Authors: Uri Harel1, Lael Gershgoren2 ,  Eli Carmeli1

1Department of Physical Therapy, University of Haifa, Haifa, Israel 
2School of Behavioral Sciences, The College of Management, Rishon-Lezion, Israel 

Corresponding Author:
Eli Carmeli
Tel: + 972507393454
Fax: + 97248288140

Uri Harel B.E.d, MA in Exe. Physiology is an athletic trainer in Maccabi Haifa Soccer Club in Israel. He plans and writes the exercise programs for major league adult and U-21 soccer players. 

Lael Gershgoren, PhD is faculty member at the The Academic College of Tel Aviv-Yaffo, expert in Sport Psychology.

Eli Carmeli PT, PhD, is faculty member at the Department of Physical Therapy, University of Haifa, ISRAEL, expert in movement performance.

Differences in activity patterns between adult and U-21 major league players


The purpose of this study was to measure differences in activity patterns between major league adult and U-21 soccer players. Four U-21 players and four adult team players were evaluated using a repeated measures technique. All eight players were affiliated with the Maccabi Haifa Soccer Club from the Israeli professional and U-21 major leagues, depending on the player’s age. GPS sensors were attached to the players during five consecutive games to identify patterns regarding running distance and speed according to the field positions. There was no significant difference in the total running distances covered by two age groups; however, when measuring high running speed, an advantage was observed for the adult group in comparison to the U-21 in general and between players playing in the same position. These findings provide valuable knowledge that may serve the principle of training specificity. First, it may assist practitioners adjust specific intensity levels to players depending on their position on the field and physical function. Moreover, it can serve coaches in transitioning U-21 players to the adult team by progressively adjusting their physical capacities to those needed at the adult level.

Key Words: physical fitness, soccer, positional differences, GPS, training specificity, Semi-Automatic Video Analysis (SAVA)


Soccer is one of the most popular spectator sports worldwide. Nevertheless, in spite of its relative accessibility to spectators, professional players require superior technical, tactical, mental, and physiological capabilities. To assist professionals in achieving this state, scholars in various fields of knowledge are engaged in studying the game. In turn, this cumulative knowledge is used to improve the training process and enhance competitiveness. Among the capabilities mentioned, the physiological component is considered one of the most significant parameters for group and personal success (17). The relationship between physical fitness performance parameters was established in studies showing a significant correlation between aerobic abilities (VO2 max) and league table ranking. Moreover, high correlation was found between a player’s oxygen consumption level and cumulative total distance within a match (23).

Duk, Kawaczynski and Chmura (13) investigated the national South Korean team during the 2010 soccer World Cup in South Africa. They concluded that the Korean team compensated for technical inferiorities with a high aerobic infrastructure. Using semi-automatic video analysis (SAVA), they revealed that in comparison to France and Brazil (who received higher scores in technical parameters) the Korean player ran on average of six kilometers more and at higher average speeds. Furthermore, they showed the Korean players’ total running distance matched that of the four semi-final qualifying national teams. They concluded that these physiological parameters enabled the Koreans to move into higher rounds despite inferiority in technical parameters compared to the two leading national teams.

To ensure effective physical training, it is essential to first identify the nature and physiological patterns of the activity and accordingly explore the energy sources exploited during the game. Furthermore, it is very important to understand the differences between the physiological requirements of the players depending on their position on the field (11,21). Accurate analysis of these measures will allow the coaching staff to quantify the physiological effort in order to develop successful training programs that rely on the principle of training specificity (12). The literature describes several methods that were developed in recent years to collect statistical data during a soccer game. The most commonly documented method is the “visual evaluation”, where experts and statisticians estimate, document and record all activity patterns according to what happens on the field. Although this method allows data to be presented during the game in “real-time”, it is labor-intensive and requires highly skilled people (18).

Another method that provides valid information and accurate analysis of the physical and technical aspects is Semi-Automatic Video Analysis (SAVA). This method, which provides data during and after the game, is based on multiple cameras located on the field. The calibrated cameras allow individual monitoring of every player on the field throughout the game. It provides technical information, such as number of complete/incomplete “passes”, number of shots on goal, turnovers, steals, etc. In addition, it provides a variety of physical data, such as the distance and speed run. In modern stadiums, cameras are used to provide “live” statistical analysis and other data to the professional staff and spectators. As the number of cameras increases, so does the amount of data recorded. Researchers have indicated that SAVA is a good tool for assessing exercise capacity in general and high intensity running distance in particular.

Global Position System (GPS) satellite tracking is an advanced technology used to collect a wide range of physiological data during a game. A tracking sensor system is attached to a player’s body. These include total distance run, positions of the players throughout the game, aggregation, and distribution of running distance at different speeds, effort through the impact force of the player on the ground, and a calculated measure of fatigue based on the ratio between the impact force and running speed variables (22).

Studies on the activity patterns of soccer players help improve the training process. For example, Barros, Misutal and Menezes (4) found that the nature of the activity varies according to field position. They demonstrated that, on average, Midfielders accumulate significantly more distance during a game than do Strikers and Central Defenders. Accordingly, they suggested integrating more endurance into Midfielders’ practices, as well as considering high oxygen consumption as a vital position parameter.

Bangsbo et al (1) have used SAVA to break down soccer games into five-minute segments. They identified three main periods of physiological fatigue: two constant and one temporary. The constant periods are the first five minutes of the second half of the game, due to a decrease in muscle temperature, and the last 5 minutes at the end of the game, due to muscle glycogen depletion. The temporary period relates to the relationship between two consecutive segments. Thus, a higher than average effort segment will be followed by five minutes of temporary fatigue. The reason for this decrease is yet unclear, but it may be related to the mechanism of the sodium-potassium pump and lactate levels. The researchers recommended a practical application of the physiological findings, such as short warm-up before the start of the second half.

Statistical analyses of activity patterns can also help target training methods that will maximize the ability to execute the skills found to be the most important. For example, a longitudinal study conducted among elite soccer players in the premier Italian league, showed that 75.8% of running at high speeds (over 19 kph) are made without the ball and for a distance of nine meters (27). Additionally, it was found that sprint running in a straight line is the most common action prior to scoring among professional soccer players (26). These data highlight the importance of training and testing players’ abilities in nine meters sprint with and without the ball, and with executing goals.

Although there are data about activity patterns in soccer, studies based on GPS advanced technology data collection are lacking. As mentioned, real-time GPS data can provide professionals with unique quantitative data that might lead to better tools to deal with the ever-increasing competitive levels. The few studies using GPS, thus far, evaluated U-19 players (26), beach soccer players (7,8), and semi-professional players (10). Hence, studies that use advanced GPS technology and elite soccer players are very importance.

Therefore, the primary aim of this study was to measure differences in activity patterns between major league adult and U-21 soccer players using GPS technologies. The physiological parameters examined were total running distance, high-speed coverage (Zone 5, between 5.5 to 7 mps, and sprint distance Zone 6, above 7 mps). Moreover, using these parameters, this study aimed to identify differences in Centeral-Defender, Fullback, Midfielder, and Striker field positions in both age groups. Based on the literature, it was hypothesized that: (a) on average, adult professional players will perform longer in Zone 6 than their U-21 counterparts will, (b) the adult Centeral Defender, Fullback, Midfielder and Striker will perform longer in Zone 6 corresponding to the same positions in the U-21 age group, and (c) no significant differences will be found between age groups and positions in total running distance and Zone 5 coverage.



Eight soccer players volunteered to participate; four adult professional players (22-31 years old) and four young players (18-19 years old). All belonged to the Maccabi Haifa Club from the Israeli premier league, playing four different positions: Fullback, Center Defender, Midfielder, and Striker.  Inclusion criteria were players who planned to play multiple consecutive games, and successfully underwent ergometry and echocardiography (ECG) physiology tests. Exclusion criteria were players who had sustained an injury in the last 6 months. The anthropometric data of the players as individuals and as a group were collected and are presented in Table 1.

Table 1


The running speed tests were performed using an optical system (Elga System, Austria). Global position system (GPS) (STATSport Viper system, Ireland) was used to monitor and analyze the activity patterns during the games. The system includes sensors (10 Hz) attached to the body of the player. The sensors are connected to a remote computer containing software that translates and records certain measures (8). Four dependent variables were recorded using the GPS: a) The total running distance covered, measured in m/sec. b) Running speed at “sub-maximal” effort that is considered as Zone 5 category. It includes the cumulative distance in meters ranging in speeds from 5.5 to 7 m/sec. c) The cumulative high-speed running in meters (considered as Zone 6 category). Zone 6 includes all fast running (i.e., sprints) performed at an intensity more than 7 m/sec. d) Maximum speed per game (mph); that is, the fastest sprint each player performed, each game.


The study was performed in accordance with the ethical standards of the Institutional Review Board of the University of Haifa. At the beginning of the soccer season, all players underwent a comprehensive physical examination, including anthropometric assessments (BMI, skin folds), ECG and running speed tests of 10, 20 and 30-meter distances. Each player performed two trials and the best result was recorded. The follow-up study was implemented over five consecutive premier league games. After each game, the GPS data were kept on a password-protected computer.

Data Analyses

Descriptive statistics were used to capture means and standard deviations in both the individual and group levels. The Cohen’s d test was used to measure the differences in effect size (ES) between and within groups and positions.


At the beginning of the season, each player’s speed was tested for 10, 20 and 30 meters. The individual and group analyses of the speed test presented in Table 2 demonstrates no average differences between the adult and the U-21 teams. These results were similar in all 3 distances measured, suggesting that differences in running speed patterns are not due to variations in speed abilities between the groups.

Table 2

The first study hypothesis centered on activity differences in high intensity running (more than 7 m/sec; Zone 6). A large effect size (ES; Cohen’s d =1.95, SD pooled = 152.37) was found between the adult team (M=428.1; SD=190.75) and the U-21 team (M=130; SD=100.24). Total distance running in Zone 6 data are presented for each group in Table 3. Figure 1 depicts the averages of the groups for this variable, throughout the five games. These results suggest that meaningful differences in Zone 6 activity patterns exist between the two research groups in favor of the adult group.

Table 3
Figure 1

Pertaining to Zone 5 running speed, we found a moderate ES (Cohen’s d = 0.36, SD pooled = 156.11) between the adult and the U-21 teams (M=533.25, SD=150.12 and M=476.4; SD=161.88, respectively). Zone 5 running data are presented in Table 4. Figure 2 demonstrates the group averages for Zone 5 throughout the five consecutive games.

Table 4
Figure 2

A Small ES (Cohen’s d = .1, SD pooled = 624.53) was calculated for total distance run. The adult team ran on average 10,246.05 m (SD=489.67) and the U-21 team ran 10,181.95 m on average (SD=575.53). Total distance running data are presented in Table 5 for the groups. Figure 3 depicts the total distance averages throughout the five consecutive games.

Table 5
Figure 3

A very large ES (Cohen’s d = 1.30, SD pooled = 1.56) was found for maximum running speed. While the adult players ran an average of 31.74 kph (SD=1.72) the U-21 players ran 29.70 kph (SD=1.39). Figure 4 presents the distribution of this variable throughout the five consecutive games.

Figure 4

The second research question of this study focused on the differences in activity patterns between the groups within each field position (Fullback, Central Defender, Midfielder or Striker). In Zone 6 running speed, very large ESs emerged between the adult players in comparison to the U-21 players. The largest ES was revealed between the Midfielders (ES = 5.22, Pooled SD = 68.66). The smallest was between the Central Defenders (ES = 1.85, Pooled SD = 79.20). Data pertaining to Zone 6 running speed for each position are presented in Table 6.

Table 6

Similar to Zone 6 running speed, very large ESs were revealed for Zone 5 running speed between the adult and the U-21 players. However, not all ES were in the same direction. The U-21 Striker covered on average a much larger distance than his adult counterpart (ES = 1.84, Pooled SD = 79.62). The other ES demonstrated an advantage for the adult players, with the largest difference between the Fullbacks (ES = 1.48, Pooled SD = 103.25) and the smallest between Midfielders (ES = 1.15, Pooled SD = 114.41). Data pertaining to Zone 5 running speed for each position are presented in Figure 5.

Figure 5

Mixed results were also revealed in the total distance-running category. Adult players outran the U-21 counterparts in both the Fullback (ES = .89, Pooled SD = 684.68) and the Striker (ES = .62, Pooled SD = 568.87) positions. No differences were found between the adult and U-21 Central Defenders (ES = .02, Pooled SD = 524.40). A very large ES emerged for the U-21 Midfielder who outran the adult (ES = 1.71, Pooled SD = 423.14). Total distance running data by positions are presented in Figure 6

Figure 6

Large ESs emerged in favor of the adult players across positions, in maximum running speed. The largest ES was revealed between the Midfielders (ES = 2.93, Pooled SD = 1.18). The smallest was between the Strikers (ES = 1.30, Pooled SD = .88). Data pertaining to this variable by position are presented in Table 7.

Table 7


The first aim of this study was to examine the difference in activity patterns in total running distance, high-speed coverage and sprint distance between U-21 and major league adult soccer players. Very large Cohen’s d ES were found between U-21 and adult players in high intensity (above 7 mps) running. On average, adult players ran almost 300 meters more in this high speed-intensity category than U-21 players did. Furthermore, a medium ES was revealed between the groups, in favor of the adult group, in the Zone 5 high-speed category (i.e., 5.5-7 mps). These findings are valuable, as scholars and practitioners consider high intensity running distance a crucial factor in determining the final game score (6).

These results are not grounded in differences in running ability, as no differences were found between the groups in 10, 20, and 30 meters sprinting. However, running economy, through physiological and motor factors, might be the underlying mechanism of the differences found. Running economy expresses the ratio between workload and VO2 in a given task. This ratio may differ from one athlete to another, despite similar VO2 max values, representing differences in ability to perform the same physical demands using higher or lower VO2 uptakes (16).

From a motor perspective, running economy is influenced by anatomical, neurological, and biomechanical factors. Biomechanical variables that effect running economy are, for example, ground reaction force, body posture, arm position, center of gravity, and synchronization of extremities (2). Physiologically, it is postulated that athletes with lower running economy are able to sustain a continuous sub-maximal effort at the cost of an earlier use of lactic and a-lactic anaerobic resources. Consequently, these athletes struggle, later on, to perform short, high intensity (i.e., above 7 mps) efforts (1). Furthermore, it was found that running economy provides an advantage for experienced as compared to younger athletes in general, and in high intensity running in particular (28).Based on these findings and postulations, it is possible that enhanced running economy allowed our experienced athletes to run the same total distance as our younger athletes did, but with lower VO2 and HR. Subsequently, more anaerobic sources were kept available for high intensity running.

This study’s findings can also be explained through psychological factors. Lefferts et al., (20) have stated that elite athletes have superior decision-making processes as compared to their less professional counterparts.  In dynamic sports, such as soccer, decision-making is a sequential process starting with situational awareness, mainly based on visual search (5).  Hence, experienced handball athletes have enhanced perceptual-cognitive (e.g., anticipation abilities) skills (14),leading to better decisions and positioning (19). Consequently, they can exert less effort in unnecessary runs and respond better with high intensity running according to situational demands.

The second purpose of this inquiry was using these aforementioned parameters to identify running pattern differences within the Fullback, Central Defender, Midfielder, and Striker field positions between both age groups. Very large Cohen’s d ESs (1.85<ES<5.22) were evident in favor of the adult players in high intensity running (above 7 mps) across all positions. Similar results (with the exception of the Striker) were found for Zone 5 intense running. As mentioned, running economy and decision-making may explain these differences.

As hypothesized, no significant differences in total running distance were found between the age groups, on average. However, in contrast to the hypothesis, differences in total running distance within positions were found between age groups. Yet, these differences varied in direction. A large ES was obtained between the defenders, indicating that the adult Fullback ran substantially more than the U-21 did. Similarly, the adult Striker covered a longer total distance (ES = .62) than his U-21 counterpart. In contrast, a very large ES was revealed between the Midfielder positions, with the U-21 player covering considerably more total distance than the adult player did. These results suggest mixed patterns of total distance running within the team, between the age groups.

Studies have examined the movement patterns of soccer players and found that Midfielders cover the most distance, while Central Defenders cover the least (9,13, 4, 24).  Investigations of the French premier league, Dellal ranked distance covered by position. They found Central Defenders had the least, followed by Fullbacks and Strikers, and Midfielders with the longest distance (12). Since the U-21 Midfielder has to cover long distances, he must perform sub-maximal runs to compensate for his lack of high intensity Zone 5 and Zone 6 running. With motor, physiological, and cognitive maturation and training, the young player can foster his running economy and decision-making. In return, he should be able to replace long sub-maximal runs with shorter and smarter high intensity runs. Furthermore, it is possible that young Midfielders are needed for high distance coverage to compensate for his teammates’ lack of skills. This postulation is supported by the results of Bradley, Lago-Penas & Diaz (2013), who found that soccer players in lower leagues run more than their more experienced counterparts do. The researchers concluded that the extra miles covered was intended to compensate for lower technical ability.

These results indicate that speed intensity depends on running economy as well as psychomotor abilities. Significant differences were found between the adult players and their U-21 counterparts in Zone 5 and Zone 6 intensity runs. These gaps must be specifically and effectively addressed in order to compare the U-21 players’ loads to those of the adults. This can be accomplished by adding mild to moderate physical content at the end of the game. However, one must ensure that this content is not too intense in order to avoid overtraining or overuse injuries. Examples of these activities are:   

  • 18 sprints of 50 meters in 10 seconds (i.e., 5 to 5.5 mps) with 10 second breaks in between. Overall, six minutes of practice.
  • 8 sprints of 60 meters in 10 seconds (i.e., above 6 mps) followed by 20 second runs of 60 meters back to the starting point. This anaerobic threshold exercise forces the players to perform 8 high intensity runs alongside 8 moderate intensity ones: 4 minutes total practice.
  • 10 sprints of 75 meters at approximately 5.5 mps, followed by a 15-second walk for 15 meters. This exercise is performed back and forth on a 90 meter course. Total of 5 minutes practice. 

These exercises are short and do not overload the player. In addition, the proposed speed is sub-maximal, yet relatively intense. Therefore, the effect in the end will contribute to reduce the physiological differences between U-21 and adult players. The use of GPS sensors during the game and these extra exercises could provide the players and the coaching staff with valuable information regarding the load to which the player should be exposed. This load can be then compared to that required from an adult professional player in the same position. HR straps can be used to illustrate improvements in recovery time for tasks with similar intensities.

An additional training option is deliberately intense runs as part of weekly training schedules. For example, using photoelectric cells to verify maximum velocity, players can execute five to seven all-out sprints of 20-30 meters with 60 seconds rest between them. Later, this practice can be performed using solely a GPS device to confirm maximum running velocity. This practice of repeated sprints is based on the High-intensity Interval Training principles, recommended for such aims (25).Moreover, monitoring a player’s HR and decrease in sprint execution time might provide valuable information to athletes regarding improvement and recovery abilities.

This study is unique for its use of GPS data collection during 5 official professional adult and U-21 games. However, despite its high validity (an official match) and reliability (the use of GPS instead of cameras) some limitations should be acknowledged. First, it included only 8 players, four in each age group. This limitation stems from the clubs’ notions that actual data should not be revealed, which prevents access to existing data and to data collection. Furthermore, the researchers had to confirm that the players a) play the same positions (to compare U-21 Fullback to an adult Fullback), b) fully and sequentially played all five games, at the same time of the season, and c) that the U-21 players were appropriate age. We call on additional clubs to provide access to both existing data and to data collection for the benefit of young players’ development through deliberate, evidence-based practice.

One may also claim that tactical instructions can affect the running patterns of the players in the two teams evaluated in this study. In fact, the club imposed similar game styles to the adult and U-21 teams. However, each game can evolve differently based on the opponent’s abilities (e.g., superior or inferior), the score (e.g., receiving or scoring an early goal), etc. Therefore, more research is needed to expand on the data collected in this study. Furthermore, these researches may even aim at addressing variables such as the time of the game and television broadcasts, which were not controlled in this study.

Despite these limitations, the data in this study were collected using advanced GPS technology and during real-time performances (i.e., official games). Therefore, the results herein are valuable in determining running patterns differences between U-21 and adult professional soccer players. Furthermore, we believe that the deliberate training options presented in this study represent evidence-based practices that can promote young soccer player toward becoming professional adult players.


Differences in high intensity running patterns between U-21 and Adult players must be examined and addressed. It is important to integrate high intensity runs into U-21 players’ practice regimes and monitor them during both practices and games. This intentional practice can increase the workloads of U-21 players to those of adults, thus preparing them better for future careers in professional soccer


Soccer coaches and athletic trainers should integrate sport sciences into the field, As such, they need to adapt a player-focused approach and to apply more specific training, and thus to better prepare each soccer player to optimized his performance in the field.


  1. Bangsbo, J., Mohr, M., & Krustrup, P. (2006). Physical and metabolic demands of training and match-play in the elite football player. Journal of Sports Sci, 24(7), 665-674. https://doi: 10.1080/02640410500482529
  2. Barnes, K.R., & Kilding, A.E. (2015). Running economy: measurement, norms, and determining factors. Journal Sports Medicine, 1:8. https://doi: 10.1007/s40279-014-0246-y.
  3. Bangsbo, J., Iaia, F.M., & Krustrup, P. (2007). Metabolic response and fatigue in soccer. International Journal of Sports Physiol and Perform, 2(2),111-127. https://doi: 10.1123/ijspp.2.2.111.
  4. Barros, R.M., Misuta, M.S., Menezes, R.P., Figueroa, P.J., Moura, F.A., Cunha, S.A., Anido, R., & Leite, N.J. (2007).  Analysis of the distances covered by first division Brazilian soccer players obtained with an automatic tracking method. Journal Sports Science Med, 6, 233-242.
  5. Basevitch, I., Tenenbaum, G., Land, W.M., & Ward, P. (2015).  Visual and skill effects on soccer passing performance, kinematics, and outcome estimations. Frontier in Psychol, 6,198. https://doi: 10.3389/fpsyg.2015.00198
  6. Bradley, P.S., Lago-Penas, C. Rey, E. & Diaz, A,G. (2013). The effect of high and low percentage ball possession on physical and technical profiles in English FA Premier League soccer matches. Journal of Sports Sci, 31(12), 1261-1270. https:// doi: 10.1080/02640414.2013.786185. 
  7. Castellano, J., & Casamichana, D. (2010). Heart rate and motion analysis by GPS in beach soccer. Journal Sports Science and Med, 9(1), 98-103.
  8. Castellano, J., Casamichana, D., Calleja-Gonzales, J., San Roman, J., & Ostojic, S.M. (2011).  Reliability and accuracy of 10 Hz GPS Devices for Short- Distance Exercise. Journal Sports Science and Med, 10(1), 233-234.
  9. Chmura, P., Andrzejewski, M., Konefał, M., Mroczek, D., Rokita, A., & Chmura, J. (2017). Analysis of Motor Activities of Professional Soccer Players during the 2014 World Cup in Brazil. Journal of Human Kinetics. 56,187-195. https://doi: 10.1515/hukin-2017-0036. 
  10. Cummins, C., Orr, R., O’Connor, H., & West, C. (2013). Global positioning systems (GPS) and microtechnology sensors in team sports: a systematic review. Sports Med, 43(10),1025-1042. https://doi: 10.1007/s40279-013-0069-2.
  11. Di salvo, V., Gegson, W., Atkinson, G., Tordoff, P., & Drust, B. (2008). Analysis of high intensity activity in premier league soccer. International Journal Sports- Med, 30, 205 – 212. https://doi: 10.1055/s-0028-1105950.
  12. Djaoui, L., Wong del, P., Pialoux, V., Hautier, C., Da Silva, C.D., Chamari, K., & Dellal, A. (2010). Physical and technical activity of soccer players in the French first league- with special reference to their playing position. International Journal Sports Med, 11(2), 278 – 290. https://doi: 10.5812/asjsm.34233.
  13. Duk, O.S., Min, K. S., Kawaczynski, A., Chmura, P., Mroczek, D., & Chmura, J. Endurance and speed capacity of the Korea republic football national team during the world cup of 2010.  Journal of Human Kinetics, 30, 115-21. https://doi:10.2478/v10078-011-0079-9
  14. Fischer, L., Baker, J., Rienhoff, R., Strauß, B., Tirp, J., Büsch, D., & Schorer, J. (2016). Perceptual-cognitive expertise of handball coaches in their young and middle adult years. Journal of Sports Sci, 34(17),1637-42. https://doi:10.1080/02640414.2015.1128558.
  15. Harley, J.A., Barns, C.A., Portas, M. Lovell, R. Barret, S. Paul, D. & Weston, M. (2010). Motion analysis of match-play in elite u-12 to 16 age group soccer players. Journal Sports Sci, 28(13), 1391-1397. https://doi: 10.1080/02640414.2010.510142.
  16. Ferrauti, A., Bergermann, M., & Fernandez, J. (2010). Effects of a concurrent strength and endurance training on running performance and running economy in recreational marathon runners. Journal of Strength and Conditioning Res, 24(10),2770-2778. https://doi: 10.1519/JSC.0b013e3181d64e9
  17. Helgerud, J., Engen, L.C., Wisloff, U., & Hoff, J. (2001). Aerobic endurance training improves soccer performance. Medicine and Science in Sports and Exer, 33(11), 1925 – 1931. https://doi: 10.1097/00005768-200111000-00019.
  18. Iaia, F.M., Fiorenza, M., Perri, E., Alberti, G., Millet, G.P., & Bangsbo, J. (2015). The Effect of Two Speed Endurance Training Regimes on Performance of Soccer Players. PLoS One, 10(9), e0138096. https://doi: 10.1371/journal.pone.0138096.‏ https://doi: 10.1111/j.1600-0838.2010.01104.x.
  19. Kannekens, R., Elferink-Gemser, M. T., & Visscher, C. (2011). Positioning and deciding: key factors for talent development in soccer. Scandinavian journal of Med & Sci in sports, 21, 846-852.‏ https://doi: 10.1111/j.1600-0838.2010.01104.x.
  20. Lefferts, W.K., DeBlois, J.P., White, C.N., Day, T.A., Heffernan, K.S., & Brutsaert, T.D (2019). Changes in cognitive function and latent processes of decision-making during incremental ascent to high altitude. Physiology Behavior. 201,139-145. https://doi: 10.1016/j.physbeh.2019.01.002
  21. Lmpellizzert, F.M., Marcora, S.M., Castagna, C., Reilly, T., Sassi, A., Iaia, F.M., & Rampinini, E. (2006). Physiological and performance effects of generic versus specific aerobic training in soccer players. International Journal Sports Med, 27, 483 – 492. https://doi: 10.1055/s-2005-865839.
  22. Oliva-Lozano JM, Rojas-Valverde D, Gómez-Carmona CD, Fortes V, & Pino-Ortega. (2020). Impact of Contextual Variables on the Representative External Load Profile of Spanish Professional Soccer Match-Play: A Full Season Study. Eur J Sport Sci, :1-10. doi: 10.1080/17461391.2020.1751305.
  23. Rampinini, E., Bishop, D., Marcora., S.M., Ferrari bravo, D., Sassi, R., & Impellizzeri, F.M. (2007). Validity of simple field tests as indicators of match-related physical performance in top-level professional soccer players. International Journal Sports Med, 28, 228 -235. https://doi: 10.1055/s-2006-924340.
  24. Rienzi, E., Drust, B., & Reilly, T. (2000). Investigation of anthropometric and work-rate profiles of elite South American international soccer players. Journal Sports Medicine Physiology Fitness, 40(2), 162-169.
  25. Schenk, K., Bizzini, M., & Gatterer, H. (2018).Exercise physiology and nutritional perspectives of elite soccer refereeing. Scandinavian Journal of Med Sci in Sports, 28(3),782-793. https://doi: 10.1111/sms.12989
  26. Tomas, M., Frantisek., Z. Lucia., M. & Jaroslav., T. (2014). Profile, correlation and structure of speed in youth elite soccer players. Journal of Human Kinetics, 40, 149-59. https://doi: 10.2478/hukin-2014-0017.
  27. Vigne, G., Gaudino., C. Rogowski., L. Alloatti., G. & Hautier., C. (2010). Activity profile in elite Italian soccer team. International Journal Sports Med, 31, 304 – 310.  https:// doi: 10.1055/s-0030-1248320.
  28. Weston,M., Castagna, C., Impellizzeri, F.M., Bizzini, M., Williams, A.M., & Gregson, W. (2012).  Science and medicine applied to soccer refereeing: an update. Sports Med, 42(7), 615-631. https://doi: 10.2165/11632360-000000000-00000.
Print Friendly, PDF & Email