The growth of female soccer as a sport was highlighted by the 2015 Women's World Cup being the first to include 24 teams. Furthermore, the female game is becoming increasingly professional; however, the understanding and research of the female game is limited compared with the male game. Previously, wearable technology could only be worn during friendly matches, but recent law changes have enabled the use of global positioning system (GPS) in competitive soccer matches (2), providing the means for a variety of factors, such as altitude, match outcomes, and opposition rankings, that affect the movement profiles of athletes to be quantified (9,23,32). Understanding the effects these factors have on performance, in addition to the natural variation of match-running metrics, is vital to analyze match performances with increased certainty. Furthermore, identifying if these factors affect female athletes differently to male athletes, because of physiological differences between sex, is of importance to improve training protocols.
Environmental challenges are ever present, with the 2010 and 2014 FIFA Men's World Cups played at high altitudes and in high temperatures (30,31). Total distance (∼10%) and accelerations (∼4%) were observed to decline at altitude (1,600–3,600 m), compared with sea level, in elite male and female youth players (1,5,17). Lower (15%) high-speed running has been observed in elite male soccer players at temperatures greater than 21° C, compared with less than 21° C (7). Only one previous investigation has observed female National Collegiate Athlete Association soccer players while playing at altitude (1,839 m), with a decrease in total distance (∼−13%, p < 0.001) and high-speed running (∼−11%, p = 0.039) distances compared to sea level (5). Physiological differences in sex may exacerbate performance changes in response to environmental factors, such as thermoregulation and menstrual cycle changes in core temperature, and further research is needed to document the changes in match-running performance in response to the environment for female soccer players.
Opposition ranking is a situational factor, which has been investigated; however, varying definitions make it difficult to compare data (9,13,34,35). Nonetheless, greater total distance (2.5%) and high-speed running distance (5.3–7.7%), in addition to greater high-speed running distance with the ball (1.2–19%), have been observed when playing more successful teams compared with less successful teams in male soccer (9,13,34,35). Elite female soccer players were reported to cover the greatest match running across all movement thresholds against similarly ranked opponents (20). Reduced leg power production in women (40) may result in shorter passing ranges for women compared with men resulting in different tactical approaches and impacting match running relative to different opposition. World rankings determined over several years may also affect match running differently compared with a single season observed in male soccer. This may be due to a larger pool of teams/players available to play, which may adjust the style of an opposition team.
With regard to the analysis of match outcomes in relation to match-running performance, Spanish Premier League players were observed to cover greater high-speed (16%) and sprint distance (5.4%) in a loss, whereas in a win, the distances covered at low (5.6%) and moderate (9.1%) speeds were higher (9). In addition, a greater percentage of time spent running at high speed (1.3%, p = 0.004) was reported in English Premier League attackers while winning a match (36). Considering these findings for the most part relating to the male game, it would make sense to examine female performances, where the differences in opposition quality and playing styles could result in different outcomes.
Finally, researchers have considered the idea of a congested schedule affecting match running because of small recovery periods between the first and subsequent matches, with often trivial findings being reported (14,16). These findings have been questioned within the elite male game, with less than 41% of players completing 90 minutes during back-to-back matches (8). However, the elite female game can often require national teams to play tournaments where 4 games are played in 8 days (such as the Cyprus and Algarve cups) or FIFA windows with 2 games played within a 3-day period. Therefore, the possibility of players being exposed to back-to-back 90-minute games is more likely, the effects of which require investigation.
Previous studies have used male athletes to examine the effects of environmental and situational factors on the match-running performance of soccer players. However, a single international female team of elite players has never been used in a single study, which enables changes to be observed within player over repeated games i.e., repeated measures design. Therefore, the purpose of this study was to examine the effects of altitude, temperature, opposition ranking, and match outcomes on the match running of elite female soccer players.
Experimental Approach to the Problem
The current longitudinal study design was designed to examine the physical demands of elite International female soccer players using GPS technology across a full competitive match. Forty-five International soccer players were observed within 47 competitive matches, with both international friendlies and competitive matches analyzed. Match data were only included for players who completed greater than 75 minutes of match play, with all games played in temperatures ranging 2–32° C and altitudes from sea level to 1,356 m.
Elite female soccer players from the same senior national team (n = 45) provided informed consent to participate in longitudinal tracking and data analysis, which was approved by the University of Victoria Human Research Ethics Board. For subjects under 18 years of age, parental consent also was obtained. Only outfield players were included in the current study, with players belonging to the following positional groups, forward (n = 18), midfield (n = 9), full back (n = 11), and center back (n = 7). Subject age range was 15–34 years of age. As this was a longitudinal study this was different each year. Informed written consent was obtained from all subjects, with those under 18 years requiring parental consent. Ethics was obtained by the University of Victoria Human Research Ethics Board.
Speed data were collected from players through GPS technology sampling at 10 Hz (Minimax S4; Catapult Innovations, Melbourne, Australia). The reliability and validity of 10 Hz devices to measure velocity (coefficient of variation [CV] = 3.1–8.3% Pearson correlation = 0.94–0.97), distance (typical error = 1.3–11.5%, no significant difference to criterion), and reliability of microsensor metrics (CV = 1.9%) have been previously reported (6,22,39). Only files from players completing a full game defined as >75 minutes were included in the analysis (Files = 606). This inclusion criterion was based on previous congested schedule research (12), and that an extra 15 minutes of playing time does not have a large effect in match data when normalized to minutes played (Table 1). Raw files were exported from manufacturer software (Sprint 5.1, Catapult Innovations) and analyzed using a custom-built MS Excel spreadsheet (2013, Microsoft, USA), with speed calculated using the Doppler shift method, as opposed to the differentiation of positional data (38). This method is associated with a higher level of precision (38). The match-to-match variation of the outcome measures of interest has been reported previously using the same data set for 90-minute performances (37). The mean number of satellites and horizontal dilution of precision for games was 11.9 ± 0.4 and 0.96 ± 0.05, respectively. The number of satellites and the horizontal dilution of precision provide an insight into the quality of the GPS data analyzed (27).
The following match factors were investigated and defined as such. Opposition ranking was defined as being higher or lower than the reference team at the time of the match, based on official FIFA Women's World Rankings (3), which are updated 4 times annually (29). Match outcome was defined as being a win, draw, or loss at the end of the game. Match congestion was defined as a player who played >75 minutes and 2 games within 72 hours of each other (Games = 17) (12,14). Altitude was defined as near sea level (Games = 40, ≤500 m) or at altitude (Games = 7, >500 m). Because of the low number of games at altitude, all games were grouped together with altitudes ranging from 671 to 1,356 m (4). The temperature was defined as cold/mild (Games = 26, <21° C) and warm (Games = 21, ≥21° C) with warm temperatures ranging from 21 to 32° C (7).
Player movement categories were defined after previously described locomotor analysis guidelines for male youth athletes and have been used in previous female literature (28,37). High-speed running was defined as an effort >4.58 m·s−1, which represented the mean maximal aerobic speed (MAS) observed during piloting. This method has been used by researchers to determine speed thresholds (28), with MAS determined using the Maximal Aerobic Speed Test, with the final completed level achieved considered as the athletes MAS (15). Low-speed running was therefore defined as any movement covered at <4.58 m·s−1. Sprinting was defined as an effort >5.55 m·s−1, a threshold representing the team mean in the 30–15 intermittent fitness test that was also the MAS plus 30% of the aerobic speed reserve (e.g., maximal sprinting speed minus MAS). This latter method has been used previously to individualize maximal speed bands (28). Maximal accelerations were defined as an effort >2.26 m·s−2, which represented 80% of players' acceleration more than 10 m and was established during pilot testing. As a player may continue to accelerate at a submaximal rate after a maximal acceleration, an acceleration effort was defined as beginning when the acceleration exceeded the threshold of 2.26 m·s−2 and finishing when the acceleration dropped below 0 m·s−2 (38). Acceleration was calculated from speed data with a 0.3-second smoothing filter. Total distance, high-speed running, and sprint thresholds were presented relative to total match time (min−1). The number of high-speed running efforts, sprinting efforts, and accelerations were presented as a count per minute of match play. A minimum effort duration of 0.3 seconds was applied to all speed data (high-speed running and sprinting).
Because of the clustering of data, the effect of match factors was examined using a negative binomial mixed model using STATA (version 13; StataCorp, College Station, TX, USA). Separate analyses were performed for all match activities, with each match factor as a fixed main effect. Match outcome and opposition ranking was analyzed as an interaction term. Random effects for player and for match were included in the model to account for repeated measures.
An inference about the true value of a given effect was based on its uncertainty in relation to the important difference, assumed to be 0.20 of the SD between players in a normal match. This was derived from the mixed model by adding the variance for the true differences between players with the match-to-match variance within players, before taking the square root. Inferences were nonclinical, with an effect deemed unclear if the 90% confidence interval (CI) included the smallest important positive and negative differences; otherwise, the effect was deemed clear. Chances of a greater or smaller substantial true difference were expressed quantitatively and calculated using a custom-made Excel spreadsheet (21). These chances were expressed qualitatively for clear outcomes as follows: >25–75%, possibly; >75–95%, likely; >95–99%, very likely; and >99%, almost certainly. The magnitude of a given effect was determined from its observed standardized value (the difference in mean divided by the between-subject SD). The magnitude was expressed qualitatively as follows: <0.20, trivial; 0.20–0.59, small; 0.60–1.19, moderate; and ≥1.20 large (29).
Descriptive data in relation to each match factor (Table 2) are presented before linear modeling.
The standardized changes in metrics at altitude for all environmental factors can be observed in Figure 1. Compared with sea level, total distance (−4.0%, CI: −5.9 to −2.1%; p = 0.001) and low-speed running (−4.0%, CI: −5.8 to −2.1%; p < 0.001) were very likely lower at altitude. Low-speed running (−2.2%, CI: −3.7 to −0.7%; p = 0.019) was likely lower in warm temperatures compared with cold or mild temperatures. Alternatively, the number of max accelerations was likely higher at altitude compared with sea level (6.8%, CI: 2.0–12%; p = 0.023), however, was very likely lower in warm temperatures compared with cold or mild temperatures (−14%, CI: −20 to −7.3%; p < 0.001).
Playing higher ranked teams in a draw, compared with lower ranked teams, resulted in very likely lower high-speed running (−24%, CI: −40 to −8.4%; p = 0.015), likely lower accelerations (−10%, CI: −20 to 0.4%; p = 0.088), likely lower high-speed running efforts (−19%, CI: −34 to −15%; p = 0.039), and very likely lower sprinting efforts (−35%, CI: −55 to −15%; p = 0.006). Losses against higher ranked teams, compared with lower ranked teams, were associated with likely higher total distance (3.8%, CI: 1.3–6.4%; p = 0.014) and very likely higher low-speed running (4.4%, CI: 2.2–6.6%; p = 0.002). Wins against higher ranked teams, compared with lower ranked teams, were associated with very likely higher total distance (4.7%, CI: 2.2–7.2%; p = 0.003), very likely higher low-speed running (4.6%, CI: 2.5–6.8%; p = 0.001), and likely higher accelerations (9.5%, CI: 3.3–16%; p = 0.015).
Playing against a lower ranked team in a loss, compared with a draw, resulted in very likely lower accelerations (−15%, CI: −25 to −6.2%; p = 0.008) and likely lower low-speed running (−3.8%, CI: −7.3 to −0.3%; p = 0.079). Playing in a win, compared with a draw against the same opposition, resulted in almost certainly lower accelerations (−16%, CI: −24 to −8.8%; p = 0.001), very likely lower sprinting efforts (−26%, CI: −43 to −9.5%; p = 0.012), and very likely lower high-speed running (−20%, CI: −34 to −6.7%; p = 0.017).
Playing against a higher ranked team in a loss, compared with a draw, was associated with very likely higher high-speed running (19%, CI: 8.1–30%; p = 0.006), very likely higher high-speed running efforts (18%, CI: 8.4–28%; p = 0.004), and likely higher sprinting efforts (20%, CI: 5.0–34%; p = 0.030). Meanwhile, a win against a higher ranked team, compared with a draw, resulted in very likely higher total distance (5.6%, CI: 2.5–8.6%; p = 0.004), very likely higher low-speed running (5.2%, CI: 2.5–7.9%; p = 0.003), and likely higher high-speed running efforts (13%, CI: 1.2–25%; p = 0.071).
The effect of a congested schedule provided no significant or meaningful findings for all outcome measures (p = 0.191–0.777).
The purpose of this study was to examine the effects of environmental and situational factors on the match-running performance of elite female soccer players. This is the first study to examine such variables in a female population using GPS technology. The major findings that were meaningful are (a) overall match-running performance, particularly the number of accelerations, was lower during higher temperatures (≥21° C) compared with lower temperatures; (b) when altitude is greater than 500 m, a greater number of accelerations and lower total distance and low-speed running distance were performed; (c) both higher and lower match-running performance was observed in relation to relative opposition ranking (higher or lower); and (d) match running was influenced by the opposition ranking and the interaction of match outcome that should be considered in future match analyses.
The effect of temperature on match-running performance in male soccer has been previously examined, with a decline in high-speed running shown in temperatures greater than 21° C (7). In the current study, a moderate decrease in the number of accelerations was observed in warm temperatures (mean = 26.5° C). It has been reported that acute performances in warm temperatures increase core temperature compared with cooler conditions, which results in increased competition between metabolic demands and heat loss requirements (33). These acute changes in core temperature may result in increased muscle temperatures, increasing the rate of glycogenolysis, and lactate accumulation within muscle (19), influencing the ability of players to sustain repeated explosive efforts throughout a match. This is of particular importance for women who may be physiologically disadvantaged in the heat because of higher body fat and surface area-to-mass ratios compared with male counterparts in addition to hormonal changes due to the menstrual cycle (10). It has been suggested that players may subconsciously adjust movement in an attempt to limit the rise of core temperature, muscle temperatures, and maintain their ability to complete high-speed running actions in male soccer (25). A small decrease in low-speed running in the heat may be indicative of this altered pacing strategy, with players remaining higher upfield or limiting normal movement patterns. With a possible trivial change in high-speed running observed in the current study, more data are required to improve the certainty of this change. With the nature of soccer, it is challenging to examine the exact physiological changes occurring, such as lactic acid accumulation and core temperature, with collection limited by the laws of the match.
Altitude is known to inhibit endurance performance (4,18), while its effects on intermittent sport are not. The current findings indicate that a small increase in the number of accelerations occurred at altitude. Because of a decrease in the partial pressure of oxygen at altitude, it is easier to accelerate and obtain maximal speed (24), while total distance and low-speed running may decrease to allow for recovery between acceleration efforts. Our findings differ to researchers who have reported a decline (3.4–4.3%) in acceleration at 1,600–3,600 m in youth male soccer players (1,17); the findings attributed to a decreased ability to recover from repeated accelerations at altitude. With a mean altitude of 810 m in the current study, it may be plausible that, with altitudes less than 1,000 m, the decline in maximal aerobic power and blood oxygen saturation may not be sufficient enough to inhibit repeated acceleration performance (18). It is important to note that several factors could be responsible for these findings, such as match situations, tactical instructions or the speed thresholds, and definitions used. Therefore, the findings of this study should be interpreted within this context, particularly if playing at higher altitudes. Physiological differences between sexes should also be considered, with further research required in both male and youth populations of both sexes.
Previous researchers have observed physical performance to be greatest against similarly ranked opponents in both male and female soccer, because of a greater perceived chance of winning (9,20). From our findings, a moderate increase in high-speed running and a small increase in the number of accelerations in a draw when playing lower ranked opponents were observed when compared with higher ranked opponents. Meanwhile, playing higher ranked opponents, compared with lower ranked opponents, a moderate increase in low-speed running was observed in both wins and losses in this study. Researchers have suggested that, in an attempt to maintain player density in their defensive half against higher ranked opponents, lower ranked teams may increase lower speed movements to maintain shape (20). The small increase in the number of accelerations observed against higher ranked teams in a win may suggest a pressing strategy that was also used by the team in the current study, with players looking to close down opposition quickly to win the ball back. Further research is required to examine team tactics, such as a pressing strategy, with respect to match-running performance and opposition rankings. The inclusion of technical information, such as possession, number of passes, and pass accuracy may also help to characterize the reference team and its opponents (26), aiding in the interpretation of findings.
In this study, match outcome had an interactive effect with opposition rankings in the examination of match running. Moderate increases in total distance and low-speed running were observed when playing higher ranked opponents in a win compared with a draw. However, the opposite was true against lower ranked opponents in a loss compared with a draw, with a small decrease in total distance and low-speed running observed. Furthermore, a loss against higher ranked opponents resulted in a moderate increase in high-speed running and high-speed running efforts, which coincided with a small increase in total distance. These findings are similar to the conclusions presented in previous research with male players. The authors suggesting that low-speed activity may decline when losing in an attempt to draw level with opposition (23). While scoring against a higher ranked opponent may increase the shared belief and overall physical effort of a team to achieve the desired outcome (36). Therefore, the evolution of the score line rather than match outcome may provide a greater insight in to the relationship between physical match demands and tactics.
A limitation of this study was that positional analysis was not possible because of the limited number of repeated measures by position. It is understood that positional match-running requirements differ among players (11), therefore generalization of findings to all members of a team is not recommended, with practitioners advised to examine these factors within their own data. The size of the data set also limited the examination of interactions, such as matches played against higher ranked opponents in hot conditions. This would have provided a greater insight in to the changes these match factors may have had on performance. Altitude in the current study can be considered as low; therefore, findings observed are likely to differ from those reported from higher altitudes. This is particularly noticeable with the number of accelerations increasing contrary to previous studies (1,17). Technical parameters, such as possession and pass accuracy, were not included within this study. Researchers have suggested that players may subconsciously adjust match running in an attempt to maintain technical proficiency (31). Furthermore, match outcome may not be as insightful as the state of the game limiting the findings of this study also.
In summary, match factors can influence the match running of elite female soccer players. Total distance seems the most susceptible to different factors, while larger magnitude changes in the number of accelerations are apparent in different environments and match outcomes. Further examination of the interaction between opposition rankings and match outcomes is warranted.
The current investigation reported for the first time the changes in female match running in response factors present in a match. Although practitioners and researchers should look to use advanced modeling techniques when examining how factors may affect match-running performance of their athletes by accounting for within player variation, allowing for greater certainty in the findings observed. Finally, any difference in findings between this study and previous male research highlights the need for sex-specific research to be completed. The playing style of the reference team may be attributed to some findings; the examination of multiple teams would remove this bias in the future. The application of this information to the training environment is recommended, with practitioners able to inform training load guidelines that fit the match-running players are likely to experience within a match.
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