The physiological demands of soccer training and match-play have been intensively studied and quantified over the past 5 decades (2–4,6,10–12,18–22,24). Match-play requires that soccer athletes cover ∼10 km at an average intensity of 70–85% of maximal heart rate (HRmax) (3,17). Lasting a minimum of 90 minutes, soccer matches are aerobically taxing and consist of walking and low-intensity running interspersed with moments of high-intensity running and sprinting, as well as other explosive sport-specific skills such as slides, jumps, and short bursts of acceleration. Bangsbo et al. (4) found that professional players in Denmark spent an average of 8.1% of match-play running at a high intensity (≥15 km·h−1) and covered an average of 10.8 km per match, whereas Mohr et al. (18) similarly found that professional players in Italy spent an average of 8.7% of time running or sprinting and covered an average of 10.9 km per match. Thus, moments of high-intensity exercise contribute greatly to the physiological load of players, while they also tend to take place during important and decisive moments of matches.
More recently, a longitudinal study of the English Premier League showed that the average distance covered by high-intensity running and sprinting increased by ∼23 and ∼35%, respectively, over 7 seasons and accounted for ∼12% of the total distance covered during matches in the most recent (2012–2013) season examined (6). These data show a trend toward an increasing physical demand on modern soccer athletes and a greater importance of fitness and ability to perform repeated bouts of high-intensity running/sprints. It follows that training of elite soccer athletes should focus on improving the ability to perform and recover rapidly from such explosive events (6,18). However, it has not been determined whether fitness is associated with individual success or is a characteristic of the top individual players within a team or league. For instance, playing style, positioning, or enhanced reading of the game by elite players may supersede the need for exertion similar to other players, which may also lead to a lower physical load experienced from the same amount of training and match-play.
Many methods for tracking training load have been developed to monitor fatigue and readiness in athletes to prevent injury and optimize performance (1,5,7,9,11–13,15,16,18,19,22). Training load can be viewed as either the external work achieved or the internal physiological and psychological stress imposed on an athlete (15). Heart rate measurement is a simple and objective method to quantify intensity and training load in soccer athletes (16). Although HR monitors and other electronic equipment such as GPS are usually prohibited in professional competitions, data on match HRs have been obtained from friendly and subprofessional matches (16,17). The average HR of players in Spain's first division (La Liga) during preseason matches was 165 bpm, corresponding to 84.7% of individual HRmax (17). Similarly, a group of second-division professional players in Portugal exhibited mean and peak HRs corresponding to 87.1% and 99.7% of HRmax for a single friendly match (2). Only 1 study to date has tracked full-match HRs of professional players in official competitions, finding an average HR corresponding to 86% HRmax over 2 seasons (23). Still, HR data from high-level competitive matches are lacking.
Several HR-based methods have been created to quantify training load (5,7) and usually rely primarily on average HR during an exercise session. Mean HR provides little information on changes in intensity during intermittent activities, such as often occurs in soccer, and may not give an accurate representation of training load. The Polar Training Load (PTL), which is used in this investigation, is an individualized method that incorporates body weight, sex, and V[Combining Dot Above]O2max in addition to HR ranges and thresholds to quantify workload of an exercise session. The PTL may, therefore, be a more appropriate measure of load in soccer training and matches.
Although substantial work has been done in tracking physiological load of athletes in training sessions or over a season, less research has examined workload/intensity during competitive matches at a high level. This is important, as a match may contribute 25% of the weekly training load or as much as 50% when 2 matches are played in a week (16). Furthermore, to our knowledge, no study has focused on the quantification of match intensity and training load of a single elite payer compared with their team. We had the unique opportunity to examine a 2-time winner of the MAC Hermann Trophy, which is awarded annually to the top male and female soccer athletes in the National Collegiate Athletic Association (NCAA). Thus, over the 2 seasons for which we recorded match data, the athlete of interest (player A) was considered the top player in the nation at the collegiate level. The purpose of this study, therefore, was to investigate the match-play intensity and training load of a collegiate men's soccer team with special reference to a single elite player.
Experimental Approach to the Problem
Match session HR data were monitored during regular season and postseason matches over 2 seasons (2012, 2013) for a single NCAA division I men's collegiate soccer team. Laboratory measurements including maximal oxygen consumption (V[Combining Dot Above]O2max) were obtained during preseasons. Player A's physiological data were analyzed in comparison with his teammates because of his high achievements, including 2 consecutive MAC Hermann Trophies as a forward in 2012 and 2013, being one of only 5 athletes in the history of this award to receive it twice. In addition, he was an NCAA soccer All-American, an All-Conference, and an All-College Cup honoree both years. Data collection took place during player A's Junior (2012) and Senior (2013) years. It is important to note that his teammates were also high-level soccer athletes as evidenced by the facts that many of them were on All-Conference first or second teams, on various age-graded national teams, and many to most have gone on to play in U.S. professional soccer leagues.
All players were informed of the risks and benefits of the study and provided their written informed consent after the protocol was approved by the Institutional Review Board of the University of Maryland, College Park for the use of their previously recorded laboratory and match physiological data. Demographics obtained from preseason measurements for all players (age range 18–22 years) are listed in Table 1. For the 2012 season, a total of 22 members of the team, in addition to player A, were tested in the period from July 11 to August 13. For the 2013 season, a total of 11 teammates were tested in the period from May 7 to July 25. Off-season and preseason conditioning for the players consisted of resistance training, endurance conditioning, flexibility, hip activation and rotation, agility, prehabilitation, and speed work. Throughout the season, HR monitoring during training sessions and matches was used by the coaching staff to track and manage player training loads by allowing appropriate rest/recovery periods.
Matches were selected for the analysis of HR data if player A and at least 3 teammates played at least 50 minutes. Thus, 10 athletes were used for comparisons with player A in both 2012 and 2013. Seven of the players were the same for both seasons, meaning there were 13 team members used for comparison in total. Teammates for comparison with player A (forward) included 5 defenders, 5 midfielders, and 3 forwards.
Maximal Oxygen Uptake and Heart Rate
V[Combining Dot Above]O2max and other laboratory measurements were obtained during the preseasons. An incremental treadmill test was used to determine V[Combining Dot Above]O2max and HRmax. Athletes were tested in the laboratory using a Jaeger Oxycon Pro (Viasys Healthcare) metabolic system. Players performed a 3–5-minute warm-up to familiarize themselves with the testing treadmill. A 2–3-minute break was given for recovery before the test. All players ran at a speed of 12.9 km·h−1 (8.0 mph) or 13.7 km·h−1 (8.5 mph). The players ran at a 0.5% incline for the first 2 minutes of the test, after which incline increased 2% every subsequent 2 minutes until volitional exhaustion. Heart rate was measured using radio telemetry with a Polar FT1 (Polar USA) HR monitor. Five minutes after the test was terminated, posttest lactate (LAmax) was measured using an Arkray Lactate Pro LT-1710 (Arkray, Inc.) to validate a maximal effort. V[Combining Dot Above]O2max and HRmax results were used to individualize the PTL scores.
Heart Rate Data and Polar Training Loads
Match session HR data were recorded using the Polar Team2 Pro (Polar USA) system. Each session began recording when the athlete started wearing his personal monitor. Matches were eliminated in cases of incomplete data for the session or irregular variations in the graphical HR response. The PTL score presented is taken directly from the Polar Team2 Software (Polar USA) and is given in arbitrary units.
A total of 50 matches were monitored between the 2012 and 2013 seasons. The first and second halves were selected for analysis from the entire recorded session. Warm-ups, half time, cool-downs, overtimes, and any waiting periods were eliminated from the match session data. Of the 50 matches, 22 match sessions were analyzed for comparisons; 13 match sessions from 2012 (8 regular season and 5 play-off/championship matches) and 9 match sessions from 2013 (all regular season). Matches were selected if player A and at least 3 comparison players each played at least 50 minutes with their monitors. A comparison of average PTL per minute was also made between player A and teammates who met these criteria for at least 3 of the 22 matches (n = 12, average number of matches per player = 8.25 ± 5.97; range = 3–21). For correlational analyses, data from all matches in which players completed at least 50 minutes were used. This included 29 matches for player A and a total of 114 matches for the other team members.
Calculations and Reported Physiological Variables
Heart rate averages are reported for the first half, second half, and entire match session. The minutes per match variable refers to minutes per match played in regulation, excluding overtime. The PTL per minute score was calculated by dividing the PTL score by the minutes played in regulation. Percent time spent in 5 separate HR zones based on percentage of HRmax (zone 1, 50–59%; zone 2, 60–69%; zone 3, 70–79%; zone 4, 80–89%; and zone 5, 90–100%) were those directly reported by the Polar Team2 System.
One-sample Student's t-tests were used to assess physiological differences in match intensities and PTL accumulation for player A and the team for the 2012 and 2013 seasons combined. T-scores were computed for each variable for player A in reference to the team average using a 2-sided test with statistical significance set at p ≤ 0.05. Pearson's correlation coefficients were calculated to assess relationships between variables. IBM SPSS Software was used to perform the statistical tests. Data are reported as mean ± SD unless otherwise stated.
Maximal Oxygen Consumption
Demographic information is presented in Table 1. The team average relative V[Combining Dot Above]O2max was 61.5 ± 4.3 and 56.9 ± 5.1 ml·kg−1·min−1 for the 2012 and 2013 seasons, respectively. The relative V[Combining Dot Above]O2max of player A was 56 and 54 ml·kg−1·min−1 for 2012 and 2013, respectively. For season 1, player A was 1.28 SDs lower than the team average, and for season 2, he was 0.58 SDs below the average.
Match-Play Heart Rate and Polar Training Load
Player A scored 17 goals and had 10 assists during the 2012 season and 19 goals and 8 assists in the 2013 season. Player A scored 27% of the team's goals in 2012 and had 17% of the team's assists in that year. In 2013, player A scored 36% of the team's goals and had 22% of the team's assists.
Player A's average match HR values (164.1 ± 7.6 b·min−1; 82.8 ± 3.9% HRmax) were similar to the average for his teammates (166.0 ± 4.8 b·min−1; 84.6 ± 2.7% HRmax) (Table 2). Player A's match average %HRmax differed significantly from the team (p ≤ 0.05) in only 1 of the 22 games. For both player A and the team, there was little difference in effort between the first and second halves when comparing average HR values.
The match-play variables in which player A showed a marked and consistent difference across both seasons are time spent in zone 3, zone 4, zone 5, and PTL per minute with 32, 68, 41, and 36% of matches significantly different from the team, respectively (Table 2). Player A's PTL per minute was consistently lower than the team average, falling at least 1 SD below in 14 of the 22 matches (Figure 1A). The average PTL per minute of player A was lower than all but one of his teammates (Figure 1B). On average, time spent in HR zone 3 and zone 4 was higher than that of his teammates (Figure 2A), with player A greater than 1 SD above the team average for time spent in HR zone 4 in 18 of the 22 matches (Figure 2B). Conversely, in 12 matches, player A was at least 1 SD below his teammates for time spent in HR zone 5 (Figure 2C).
Percentage of time spent in HR zone 5 correlated strongly with PTL per minute for both player A (r = 0.800, p < 0.0001) and the other team members (r = 0.716, p < 0.0001) (Figure 3A). There was a strong negative correlation between PTL per minute and time spent in HR zone 4 for player A (r = −0.735, p < 0.0001) and the rest of the team (r = −0.505, p < 0.0001) (Figure 3B). However, there was no correlation between PTL per minute and average %HRmax for player A (r = 0.140, p = 0.468), whereas there was a moderate, yet significant, correlation for the other players (r = 0.449, p < 0.0001) (Figure 3C).
This study compared match-play physiological data from an elite collegiate soccer athlete with that of his teammates over 2 seasons. The average V[Combining Dot Above]O2max values of the team are similar to other elite soccer teams, whereas player A's V[Combining Dot Above]O2max was on the low end of the range of other elite soccer athletes. Silvestre et al. (22) reported an average V[Combining Dot Above]O2max of 59.8 ml·kg−1·min−1 (estimated from a Yo-Yo endurance test) for an NCAA division I team during the preseason, whereas Bangsbo et al. (4) found a mean V[Combining Dot Above]O2max of 60.6 ml·kg−1·min−1 for professional and semiprofessional players in Denmark. These values are close to the average V[Combining Dot Above]O2max of 61.5 ml·kg−1·min−1 that we found for the team in 2012 but are slightly higher than the average for 2013. This could be due to our smaller sample size in 2013 and the fact that testing was performed earlier in the preseason than the tests in 2012. Player A's V[Combining Dot Above]O2max values of 56 and 54 ml·kg−1·min−1 were lower than the team averages for both seasons but are close to the average of 55.1 ml·kg−1·min−1 found for similarly aged players in the secondary divisions of Portugal (2). Player A's lower relative V[Combining Dot Above]O2max as compared to his teammates suggests that a relatively high value for this measure of aerobic capacity is not a predictor of personal success or a requirement for elite performance in collegiate soccer. It remains possible that player A's V[Combining Dot Above]O2max value may fluctuate from off-season to in-season more than other players, although the change in V[Combining Dot Above]O2max among starters on another NCAA division I soccer team was nonsignificant from preseason to postseason (22). Likewise, there was no significant change in V[Combining Dot Above]O2max across a season for professional players in the Spanish La Liga (8).
The average match HR values found for player A (82.8% HRmax) and the team (84.6% HRmax) are similar to previously reported values of around 85% HRmax (2,4,17). Player A's average HR did not consistently differ from that of the team, although he did exhibit differences for 4 variables in match-play intensity: time spent in zone 3 (70–79% HRmax), time spent in zone 4 (80–89% HRmax), time spent in zone 5 (90–100% HRmax), and PTL per minute. To our knowledge, this is the first study to report such physiological data for competitive matches over a season. A recent investigation described the HR response and training load of elite youth-level Portuguese players in a simulated match with 25-minute halves (14). In their study, midfielders and defenders spent more than 50% of the match at or above 93% HRmax, whereas forwards spent only 33% at that intensity. These are similar to our results comparing player A to his teammates, although because of our small sample sizes we could not make meaningful comparisons between positions.
Player A's time spent in HR zone 5 and PTL per minute were consistently lower, whereas time spent in zones 3 and 4 was consistently higher, than the team average. The largest differences were observed for time spent in the highest 2 HR zones, which may explain the contrast in PTL per minute. Interestingly, although the proportion of time spent in zone 5 correlated positively with PTL per minute, time spent in zone 4 exhibited a negative correlation, which suggests that time spent above 90% of HRmax is given a high weight in the Polar system's calculation of training load. We found that on average, player A spent 23.1% of matches at ≥90% HRmax (zone 5), whereas the rest of the team averaged 45.4% at this intensity. When time spent at HR zones 4 and 5 is combined however, it can be seen that player A (77.4%) and the team (74.7%) spent a similar proportion of the match above 80% HRmax. Thus, it should be verified whether an altered proportion of time spent between HR zone 4 and HR zone 5 actually translates to a meaningful difference in training load, as is suggested by the PTL equation.
A PTL score of 350 was reported as an average score for an NCAA division I soccer athlete competing in a full 90-minute preseason match (22), which is similar to player A's average of 297 when his lesser 82 minutes per match is considered. There was not a consistent significant difference between the PTL average of player A and that of the team. We believe that this is because of player A's greater time spent in zones 3 and 4 and slightly higher minutes played. We used the PTL per minute score to correct for this difference and found that player A was consistently lower than the team average. Conversely, player A's average %HRmax was similar to that of his teammates. There was also no correlation between player A's average %HRmax and PTL per minute, whereas there was a moderate correlation for the other players. Given the correlations discussed above, these discrepancies may be due to differences in player A's time spent at HR zones 3–5. In the current study, we were unable to determine if this was due to a positional difference or if it reflects individual differences caused by his playing style or aerobic capacity. Some positional differences in soccer have been identified, whereas other characteristics seem to be consistent, regardless of position (3,9,10,14,18,19,22,23). Total distance covered during match-play is one positional difference, with midfielders typically covering the greatest distance, followed by forwards and defenders (4,10). Lower total distance covered in match-play could be one reason why player A consistently accumulated a lower PTL per minute score. Team strategy is another factor that may have influenced player A's training load. According to the team's head coach, team strategy was never focused on a single player, although player A was clearly the focal point of the team's attack over the 2 seasons studied.
Training load during competitive matches has only recently been reported by 1 study of a professional Norwegian team over 3 seasons, which calculated load based on number of accelerations (9). To our knowledge, time spent in HR zones has not been reported for high-level competitive soccer matches over a season, much less 2 seasons, and further, have not been used to compare elite players with respect to their teams. One mechanism for the elite performance of player A could be that lower accumulated training load may result in a lower injury rate or lower fatigue which may increase readiness for the next match (15). Therefore, the advantage of consistently accumulating a lower training load in comparison with other players may confer a season-long advantage. Another mechanism of increased performance is that by accumulating a lower training load during a match, an elite player may be better prepared to increase intensity and exert more energy for a single important play in comparison with other players. This may explain player A's lower proportion of time spent at the highest HR intensity as compared to his teammates. Whether this is a conscious strategy by player A or a by-product of enhanced ability to read the game (i.e., anticipate situations/opportunities) or positional awareness is unknown. It would be useful to make comparisons between an elite player, such as player A, and other players of the same position, although this would be difficult because of differences within positions of the same team (e.g., defensive midfielders vs. wingers vs. central attacking midfielders) and tactical differences between teams. Again, a limitation of our study is that we were unable to make positional comparisons because of small sample sizes and differences in average minutes and matches played.
In conclusion, player A consistently spent more time at 70–89% HRmax and less time at 90–100% HRmax, which led to a lower PTL per minute than his teammates. The altered time spent in HR zones as compared to his teammates may be an effect of player A's aerobic capacity, playing style, or positioning. Meanwhile, his lower accumulated PTL per minute may be a reason for his success. Future studies may focus on whether tracking training and match time spent at specific HR zones is an effective training strategy or is a predictor of individual success.
Our data suggest that a high V[Combining Dot Above]O2max is not a requirement for success or elite play in soccer. Therefore, given a reasonably adequate aerobic capacity, relative V[Combining Dot Above]O2max should not be used to predict or assess competency of players. Time spent in different HR zones may be related to individual success through alterations in training load. We found that despite similar average match HR values, time spent in HR zones differed greatly between the elite player and the team, which translated to significant differences in training load accumulation. This is evidence that average %HRmax is not informative enough, and that times spent at specific %HRmax zones should be considered when evaluating player training load. Specifically, reduced match time at maximal effort (above 90% HRmax) lessens training load, which is advantageous to a player over matches and may be an effective characteristic of elite players. In this respect, it may be beneficial to focus on enhancing players' abilities to read the game by augmenting positional, situational, and tactical awareness so that they are better able to regulate moments of maximal exertion, as opposed to specifically training them to stay under a certain HR threshold (e.g., 90% HRmax).
The authors thank the coach of the team (Sasho Cirovski) and his training staff for generating and providing these data. They especially thank the elite soccer athlete in this study for being willing to share this information.
1. Akubat I, Abt G. Intermittent exercise alters the heart rate
-blood lactate relationship used for calculating the training impulse (TRIMP) in team sport players. J Sci Med Sport 14: 249–253, 2011.
2. Ascensão A, Rebelo A, Oliveira E, Marques F, Pereira L, Magalhães J. Biochemical impact of a soccer match - analysis of oxidative stress and muscle damage markers throughout recovery. Clin Biochem 41: 841–851, 2008.
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, Nørregaard L, Thorso F. Activity profile of competition soccer. Can J Sport Sci 16: 110–116, 1991.
5. Banister EW. Modelling elite athletic performance
. In: Physiological Testing of the High-performance
Athlete. MacDougall JD, Wenger HA, Green HJ, eds. (2nd ed.). Champaign, IL: Human Kinetics Publishers Ltd, 1991. pp. 403–424.
6. Barnes C, Archer DT, Hogg B, Bush M, Bradley PS. The evolution of physical and technical performance
parameters in the english premier league. Int J Sports Med 35: 1095–1100, 2014.
7. Borresen J, Lambert MI. The quantification of training load
, the training response and the effect on performance
. Sports Med 39: 779–795, 2009.
8. Casajus JA. Seasonal variation in fitness variables in professional soccer players. J Sports Med Phys Fitness 41: 463–469, 2001.
9. Dalen T, Ingebrigsten J, Ettema G, Hjelde GH, Wisloff U. Player load, acceleration, and deceleration during forty-five competitive matches of elite soccer. J Strength Cond Res 30: 351–359, 2016.
10. Dupont G, Nedelec M, McCall A, McCormack D, Berthoin S, Wisløff U. Effect of 2 soccer matches in a week on physical performance
and injury rate. Am J Sports Med 38: 1752–1758, 2010.
11. Ekstrand J, Waldén M, Hägglund M. A congested football
calendar and the wellbeing of players: Correlation between match exposure of european footballers before the world Cup 2002 and their injuries and performances during that world Cup. Br J Sports Med 38: 493–497, 2004.
12. Eniseler N. Heart rate
and blood lactate concentrations as predictors of physiological load on elite soccer players during various soccer training activities. J Strength Cond Res 19: 799–804, 2005.
13. Gabbett TJ, Domrow N. Relationships between training load
, injury, and fitness in sub-elite collision sport athletes. J Sports Sci 25: 1507–1519, 2007.
14. Goncalves BV, Figueira BE, Macas V, Sampaio J. Effect of player position on movement behaviour, physical and physiological performances during an 11-a-side football
game. J Sports Sci 32: 191–199, 2014.
15. Halson SL. Monitoring training load
to understand fatigue in athletes. Sports Med 44: 139–147, 2014.
16. Impellizzeri FM, Rampinini E, Marcora SM. Physiological assessment of aerobic training in soccer. J Sports Sci 23: 583–592, 2005.
17. Mallo J, Mena E, Nevado F, Paredes V. Physical demands of top-class soccer friendly matches in relation to a playing position using global positioning system technology. J Hum Kinet 47: 179–188, 2015.
18. 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.
19. Reilly T. Energetics of high-intensity exercise (soccer) with particular reference to fatigue. J Sports Sci 15: 257–263, 1997.
20. Reilly T, Thomas V. A motion analysis of work rate in different positional roles in professional football
match-play. J Hum Mov Stud 2: 87–97, 1976.
21. Rogalski B, Dawson B, Heasman J, Gabbett TJ. Training and game loads and injury risk in elite Australian footballers. J Sci Med Sport 16: 499–503, 2013.
22. Silvestre R, Kraemer WJ, West C, Judelson DA, Spiering BA, Vingren JL, Hatfield DL, Anderson JM, Maresh CM. Body composition and physical performance
during a national collegiate athletic association division I men's soccer season. J Strength Cond Res 20: 962–970, 2006.
23. Torreno N, Munguia-Izquierdo D, Coutts A, de Villarreal ES, Asian-Clemente J, Suarez-Arrones L. Relationship between external and internal loads of professional soccer players during full matches in official games using global positioning systems and heart-rate technology. Int J Sports Physiol Perfom 11: 940–946, 2016.
24. Tumilty D. Physiological characteristics of elite soccer players. Sports Med 16: 80–96, 1993.
Keywords:Copyright © 2017 by the National Strength & Conditioning Association.
training load; V[Combining Dot Above]O2max; heart rate; performance; football