2026 – Page 3 – The Sport Journal

Positional Differences and Workload Requirements of a 3-5-2 Formation in Women’s Soccer

Authors: Asher L. Flynn¹, Joanne Spalding², and Sellena Dixon³

¹Department of Sport & Exercise Science, Lincoln Memorial University, Harrogate, TN, USA

²Department of Health Sciences, Georgia College & State University, Milledgeville, GA, USA

³Athletics Department, Lincoln Memorial University, Harrogate, TN, USA

Corresponding Author:

Asher L. Flynn, PhD, CSCS

6965 Cumberland Gap Parkway

[email protected]

423.869.6828

Asher L Flynn, PhD, CSCS is an Assistant Professor of Sport and Exercise Science at Lincoln Memorial University, TN. His research interests focus on fatigue and athlete monitoring in colligate athletes, and aspects of women’s soccer performance.

Joanne Spalding, PhD, is an Assistant Professor in Exercise Science at Georgia College & State University, GA. Her research interests include athlete monitoring and long term athlete development in female athletes.

Sellena Dixon, BS, is the graduate assistant for the women’s soccer team at Lincoln Memorial University, TN.

ABSTRACT

The purpose of this research was to investigated the match demands of 24 female soccer players over one season (15 conference matches) playing in a 3-5-2 formation to determine the match demands and work rates of each position. In order to determine formation specific workload, positions were grouped as center-backs (CB), wingbacks (WB), midfielders (MF), and forwards (FW). Velocity bands used to compare distances and work rates were total distance (TD), 2-3 m/s, 3-4 m/s, 4-5 m/s, 5-6 m/s, and >6 m/s. Results revealed that MF covered significantly more total distance (TD; 10743 ±1520 m) and distance between 2-3 m/s (3444 ± 423 m) than all other positions (CB TD: 8549 ± 1106 m, 2-3: 2497 ± 276 m; WB TD: 8860 ± 1216 m, 2-3: 2324 ± 344 m; FW TD: 8069 ± 1286 m) and greater distance between 3-4 m/s (2515 ± 382m) compared to CB (1574 ± 214 m) and FW (1270 ± 281 m), but not WB (1814 ± 258 m). There were no significant differences between any of the higher velocity bands (>4 m/s). These results can be useful for the coaching staff as descriptive data for the expected distances covered and work rates of Division 2 Women’s soccer teams playing a 3-5-2 formation. These data can be used to determine practice plans in season and for off-season training plans, to prepare athletes for reporting for preseason in appropriate condition.

KEYWORDS: GPS, Sport Science, Athlete Monitoring, Fitness, NCAA

Abbreviations: CB: Center-back, WB: Wingback, MF: Midfield, FW: Forward, GPS: Global Positioning System, MD: Match Day, vVO_2max: Velocity at VO_2max

INTRODUCTION

There is a growing interest in the physical demands of women’s soccer, but the majority of research has focused on the top levels of competition (International and Professional; 23). Although this information is important, it is likely to have limited use for lower levels of competition. It has been reported that “top class” females perform more high intensity running compared to “high-level” females (16, 17, 19). Currently, there is a lack of research investigating match demands at other levels of women’s soccer, especially at lower levels of collegiate soccer in the USA.

The majority of the literature investigating the demands of lower levels of women’s soccer has focused on the match demands of NCAA Division I (2, 5, 12, 20, 21), two articles focused on NCAA Division II athletes (9, 11), and one article focused on NCAA DIII (22), with no studies investigating the match demands of women’s NAIA soccer. Gentles et al (2018) observed that NCAA DII female players covered approximately 5480 m in 45 minutes of match play, while NCAA Division I players covered between 9486m and 9930 m in 90 minutes (20, 22). With little evidence exploring the activity profiles of the lower divisions of collegiate women’s soccer, further investigation is needed.

Many of the studies to date, at any level, have been conducted using only a few matches (3, 17), which, due to the high standard deviations (approximately 10 – 40%; 3, 11, 13, 20, 21-23), raise the question of whether a small number of matches truly reflects the average match demands. Previous studies have also shown differences in match demands based on position (20, 21). On average, defenders covered less distance than midfielders and attackers (forwards), and forwards covered more distance than midfielders (20, 21). Another complication when extrapolating information from current research is that the formation type may also alter the match demands (4, 6, 7). These limitations can lead to complications when inferring information from current research for use in a practical setting with different teams.

Factors that alter expected match demands, level of play and formation used, imply that it would be improper for the coaching staff to use data from a different level of play and unknown formation to determine the expected demands for their specific situation; that is, a high school coach should not be implementing a training plan based on data from college, professional, or international level teams. As such, the purpose of this research was to observe the match demands (distances and work rates) of an NCAA Division II women’s soccer team playing in a 3-5-2 formation, and to determine if there are significant differences in distances covered and/or work rates between positions.

METHODS

Participants

Retrospective data from 24 field players (age: 19.90 ± 1.56 years old; height: 163.43 ± 6.18 cm, weight: 59.35 ± 7.20 kg,) on the same team were included in this study. A total of 194 observations were included in the analysis (center back (CB), n = 49; wingback (WB), n = 36; midfield (MF), n = 61; and forward (FW), n = 48). This study was approved by the institutional review board of Lincoln Memorial University.

Procedures

Match data from an NCAA division II women’s soccer team playing a 3-5-2 formation were collected during a single competitive season. Only conference regular season matches, conference tournament, and national tournament matches were included, with 15 matches used for analysis.

Global Positioning System (GPS) devices (TITAN sports, Houston, TX, USA), sampling at 10 Hz, were used to track player movement duringcompetition. GPS units were activated at least 20-minutes prior to kick-off and were worn in a provided chest halter that secured the device between the shoulder blades under their game uniform. GPS devices were provided to all athletes (starters and substitutes) during the 10-minute window after the team warm-up and before the start of the match, and were collected again after the final whistle. All data from the duration of the match (substitute warm-up and half-time warm-up) were included in the analysis.

For positional analysis, distances accumulated by all athletes who played in a specific position for a match were summed and then divided by the number of positions in the formation. For example, in the forward position, if one athlete was substituted for a portion of the match, the total distance covered by all three athletes playing in the forward position was summed and then divided by two for the two forward positions in the 3-5-2 system. For the work rate analysis, meters covered per minute at each threshold (distances divided by total match time) were averaged for all players in that position (20). Due to the different substitution rules in college soccer, this analysis was deemed optimal to determine the distances and work rate of each position, instead of specific players. Variables of interest included total distance and distances covered in velocity thresholds; 2 – 3 m/s (7.2 – 10.8 km·h^-1), 3 – 4 m/s (10.8 – 14.4 km·h^-1), 4 – 5 m/s (14.4 – 18.0 km·h^-1), 5 – 6 m/s (18 – 21.6 km·h^-1), and >6 m/s (>21.6 km·h^-1).

All matches analyzed were official NCAA matches consisting of two 45-minute halves with a 15-minute half-time period, with a maximum of two 10-minute extra-time periods with a 2-minute intermission, with extra time being stopped in the event of a goal if the competition was tied at the end of the normal 90-minute match.

Data Analyses

Two Repeated Measures ANOVA analyses with Bonferroni corrections were performed to determine whether there was a significant difference in distances and work rates for each position at each threshold (TD, 2 – 3 m/s, 3 – 4 m/s, 4 – 5 m/s, 5 – 6 m/s, and >6 m/s). Statistical analyses were performed using JASP (Version 0.16.2). The alpha level was set at 0.05.

RESULTS

The first RM-ANOVA revealed significantly higher distances (F(3.37, 62.82) = 11.96, p < 0.001) covered in the MF position compared to all other positions for TD and 2 – 3 m/s. Midfielders also covered significantly more distances than CBs and FWs, but not WBs, at 3 – 4 m/s. The only other significant difference observed was between the WB and FW positions in TD covered. There were no significant differences between any other positions at any other threshold. Descriptive statistics are provided in Table 1.

Table 1

Mean distance covered by position in each velocity zone (meters).

	CB	WB	MF	FW
TD	8549 ± 1106 (6467 – 11066)	8860 ± 1216# (5443 – 10854)	10743 ± 1520* (7060 – 12864)	8069 ± 1286# (6204 – 10537)
7.2 – 10.8 km·h-1	2497 ± 276 (1827 – 2972)	2324 ± 344 (1389 – 2842)	3444 ± 423* (2180 – 3916)	2301 ± 400 (1668 – 2942)
10.8 – 14.4 km·h-1	1574 ± 214 ǂ (1191 – 1959)	1814 ± 258 (1068 – 2218)	2515 ± 382# ǂ (1771 – 3129)	1270 ± 281# (759 – 1688)
14.4 – 18.0 km·h-1	607 ± 113 (449 – 769)	878 ± 135 (639 – 1124)	1092 ± 211 (792 – 1453)	587 ± 109 (395 – 734)
18.0 – 21.6 km·h-1	246 ± 58 (155 – 342)	404 ± 86 (252 – 552)	381 ± 79 (241 – 533)	272 ± 60 (149 – 357)
>21.6 km·h-1	96 ± 35 (45 – 175)	200 ± 102 (68 – 426)	120 ± 55 (68 – 295)	180 ± 112 (45 – 530)

Note: Data are presented as mean ± Standard Deviation (Range).
TD: Total distance, CB: Center back, WB: Wingback, MF: Midfield, FW: Forward
* = p < 0.05 compared to all other positions in that velocity range
# = p < 0.05 compared to the other indicated positions in that velocity range
ǂ = p < 0.05 compared to the other indicated positions in that velocity range

The RM-ANOVA performed to determine differences in work rates at each threshold revealed significantly higher work rates (F(3.47, 64.82) = 6.05, p < 0.001) over the entire match (TD) for the MF position compared to all other positions, and a significantly higher work rate from the MF position in the 3 – 4 m/s velocity range compared to the FW position. There were no other significant differences between any of the other positions at any other threshold. The results are presented in Table 2.

Table 2

Mean work rate by position in each velocity zone (meters per minute).

	CB	WB	MF	FW
TD	96 ± 8 (88 – 123)	101 ± 7 (85 – 117)	119 ± 20 * (88 – 167)	101 ± 24 (72 – 156)
7.2 – 10.8 km·h^-1	28 ± 2 (25 – 33)	26 ± 3 (21 – 35)	36 ± 3 (29 – 40)	27 ± 5 (19 – 35)
10.8 – 14.4 km·h^-1	18 ± 2 (14 – 21)	21 ± 2 (18 – 26)	27 ± 3 # (21 – 32)	16 ± 4 # (8 – 21)
14.4 – 18.0 km·h^-1	7 ± 1 (5 – 10)	10 ± 1 (8 – 12)	12 ± 3 (9 – 19)	8 ± 3 (4 – 14)
18.0 – 21.6 km·h^-1	3 ± 1 (2 – 4)	5 ± 1 (4 – 6)	4 ± 1 (2 – 7)	4 ± 2 (2 – 9)
>21.6 km·h-1	1.3 ± 1 (1 – 3)	2.3 ± 1 (1 – 4)	1.3 ± 1 (1 – 3)	3.4 ± 4 (1 – 13)

Note. Data are presented as mean ± Standard Deviation (Range).
TD: Total distance, CB: Center back, WB: Wingback, MF: Midfield, FW: Forward

* = p < 0.05 compared to all other positions in that velocity range
# = p < 0.05 compared to the other indicated positions in that velocity range

DISCUSSION

The purpose of this study was to examine the external demands of NCAA DII women’s college soccer playing in a 3-5-2 formation. The most interesting finding from these data was that the WB position was only significantly higher in total distance covered compared to the FW position (p = 0.044), but there were no other significant differences in distances or work rates compared to other positions. This was interesting, given that the WB position is commonly accepted as the most demanding position. Another interesting result was that the MF position had significantly higher distances at lower speeds (2-3 m/s, p < 0.001; 3-4 m/s, p < 0.001) only when compared to FW position. Other studies have reported that different positions have significantly different total distances covered in a match (1, 14). Abbot et al.(2018) reported that central midfielders covered the greatest distance (11,570 ± 469 m), followed by wide attackers (10,918 ± 353 m), wide defenders (10,747 ± 420 m), strikers (10,320 ± 420 m), and central defenders covering the least amount of distance (9,830 ± 428 m), while Lago-Peñas (2009) reported significant differences between nearly every position for each threshold, except for total distance (11.1 – 14 km·h^-1, 14.1 – 19 km·h^-1, 19.1 – 23 km·h^-1, > 23 km·h^-1). Both these studies observed high-level male soccer teams (U23 English Premier League, Professional Spanish Premier League) and as such they would not be expected to mimic the results of this study and highlight the importance of sex- and level-specific research.

In a more direct comparison with other women’s college soccer research, Sausaman et al(2019) and Alaxander et al (2014) reported significant differences in distances covered by position, whereas Corrales (2020) reported no differences, regardless of position. Sausaman et al (2019) reported that attackers covered more high-speed (> 15 km·h^-1) and sprint (> 18 km·h^-1) distances than midfielders and defenders, with no difference between midfielders and defenders. Alexander et al (2104) reported that central defenders covered the least amount of total distance (8041.2 ± 371.0 m), followed by central attacking midfielders (9236.1 ± 491.3 m), fullbacks (9306.2 ± 367.8 m), and wide midfielders (9500.4 ± 847.0 m), with central defensive midfielders (9947.4 ± 577.9 m) covering the greatest amount of total distance. Fullbacks (1321.5 ± 173.7 m) and wide midfielders (1208.2 ± 314.1 m) covered significantly more distance at high speed (> 15 km·h^-1) compared to central defensive midfielders (847.7 ± 234.9 m), central attacking midfielders (747.64 ± 196.5 m), and central defenders (614.1 ± 98.9 m). The difference in results between these studies and the current investigation could be due to a different level of competition (NCAA Division 1), a possible difference in formation (not reported), or due to the current studies banding velocity zones (i.e. 4 -5 m/s, 5 – 6 m/s) instead of summing distances above thresholds (> 15 km·h^-1).

CONCLUSION

This present study provides information on the expected work and work rates of division 2 women’s soccer. Data analysis revealed minimal differences based on position, with the midfield position being the only position with significant differences and only at low intensity thresholds (TD, 2-3 m/s). All other positions and intensities were not statistically different, highlighting the possibility that training for these positions likely does not need to be modified to fit each position but rather each athlete.

APPLICATIONS IN SPORT

The primary application of this research is to allow the coaching staff to determine the appropriate fitness, conditioning, and practice workloads for their team with respect to their level of competition, formation, and style of play. Using typical tactical periodization plans for match-day preparation (Match day (MD) +1, MD -2, MD -1, etc.), position-specific workloads can be determined and monitored to ensure optimal loading during each practice session. This information can also be used to determine fitness testing requirements. Since there was a significant drop (43.7%) in distance covered and work rate (approximately 43% decrease) at intensities above 4 m/s (14.4 km·h^-1) observed from this study, minimum criteria for aerobic fitness tests could be set at 15.5 km·h^-1, which would allow players to do the majority of the work expected below their estimated lactate threshold (85% vVO2max; 8, 18)

In addition, these data can be used to determine the appropriate time requirements for different conditioning drills. For example, making a time criterion of 3:40 for an 800 m run would meet the Long Interval definition for an individual with a vVO2max of 15.5 km·h^-1, but if a team had higher/lower requirements, adjusting time cut offs would be suggested (15). These data can also be used to create game-specific conditioning drills, such as creating an interval training exercise (100m active running, 100m recovery jog; Table 3) that would provide about 30 – 35% of game distances at the higher end of expected game work rates.

Table 3

Interval conditioning exercise.

Speed Level	Reps	Time	Work Rate	Accumulated Distance
8.0 km·h^-1	22	45s	89 m/min	2200
12.0 km·h^-1	10	30s	36 m/min	1000
15.0 km·h^-1	5	24s	20 m/min	500
20.0 km·h^-1	2	18s	8 m/min	200
>21.6 km·h^-1	1	<16s	4 m/min	100

Note: Work Rate was calculated as the total distance covered in each velocity threshold divided by the total exercise time (24.5 min).

When retroactively analyzing team distances and work rates to create a training plan, it is important to note that since the majority of the total distance covered during a match is at low intensities (<3 m/s; 11), focusing on “running” the observed TD is likely unnecessary. Lower speed distances (<4 m/s) would be expected to be accumulated through daily technical drills and exercises. Higher speed distances (>4 m/s) could be accumulated in any manner chosen by the coach, such as a mixture of soccer technical drills and/or conditioning drills.

When using workload data in this manner, this would allow the coaching staff to create training plans that develop physical characteristics in a manner appropriate to the athletes level and expected match play requirements instead of arbitrarily spending time and effort developing a specific characteristic beyond projected usefulness. For example, since the majority of work is performed below 14.4 kph, spending the time and training effort for a vVO2max above 16 kph would be counterproductive. The extra focus could be better spent on improving other training targets to improve performance (technical, tactical, sprint, acceleration, change of direction)

ACKNOWLEDGMENTS

The authors declare no conflicts of interest, and no funding was received for this research.

REFERENCES

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6. Bradley, P. S., Carling, C., Archer, D., Roberts, J., Dodds, A., Di Mascio, M., Paul, D., Gomez Diaz, A., Peart D., & Krustrup, P. (2011). The effect of playing formation on high-intensity running and technical profiles in English FA Premier League soccer matches. Journal of sports sciences, 29(8), 821-830.

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9. Choice, E. E., Tufano, J. J., Jagger, K. L., & Cochrane-Snyman, K. C. (2022). Match-play external load and internal load in NCAA division II women’s soccer. The Journal of Strength & Conditioning Research, 10-1519.

10. Corrales, I. (2020). The Physical Demands of Women’s Collegiate Soccer Matches Assessed Using GPS Devices [master’s thesis, California State University, Fullerton], https://scholarworks.calstate.edu/.

11. Gentles, J. A., Coniglio, C. L., Besemer, M. M., Morgan, J. M., & Mahnken, M. T. (2018). The demands of a women’s college soccer season. Sports, 6(1), 16.

12. Ishida, A., Travis, S. K., Draper, G., White, J. B., & Stone, M. H. (2022). Player position affects relationship between internal and external training loads during Division I collegiate female soccer season. The Journal of Strength & Conditioning Research, 36(2), 513-517.

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14. Lago-Peñas, C., Rey, E., Lago-Ballesteros, J., Casais, L., & Dominguez, E. (2009). Analysis of work-rate in soccer according to playing positions. International Journal of Performance Analysis in Sport, 9(2), 218-227.

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17. Mohr, M., Krustrup, P., Andersson, H., Kirkendal, D., & Bangsbo, J. (2008). Match activities of elite women soccer players at different performance levels. The Journal of Strength & Conditioning Research, 22(2), 341-349..

18. Parpa, K., & Michaelides, M. A. (2025). Ventilatory thresholds in professional female soccer players. International Journal of Sports Medicine, 46(02), 97-103.

19. Ramos, G. P., Nakamura, F. Y., Penna, E. M., Wilke, C. F., Pereira, L. A., Loturco, I., Capelli, L., Mahseredjian F., Silami-Garcia E., & Coimbra, C. C. (2019). Activity profiles in U17, U20, and senior women’s Brazilian national soccer teams during international competitions: are there meaningful differences?. The Journal of Strength & Conditioning Research, 33(12), 3414-3422.

20. Sausaman, R. W., Sams, M. L., Mizuguchi, S., DeWeese, B. H., & Stone, M. H. (2019). The physical demands of NCAA division I women’s college soccer. Journal of Functional Morphology and Kinesiology, 4(4), 73.

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Troy Palmer2025-09-30T16:08:05-05:00March 4th, 2026|Research, Sport Education, Sport Training, Sports Coaching, Women and Sports|Comments Off

Supplemental lessons to the Peak Health and Performance curriculum: Nutritional considerations for injury, energy management, and gastrointestinal issues

Authors: Tyler B. Becker¹², Ronald L. Gibbs, Jr²

¹Department of Food Science and Human Nutrition, Michigan State University, East Lansing, MI, USA

²Michigan State University Extension, Health and Nutrition Institute, Michigan State University, East Lansing, Michigan, USA

Corresponding Author:

Tyler B. Becker, PhD, CSCS

469 Wilson Road, Room 125

East Lansing, MI 48824

[email protected]

517-353-3338

Tyler B. Becker, PhD, CSCS is an Associate Professor of Nutritional Sciences at Michigan State University in East Lansing, MI. His research interests focus on sports nutrition practices and strategies in youth athletes and higher education andragogy.

Ronald L. Gibbs, Jr, PhD, MCHES is currently a Program Evaluation Specialist for Michigan State University in East Lansing, MI. His research interests focus on coach and athlete education, long-term athlete development (LTAD), psychosocial aspects of sports and physical activity, adolescent nutrition and physical activity behavior change through sport participation, sports performance, and reducing childhood obesity.

ABSTRACT

Youth sports injuries are quite common in sport and have several negative consequences, including healthcare costs, loss of playing time, and producing mental stress. Nutritional strategies have been suggested to improve recovery from sports-related injuries. The Peak Health and Performance (PHP) youth-sports curriculum was developed to use sport to promote healthy eating behaviors. Six additional lessons on nutrition for recovery from injury, energy management, and gastrointestinal issues have been added as addendums to PHP. Lesson A discusses the importance of key nutrients (eg., protein, complex carbohydrates, unsaturated fatty acids, water) for promoting tissue healing following an injury. Lesson B describes several micronutrients and the possible role of nitrates for aiding in injury recovery. Lesson C discusses the implications of low energy availability, including how to identify and prevent it. In Lesson D, several nutritional strategies for addressing mild traumatic brain injuries are explored. Lesson E discusses the importance of sleep for injury recovery and describes nutritional strategies for improving sleep quality. The final lesson (Lesson F) describes various gastrointestinal issues encountered in sport and how to prevent them. Future research will examine youth athlete knowledge of nutritional strategies for recovering from a sports-related injury following these lessons.

KEYWORDS: adolescent sports; sports nutrition; injury management

INTRODUCTION

Sports-related injuries are a significant concern among adolescent athletes, with prevalence rates ranging from 34.1% to 65% (2). Certain groups, including female athletes, obese athletes, and those participating in contact sports, are at particularly high risk. In the US, the rate of injuries in sports, recreation, and leisure activities is 117.1 per 1000 children and adolescents aged 12-17 years of age (57). These injuries impose substantial financial burdens; for example, over a 5-year period in Florida, inpatient care costs for pediatric sports injuries totaled $24.55 million, while emergency care expenses reached $87 million (61). Beyond the economic impact, sports-related injuries also incur both physical and mental challenges to the athlete, including lost playing time, with female athletes averaging 10 days of missed competition per injury (5). This contributes to social isolation and depressive symptoms in adolescent athletes during recovery (63). In addition, gastrointestinal (GI) problems, including diarrhea, vomiting, and abdominal injuries are common place among athletes (12,73), and can contribute to decreases in performance and a loss of playing time (39). Given these multifaceted challenges, there is a critical need to optimize injury prevention and rehabilitation strategies to support young athletes’ physical and psychological well-being.

Proper nutritional intake plays a critical role in injury prevention and rehabilitation among youth athletes (3). In addition to supporting overall health and well-being, adequate nutrition is essential during adolescence–a period marked by rapid growth and development–and contributes to athletic performance and post-workout recovery (15, 66). Despite its importance, many adolescent athletes demonstrate a lack of knowledge regarding both general and sport-specific nutritional practices (6). A recent systematic review by Hulland et al. (2023) revealed that adolescent athletes are more familiar with general than sport-specific dietary strategies (32), while Gibbs and Becker (2025) found that both male and female adolescent athletes scored below 50% on assessments covering both areas (24). These findings underscore a significant gap in nutritional literacy among youth athletes, indicating the need for targeted education to optimize their health development and athletic outcomes.

Several nutritional strategies have emerged highlighting the importance of it for injury rehabilitation primarily in adult athletes (26, 54, 65). For example, kilocaloric and protein needs often increase following injury due to a need for recovery and maintenance of lean body mass resulting from disuse (58, 65). However, the application of nutritional strategies for recovering from injury for youth athletes remains understudied. Alcock et al. (2024) offered a comprehensive overview of injury rehabilitation strategies for youth, indicating practical applications; however there remains a critical gap in understanding how adolescent athletes perceive and apply nutrition during recovery (3). To date, no research has directly examined youth athletes’ knowledge of nutrition for injury rehabilitation, but existing evidence suggests they are likely deficient in this area as well (46). Research suggests that poor food literacy and nutrition knowledge could theoretically contribute to increased injury risk (3, 15). This reinforces the urgency of developing age-appropriate interventions that address both performance and recovery nutrition, particularly in the context of injury.

The Peak Health and Performance (PHP) curriculum was designed from a collaboration by faculty and staff at Michigan State University, Division of Sports and Cardiovascular Nutrition, College of Osteopathic Medicine, East Lansing, MI and Spartan Performance Training, East Lansing, MI (25). This curriculum incorporates various sports nutrition best practices from several areas of literature providing sports nutrition recommendations (17, 66, 69). Fruit and vegetable intake significantly increased in 290 children and adolescents who completed the PHP curriculum (4). Due to the success of the program in modifying nutrition behaviors, additional lessons were created to educate youth on nutritional strategies for injury recovery, energy management, and managing GI issues. These topics include nutritional strategies for musculoskeletal and mild traumatic brain injury (mTBI) recovery, and other nutritional considerations around injury risk and recovery including sleep, low energy availability (LEA), and GI issues. This manuscript describes the rationality and creation of these addendum lessons for the PHP curriculum.

LESSON CONTENT

The original PHP curriculum consists of six lessons labeled as: Lesson 1- Nutrition Basics; Lesson 2- Athletes Performance Plates; Lesson 3- Timing of Intake; Lesson 4- Hydration, Energy Drinks, and Sugary Beverages; Lesson 5- Convenience Foods; and Lesson 6- More Than a Game (25). Further information on PHP learning objectives and topics inclusion can be found in Gibbs & Becker (25). The new additional lessons and their learning objectives can be found in Table 1. These additional lessons are meant to serve as their own lesson series, a single lesson session, or as supplemental lessons to the original PHP lessons.

Table 1.
Additional Lessons for the Peak Health and Performance Curriculum: Learning Objectives
Lesson	Learning Objectives
A: Macronutrients for Injury Rehabilitation	• Explain the four phases of an injury • Understand the importance of consuming enough calories following an injury • Explain why protein is needed during the health process and recall amounts needed • Explain the role that carbohydrates have during the healing process • Describe what role unsaturated fatty acids, such as omega-3s, have while healing an injury. • Understand the importance of water during the healing process
B: Micronutrients for Injury Rehabilitation	• Explain the importance of choosing food sources of vitamins and minerals over dietary supplements • List and understand the roles that vitamins A, C, D, and E have in injury healing • Identify good food sources of vitamins A, C, D, and E • List and understand the roles that calcium, zinc, and iron have in injury healing • Identify good food sources of calcium, zine, and iron • Explain why foods high in nitrates may promote injury healing and identify good food sources of them
C: Low Energy Availability	• Explain what low energy availability is • Identify what causes low energy availability • Understand how low energy availability negatively impacts performance and recovery • Explain how low energy availability may lead to other negative health outcomes • Recognize the symptoms of low energy availability • Describe prevention and treatment strategies for low energy availability
D: Nutrition for Head Injuries	• Explain what happens during a head injury in sport • List the different phases of concussion recovery • Explain the benefits of creatine, magnesium, and flavonoids for head injury recovery • Identify good food sources of creatine, magnesium, and flavonoids • Identify other nutritional considerations to have when recovering from a head injury
E: Nutrition and Sleep for Injury Reduction and Recovery	• Explain why sleep is important for performance and reducing and healing injuries • Identify how much sleep an athlete should be getting each night • Explain the benefits of melatonin and serotonin rich foods for improving sleep quality • Identify other nutrients of interest that are related to sleep quality • Identify foods to avoid prior to sleep • List strategies to set up an ideal bedtime routine
F: Gastrointestinal Issues and Sport	• Understand how vomiting and nausea symptoms may appear during practice and sport • Provide strategies to reduce vomiting and nausea symptoms during practice and sport • Explain how diarrhea can happen during practice and sport • Identify strategies to prevent diarrhea during practice and sport • Explain how probiotics and prebiotics are important for gut health

Each of these six lessons will be discussed in detail in the next section. These supplemental lessons were created in a manner to instruct participants to refer back to the original lessons for further information.

Lesson A: Macronutrients for Injury Prevention

This lesson begins by describing how musculoskeletal injuries heal and the importance of proper caloric intake and macronutrients during recovery from sports-related injuries. Each macronutrient is then highlighted to show its main role in providing both energy and nutritional needs to promote recovery. Macronutrient roles and responsibilities are described in detail in PHP Lesson 1 of the original curriculum (25).

Caloric Intake: The following section of the lesson describes the importance of meeting kilocalorie (kcal) needs to help heal an injury. Research on adult athletes suggest increasing kcal consumption by 10-15% during injury and recovery (58). Additionally, to offset sarcopenia in adults resulting from injury and disuse, energy intake should be between 25-40 kcal/kg of bodyweight per day (54). Independent of injury status, growth and development demands of children aged 9 and up typically require 60-65 kcal/kg of bodyweight per day (21). Taking energy needs during injury into account, coupled with normal demands for growth and development (21), an injured adolescent would need slightly more than the recommended 60-65 kcal/kg of bodyweight per day.

Protein: Following injury, protein requirements are significantly elevated to offset bodily stress incurred from the injury (65). Additionally, protein intake helps offset muscle atrophy due to disuse (47). Protein requirements for adult athletes and recreationally active adults is between 1.2 to 2.0 g/kg of bodyweight per day (69), with protein recommendations for adolescent athletes being in a similar range (15, 41). Following injury, it is suggested to increase daily intake of protein to 2.0 to 3.0 g/kg of bodyweight in athletic adults (65), which likely suffices for protein requirements for adolescent athletes.

Carbohydrates: Carbohydrates can provide a source of energy while healing through an injury (65), and aid in muscle adaptations and recovery (69). Due to a decrease in the amount of high-intensity exercise that can be performed while injured, carbohydrate needs are not as large as what is needed in an uninjured athlete (65). Thus, to meet demand while recovering from an injury, up to 60% of daily kcals should come from carbohydrates (65), with an emphasis on complex carbohydrates. Additionally, fatty acids are important in the recovery process as they synthesize several hormones and aid in the absorption of several vitamins (3, 27). Unsaturated fatty acids, such as omega-3 fatty acids may reduce inflammation, thereby making their need instrumental during the recovery process (27). It is recommended to consume good sources of omega-3 fatty acids including fatty fish, walnuts, flaxseed, and avocado, which this lessons includes as suggested food sources (27, 65).

Fluid Intake: Hydration for performance is covered in Lesson 4 of the original PHP, but in this lesson, it is explored in more detail pertaining to injury risk and recovery. Over half of US children are inadequately hydrated (37), and being in this state can increase risk of injury and prolong recovery (10). Muscles on average are 75% water with bones comprising 25% of it, suggesting that a lowered consumption of it could further exacerbate healing of injuries to these structures (27). Males aged 9 to 13 years need at least 8 cups of fluid per day, while females of the same age need at least 7 cups per day (34). Adolescent males aged 14 to 18 years of age, need at least 11 cups of fluid per day, while females of the same age need 8 cups. Thus, it could be hypothesized that an injured youth athlete should strive to meet and exceed these recommendations for fluid consumption.

Lesson B: Micronutrients for Injury Rehabilitation

Lesson B highlights the importance of specific micronutrients that provide a key role in injury rehabilitation (3, 26). Consuming adequate nutrients, including micronutrients, from whole food sources, is a major goal of the PHP curriculum (21). This lesson begins with a discussion on the concerns with the use of dietary supplements to meet micronutrient recommendations such as issues with regulation (20), and possible contamination (40). Each section of the lesson describes how the micronutrient of interest is implicated in the recovery process, how much is needed, other important functions it provides in the body, and suggested foods that are good sources for the micronutrient of interest.

Vitamin D and Calcium: As summarized in Alcock et al. (2024) micronutrients of interest for bone injury rehabilitation include vitamin D and calcium (3). Calcium is needed to increase bone mineral density and bone remodeling such as when following an injury (27). Vitamin D is needed for calcium absorption and maintenance. Children and adolescents between 9 and 18 years old, need 1,300 mg of calcium every day (23). Adolescents between 14- and 18-years old need at least 15 mcg (600 IUs) of vitamin D daily. Food sources of calcium listed in the lesson include milk, yogurt, salmon, fortified fruit juice, and collard greens (27). Good food sources of vitamin D suggested in the lesson includes salmon, fortified milk, tuna, and cashews.

Zinc and Iron: Other micronutrients of interest for muscle injury also include zinc and iron (3). Zinc and iron are both trace minerals that have several important functions in the human body (27). Zinc is involved in hundreds of functions in the body, such as involvement in DNA synthesis and wound healing, and immune system function (27). Zinc is needed for protein synthesis and iron is needed for the transport of oxygen to several tissues in the body which would increase healing (27). Youth aged 9 to 13 years, need 8 mg of zinc per day (23). Male adolescents aged 14-18 years of age need 11 mg of zinc per day, while females of the same age require 9 mg each day. Children aged 9-13 years of age need 8 mg of iron per day (23). Males aged 14-18 years of age need 11 mg of iron per day, and females of the same age need 15 mg per day. Good sources of zinc include dark meat, legumes, shrimp, and nuts (27). Good food sources of iron includes dark meat, and also spinach and cashews.

Vitamins A, C, and E: Vitamin C plays a pivotal role in the synthesis of collagen (3). Similar to vitamin C, vitamin A aids in collagen formation, specifically the laying down of new collagen (65). Vitamin E can reduce muscle breakdown and promote muscle repair (27). Each of these vitamins can reduce oxidative stress and inflammation and improve tissue healing (27). Children aged 9 to 13 years old need 1,200 mg of vitamin C every day, and adolescents aged 14 to 18 years old, need 1,800 mg per day (23). Good food sources of vitamin C include kiwis, green peppers, strawberries, and cantaloupe (27). Youth aged 9-13 years need 600 mcg of retinol activity equivalents (vitamin A) per day, while adolescents aged 14-18 years need 600 mcg of retinol activity equivalents each day (23). Youth aged 9-13 years of age need 11 mg of vitamin E per day, while adolescents over the age of 14 need 15 mg per day (23). Dietary sources of vitamin A include sweet potatoes, pumpkins, spinach, and squash, while good sources of vitamin E include sunflower seeds, apricots, avocados, and almonds (65).

Although not a micronutrient, eating foods high in nitrates, like beets, could theoretically help heal an injury (76). About 20% of the nitrates consumed in food is converted to nitrite by bacteria found in the oral cavity (76). In turn, the stomach transforms this nitrite into nitrous oxide which can cause vasodilation. Thus, more oxygen and nutrients are transported to the injured area, supporting the healing process. A recent systematic review examined nine studies and concluded that short-term consumption of beetroot may accelerate the recovery of muscle soreness and various functional markers due to its antioxidant and inflammatory properties likely exerted by its nitrate content and several phenolic compounds (60). Therefore, it could be assumed that consuming foods high in nitrates and phenolic compounds could expedite the injury healing process. Aside from beets, good food sources of nitrates include spinach, radishes, celery, and rhubarb (36).

Lesson C: Low Energy Availability

Energy availability is the amount of energy available after energy expenditure, that is used for bodily functions (9). Thus, LEA is the state of inadequate energy intake relative to energy expenditure (9) and the prevalence for LEA in athletes ranges from 22% to 58% in a given sport (44). LEA can lead to several negative impacts on performance including decreased muscular strength, decreased endurance performance, and decreased responses to training responses and adaptations (50, 70). Additionally, there is an increased injury risk with LEA (29, 56).

The next section of this lesson discusses how LEA can negatively impact the growth and development of a child or adolescent, potentially resulting in poor bone health, delayed puberty, short stature, and menstrual irregularities (15). It also highlights several signs and symptoms felt by an athlete that could indicate LEA (9, 70).

LEA, with or without the presence of an eating disorder, is a characteristic of the Female Athlete Triad, which is a condition that also includes decreased bone mineral density, and menstrual dysfunction (53, 59). The concept of Relative energy deficiency in sport (RED-S) expands upon the Female Athlete Triad by recognizing a broader range of health consequences including disruptions to the endocrine system, immune system, and cardiovascular health (9). Raising awareness of these signs and symptoms is essential, especially given that knowledge of LEA remains low among both athletes and coaches (44). The lesson concludes with evidence-based strategies to prevent LEA, as well as treatment options to address its underlying causes (9).

Lesson D: Nutrition for Head Injuries

This lesson discusses various nutritional considerations to assist in the healing process for someone who has had a concussion, or other types of mTBI (22, 62). Current concussion rates in youth sports are 4.17 cases per 10,000 athlete exposures (38). There are several nutritional aspects that may support brain health among those recovering from mTBIs (22, 62). Although several macronutrients are considered nutrients of interest during this process (22, 62), this lesson discusses other nutrients and micronutrients (aside from those discussed in previous lessons) that may have a place while recovering from a mTBI, including creatine, magnesium, and flavonoids.

Creatine: Creatine is a compound that is formed in protein metabolism and works to recycle adenosine triphosphate (ATP) for energy metabolism (42). It has been shown that creatine content in the brain is diminished after a mTBI, and increasing its intake could maintain ATP levels in the brain (1, 65). This could help offset injury sustained from the mTBI, such as decreasing protease activation that degrades axon structures (1). Good food sources of creatine listed in this lesson includes lean red meats, fatty fish, pork, and wild game (72).

Magnesium: Magnesium is a trace mineral that has several functions within the body (27). In the brain, magnesium is involved in efficient nerve signaling and maintaining the blood brain barrier (45). Following a mTBI, magnesium levels decrease in the brain (67), and low magnesium levels have been associated with neuroinflammation and neurodegeneration, including several diseases such as Alzheimer’s and Parkinson’s diseases (67). Research has revealed that magnesium supplementation can reduce concussion symptoms in adolescents following injury (67). Youth aged 9 to 13 years of age need 240 mg of magnesium per day (23). Older adolescent, males aged 14-18 years of age need 410 mg or magnesium per day while their female counterparts need 360 mg per day. Good food sources of magnesium include almonds, cashews, peanut butter, and spinach (27).

Flavonoids: Lastly, flavonoids are phytochemicals found in many fruits and vegetables, that have anti-inflammatory and antioxidant effects, which may reduce swelling after a mTBI (28). Blueberries contain high amounts of flavonoids including anthocyanins, which contribute to the blueberry’s dark color (11). Anthocyanins could lower brain inflammation and stress caused by mTBI (30). Laboratory studies have shown beneficial effects from blueberry supplementation on various cognitive performance outcomes and symptoms following a mTBI (43, 68). Therefore, consuming foods high in flavonoids, including blueberries, could offer a benefit for healing from a head injury.

This lesson concludes with additional nutritional considerations for those recovering from a mTBI. For example, it is suggested to eliminate the consumption of caffeine following a mTBI (65). Other suggestions include taking note of any foods or drinks that cause vomiting or feelings of nausea, and reducing their consumption for a period of time while mTBI symptoms decrease (72).

Lesson E: Nutrition and Sleep for Injury Reduction and Recovery

This lesson highlights the importance of sleep for performance and injury recovery (19, 49). Youth athletes not getting enough sleep are 1.7 times more likely to get injured (52). School-aged children need 9-11 hours of sleep each night, while teenagers need 8-10 hours of sleep per night (31). It is likely an injured athlete should aim for the upper amount of sleep needed per day. Currently, adolescents aged 13-18 years of age are getting on average 7.7 hours of sleep per night, slightly less than the minimum amount needed (48). Several nutrients have been identified that can naturally aid in hormone regulation associated with sleep (55).

Melatonin: Melatonin is a hormone secreted by the pineal gland that is involved in circadian rhythm and increases total sleep time and may reduce time to fall asleep (13, 55). It is found naturally in several foods including tart cherries (18, 51). In addition, tart cherries include other constituents that have anti-inflammatory and antioxidant effects, which may aid in sleep and recovery (8). Other foods with a high melatonin content include milk, pineapples, oranges, and bananas (18, 55).

Serotonin: Serotonin is another hormone involved in sleep by synthesizing hypogenic substances that influence sleep quality (7, 55). Kiwi fruits are a good source of serotonin and contain several minerals, dietary fiber, and phytochemicals that also may aid in sleep (18, 55).

This section of the lesson also includes other nutritional considerations for quality sleep. For example, some foods that contain caffeine, can make it difficult to fall asleep and the recommendation is to reduce or eliminate its intake closer to bedtime (33). This lesson concludes with tips on how to establish an effective sleep routine such as minimizing screen time before it (33).

Lesson F: Gastrointestinal Issues and Sport

This lesson addresses common GI issues encountered in sport and concludes with practical applications for maintaining gut health.

Nausea and Vomiting: Nausea and vomiting are frequent complaints among athletes across various disciplines (77). These symptoms may result from elevated levels of norepinephrine reducing splanchnic blood flow to the gut, delayed gastric emptying, or increased production of gastric bile acids (77). This lesson outlines several risk factors that may contribute to these symptoms along with simple strategies to help prevent them.

Diarrhea: Diarrhea is a common condition experienced by athletes, particularly among endurance athletes (77). Proposed mechanisms include the secretion of vasoactive intestinal peptide which relaxes smooth muscle in the digestive system (35), and changes in gut motility (77). Many of the risk factors associated with diarrhea overlap with those linked to nausea and vomiting. This section concludes with evidence-informed approaches for minimizing the risk of diarrhea during training and competition.

Heartburn: Heartburn is another GI issue sometimes encountered by athletes during exercise and sport and can be caused by increased abdominal pressure, changes in posture, and changes in exercise intensity (74). Additionally, consuming large meals prior to exercise, not being properly hydrated, and having high levels of stress or anxiety can also trigger heartburn. Chronic heartburn could be caused by gastroesophageal reflux disease or GERD (74). This section provides strategies to prevent heartburn during practice or a game, with an emphasis on taking note of such foods that sometimes cause heartburn in an individual.

This lesson concludes by discussing several strategies to maintain gut health and gut microbiota which may impact immunological function and thus injury risk and recovery from them (75). Rationale for its inclusion within this lesson is from the US Olympic & Paralympic Committee sports nutrition handout on nutrients for GI injury (71). Consuming foods high in probiotics may maintain digestion and absorption while also preventing several GI issues described in this lesson (71). Prebiotic fibers are a type of fermentable fiber that stimulates intestinal bacteria growth and activity (64). In addition, prebiotic fiber consumption is associated with several other benefits including increasing the absorption of calcium, improving cognitive health, and reducing risk of some diseases (14). Therefore, it is important to incorporate prebiotic fibers into one’s diet.

CONCLUSIONS

Nutrition is a cornerstone of health and performance for adolescent athletes not only supporting their growth and development but also their ability to train, compete, and recover effectively (15). Integrating sound nutrition practices into youth athlete development programs is essential for promoting lifelong well-being and optimal athletic potential (16). In addition to enhancing performance, proper nutrition can play a key role in preventing injuries and accelerating recovery when injuries occur (3). To emphasize these critical areas, several new lesson have been added as targeted addendums to the PHP curriculum (25). When combined with the original PHP content, these additions aim to strengthen both general and sport-specific nutrition behaviors, equipping young athletes with the knowledge and habits needed to thrive on and off the field.

Following an injury, it is important to consume adequate kcals from protein, carbohydrates, and unsaturated fatty acids, along with being properly hydrated to facilitate recovery (3). Emphasizing certain micronutrients from food may also improve recovery from injury (3). Additionally, nutritional support is needed for athletes recovering from an mTBI (65). LEA is a common problem in youth sports and understanding its consequences and how to prevent it are important for reducing injury risk (9). Getting adequate sleep is important not only for athletic performance, but also injury prevention and healing from an injury (19, 49). Although not a direct injury caused by sport, GI issues can occur during it, and can be prevented using evidence-based nutritional strategies (77). Next steps are to examine adolescent knowledge of nutritional best practices for recovering from sports-induced injuries.

APPLICATIONS IN SPORT
These supplemental lessons are to serve as adjunct lessons to the PHP curriculum and to provide youth athletes with knowledge on injury management and other sports nutrition topics not otherwise discussed in athletic circles. Additionally, the hope is to encourage further research in this understudied area and add to the growing body of literature examining nutrition practices for injury management in youth athletes.

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Troy Palmer2025-09-25T16:05:07-05:00February 18th, 2026|Research, Sport Education, Sport Training, Sports Medicine, Sports Nutrition|Comments Off

Match Demands and Positional Differences of NCAA Division II Women’s Soccer Players Over a Competitive Season

Authors: Joanne Spalding¹, Jacob L. Grazer²

¹Department of Health & Human Performance, Georgia College & State University, Milledgeville, GA, USA

²Department of Exercise Science & Sports Management, Kennesaw State University, Kennesaw, GA, USA

Corresponding Author:

Joanne Spalding, PhD, CSCS, CPSS, ACSM-EP

231 West Hancock Street

Milledgeville, GA 31061

[email protected]

478-445-2135

Joanne Spalding, PhD is Assistant Professor in Exercise Science at Georgia College and State University. Her research interests include long term athletic development and monitoring at the club, high school, and college level with an emphasis on neuromuscular fatigue.

Jacob L. Grazer, PhD, CSCS, USAW-1, ACSM-EP is currently a faculty member in the Department of Exercise Science and Sport Management at Kennesaw State University. Jacob’s research interests include: accentuated eccentric loading, countermovement jump analysis, and sport technology.

ABSTRACT

Purpose: There has been a growing interest in American women’s college soccer with the majority of research focused on the NCAA Division I level. With 250+ Division II programs competing in 2024, there is an underrepresentation of this group in the literature. The purpose of this study is to examine the physical demands in Division II women’s soccer and analyze whether these physical demands vary by position.

Methods: Twenty-two Division II female soccer field players from one DII college team were monitored over an 18-match season wearing GPS devices, a technology that is more commonly used at prevalent at Division I universities. Field players were divided into one of four positions: (1) Center Back Defenders; (2) Outside Back Defenders; (3) Midfielders; and (4) Forwards. The GPS devices provided data on: (a) total distance traveled; (b) total distance traveled per minute; (c) high-speed running distance traveled per minute; and (d) sprint distance traveled per minute. Due to NCAA Division II substitution rules, the focus in this study is the relative variables calculated as distance traveled per minute.

Results: Descriptive statistics for each GPS measurement are calculated based on the position played. Then, Analysis of Variance (ANOVA) is used to identify any differences in player workload physical demands based on the position played. For distance traveled during a match, Center Back Defenders traveled the least distance compared to other positions. Similarly, for high-speed running distance, Center Back Defenders traveled the least distance compared to other positions. Finally, for Sprint Distance per minute, both Forwards and Outside Back Defenders traveled more distance compared to Center Back Defenders and Midfielders.

Conclusions: Findings suggest that the physical demands of Division II women’s soccer differ by position. Center Back Defenders tend to ‘stay at home’ and defend the goal while traveling less distance and sprinting less often. Forwards and Outside Back Defenders tend to spend more time sprinting compared to other players.

Applications in Sport: Coaches and practitioners can use this data when designing training programs to ensure their athletes are well-prepared for the differing physical demands of their sport based on the position played.

KEYWORDS: college soccer, GPS, match demands, women’s soccer, physical match performance

INTRODUCTION

Over the past several years, there has been an increase in the number of peer-reviewed research articles focusing on the physical demands of women’s soccer. The majority of the research that has been conducted has focused on athletes who compete at the professional and international levels (E. Choice et al., 2022). Recently, there has been a growing interest in American college women’s soccer, with the majority of this research focused on National Collegiate Athletic Association (NCAA) Division I level. This can likely be attributed to the fact that wearable technology has become more prevalent at the collegiate Division I level making it easier to collect information regarding the physical demands experienced by female collegiate athletes. However, with 250+ Division II programs competing in the 2024 season, this significant population has been under-represented in research efforts to examine the physical demands on the athlete when competing. As such, further analysis is needed to better understand the physical demands on athletes participating in the various levels of collegiate women’s soccer.

Soccer is a high intensity, intermittent sport that requires running, jogging, walking, repeated sprints, jumping and change of direction (Al-Hazzaa et al., 2001; Bloomfield et al., n.d.; Wisløff et al., 2004). Matches consist of two 45-minute halves with a 15-minute halftime period. For international competition, the Fédération Internationale de Football Association (better known as FIFA) sanctions allow for five substitutions per game with a typical squad size of 26 players. However, NCAA soccer permits an unlimited number of players to be on the official roster during the regular season. Substitutions rules for the NCAA also differ from FIFA in that the number of substitutions is unlimited per game, and players are allowed to re-enter the match once in the second half. This difference is notable because it may influence the physical demands placed on players during match play.

Current literature has explored match play data at various playing levels and reports that there are differences in match-play demands at different levels in women’s soccer (E. Choice et al., 2022). A perspective review by Vescovi et al (2021) reported that there is a linear increase in total distance covered across playing levels ranging from youth (~7,500m), collegiate (~9,500m), to professional levels (~10,200m). A systematic review of women’s soccer also identified that playing demands increase between playing levels, and that there are positional differences in terms of total distance covered, high-speed running distance, and sprint efforts where midfielders covered greater total distances and sprint efforts compared to central defenders (Alexander, 2014; E. E. Choice et al., 2023).

Challenges arise when comparing research due to variations of inclusion criteria and determination of velocity thresholds. In addition to inclusion criteria varying, values are typically reported in total distances rather than relative to time spent playing on the field. As highlighted previously, the unique NCAA substitution rules make it challenging to fully grasp the physical demands that are placed on the athletes since it is uncommon for someone to play a full 90-minute match in women’s collegiate soccer. Due to the potential influence of substitution rules on physical demand, more research is needed to gain a greater understanding of the relative demands with respect to playing time.

Very few studies have investigated the match demands of NCAA DII women’s soccer (E. E. Choice et al., 2023; Gentles et al., 2018). Gentles et al. (2018) assessed and compared accelerometry data to data collected using GPS signal. Although total distance and distance covered in specific velocity zones were reported, statistical analyses were not made comparing playing positions. Whereas Choice et al., (2023) investigated the external and internal load of players, including positional and time-specific differences.

Due to the lack of research in women’s soccer in general, and specifically at the lower playing levels, there is a need for more research. The purpose of this study is to examine the physical demands in Division II women’s soccer and analyze whether these physical demands vary by position.

SOCCER TEAM ROLES AND ROLE EXPECTATIONS

Given this study examines the possible differences in physical demands on soccer athletes based on the position played, it is advantageous to clearly outline each position and the expectation of athletes who play that position. Each position carries distinct responsibilities within a team’s formation and can change based on the formation. This study focused on a team that utilized the 4-3-3 formation where positions are separated into center backs, outside backs (right and left), midfielders (center and attacking), and Forwards (central striker and right/left wing) (see Figure 1).

Figure 1 – Visual Presentation of 4-3-3 Soccer Formation

Center Back Defenders

Center back defenders are primarily responsible for defensive organization and central coverage. When in a defensive scenario, center backs are responsible for defending the opposing forwards, intercepting through balls, contesting aerial duels and tackling to gain possession. In possession, center backs initiate playing out from the back, with passes to midfielders or out to forwards, and or switching play across the back line.

Outside Back Defenders

Outside backs (right and left) provide width for the back line and help with transitional play from the defensive half to the attacking half. Out of possession, outside backs typically deal with the opposing forward or winger depending on the opposition’s formation. Similar to center backs, outside backs with attempt to win aerial duels and tackles. In possession, outside backs will look to support the team by overlapping the forward, dribbling the ball up the field, delivering crosses and well as helping maintain possession in the attacking half.

Midfielders

The center midfielders are composed of two defensive midfielders and one attacking midfielder. The defensive midfielders attempt to shield the back line, break up opposition attacks, and in possession help to maintain and build possession from the defensive to attacking half. The attacking midfielder helps the forward and midfielders with defensive duties and in possession looks to support the forwards, maintain possession as well provide goals and assists.

Forwards

Forwards help to provide width and are important for stretching the oppositions defense. They will primarily defend the oppositions outside back and recover into midfield positions when out of possession. In possession they will aim to beat defenders one-on-one, deliver crosses and create shooting opportunities. When a player is in the central striker position, they are the focal point of the attacking line and are primarily there to convert scoring opportunities by finishing inside and around the 18-yard box. In possession, the central striker will look to hold the ball up allowing midfielders and other forwards to join the play. They will attempt to make runs to disrupt the defensive lines and apply pressure to the opposition center backs.

METHODS

Participants

A total of 22 NCAA Division II female soccer players participated in this study. Participants were athletes from a team already using a GPS athlete monitoring system. Players were assigned 1 of 4 positions by the team’s coaching staff (Sporis et al., 2009).

Center Backs (CB) (n = 4)
Outside Backs (OB) (n = 5)
Midfielders (MID) (n=6)
Forwards (FWD) (n= 7).

Procedures

A total of 18 regular season matches were included in this analysis from a single competitive season. Global Positional System (GPS) devices (TITAN Sports, Titan2, Houston, TX, USA) sampling at 10 Hz were used to track player movement during competition. Data included 234 observations (n= 79 FWD, n= 70 MID, n= 50 OB, n= 35 CB). The team used for this study played 1-4-3-3 formation (please note 1 = goalkeeper) during all matches included in the analyses. All matches were official NCAA matches with two 45-minute halves and a 15-minute halftime period. All players were informed of the risks and benefits of this study and voluntarily signed an informed consent. This study was approved by the Institutional Review Board.

Variables for Analysis

Data was collected on the following variables to better understand the physical demands on each player based on position played (see Andersson, 2010).

Total Distance – The total distance a player covers during a game. This value is needed to determine the distance covered related to minutes played.

Relative Total Distance (TDREL) – The distance covered per unit of time, expressed as meters per minute (m/min).

Relative High-Speed Running Distance (HSRREL) – Distance covered per minute while running at high speeds, in this case, over 15 kilometers per hour (km/h), or 9.3 miles per hour.

Relative Sprint Distance (SDREL) – Distance covered per minute while sprinting, usually above a higher threshold. In this case, over 18 kilometers per hour (km/h) or 11.2 miles per hour.

Using the Global Navigation Satellite System to Measure Match Load

GPS units were worn in a harness that secured the device between the scapulae (i.e., shoulder blades). The units were turned on 10 minutes before being placed in the harness to ensure sufficient GPS signal was attained per manufacturer’s instructions. Due to the substitution rules in NCAA college soccer, calculating the variables per minutes played was deemed the optimal solution for comparing workload across playing position.

RESULTS

Three separate one-way ANOVA analyses with Bonferroni corrections were conducted to determine if there were differences between positions for Total Distance (TDREL), High Speed Running Distance (HSR_REL) and Sprint Distance (SD_REL). Statistical analyses were performed in SPSS (Version 27.0.1). Alpha level was set at p <0.05. Eta-squared effect sizes were calculated to determine magnitude of differences for each variable. For pairwise comparisons, Cohen’s d effect sizes were calculated to determine magnitude of difference between playing positions (Hopkins, 2002). Descriptive statistics for match physical performance for this specific team are summarized in Table 1.

Table 1 – Match Performance Data

Variables	Midfielders (n=70)	Forwards (n=79)	Outside Back Defenders (n=50)	Center Back Defenders (n=35)	Total (n=234)
Minutes Played per Match	51.6 ± 13.6	52.8 ± 15.1	52.1 ±13.9	68.0 ±19.9	57.1 ± 15.6
Total Distance (m)	5,643.9 ± 1,435.0	5,507.9 ± 1,214.1	5,506.3 ±1,373.8	6365.1 ± 1884.9 Greatest total distance covered due to greatest minutes played	5,855.8 ±1,476.8
Relative Total Distance (m/min) The distance covered per unit of time, usually expressed as meters per minute (m/min).	110.4 ± 14.2 Significantly greater than Center Back Defender	106.8 ± 14.4 Significantly greater than Center Back Defender	106.8 ±12.6 Significantly greater than Center Back Defender	93.8 ± 11.6	105.9 ± 14.5
Relative High Speed Running Distance (m/min) Distance covered per minute while running at high speeds, in this case, over 15 kilometers per hour (km/h), or 9.3 miles per hour.	10.06 ± 3.45 Significantly greater than Center Back Defender	12.96 ±4.01 Significantly greater than Center Back Defender	13.18 ± 4.21 Significantly greater than Center Back Defender	7.53 ± 2.90	11.33 ± 4.26
Relative Sprint Distance (m/min) Distance covered per minute while sprinting, usually above a higher threshold. In this case, over 18 kilometers per hour (km/h) or 11.2 miles per hour	3.45 ± 1.98	6.06 ± 2.64 Significantly greater than Center Back Defender Significantly different than Midfielders	5.53 ± 1.85 Significantly greater than Center Back Defender Significantly different than Midfielders	3.05 ± 1.92	4.72 ± 2.53

Relative Total Distance (TDREL)

A one-way ANOVA revealed a significant difference in positions for TD_REL, F(_{3, 230}) = 11.9, p = <0.001. An effect size (η²=0.135) indicated a medium effect. Post Hoc analysis indicated that there were mean differences between FWD and CB (p = <0.001), MID and CB (p = <0.001), and OB and CB (p = <0.001).

Relative High-Speed Running Distance (HSRREL)

There were statistically significant difference for HSR_REL, F(_{3, 230}) = 23.73 p < 0.001, with a large effect (η²=0.23). Post Hoc analysis showed mean differences between FWD and MID (p = <0.001), FWD and CB (p = <0.001), MID and OB (p = <0.001), MID and CB (p = <0.008) and OB and CB (p = <0.001) as seen in Figure 2.

Relative Sprint Distance (SDREL)

Finally, there were statistically significant differences for positions in SD_REL, F=(_3,230), 25.547, p <0.001 with a large effect η²=0.25 Post Hoc analysis revealed mean differences for FWD and MID (p = <0.001), FWD and CB (p = <0.001), MID and OB (p = <0.001), OB and CB (p = <0.001).

DISCUSSION

The purpose of this study is to determine the physical demands for NCAA Division II Women’s Soccer and to assess positional differences and physical demands relative to minutes played. The study’s results include the average demands during match play of each field position as previously shown in Table 1. The main findings of this study indicate that center backs accumulate the greatest total distance, however, this is mainly attributable to extended playing time as they frequently remained on the field 15-17 minutes longer than other positions. When normalized for minutes outside backs, midfielders and forwards covered greater total distance and high-speed running distance than center backs. In addition, outside backs and forwards demonstrated greater sprint distance per minute compared to both center backs and midfielders.

The average total distance covered for players included in analyses was 5,855 ± 1,476 meters, which differs from prior findings by Choice et al. (2023) who saw starters cover 9,463 ± 2,591 meters. It should be noted that the analysis by Choice et al. (2023) only included players who participated in over 50% of match play. Findings from this study are similar to results reported by Gentles et al. (2018) who reported findings of 5,480 ± 2,350 meters and average minutes played of 45.32 ± 26.01 which are similar to minutes played in this study. The maximum distance covered by an individual in this study was 9,810 meters, which is much lower than distance covered by the sample in Gentles (13,850 meters) and Choice (12,054 meters), but similar to distances reported in Division I athletes, 9,786 meters (Sausaman, 2019). Both maximum and average distance covered varied across levels may indicate that more research is needed across differing playing levels. Also, from a practical point of view, this should help sport practitioners implement appropriate programs that develop players for both maximum and mean distances, regardless of minutes played.

To our knowledge, this is the first study to report TD_REL (105 ± 14.5 m/min), HSR_REL (11.3 ± 4.2 m/min) and SD_REL (4.72 ± 2.53) in NCAA Division II athletes. Gentles (2018) reported total distance accumulated at velocity zones 15.00 – 19.99 kph and 20.00-24.99 kph but not relative distances. Due to the substitution rules in NCAA College soccer, there is a high variance in match-to-match playing variables. Other studies have only included players who have played over 50% of the match or the entire match (E. E. Choice et al., 2023), whereas other research only included participants who completed the match in its entirety (Alexander, 2014; Sausaman et al., 2019). If training programs are only based on average totals, then the whole picture may not be clear when designing training programs for those not participating in 90 minutes. Therefore, reporting data per minute may be more beneficial as reserves need to be trained throughout the season to compensate for minutes not played.

There was a difference in positional demands for HSR_REL, with FWDs covering higher distances than MIDs and CBs. Whereas OBs covered more HSR_REL than CBs and MIDs, and MIDs only covered more distance than CBs. Although there was no statistical difference between OBs and FWDs, OBs covered slightly more distance per minute. Although the lack of statistical difference between FWDs and OBs may be due to formation and playing style. In the 4-3-3 OB and FWDs may have similar positional demands as OBs are often part of the attack by providing width higher up the field. Formation on the field was not controlled for in this study so more research is warranted to understand positional differences across a variety of formation styles.

Results also showed that FWDs covered more SD_REL than MIDs and CBs, and OBs covered more than CBs. Although variables are different within this study, results show similar trend within the literature, that forwards cover more HSR distance and sprint distance than midfielders (Sausaman et al., 2019; J. Vescovi, 2013). It should also be noted that CB and OB positions are typically grouped together as a singular position group (commonly reported as defender) (Stolen et al., 2005; J. D. Vescovi, 2012).

This study provides further evidence, particularly in women’s soccer literature, that there is a need to differentiate between central and outside defenders when evaluating physical demands during competition due to the different physical demands during competition (Alexander, 2014; Harkness-Armstrong et al., 2022). It is also important to note that in NCAA Division I studies, players covered more high-speed running and sprint distance on average compared to NCAA division II athletes. This indicates that the Division I game may be played at a faster pace, especially in those critical moments of the game (defensive recovery run, sprinting to goal etc.) As this study reports HSR and sprint distance per minute, it prevents the authors from making additional conclusions or comparisons.

There are limitations to the study that may have impacted the results. The data for this study was collected for one team playing one season of competition. The level of soccer players at all NCAA level can vary greatly across teams and conferences. Therefore, further research is needed to confirm the current findings. Differences in match demands as well as positional demands may be a result of opposition level, formations, match status, and match location. As such, additional research is needed to assess these factors to determine their possible influence on the physical demands of Division II women’s soccer.

CONCLUSION

This study examined the physical demands of women’s soccer at the NCAA Division II level and analyzed differences among position groups. Unlike previous research, this study assessed Relative total distance (TD_REL), relative high-speed running distance (HSR_REL), and relative sprint distance (SD_REL), providing average values for all players and specific positions to describe match demands. The findings highlight differences in physical demands based on position played. The findings from this study emphasize the importance of evaluating workload relative to minutes played and recognizing the unique demands of each position at the NCAA Division II level. Such insights provide a framework for tailoring training, conditioning and substitution strategies that reflect position-specific requirements rather than relying on team-wide averages. Future research should continue to examine how positional workloads vary across divisions and competitive levels, as well as how these demands interact with injury risk, recovery and long-term players development.

APPLICATIONS IN SPORT

These findings provide an insight into general and positional demands of women’s soccer at the NCAA Division II level. These results are particularly valuable to coaches who may strategically utilize the NCAA substitution rules to manage player workloads. Caution should be exercised when applying published findings from other levels of play and relying on total averages as positional differences can substantially influence match demands.

The results emphasize the importance of aligning athlete’s physical attributes with the requirements of their playing positions. Forwards and outsides backs perform the highest sprinting and high-speed running demands per minute and may require players that have higher acceleration and sprint capacities. On the other hand, players that have less high-speed and sprinting demands, but positional awareness may be better suited at center back.

This may have implications for recruitment. Coaches may want to target athletes with performance profiles that align with the positional demands, rather than relying on general fitness. In regard to player development, training and conditioning programs should be tailored to the requirements of each position moving beyond a one-size-fits-all approach. For example, midfielders may benefit from training designed to sustain higher per-minute workloads, while forwards and outside backs should focus on repeated spring ability as well as increasing top speed.

Finally, these results may extend to tactical decision making and injury prevention. Coaches may rotate outside backs and forwards more frequently to preserve sprint performance later in matches. These findings provide sport practitioners with evidence-based guidance to optimize recruitment, training and match management in NCAA division II soccer.

REFERENCES

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Andersson, H. A., Randers, M. B., Heiner-Møller, A., Krustrup, P., & Mohr, M. (2010). Elite female soccer players perform more high-intensity running when playing in international games compared with domestic league games. Journal of Strength & Conditioning Research, 24(4), 912–919. https://doi.org/10.1519/JSC.0b013e3181d09f21
Bloomfield, J., Polman, R., & O’Donoghue, P. (n.d.). Physical demands of different positions in FA Premier League soccer.
Choice, E. E., Tufano, J. J., Jagger, K., L., & Cochrane-Synman, K. C. (2023). Match-Play External Load and Internal Load in NCAA Division II Women’s Soccer. Journal of Strength & Conditioning Research, 37(12), 633–639. https://doi.org/10.1519/JSC.0000000000004578
Choice, E., Tufano, J., Jagger, K., Hooker, K., & Cochrane-Snyman, K. C. (2022). Differences across Playing Levels for Match-Play Physical Demands in Women’s Professional and Collegiate Soccer: A Narrative Review. Sports, 10(10), 141. https://doi.org/10.3390/sports10100141
Gentles, J., Coniglio, C., Besemer, M., Morgan, J., & Mahnken, M. (2018). The Demands of a Women’s College Soccer Season. Sports, 6(1), 16. https://doi.org/10.3390/sports6010016
Harkness-Armstrong, A., Till, K., Datson, N., Myhill, N., & Emmonds, S. (2022). A systematic review of match-play characteristics in women’s soccer. PLOS ONE, 17(6), e0268334. https://doi.org/10.1371/journal.pone.0268334
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Sausaman, R. W., Sams, M. L., Mizuguchi, S., DeWeese, B. H., & Stone, M. H. (2019). The Physical Demands of NCAA Division I Women’s College Soccer. Journal of Functional Morphology and Kinesiology, 4(4), 73. https://doi.org/10.3390/jfmk4040073
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Troy Palmer2025-09-25T15:33:55-05:00February 4th, 2026|Research, Sport Education, Sport Training, Sports Coaching, Sports Health & Fitness, Women and Sports|Comments Off

Fundraising in Sports: A case study

Author: Francisco J. Quevedo¹

Corresponding Author:

Francisco J. Quevedo

72 Maple Street

Watchung, NJ, 07069

[email protected]

929-208-5289

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

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

ABSTRACT

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

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

INTRODUCTION

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

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

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

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

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

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

BACKGROUND

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

Chart 1: Nonprofit Revenues in the US

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

METHODS

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

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

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

Data Analyses

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

Prior Research Studies

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

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

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

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

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

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

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

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

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

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

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

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

The Case of the WSKF Sports Foundation

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

Illustration # 3: The Winning Strategy

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

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

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

Chart 3: Structure of Nonprofit Revenues

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

Illustration # 4: The WSKF Fundraising Process

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

Illustration # 5: The WSKF Newsletter

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

Chart 4: The WSKF Venezuela Medal Count

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

DISCUSSION

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

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

CONCLUSIONS

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

Limitations and Further Research

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

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

APPLICATIONS IN SPORT

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

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

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

Social Implications

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

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

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

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

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

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

Corresponding Author:

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

75 College Drive

Montevallo, AL 35115

[email protected]

205-665-6118

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

ABSTRACT

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

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

INTRODUCTION

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

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

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

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

METHODS

Participants

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

Procedures

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

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

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

Data Analyses

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

RESULTS

Descriptive Statistics

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

Anthropometric Correlation Analysis

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

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

Performance Correlation Analysis

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

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

Total Correlation Analysis

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

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

Discussion

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

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

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

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

CONCLUSIONS

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

APPLICATIONS IN SPORT

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

REFERENCES

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