Sports Exercise Science – The Sport Journal

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

Authors: Moez Baklouti¹

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

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

Corresponding Author:

[email protected]

ABSTRACT

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

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

INTRODUCTION

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

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

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

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

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

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

METHODS

Research Design and Data Source

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

Participants

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

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

Table 1: Sample Size and Gender Distribution by Grade Cohort

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

Measures and Variables

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

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

Data Analysis

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

RESULTS

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

Developmental Trends Across Grade Levels

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

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

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

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

Figure 1. Mean Horizontal Jump Distance by Grade Level

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

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

The Development of Jump Rope Coordination

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

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

Gender Differences in Physical Performance

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

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

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

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

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

Body Composition Trends

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

DISCUSSION

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

The Jump Rope as a Diagnostic Tool for Physical Literacy

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

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

Interpreting Broader Developmental Trajectories

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

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

Addressing the Emergent Gender Gap and Skill Equity

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

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

CONCLUSION

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

LIMITATIONS AND FUTURE RESEARCH

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

APPLICATIONS IN SPORT

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

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

REFERENCES

Barnett, L. M., Webster, E. K., Hulteen, R. M., et al. (2022). Through the looking glass: A systematic review of longitudinal evidence, providing new insight for motor competence and health. Sports Medicine, 52 (4), 875–920. https://doi.org/10.1007/s40279-021-01516-8
Behringer, M., Vom Heede, A., Matthews, M., & Mester, J. (2024). Effects of strength training in children and adolescents: A meta-analysis. Pediatrics, 153 (1), e2023062512. https://doi.org/10.1542/peds.2023-062512
Cattuzzo, M. T., dos Santos Henrique, R., Ré, A. H. N., et al. (2016). Motor competence and health-related physical fitness in youth: A systematic review. Journal of Science and Medicine in Sport, 19 (2), 123–129. https://doi.org/10.1016/j.jsams.2014.12.004
Donnelly, J. E., Hillman, C. H., Castelli, D., et al. (2024). Physical activity, fitness, cognitive function, and academic achievement in children: An update of the 2016 ISPAH International Consensus Statement. Journal of Sport and Health Science, 13(1), 1–10. https://doi.org/10.1016/j.jshs.2023.09.002
Drenowatz, C., Greier, K., Ruedl, G., & Kopp, M. (2021). Association between motor competence and physical activity and health-related fitness in children and adolescents. European Journal of Sport Science, 21 (10), 1450–1459. https://doi.org/10.1080/17461391.2020.1842512
Edwards, L. C., Bryant, A. S., Keegan, R. J., Morgan, K., & Jones, A. M. (2023). Definitions, foundations and associations of physical literacy: A systematic review. Sports Medicine, 53 (1), 1–21. https://doi.org/10.1007/s40279-022-01761-5
Guthold, R., Stevens, G. A., Riley, L. M., & Bull, F. C. (2020). Global trends in insufficient physical activity among adolescents: A pooled analysis of 298 population-based surveys with 1.6 million participants. The Lancet Child & Adolescent Health, 4 (1), 23–35. https://doi.org/10.1016/S2352-4642(19)30323-2
Haywood, K. M., & Getchell, N. (2020). Life span motor development (7^th ed.). Human Kinetics.
Lloyd, R. S., Faigenbaum, A. D., Stone, M. H., et al. (2014). Position statement on youth resistance training: The 2014 International Consensus. British Journal of Sports Medicine, 48 (7), 498–505. https://doi.org/10.1136/bjsports-2013-092952
López-Gil, J. F., Brazo-Sayavera, J., & Tárraga-López, P. J. (2023). Associations between physical fitness and academic achievement in Spanish schoolchildren. European Journal of Pediatrics, 182 (2), 893–902. https://doi.org/10.1007/s00431-022-04748-6
Malina, R. M., Bouchard, C., & Bar-Or, O. (2004). Growth, maturation, and physical activity (2^nd ed.). Human Kinetics.
Robinson, L. E., & Goodway, J. D. (2021). Instructional climates in preschool children who are at-risk. Part I: Object-control skill development. Research Quarterly for Exercise and Sport, 92 (1), 1–10. https://doi.org/10.1080/02701367.2020.1712316
Robinson, L. E., Stodden, D. F., Barnett, L. M., et al. (2015). Motor competence and its effect on positive developmental trajectories of health. Sports Medicine, 45 (9), 1273–1284. https://doi.org/10.1007/s40279-015-0351-6
Schranz, N., Tomkinson, G., Olds, T., & Dannecker, L. (2020). What is the effect of resistance training on the strength, body composition and psychosocial status of overweight and obese children and adolescents? A systematic review and meta-analysis. Sports Medicine, 50 (1), 1–10. https://doi.org/10.1007/s40279-019-01238-y
Stodden, D. F., Goodway, J. D., Langendorfer, S. J., et al. (2008). A developmental perspective on the role of motor skill competence in physical activity: An emergent relationship. Quest, 60 (2), 290–306. https://doi.org/10.1080/00336297.2008.10483582
Thomas, E., Bianco, A., Mancuso, E. P., et al. (2022). The impact of the COVID-19 pandemic on physical activity and sedentary behavior in Italian children and adolescents. International Journal of Environmental Research and Public Health, 19 (5), 2602. https://doi.org/10.3390/ijerph19052602
Whitehead, M. (Ed.). (2019). Physical literacy across the world. Routledge.

Troy Palmer2025-12-12T09:51:04-06:00July 1st, 2026|General, Sport Training, Sports Coaching, Sports Exercise Science, Sports Health & Fitness, Sports Studies|Comments Off

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

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

¹Neumann University

²Conestoga High School

³Brigham Young University

Corresponding Author:

Ryan Savitz

[email protected]

ABSTRACT

Purpose:

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

Methods:

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

Results:

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

Conclusions:

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

Applications in Sport:

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

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

INTRODUCTION AND LITERATURE REVIEW

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

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

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

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

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

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

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

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

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

METHODS

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

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

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

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

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

RESULTS

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

Men

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

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

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

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

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

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

(0.516)

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

(1.973)

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

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

H₀: b₂₁= b₂₂

H₁: b₂₁ ≠ b₂₂

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

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

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

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

Women

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

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

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

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

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

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

(1.127)

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

(3.521)

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

We now test the following hypothesis for the women:

H₀: a₂₁= a₂₂

H₁: a₂₁ ≠ a₂₂

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

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

Comparison of Men and Women

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

H₀: c_4m= c_4f

H₁: c_4m ≠ c_4f,

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

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

(0.208) (8.54) (2.06)

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

(0.90) (48.64) (3.36)

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

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

DISCUSSION

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

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

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

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

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

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

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

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

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

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

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

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

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

CONCLUSION

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

APPLICATIONS IN SPORT

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

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

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

REFERENCES

Batista, K., Peel, S., Healey, L., & Paquette, M. (2025). The effects of forefoot curvature in “super-shoes” on the biomechanics and metabolic cost of female runners. Footwear Science, 17(sup1), S181-S182.
Bjorkelo, A., Savitz, R., Ward, J., & Waggoner, B. (2024). Super shoes: How super are they? Journal of Sports Analytics, 10(1), 137-140.
Clark, K.P., Ryan, L.J., & Weyand, P.G. (2017).A general relationship links gait mechanics and running ground reaction forces. Journal of Experimental Biology, 220(2), 247-258.
Cochrane, D., and Orcutt, G.H., 1949. Application of least squares regression to relationships containing auto-correlated error terms. Journal of the American Statistical Association, 44(245), 32-61. doi: 10.1080/01621459.1949.10483290
Herbert-Losier, K., & Pamment, M. (2022). Advancements in running shoe technology and their effects on running economy and performance– a current concepts overview. Sports Biomechanics, pp.1–16. doi:10.1080/14763141.2022. 2110512
World Athletics. (2024). Records. https://worldathletics.org/records
Hunter, I., McLeod, A., Valentine, D., Low, T., Ward, J., & Hager, R. (2019). Running economy, mechanics, and marathon racing shoes. Journal of Sports Sciences, 37(20), 2367-2373
Morgan, D.W., Martin, P.E. and Krahenbuhl, G.S. (1989). Factors affecting running economy. Sports Medicine, 7(5), 310–330. doi: 10.2165/00007256-198907050-00003
Paradisis, G. P., Zacharogiannis, E., Bissas, A., & Hanley, B. (2023). Recreational runners gain physiological and biomechanical benefits from super shoes at marathon paces. International journal of sports physiology and performance, 18(12), 1420-1426.
Pfister, A. (2024). The potential placebo effect of advanced footwear technology on running economy and comfort in female recreational runners (Doctoral dissertation, The University of Waikato).
Robbin, J., Mai, P., Helwig, J., and Willwacher, S. (2023) Does an analysis of the world top 100 track and road running performances provide an indication for the effects of super shoes and spikes?, Footwear Science, 15:sup1, S16-S17, doi: 10.1080/19424280.2023.2199262
Ruiz-Alias, S. A., Pérez-Castilla, A., Soto-Hermoso, V. M., & García-Pinillos, F. (2023). The effect of using marathon shoes or track spikes on neuromuscular fatigue caused by a long-distance track training session. International Journal of Sports Medicine, 44(13), 976-982.
von Davier, M. (2024). A Modified Satterthwaite (1941, 1946) Effective degrees of freedom approximation. arXiv preprint arXiv:2409.14606.
Wooten, J. O. (2022). Leaps in innovation and the Bannister effect in contests. Production and Operations Management, 31(6), 2646-2663.

Troy Palmer2026-06-29T08:43:40-05:00June 26th, 2026|Contemporary Sports Issues, General, Olympics, Sport Training, Sports Coaching, Sports Exercise Science, Sports Marketing|Comments Off

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

Authors: Daniel J. Marcolongo¹, Bret R. Myers²

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

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

Corresponding Author:

Bret R, Myers, Ph.D.

Department of Management and Operations

Villanova School of Business

800 E Lancaster Avenue

Villanova, PA 19085

[email protected]

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

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

ABSTRACT

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

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

INTRODUCTION

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

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

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

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

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

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

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

METHODS

Dataset and Sampling

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

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

Defensive Statistics

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

Shot Stopping

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

2. Cross Stopping

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

3. Sweeping

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

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

Offensive Statistics

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

Pass Completion Percentage (in buildup)

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

2. Long Pass Completion Percentage

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

Component Weighting and Possession-Based Adjustments

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Analyses and Visualizations

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

RESULTS

Relationship between TGP and Team Success

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

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

Figure 1

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

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

Figure 2

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

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

PPG=0.143+0.084×TGP

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

Individual TGP Rankings and Year-over-Year Analysis

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

Table 2

2024-2025 TGP Rankings in the EPL

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

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

Table 3

Year-over year TGP performances of qualifying Goalkeepers

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

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

Figure 3

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

TGP vs. Player Market Value

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

Figure 4

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

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

DISCUSSION

Interpretation of Findings

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

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

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

Tactical or Practical Implications

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

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

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

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

Limitations

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

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

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

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

CONCLUSION

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

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

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

APPLICATIONS IN SPORT

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

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

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

REFERENCES

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

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

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.

REFERENCES

Baker, J., Côté, J., & Abernethy, B. (2003). Sport-specific practice and the development of expert decision-making in team ball sports. Journal of Applied Sport Psychology, 15(1), 12-25.

Barynina I., & Vaitsekhovskii, S. (1992). The aftermath of early sports specialization for highly qualified swimmers. Fitness & Sports Review International, 27(4), 132-133.

Bell, D., Post, E., Trigsted, S., Hetzel, S., McGuine, T., & Brooks, M. (2016). Prevalence of sport specialization in high school athletics: A 1-year observational study. The American Journal of Sports Medicine, 44(6), 1469-1474.

Bell, D. R., Post, E. G., Trigsted, S. M., Schaefer, D. A., McGuine, T. A., Watson, A. M., & Brooks, M. A. (2018). Sport Specialization Characteristics Between Rural and Suburban High School Athletes. Orthopaedic Jjournal of Sports Medicine, 6(1).

Buhrow, C., Digman, J., Waldron, J., Gienau, D., Thomas, S., & Sigler, D. (2017) The relationship between sport specialization and mental toughness in college athletes. International Journal of Exercise and Science. 10(1), 44-52.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd Ed.). Lawrence Earlbaum Associates.

Côté, J. & Hay, J. (2002), Children’s involvement in sport: A developmental perspective. In J. In J.M. Côté & D.M. Stevens (Eds.), Psychological Foundations of Sport. (pp. 484-502), Allyn & Bacon.

Côté, J. (1999). The influence of the family in the development of talent in sport. The Sport Psychologist, 13(4), 395–417.

Coté, J., & Fraser-Thomas, J. (2007). Youth involvement in sport. In P. R. E. Crocker (Ed.), Introduction to sport psychology: A Canadian Perspective (pp. 270-298). Pearson

Cox, B., Enns, W., & Clara, I. (2002). The multidimensional structure of perfectionism in clinically distressed and college student samples. Psychological Assessment, 14(3), 365-373.

Emery, C. (2003). Risk factors for injury in child and adolescent sport. Clinical Journal of Sport Medicine, 13(4), 256-268.

Fleisig, G., Andrews, J., & Cutter, G. (2011). Risk of serious injury for young baseball pitchers: a 10-year prospective study. American Journal of Sports Medicine, 39(2), 253-257.

Flett, G., & Hewitt, P. (Eds.). (2002). Perfectionism: Theory, research, and treatment. American Psychological Association (pp. 5-31).

Ford, P., & Williams, M. (2012). The developmental activities engaged in by elite youth soccer players who progressed to professional status compared to those who did not. Psychology of Sport and Exercise, 13(3), 349-352.

Frost, R., Marten, P., Lahart, C., & Rosenblate, R. (1990). The dimensions of perfectionism. Cognitive Therapy and Research, 15(5), 449-468.

Gould, D., Tuffey, S., Udry, E., & Loehr, J. (1996). Burnout in competitive junior tennis players: A quantitative psychological assessment. The Sport Psychologist, 10(4), 322-340.

Gotwals, J., & Dunn, J. (2009). A multi-method multi-analytic approach to establishing internal construct validity evidence: The Sport Multidimensional Perfectionism Scale 2. Measurement in Physical Education and Exercise Science, 13(2), 71-92.

Gotwals, J., Stoeber, J., Dunn, J., & Stoll, O. (2012). Are perfectionistic strivings in sport adaptive? A systematic review of confirmatory, contradictory, and mixed evidence. Canadian Psychology/Psychologie Canadienne, 53(4), 263-279.

Güllich, A., Macnamara, B. N., & Hambrick, D. Z. (2022). What Makes a Champion? Early Multidisciplinary Practice, Not Early Specialization, Predicts World-Class Performance. Perspectives on Psychological Science, 17(1), 6–29.

Hewitt, P., & Flett, G. (1991). Perfectionism in the self and social contexts: Conceptualization, assessment, and association with psychopathology. Journal of Personality and Social Psychology, 60(3), 456-470.

Hill, A., & Mallison, S., & Jowett, G. (2018). Multidimensional perfectionism in sport: A meta-analytic review. Sport, Exercise, and Performance Psychology, 7(3), 235-270.

Jayanthi, N., Pinkham, C., Dugas, L., Patrick, B., & LaBella C. (2013). Sports specialization in young athletes: Evidence-based recommendations. Sports Health, 5(3), 251-257.

Jayanthi, N., LaBella, C., Fischer, D., Pasulka, J., & Dugas, L. (2015). Sports-specialized intensive training and the risk of injury in young athletes: A clinical case-control study. American Journal of Sports Medicine, 43(4), 794-801.

Kelto, A. (2015, September 4). How likely is it, really, that your athletic kid will turn pro? http://www.npr.org/sections/health-shots/2015/09/04/432795481/howlikely-is-it-really-that-your-athletic-kid-will-turn-pro

Lizmore, M., Dunn, Jo, Dunn, Ja., & Hill, A. (2019). Perfectionism and performance following failure in a competitive task. Psychology of Sport & Exercise, 45, 101582.

Malina, R. M. (2009). Organized youth sports: Background, trends, benefits and risks. Youth Sports: Participation, Trainability and Readiness, 2–27.

Malina R. (2010). Early sport specialization: Roots, effectiveness, risks. Current Sports Medicine Reports, 9(6), 364-371.

Malina, R., Bouchard, C., & Bar-Or, O. (2004). Growth, maturation, and physical activity (2nd ed.). Human Kinetics.

National Collegiate Athletic Association (n.d.). Retrieved February 2, 2025, from https://www.ncaa.org/sports/2015/3/6/estimated-probability-of-competing-in-professional-athletics.aspx

Post, E. G., Trigsted, S. M., Riekena, J. W., Hetzel, S., McGuine, T. A., Brooks, M. A., & Bell, D. R. (2017). The Association of Sport Specialization and Training Volume With Injury History in Youth Athletes. The American journal of sports medicine, 45(6), 1405–1412.

Rose S., Emery, C., & Meeuwisse, W. (2009). Sociodemographic predictors of sport injury in adolescents. Medicine and Science in Sports Exercise, 40(3), 444-450.

Sabato, T., Walch, T., & Caine, D. (2016). The elite young athlete: Strategies to ensure physical and emotional health. Journal of Sports Medicine, 7, 99–113.

Stoeber, J. (2011). The dual nature of perfectionism in sports: Relationships with emotion, motivation, and performance. International Review of Sport and Exercise Psychology, 4(2), 128-145.

Stoeber, J. (2014). Perfectionism. In R. C. Eklund & G. Tenenbaum (Eds.), Encyclopedia of sport and exercise psychology, Vol. 2, 527-530. SAGE Publications, Inc.

Wall, M., & Côté, J. (2007). Developmental activities that lead to dropout and investment in sport. Physical Educational Sport Pedagogy, 12(1), 77-87.

Weiss, M.R., & Petlichkoff, L.M. (1989). Childrenʼs motivation for participation in and withdrawal from sport: Identifying the missing links. Pediatric Exercise Science, 1, 195-211.

Wiersma L. (2000). Risks and benefits of youth sport specialization: Perspectives and recommendations. Pediatric Exercise Science, 12(1), 13-22.

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.

References:

Argus CK, Gill ND, Keogh JW, Hopkins WG. Acute Effects of Verbal Feedback on Upper-Body Performance in Elite Athletes. Journal of Strength and Conditioning Research. 2011;25(12):3282-3287. doi:https://doi.org/10.1519/jsc.0b013e3182133b8c

Askow A, Stone J, Arndts D, et al. Validity and Reliability of a Commercially-Available Velocity and Power Testing Device. Sports. 2018;6(4):170. doi:https://doi.org/10.3390/sports6040170

Banyard HG, Nosaka K, Sato K, Haff GG. Validity of Various Methods for Determining Velocity, Force, and Power in the Back Squat. International Journal of Sports Physiology and Performance. 2017;12(9):1170-1176. doi:https://doi.org/10.1123/ijspp.2016-0627

Banyard HG, Tufano JJ, Weakley JJS, Wu S, Jukic I, Nosaka K. Superior Changes in Jump, Sprint, and Change-of-Direction Performance but Not Maximal Strength Following 6 Weeks of Velocity-Based Training Compared With 1-Repetition-Maximum Percentage-Based Training. International Journal of Sports Physiology and Performance. 2020;16(2):1-11. doi:https://doi.org/10.1123/ijspp.2019-0999

Beckham GK, Layne DK, Kim SB, Martin EA, Perez BG, Adams KJ. Reliability and Criterion Validity of the Assess2Perform Bar Sensei. Sports. 2019;7(11). doi:https://doi.org/10.3390/sports7110230

Crewther BT, Kilduff LP, Cunningham DJ, Cook C, Owen N, Yang GZ . Validating Two Systems for Estimating Force and Power. International Journal of Sports Medicine. 2011;32(04):254-258. doi:https://doi.org/10.1055/s-0030-1270487

Dorrell HF, Smith MF, Gee TI. Comparison of Velocity-Based and Traditional Percentage-Based Loading Methods on Maximal Strength and Power Adaptations. Journal of Strength and Conditioning Research. 2020;34(1):46-53. doi:https://doi.org/10.1519/jsc.0000000000003089

Dorrell HF, Moore JM, Smith MF, Gee TI. Validity and reliability of a linear positional transducer across commonly practised resistance training exercises. Journal of Sports Sciences. 2018;37(1):67-73. doi:https://doi.org/10.1080/02640414.2018.1482588

Fernandes JFT, Lamb KL, Clark CCT, et al. Comparison of the FitroDyne and GymAware Rotary Encoders for Quantifying Peak and Mean Velocity During Traditional Multijointed Exercises. Journal of Strength and Conditioning Research. 2021;35(6):1760-1765. doi:https://doi.org/10.1519/JSC.0000000000002952

García-Ramos A, Barboza-González P, Ulloa-Díaz D, et al. Reliability and validity of different methods of estimating the one-repetition maximum during the free-weight prone bench pull exercise. Journal of Sports Sciences. 2019;37(19):2205-2212. doi:https://doi.org/10.1080/02640414.2019.1626071

García-Ramos A, Janicijevic D, González-Hernández JM, Keogh JWL, Weakley J. Reliability of the velocity achieved during the last repetition of sets to failure and its association with the velocity of the 1-repetition maximum. PeerJ. 2020;8:e8760. doi:https://doi.org/10.7717/peerj.8760

González-Badillo JJ, Rodríguez-Rosell D, Sánchez-Medina L, Gorostiaga EM, Pareja-Blanco F. Maximal intended velocity training induces greater gains in bench press performance than deliberately slower half-velocity training. European Journal of Sport Science. 2014;14(8):772-781. doi:https://doi.org/10.1080/17461391.2014.905987

Hart PD, Buck DJ. The effect of resistance training on health-related quality of life in older adults: Systematic review and meta-analysis. Health Promotion Perspectives. 2019;9(1):1-12. doi:https://doi.org/10.15171/hpp.2019.01

Lorenzetti S, Lamparter T, Lüthy F. Validity and reliability of simple measurement device to assess the velocity of the barbell during squats. BMC Research Notes. 2017;10(1). doi:https://doi.org/10.1186/s13104-017-3012-z

McBride JM, McCaulley GO, Cormie P, Nuzzo JL, Cavill MJ, Triplett NT. Comparison of Methods to Quantify Volume During Resistance Exercise. Journal of Strength and Conditioning Research. 2009;23(1):106-110. doi:https://doi.org/10.1519/jsc.0b013e31818efdfe

Menrad T, Edelmann-Nusser J. Validation of Velocity Measuring Devices in Velocity Based Strength Training. International Journal of Computer Science in Sport. 2021;20(1):106-118. doi:https://doi.org/10.2478/ijcss-2021-0007

Orange ST, Metcalfe JW, Marshall P, Vince RV, Madden LA, Liefeith A. Test-Retest Reliability of a Commercial Linear Position Transducer (GymAware PowerTool) to Measure Velocity and Power in the Back Squat and Bench Press. Journal of Strength and Conditioning Research. 2020;34(3):728-737. doi:https://doi.org/10.1519/jsc.0000000000002715

Pareja-Blanco F, Rodríguez-Rosell D, Sánchez-Medina L, Gorostiaga E, González-Badillo J. Effect of Movement Velocity during Resistance Training on Neuromuscular Performance. International Journal of Sports Medicine. 2014;35(11):916-924. doi:https://doi.org/10.1055/s-0033-1363985

Scott BR, Duthie GM, Thornton HR, Dascombe BJ. Training Monitoring for Resistance Exercise: Theory and Applications. Sports Medicine. 2016;46(5):687-698. doi:https://doi.org/10.1007/s40279-015-0454-0

Suchomel TJ, Nimphius S, Bellon CR, Stone MH. The Importance of Muscular Strength: Training Considerations. Sports Medicine. 2018;48(4):765-785. https://pubmed.ncbi.nlm.nih.gov/29372481/

Thompson SW, Rogerson D, Dorrell HF, Ruddock A, Barnes A. The Reliability and Validity of Current Technologies for Measuring Barbell Velocity in the Free-Weight Back Squat and Power Clean. Sports. 2020;8(7):94. doi:https://doi.org/10.3390/sports8070094

Weakley JJS, Till K, Read DB, et al. The Effects of traditional, superset, and tri-set Resistance Training Structures on Perceived Intensity and Physiological Responses. European Journal of Applied Physiology. 2017;117(9):1877-1889. doi:https://doi.org/10.1007/s00421-017-3680-3

Weakley JJS, Wilson KM, Till K, et al. Visual Feedback Attenuates Mean Concentric Barbell Velocity Loss and Improves Motivation, Competitiveness, and Perceived Workload in Male Adolescent Athletes. Journal of Strength and Conditioning Research. 2019;33(9):2420-2425. doi:https://doi.org/10.1519/jsc.0000000000002133

Weakley J, McLaren S, Ramirez-Lopez C, et al. Application of velocity loss thresholds during free-weight resistance training: Responses and reproducibility of perceptual, metabolic, and neuromuscular outcomes. Journal of Sports Sciences. 2019;38(5):477-485. doi:https://doi.org/10.1080/02640414.2019.1706831

Weakley, J., Morrison, M., Garcia-Ramos, A., Johnston, R., James, L., and Cole, M. H. (2021). The validity and reliability of commercially available resistance training monitoring devices: A systematic review. Sports Medicine 51, 443-502.

Weakley J, Till K, Sampson J, et al. The Effects of Augmented Feedback on Sprint, Jump, and Strength Adaptations in Rugby Union Players Following a Four Week Training Programme. International Journal of Sports Physiology and Performance. 2019;14(9):1-21. doi:https://doi.org/10.1123/ijspp.2018-0523

Westcott WL. Resistance training is medicine: Effects of strength training on health. Current Sports Medicine Reports. 2012;11(4):209-216. doi:https://doi.org/10.1249/JSR.0b013e31825dabb8

Wilson KM, Helton WS, de Joux NR, Head JR, Weakley JJS. Real-time quantitative performance feedback during strength exercise improves motivation, competitiveness, mood, and performance. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2017;61(1):1546-1550. doi:https://doi.org/10.1177/1541931213601750

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