Secondary Logo

Journal Logo

Original Research

Comparison of Internal and External Training Loads in Male and Female Collegiate Soccer Players During Practices vs. Games

McFadden, Bridget A.1; Walker, Alan J.2; Bozzini, Brittany N.1; Sanders, David J.3; Arent, Shawn M.1,3

Author Information
Journal of Strength and Conditioning Research: April 2020 - Volume 34 - Issue 4 - p 969-974
doi: 10.1519/JSC.0000000000003485
  • Free



Soccer is a physically demanding sport, which involves frequent bouts of high-intensity activity, including sprinting and high-speed running, coupled with low-intensity periods of walking and jogging (3). A 90-minute soccer match is further characterized by a large caloric expenditure estimated to be around 20.3–23.6 Kcal·kg−1 body mass and an average distance covered of 10 km at an intensity close to anaerobic threshold (80–90% HRmax) (22). In addition, sprints constitute 1–11% of the total distance covered (17,22), with players performing around 10–20 sprints during a game (22). In addition, high-intensity running occurs approximately every 70 seconds in a game (22). Although the physiological demands of a competitive soccer season are often believed to vary between men and women, these workload outputs as a function of sex have rarely been assessed.

Technological advances in athlete tracking have afforded the ability to monitor training workloads both in-game and during practice. Sports science programs have the capabilities to track the physiological response of the athlete, often termed internal load, using techniques such as heart rate (HR) monitoring. The physical demands, often described as external load, can be tracked using global positioning satellite (GPS) systems that determine distance covered as well as the speed at which the athletes travel. Although research that quantifies these workload demands has mainly been limited to in-game performance (6,16) and particularly assessed in elite professional men during select training periods (1,13,15,19,20), equally important are the workload demands required at practices. More research comparing the total competitive season demands, specifically both internal and external loads, on male and female soccer players is warranted to determine potential differences in recovery needs and practice strategies.

Research designed to assess the current stressors placed on the players in competition as well as the performance characteristics of men's soccer has grown rapidly (5), yet less progress has been made in women's soccer. Conclusions regarding sex differences are based on a relatively small number of in-game studies in high-level to elite female players (6). The few studies that have addressed sex differences in workload have found the relative physiological demands, represented by percent of HRmax and percent of maximum oxygen consumption (V[Combining Dot Above]O2max), for male and female elite soccer players during a game to be similar (22). Although previous research suggests that both female and male players tax the aerobic and anaerobic energy systems to a comparable level (12,22), female soccer players often experience less of an external load (e.g., female players run a shorter distance during a typical game compared with male players) (6,22). Furthermore, high-intensity running in an elite female game is reported to be ∼30% lower than male athletes of a similar competitive standard (6,14,16). For example, elite female players cover less distance at high intensity (speed > 15 km·h−1) than male players matched for age and competitive standards (16,18). More research is needed to determine total competitive season demands including practice and games on combined internal and external load metrics in high-level male and female collegiate soccer players. With multiple games per week, frequent travel, and further burdens of academic requirements, quantifying workload becomes increasingly important in a collegiate population to enhance recovery throughout the season (26).

The purpose of this study was to compare the internal and external training loads (TLs) in men and women throughout an entire Division 1 soccer season during both practices and games. It was hypothesized that male and female high-level soccer players would have a similar relative internal load in regards to both practices and games; however, men would accumulate more high-speed running on an absolute basis, which may be a function of greater power capabilities.


Experimental Approach to the Problem

This observational study sought to determine differences in workload and performance characteristics between men's and women's Division I collegiate soccer teams. Differences in internal load and external load were monitored during both practice and games utilizing GPS and HR technology throughout the course of a competitive collegiate soccer season. In addition, maximal performance testing and body composition characteristics were evaluated before the start of the season to determine differences in aerobic capacity, power output, and body composition.


Female (N = 16) and male (N = 12) Division I collegiate soccer players (ages 18–22 years) were monitored throughout the competitive season. Descriptive characteristics are presented in Table 1. All subjects performed testing as part of regular team activity and in association with their sports science program. All subjects received clearance by the Rutgers University Sports Medicine staff before testing. Research was approved, and written consent waived, by the Rutgers University Institutional Review Board for the Protection of Human Subjects and conducted in accordance with the Declaration of Helsinki.

Table 1
Table 1:
Descriptive characteristics.*


Performance Testing

Athletes reported to the Rutgers University Center for Health and Human Performance (CHHP) before the start of preseason to complete a battery of tests. Subjects were instructed to arrive euhydrated, at least 2 hours fasted, and having abstained from exercise 24 hours before testing. Body composition was assessed by air displacement plethysmography by using the BodPod (BOD POD; COSMED, Concord, CA) to determine percent body fat (%BF), fat free mass (FFM), and fat mass using the Brozek formula (8). After a self-selected warm-up, subjects were given 3 attempts for maximal countermovement vertical jump (CMJ) with arm swing methods assessed using the Just Jump system (Probotics, Huntsville, AL). Performance was scored as the highest jump recorded. After this, a maximal graded treadmill exercise test (GXT) was used to measure maximal aerobic capacity (V[Combining Dot Above]O2max) and ventilatory threshold (VT) via direct gas exchange via a COSMED Quark CPET (COSMED). A speed-based protocol was used with stages that were MET equated to the Bruce protocol. This protocol included two-minute stages at a constant 2% incline. The speeds were as follows: 6.4, 7.9, 10.0, 11.7, 13.7, 15.6, 17.1, 18.2, 19.8, and 21.1 (km·h−1). Subjects continued the test with encouragement from the laboratory staff until volitional fatigue. At least 3 of the following criteria were met for attainment of V[Combining Dot Above]O2max: a leveling off or plateauing of V[Combining Dot Above]O2 with an increase in exercise intensity, attainment of age-predicted HRmax, a RER >1.10, or an RPE ≥18. Heart rate was continuously monitored using a Polar S610 HR monitor to accurately obtain HRmax (Polar Electro, Co, Woodbury, NY). Subject's VT was calculated after the completion of each test as the point where ventilation increased nonlinearly with V[Combining Dot Above]O2, which is expressed as a percentage of V[Combining Dot Above]O2max.

In-Game and Practice Monitoring

Players were evaluated during all practices (M = 42; F = 60 practices) and regulation game play (M = 17; F = 23 games) using the Polar TeamPro system that utilizes HR, GPS, and accelerometry technology. Physiological attributes of the player obtained from laboratory testing (age, height, body mass, sex, V[Combining Dot Above]O2max, HRmax, and VT) were used to program each individual player's monitor. The quantification of each individual player's workload was calculated by TL, energy expenditure via HR analysis (9) expressed as a function of body mass (Kcal·kg−1), time spent in HR zones expressed as a percent of HRmax (HRZ1 = 50–59%; HRZ2 = 60–69%; HRZ3 = 70–79% HRZ4 = 80–89%; HRZ5 = 90–100% of HRmax), total distance (DIS), number of sprints, average speed (SPDAVG), and distance covered in each speed zone (DISZ1 = 3.0–6.99 km·h−1; DISZ2 = 7.0–10.99 km·h−1; DISZ3 = 11.0–14.99 km·h−1; DISZ4 = 15.0–18.99 km·h−1; DISZ5 = ≥19 km·h−1) based on similar speed zones used for female soccer athletes (7). Training load was calculated using an algorithm developed by Polar based on the quantification of an individual player's output (10). Speed zone thresholds were designated by the Polar TeamPro system. A sprint was considered to be any movement greater than 2.8 m·s−2 (23,24).

Statistical Analyses

For the purposes of this study, only field players were included in analysis. Furthermore, only players who participated in at least 50% of the matches and maintained a minimum playing time of 45 minutes per match were included in the analysis (male: N = 9; female: N = 9). A secondary analysis was run to account for players who were substituted into at least 50% of the matches but did not meet the 45-minute playing time criteria (male: N = 12; female: N = 16). All overtime and half-time minutes were factored out of the match data so that a maximum of 90 minutes of regulation play was included in analysis. All Polar TeamPro metrics were averaged for each player for both games and practices separately. Multivariate analyses of variance were conducted for the physiological attributes, internal load, and external load variables. Univariate follow-ups using analysis of variance were used to compare differences between sexes. All analyses were conducted using SPSS statistical software (SPSS version 23; IBM) with significance set at p < 0.05. Values are expressed as means ± SD. Difference adjustments were examined, and effect sizes (ES) were calculated using Hedge's d.


When comparing performance and body composition characteristics between women and men, differences were seen for FFM (FFMF = 50.69 ± 4.6 kg; FFMM = 67.78 ± 8.6 kg; ES = 2.59; p < 0.05), %BF (%BFF = 20.331 ± 3.4; %BFM = 12.27 ± 3.5; ES = −2.34; p < 0.05), CMJ (CMJF = 53.64 ± 7.3 cm; CMJM = 61.52 ± 7.6 cm; ES = 1.06; p < 0.05), and V[Combining Dot Above]O2max (V[Combining Dot Above]O2maxF = 51.13 ± 2.8 ml·kg−1·min−1; V[Combining Dot Above]O2maxM = 57.53 ± 5.1 ml·kg−1·min−1; ES = 1.62; p < 0.05). Men exhibited higher aerobic capacity, power production, and FFM compared with women, whereas the women showed greater %BF. Differences in body composition and performance characteristics between men and women are presented in Table 2.

Table 2
Table 2:
Performance and body composition comparison.*†

Internal and external load comparisons for men and women can be found in Tables 3 and 4. No significant differences in game playing time were seen between men and women (playing timeF = 78.6 ± 3.9 minutes; playing timeM = 74.1 ± 3.9 minutes; ES = 1.15; p > 0.05). Furthermore, no significant differences in game analytics between men and women were seen for TL, Kcal·kg−1, HRZ1–HRZ5, SPDAVG, DIS, DISZ1, DISZ3, and DISZ4 (p > 0.05). All comparisons between distance covered in speed zones and time spent in HR zones are presented in Figures 1 and 2 (respectively). However, men accumulated a significantly greater number of sprints (sprintsF = 13.8 ± 5.0; sprintsM = 21.9 ± 3.2; ES = 1.95; p < 0.05), and DISZ5 (DISZ5F = 400.9 ± 157.6 m; DISZ5M = 680.0 ± 114.2 m; ES = 2.02; p < 0.05) during games. Women covered more ground in DISZ2 (DISZ2F = 2,584.5 ± 311.1 m; DISZ2M = 2,116.4 ± 508.3 m; ES = −1.11; p < 0.05).

Table 3
Table 3:
Male vs. female external load practice and game comparison.*
Table 4
Table 4:
Male v. female internal load practice and game comparison.*
Figure 1
Figure 1:
Distance covered in speed zones (N M = 9; N F = 9). *Significant differences between men and women.
Figure 2
Figure 2:
Time spent in HR zones (N M = 9; N F = 9). *Significant differences between men and women. HR = heart rate.

When substituted players were included in the game analysis (playing timeF = 56.4 ± 27.3 minutes; playing timeM = 65.9 ± 19.6 minutes; ES = 0.39; p > 0.05), differences in DISZ4 (ES = 0.90; p < 0.05) as well as time spent in HRZ3 (ES = 0.79; p < 0.05) were observed, with values found to be higher in men. Furthermore, TL (ES = 0.67; p = 0.09) and DISZ1 (ES = 0.70; p = 0.079) trended toward being higher in male than female players.

The average recorded practice time for men was 90.3 ± 30.3 minutes, whereas average practice time for women was 98.1 ± 22.0 minutes. During practices, no differences were seen for TL, DIS, sprints, Kcal·kg−1, DISZ2, DISZ3, and HRZ1–Z5 (p > 0.05). However, men exhibited higher SPDAVG (ES = 2.60; p < 0.05), DISZ1 (ES = 2.57; p < 0.05), DISZ4 (ES = 1.41; p < 0.05), and DISZ5 (ES = 2.03; p < 0.05). A higher average DIS (ES = 1.04; p < 0.05) was seen in men when substituted players were included in practice analysis, whereas a greater time spent in HRZ3 (HRZ3F = 18.7 ± 3.6 minutes; HRZ3M = 15.4 ± 2.5 minutes; ES = −1.04; p < 0.05) was seen in women.


Despite differences in performance characteristics, game and training demands were remarkably similar between male and female DI college soccer players over the competitive season. The parallels in TL, HR, and Kcal·kg−1 among the players indicate a similar relative workload between sexes. DIS and SPDAVG, assessed using absolute measures, were also found to be comparable. However, the distance covered in the higher speed zones across practices and games was found to be greater in men than women, with the differences most pronounced at the highest speed zones. In addition, male players accumulated a greater number of sprints during games than female players, although no differences were seen in the practice analysis.

Similar to the current study, Bradley et al. assessed sex differences in game performance characteristics of elite soccer players using a multicamera system (6). Researchers found male players covered more distance than female players at higher speed thresholds (>15, 18–21, 21–23, 23–25, and >27 km·h−1), but minimal differences were depicted at speeds <12 km·h−1. In addition, a study by Krustrup et al. evaluating the physical demands during an elite women's soccer game found the average distance covered by high-intensity running (speeds >15 km·h−1) was 1.3 km, which was about 66% less than that of elite male players (1.9–2.4 km) (14,17). It seems that male players have an increased ability to reach (and sustain) higher speed thresholds than their female counterparts, which is not surprising. Although relative physiological loads in men and women have been shown to be similar, female players have a lower absolute aerobic and anaerobic physical fitness capacity (6,18). Therefore, the increased ability to cover distance in higher speed zones is an expected finding in men due to a greater proportion of muscle mass and capacity for power production as seen with greater FFM values and higher CMJ performance among the men in the current study. This becomes further evident in a study by Mujika et al. comparing sex differences in physical performance outcomes. Men produced 31.7–33.9% greater power via CMJ than women and 13.6–16.2% faster 15-minute sprint performance (18). Despite the greater ability to reach top speed thresholds, the current study shows both men and women covered the greatest proportion of their distance at speeds less than 15 km·h−1.

In addition, the current study concluded both sexes covered a similar distance in games and practices, yet research comparing the total distance covered between men and women in a typical soccer game has varied (6,14,16,22). Bradley et al. found elite male players covered more distance in total than women (11.14 and 10.75 km, respectively). However, sex differences were more pronounced at the higher speed thresholds (ES = 0.7–1.4) than for the total distance covered in a match (ES = 0.5) as the female players covered more of the distance at speeds <12 km·h−1 (6). Krustrup et al. found total distances covered in elite female soccer players ranged from 9.7 to 11.3 km (average 10.3 km), similar to the values reported for moderate and top-class male players (10.33 and 10.86 km, respectively) (17). Technical ability of the teams as well as their opponents may affect relative game workloads and differences seen in certain metrics (5). Furthermore, it is important to consider that similarities in distance covered may not be reflective of the overall speed of play. Future studies may consider ball movement and change of field differences that may enhance or reduce the total distance traveled during a game.

As with the existing research on this topic, one limitation to this study is the relatively small sample size used. However, the limited sample of athletes who met the playing time criteria is indicative of a typical collegiate soccer match. This nine-player rotation is a function of game substitution strategies typically seen at this level and is important to consider to make reasonable male and female comparisons. Future studies evaluating sex differences in collegiate soccer players may consider utilizing multiple teams within a collegiate conference to reduce the variability inherent with the nature of the sport.

Another limitation that seems to be inherent across the existing literature is the differential ranges used to classify high-intensity running. Speed zones are often defined according to distinct thresholds or determined by the proprietary software of the tracking system (23). A high-intensity running speed threshold is typically set at >15 km·h−1 for elite male soccer players (2,4). The rationale for these thresholds are generally chosen based on speeds seen in soccer that equate to those obtained during maximal oxygen uptake (2,6,11); however, these speed thresholds vary from study to study. Typically, elite male players reach V[Combining Dot Above]O2max at ∼19 km·h−1 during treadmill running (2), which is ∼3 km·h−1 higher than seen with women (2,6,14). Although treadmill running does not equate to the intensities seen during match performance due to changes in acceleration and direction as well as movements on the ball, relative intensities may be comparable for men and women. Bradley et al. found men ran a greater distance at speeds >15 km·h−1 but found minimal differences at <12 km·h−1 (6). The speed zones used in this study found men ran a greater distance in games and practices at speed zones above >19 km·h−1 but no difference between 11 and 14.99 km·h−1. Although differential speed zones' thresholds for men and women would have been desirable, the capabilities to individualize speed zones were not available in the Polar software at the time this study was conducted. Alternatively, individualized speed zones based on physical capacity tests have been suggested (7) and may prove more beneficial than generalized male/female speed thresholds. This may aid in understanding differences between individual athletes as well as to more fully recognize relative sex differences in external load during practices and games and may be necessary if one hopes to optimize the utility of GPS monitoring. However, the type of testing necessary to determine adequate speed zone thresholds warrants further consideration (21).

Furthermore, there is currently no consensus on the definition of a “sprint,” including the necessary acceleration/speed and the minimum duration required to be considered a sprint (23). Although some studies categorize sprints as distances in certain speed thresholds, such as distance covered >25 km·h−1 (16,25), others (including the current study) define them using velocity thresholds, such as efforts exceeding greater than 2.8 m·s−2 (23,24). It is important to note that linear movement does not take into consideration change of direction or acceleration. Future research is warranted to determine sprint classifications specific to the sport of soccer.

When all players were considered in analysis, the trends for higher TL seen in men versus women could indicate a possible difference in coaching strategies. Women may tend to share the workload among substituted players to a greater extent than men. Sex differences may be a function of substitution strategies rather than just physiological demands of the sport. For example, coaches of the female players tended to share the team's TL by substituting different players more frequently, as evident with 16 players who were included in the greater than 50% of games criteria as well as the lower playing time averages seen across the women. This is in direct comparison with the men's substitution strategies that only included a total of 12 players who met the minimum of 50% of games. It is important to note that TL is expressed in arbitrary units and is not a validated measure; therefore, should not be used as a stand-alone metric. However, it combines the use of other metrics such as HR and energy expenditure to reflect the overall workload of the players. In addition, it can be used as a tool to communicate to the coaches and training staff as to the physiological strain placed on the players.

One strength of the current study is that male and female collegiate players were analyzed during the whole competitive season to more fully understand the workload demands throughout practices and games. Furthermore, HRmax and V[Combining Dot Above]O2max were directly measured during a GXT test before the start of the season. This allowed for more accurate HR measurements and energy expenditure throughout the season. Establishing the relationship between HR and V[Combining Dot Above]O2 during a fitness test allows an accurate indirect measurement of V[Combining Dot Above]O2 during soccer matches. Establishing each player's relationship between HR and V[Combining Dot Above]O2 may accurately reflect the energy expenditure in a soccer match (22), thus giving a good representation of the internal physiological load of the player. Although is it acknowledged that periodic maximal performance tests would have been optimal to determine changes in players' physiological capabilities, the added fatigue incurred by the athlete during an already demanding training cycle limits the feasibility of implementing these tests during the season.

In this study, the internal physiological load was found to be similar for men and women despite differences seen in performance testing capabilities. Internal load metrics, TL, HR, and Kcal·kg−1 were determined on an individual basis based on data from V[Combining Dot Above]O2max testing. However, differences in power production and muscle mass, as seen with CMJ testing and body composition testing, were not quantified in any load metrics. There may be a benefit to incorporating individual power and speed metrics into athlete tracking systems in the future; however, determining the type of test necessary to quantify these metrics may require further research. In addition, because of the large variations seen in distances covered at different intensities, it has been suggested that game intensity be expressed as a percentage of HRmax and to include the number and duration of sprints (22). Future studies should consider both the internal and external load derived from maximal capacity testing data to understand the relative differences in workload among individual players. This study further lends support for the notion that there are few apparent differences in workload between men and women during a typical soccer game if you are able to account for relative differences between sexes.

Practical Applications

Coaches and sports scientists have the ability to implement monitoring techniques that track relative player workloads throughout practices and games to enhance player health and performance during the season. The similarities in workload throughout practices and games indicate similar relative demands for men and women. Furthermore, the application of male-oriented speed zones to a female team tracking system may result in the underestimation of external load. For comparative purposes, relative speed zones based on individual speed testing data may be more useful than team speed zone thresholds. Furthermore, the capability to customize speed zones based on tested speed values would be a desirable characteristic to quantify an athlete's workload. An individualized approach to tracking high-intensity running may improve workload prescriptions on a per player basis.


Special thanks to the Rutgers men's and women's soccer team. The results of this study do not constitute endorsement of the product by the authors or the NSCA.


1. Anderson J, Orme P, Di Michele R, et al. Quantification of training load during one-, two- and three-game week schedules in professional soccer players from the English premier league: Implications for carbohydrate periodisation. J Sport Sci 34: 1250–1259, 2016.
2. Bangsbo J. Energy demands in competitive soccer. J Sports Sci 12: S5–S12, 1994.
3. Bangsbo J, Mohr M, Krustrup P. Physical and metabolic demands of training and match-play in the elite football player. J Sports Sci 24: 665–674, 2006.
4. Bangsbo J, Norregaard L, Thorso F. Activity profile of competitive soccer. Can J Sports Sci 16: 110–116, 1991.
5. Bradley PS, Carling C, Archer D, et al. The effect of playing formation on high-intensity running and technical profiles in English FA premier league soccer matches. J Sports Sci 29: 821–830, 2011.
6. Bradley PS, Dellal A, Mohr M, Castellano J, Wilkie A. Gender differences in match performance characteristics of soccer players competing in the UEFA Champions League. Hum Mov Sci 33: 159–171, 2014.
7. Bradley PS, Vescovi JD. Velocity thresholds for women's soccer matches: Sex specificity dictates high-speed-running and sprinting thresholds—female athletes in motion (FAiM). Int J Sports Physiol Perform 10: 112–116, 2015.
8. Brozek J, Grande F, Anderson J, Keys A. Densitometric analysis of body composition: Revision of some quantitative assumptions. Ann N Acad Sci 110: 113–140, 1963.
9. Ceesay S, Prentice A, Day K, et al. The use of heart rate monitoring in the estimation of energy expenditure:validation study using indirect whole-body calorimetry. Br J Nutr 61: 175–186, 1989.
10. Giersch G, Huggins R, Benjamin C, et al. Validity and reliability of a shirt-based integrated GPS sensor: 2759 board #42. Med Sci Sports Exerc 50: 673, 2018.
11. Greig MP, McNaughton LR, Lovell RJ. Physiological and mechanical response to soccer-specific intermittent activity and steady-state activity. Res Sports Med 14: 29–52, 2006.
12. Helgerud J, Hoff J, Wisloff U. Gender differences in strength and endurance of elite soccer players. In: Science and Football IV. Spinks WR, Reilly T, Murphy A, eds. Sydney, Australia: Taylor and Francis, 2002. pp. 382.
13. Jeong T, Reilly T, Morton J, Bae S, Drust B. Quantification of the physiological loading of one week of “pre-season” and one week of “in-season” training in professional soccer players. J Sports Sci 29: 1161–1166, 2011.
14. Krustrup P, Mohr M, Ellingsgaard H, Bangsbo J. Physical demands during an elite female soccer game: Importance of training status. Med Sci Sports Exerc 37: 1242–1248, 2005.
15. Malone J, di Michele R, Morgans R, et al. Seasonal training-load quantification in elite English premier league soccer players. Int J Sports Physiol Perform 10: 489–497, 2015.
16. Mohr M, Krustrup P, Andersson H, Kirkendal D, Bangsbo J. Match activities of elite women soccer players at different performance levels. J Strength Cond Res 22: 341–349, 2008.
17. Mohr M, Krustrup P, Bangsbo J. Match performance of high-standard soccer players with special reference to development of fatigue. J Sports Sci 21: 519–528, 2003.
18. Mujika I, Santisteban J, Impellizzeri FM, Castagna C. Fitness determinants of success in men's and women's football. J Sports Sci 27: 107–114, 2009.
19. Owen AL, Lago-Penas C, Gomez M, Mendes B, Dellal A. Analysis of a training mesocycle and positional quantification in elite European soccer players. Int J Sports Sci Coach 12: 665–676, 2017.
20. Owen AL, Wong D, Dunlop G, et al. High-intensity training and salivary immunoglobulin A responses in professional top-level soccer players: Effect of training intensity. J Strength Cond Res 30: 2460–2469, 2016.
21. Scott D, Lovell R. Individualisation of speed thresholds does not enhance the dose-response determination in football training. J Sports Sci 36: 1523–1532, 2018.
22. Stolen T, Chamari K, Castagna C, Wisloff U. Physiology of soccer: An update. Sports Med 35: 501–536, 2005.
23. Sweeting AJ, Cormack SJ, Morgan S, Aughey RJ. When is a sprint a sprint? A Review of the analysis of team-sport athlete activity profile. Front Physiol 8: 432, 2017.
24. Varley M, Gabbett T, Aughey R. Acceleration profiles in elite Australian soccer. Int J Sports Med 34: 34–39, 2013a.
25. Vescovi JD. Sprint speed characteristics of high-level American female soccer players: Female athletes in motion (FAiM) study. J Sci Med Sport 15: 474–478, 2012.
26. Walker A, McFadden B, Sanders D, et al. Biomarker response to a competitive season in Division I female soccer players. J Strength Cond Res 33: 2622–2628, 2019.

athlete monitoring; high-speed running; heart rate; GPS

Copyright © 2020 by the National Strength & Conditioning Association.