selections from 1969 to the most 2024 season. The table reveals that 6 of the 35 (17%) of the MVP Finals MVPs were from France, Greece, Nigeria, Serbia, U.S. Virgin Islands, and Germany, while 29 of the 35 (83%) were of United States nationality. As a result of these findings, it can be assumed that over a period of 55 years of NBA Finals from 1969-2024, pre-

1992 and the Olympic Dream Team in Barcelona, all Finals MVP’s were of U.S. Nationality, while post-1992 and until most recently, in 2023, there six individuals that have won the coveted title of NBA Finals MVP as a direct result of globalization of basketball. Table 10 shows the following outcomes in the competition from those involved with the NBA Finals and their background:

Table 10, according to (4), indicates how the players came from the San Antonio Spurs the majority of the times which indicates a priority of producing MVPs might be emphasized within that organization. These players primarily came from the U.S. Virgin Islands which also might indicate a pipeline being utilized to recruit players from that area. Nevertheless, with globalization, competition matters.

A View on The Team and Medals Received in Women’s Basketball for Globalization

Competition matters, too, for women’s basketball when considering globalization. As Tables 11-13 show aggregately and collectively, the United States has won 77% of the gold medals, while two countries, Australia (23%) and France (15%) have won silver medals with eight countries winning at least one silver medal each to make up the remaining 62% of medal recipients; whereas two countries, Australia (23%) and Russia (15%) have won bronze medals with eight countries winning at least one bronze medal each to make up the remaining 62% of medal recipients. Table 11 highlights the United Sates in comparison to other teams.

Table 11 is as follows:

Women’s Olympic Gold Medals Since 1976 (N=13)

Table 11, according to (10), indicates the Soviet Union as only having received one gold medal since 1976. The United States Women’s Team has had ten (10) gold medals within this time. Table 12, however, highlights the silver medals where Australia had the highest number of silver medal at three (3). Table 12 is as follows:

Women’s Olympic Silver Medals Since 1976 (N=13)

Table 12, according to (10), shows several countries with only one silver medal. Some of those countries include China, Australia, South Korea, Spain, and others. Table 13 highlights those countries that have received bronze medals since 1976. Table 13 is as follows:

Women’s Olympic Bronze Medals Since 1976 (N=13)

Table 13, according to (10), indicates Australia with the highest number of bronze medals. Russia has received two (2) silver medals while several countries received one (1) bronze medal. What becomes evident is the consistency of the United States as the recipient of gold medals throughout the years. Australia is identified as the country that is next in terms of the medals received since this time. Competition matters.

Shared Implications on Context and Competition Matter: The NBA, WNBA, Olympics, and Globalization for Basketball

Context and competition have shared implications for globalization when considering the NBA, WNBA, and the Olympics. From historic Olympic, NBA, and WNBA games to the more recent Olympic, NBA, and WNBA games, it remains important to continuously consider the sociohistorical and current impact upon the globalization of the game of basketball. Both the NBA and WNBA markets are continuing to evolve into the vision first spoken by late NBA Commissioner, David Stern vision of globalization and during the WNBA’s first president, Val Ackerman, service as a U.S. representative to the International Basketball Federation (FIBA), to grow the game of basketball. Currently, as it stands in 2024, the economic, social, political, and technological changes that are taking place are evident as the game of basketball is part of the global sports industry, that is worth $484 Billion Dollars in 2023, according to The Business Research Company in April of 2024, with an expected market growth rate of 6.1% over the next five years from $484 Billion in 2023 to an estimated $862 Billion in 2028.(15) Such financial outcomes collectively shape the context and competition for professional basketball.

Furthermore, the Olympics Games of 2024 has provided a unique example of how much the game has grown ever since the 1992 Dream Team of NBA Players entered the competition. Through the vision of the late NBA Commissioner, David Stern, and the continued efforts of current NBA Commissioner, Adam Silver, the game and competition continued to improve. This year’s Olympic Game Gold Medal Games was another example of how far globalization has come as the United States of America competed in the Men’s and Women’s finals again the host country of France, with each of these games featuring players from not only globally, but from the NBA in the Men’s Gold Medal Game and from the WNBA in the Women’s Gold Medal Game.

To that end, from both context and competition stances, the game will continue to build upon the past success of this year’s Olympic Games as it was viewed globally by millions. With almost 400 million fans in 2024, basketball continues to expand across the globe. For example, this year’s Men’s Olympic Games gold medal game averaged 19.5 million viewers on NBC and Peacock, which according to the (3) in the New York Times (2024). According to LeBron James in that same article regarding the United States Olympic Games Gold Medal Game, “we got our moment…it’s a basketball world and everybody loves the game; we just hope that we continue to inspire people all over the world”. As one of the most recognizable figures in the game and the first active NBA billionaire player, LeBron James, along with Kevin Duran, Steph Curry and the 2024 Olympic Gold Media winning team of NBA superstars, the U.S. Team was able to capture the gold and continue in the legacy of past U.S. Olympics teams made up of NBA superstars.

Additionally, from an WNBA perspective, the U.S. Women’s Olympic Team, led by WNBA MVP, Aja Wilson of the Las Vegas Aces’ and her fellow WNBA and Olympic teammates was able to win the gold medal over France with “a peak viewership of 10.9 million for the final half hour of the one-point affair” (8). With the growth of women’s basketball on the collegiate level, through the emergence of budding stars, Caitlin Clark (Iowa) and Angel Reese (LSU), they are now in the WNBA, with Clark, with the Indiana Fever and Reese, now with the Chicago Sky and will potentially be in the 2028 Olympics to help extend their record eight straight goal medal streak started in 1996. As a result, the future is very bright with the new stars emerging in the NBA, WNBA and Olympic games, while the old guard passes the torch to the next generation. Therefore, as the past is cherished, the present is held and the future is embarked upon, basketball is changing because of the demographic makeup of National Basketball Association (NBA), Women’s National Basketball Association (WNBA) and Olympic team rosters in 2024 and beyond (12). Context and competition matter.

In closing, since the founding of basketball at Springfield College by Dr. James Naismith in 1891, for both men and women now, the pathways into the globalization of professional basketball has expanded from a small college to larger colleges and universities to professional leagues to countries from across the world. With there being no boundaries, the opportunities for globalization remain limitless. Thus, the success of individual teams led by those individual basketball players born outside of the United States has not only led to an increased fanbase, but also has allowed the Olympic game talent to become more talented. As “Table 1: Birthplace of non-U.S. Born NBA and ABA Players” and “Table 2: NBA Rosters from a Global Perspective, 2023-2024” show, the nationalities of players have grown exponentially, while at the same time, selection of MVP’s has grown as well. The cities of Houston, San Antonio, Dallas, Milwaukee, and Denver, which now boast NBA Finals MVP’s have all represented their counties well, along with those respectful induvial players.

When considering both context and competition, with the U.S. dominance in both Men’s and Women’s Gold Medal games, the next four years will offer interesting perspectives to consider as countries seek to close the talent gap between those teams that have and those two teams that have not. These are tremendous efforts, particularly since 2020/2021 during the pandemic when the teams of the NBA and WNBA, had to play in the bubble, the unintended yet, resulting, outcome has led to higher medical protocols and concerns for those participating then and even now. In effect, many will wonder how globalization will influence context and competition for the next four years. With the Olympics coming to Los Angeles in 2028, it will be critical that those involved in sports stay encouraged as the games continue to grow as the growth will foster itself as new markets come aboard. Moreover, as new forms of gaming enter the technical arena, having knowledge of the past histories allows one to be able to learn the necessities for current and future matters of context and competition, particularly given the rise of e-sports and related virtual gaming. By learning the game through e-sports and video games, participants can utilize their movements into today’s face to face games. Strategic planning and coaching sessions help to make today’s understanding of the globalized basketball game in a more reflective and projected manner. Within these types of sessions, learning about the world of gaming offers more engaging and relevant experiences. Such sessions create the platform for further advancing the globalized game of basketball for engaging professional and amateur worlds. With the popularity of the NBA and WNBA and the Olympics being at an all-time high, understanding the globalization of basketball, particularly given the implications and impact of context and competition, becomes important for how the future game of professional basketball is shaped for future generations

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George Minoso2024-12-03T15:53:49-06:00December 20th, 2024|Contemporary Sports Issues, General, Olympics, Research, Sports Exercise Science|Comments Off