The influence of situational and environmental factors on match-running in soccer: a systematic review

Abstract

It is common practice amongst researchers and practitioners to monitor the effects of external load via
electronic tracking systems. Both semi-automatic multiple camera systems and global positioning system
technologies are used worldwide to quantify match-running. As soccer is a global game, the playing environments can differ, with heat and altitude being factors that may impact players running performance. Further,
tactical and situational match factors may also have an effect on match-running performance. Therefore, the
purpose of this article is to systematically review current literature on the environmental and situational factors
affecting match-running in soccer. An electronic database search (PubMed, EBSCOHost and Web of Science)
was conducted. Further articles known to the authors were also included. A total of 1806 studies were
identified, with only 28 meeting the specific search criteria. The main findings were that trivial changes in
match-running were observed with regards to possession, team formation and match status (win, lose, draw).
Match-running was affected by temperatures as low as 20°C, with both high- and very-high speed running
decreasing (8.5% and 15% respectively), whilst altitude lowers the number of high-speed efforts completed by
players (7.1–25%). Findings indicate that environmental factors have a strong influence on the variability and
differences observed in match-running performances from match-to-match. Further understanding of the
effect of match factors on match-running would allow better planning to minimise possible detrimental
factors, particularly in relation to gender.

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The influence of situational and environmental
factors on match-running in soccer: a systematic
review
Joshua Trewin , César Meylan, Matthew C. Varley & John Cronin
To cite this article: Joshua Trewin , César Meylan, Matthew C. Varley & John Cronin (2017): The
influence of situational and environmental factors on match-running in soccer: a systematic review,
Science and Medicine in Football
To link to this article: http://dx.doi.org/10.1080/24733938.2017.1329589
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REVIEW
The influence of situational and environmental factors on match-running in
soccer: a systematic review
Joshua Trewin
a,b,c
, César Meylan
a,b,c
, Matthew C. Varley
d
and John Cronin
a,e
a
Sports Performance Research Institute New Zealand, Auckland University of Technology, Auckland, New Zealand;
b
Women’s EXCEL Program Sport
Science, Canadian Soccer Association, Ottawa, Canada;
c
Strength and Conditioning, Canadian Sport Institute - Pacific, Vancouver, Canada;
d
Institute of Sport, Exercise and Active Living, College of Sport and Exercise Science, Victoria University, Melbourne, Australia;
e
School of Exercise,
Biomedical and Health Sciences, Edith Cowan University, Perth, Australia
ABSTRACT
It is common practice amongst researchers and practitioners to monitor the effects of external load via
electronic tracking systems. Both semi-automatic multiple camera systems and global positioning system
technologies are used worldwide to quantify match-running. As soccer is a global game, the playing environ-
ments can differ, with heat and altitude being factors that may impact players running performance. Further,
tactical and situational match factors may also have an effect on match-running performance. Therefore, the
purpose of this article is to systematically review current literature on theenvironmental and situational factors
affecting match-running in soccer. An electronic database search (PubMed, EBSCOHost and Web of Science)
was conducted. Further articles known to the authors were also included. A total of 1806 studies were
identified, with only 28 meeting the specific search criteria. The main findings were that trivial changes in
match-running were observed with regards to possession, team formation and match status (win, lose, draw).
Match-running was affected by temperatures as low as 20°C, with both high- and very-high speed running
decreasing (8.5% and 15% respectively), whilst altitude lowers the number of high-speed efforts completed by
players (7.1–25%). Findings indicate that environmental factors have a strong influence on the variability and
differences observed in match-running performances from match-to-match. Further understanding of the
effect of match factors on match-running would allow better planning to minimise possible detrimental
factors, particularly in relation to gender.
ARTICLE HISTORY
Accepted 9 January 2017
KEYWORDS
GPS; match analysis;
time–motion analysis;
altitude; temperature;
football
Introduction
The use of technology in soccer to monitor, forecast and adjust the
cardiovascular and neuromuscular stress (i.e., external running
load) imposed by training, or matches, is increasing in prevalence
(Bradley et al. 2009;Halson2014). Modern semi-automatic multi-
camera analysis systems (e.g., Prozone® and Amisco®) monitor
physical performance, in combination with technical activities
(e.g., ball possession, passes, tackles) during matches (Castellano
et al. 2014). Similarly, the use of global positioning system (GPS)
technology is common across a range of football codes, which
allows tracking of match-running and accelerometry-based
metrics (Cummins et al. 2013), and recently has been cleared for
use within official competitive matches(e.g.,FIFAWorldCup).Past
reviews have discussed the use of technology and match-running
analysis in soccer (Carling 2013; Cummins et al. 2013;Mackenzie&
Cushion 2013; Castellano et al. 2014); however, the effect of spe-
cific factors on match-running demands have only briefly been
reviewed (Paul et al. 2015) and requires further investigation.
Soccer is subject to various situational and environmental fac-
tors, proposed to affect match-running (Lago-Penas 2012;Paul
et al. 2015). Within match situational factors, such as formation
(Bradley et al. 2011;Carling2011), ball possession (Di Salvo et al.
2009;Carling2010; Bradley et al. 2013), opposition ranking
(Rampinini et al. 2007;Hewittetal.2014;Hoppeetal.2015)and
match status (Redwood-Brown et al. 2012), have been assessed
with varying effects observed. Environmental factors such as alti-
tude (Aughey et al. 2013;Garvicanetal.2014; Nassis 2013; Bohner
et al. 2015) and temperature (Mohr et al. 2010; Carling et al. 2011;
Nassis et al. 2015) have been found to affect match-running due to
physiological limitations and possible subconscious pacing whilst
performing in these environments (Waldron & Highton 2014). As
soccer is an ever-changing game performed in a range of environ-
mental conditions, understanding the effects of these factors is
required to optimise player performance. Finally, players can be
subject to periods of fixture congestion, playing multiple matches
within a short period of time (Arruda et al., 2014; Rey et al. 2010;
Lago et al. 2011). However, exposure to congested schedules has
been questioned, along with the current methodology used to
examine these periods (Carling et al. 2015a,2015b).
As match-running is of interest to practitioners in terms of
performance analysis, team strategies and load management, it
is pertinent to investigate the effect of different factors on
match-running. The authors believe all factors should be consid-
ered in combination, rather than independently, whilst it is also
not feasible to include all factors (e.g., grass vs. artificial turf) in a
detailed review such as this. Therefore, the primary aim of this
review is to examine, in a systematic way, the effects of selected
situational and environmental factors on the match-running of
soccer players. This review will elaborate on the findings of Paul
CONTACT Joshua Trewin jtrewin@aut.ac.nz
SCIENCE AND MEDICINE IN FOOTBALL, 2017
https://doi.org/10.1080/24733938.2017.1329589
© 2017 Informa UK Limited, trading as Taylor & Francis Group

et al. (2015) by specifically expanding on the match-factors
affecting match-running. Recommendations will be provided to
improve future match-analysis and research protocols with the
aim of better understanding elite soccer performances.
Methods
Data source
Studies investigating match-running, where situational variables
were examined, or where games were played in a range of outdoor
environments, were included in this review. A systematic literature
search of electronic databases (PubMed, EBSCOHost and Web of
Science) was conducted for the time period of Jan 2000 until
October 2015. Further articles known to the authors that were
not identified during the literature search were also included for
analysis. The search terms included football or soccer combined
with performance analysis, movement analysis, activity profiles,
time–motion analysis, congested schedule, GPS, Prozone® and
Amisco®.
Study selection
After eliminating duplicates, titles were screened for eligibility.
Titles which indicated the investigation was not relevant to the
scope of this review were eliminated. Following title screening,
search results were independently screened by 2 researchers
against the eligibility criteria. Abstracts were examined as to rele-
vance, with articles retrieved for further review if required.
Following independent screening, the 2 researchers discussed
any differences and finalised the studies for inclusion in this review.
Papers were only included if they were in English, with abstracts of
conference proceedings excluded. Studies were included if they
reported physical performance outcome measures and assessed
the effects of situational (formation, possession, match outcome,
team success or congested schedule) or environmental factors
(altitude or temperature). Studies that utilised outdated time–
motion techniques, such as notation of manual video analysis
(Mohr et al. 2003; Gabbett & Mulvey 2008), were also excluded.
Following screening, a total of 27 studies were included in this
review (Figure 1) with the quality criteria (Table 1)andcharacter-
istics and quality index of selected studies shown in Table 2.The
methods of Castellano et al. (2014), where a detailed explanation
can be found, were used to rate study quality out of 9 criteria, with
amaximumscoreof10.
Outcome measures
Technology
Before indicating the outcome measures of importance, it must
be noted that it is difficult to compare data from a variety of
technological sources (e.g., GPS and Prozone®). For example,
Records identified through
database searching
(n = 1802)
ScreeningIncluded Eligibility Identification
Additional records identified
through other sources
(n = 5)
Records after duplicates removed
(n = 1385)
Records abstract screened
(n = 196)
Records excluded
(n = 128)
Full-text articles assessed
for eligibility
(n = 68)
Full-text articles excluded,
with reasons
(n = 40).
No situational
environmental variables
(n = 30).
Studies included in
qualitative synthesis
(n =28)
Records excluded
(n = 1189)
Figure 1. Flow-diagram of study identification and exclusion process.
2J. TREWIN ET AL.

total distance covered during a game has been reported to be
greater (7%) using GPS as compared to using Prozone® (Buchheit
et al. 2014). However, Prozone® reported higher distances for
sprinting and high intensity running than GPS (70% and 16%,
respectively) at the same speed thresholds. Furthermore, GPS is
subject to intra-unit (same manufacturer) variation and between
manufacturer variation. Therefore, caution should be applied
when comparing or interchangeably using data from GPS or
Prozone®/Amisco Pro® technology, with players advised to
wear the same GPS unit, from the same brand, to minimise
inter-unit/manufacturer variability affecting data (Malone et al.
2016). Effects presented in this review were observed as within-
study changes using the same technology, removing possible
technological error.
Match activity
Match-running has been analysed, using a range of different
movement categories including total distances and distance
covered within specific speed thresholds (such as moderate-
speed, high-speed and sprinting) (Di Salvo et al. 2009;Bradley
et al. 2010). The frequency or occurrence of accelerations and
high speed or sprinting efforts have also been reported by some
researchers (Aughey et al. 2013; Garvican et al. 2014). Distance
covered at high-speed alone may underestimate energy expen-
diture by 6–8% (Osgnach et al. 2010; Özgünen et al. 2010;
Gaudino et al. 2013), whilst not accounting for accelerations.
Temperatures was defined as; cold (0–10°C), moderate (10–
20°C), warm (20–30°C) and hot (>30°C), as classified in pre-
vious studies (Mohr et al. 2010; Carling et al. 2011). Recently,
there has been a shift to the use of wet bulb globe tempera-
ture to define heat stress; however, this was only used by 1
study included in this review (Nassis et al. 2015).
Altitude was defined as –near sea level (0–500 m), low
altitude (500–2000 m), moderate altitude (2000–3000 m) and
high altitude (3000–5500 m) as previously classified (Bärtsch
et al. 2008). A congested schedule was defined as when a
player played in multiple matches within a 7-day period
(Arruda et al., 2014; Rey et al. 2010).
Methodological considerations
Whilst this review does not perform any statistical analysis, the
authors feel statistical considerations should be made when
Table 1. Study quality criteria.
Criteria (from Castellano’s review)
Q1 The study is published in a peer-reviewed journal or book No = 0 Yes = 1
Q2 The study is published in an indexed journal No = 0 Yes = 1
Q3 The study objective(s) is/are clearly set out No = 0 Yes = 1
Q4 Either the number of recordings is specified or the distribution of players/recordings used is known No = 0 Yes = 1
Q5 The duration of player recordings (an entire half, a complete match, etc.) is clearly indicated. No = 0 Yes = 1
Q6 A distinction is made according to player positions No = 0 Yes = 1
Q7 The reliability/validity of the instrument is not stated, is mentioned or is measured Not Stated = 0 Mentioned = 1 Measured = 2
Q8 Certain contextual variables (e.g., match status, match location, type of competition or quality of the
opponent) are taken into account
No = 0 Yes = 1
Q9 The results are clearly presented No = 0 Yes = 1
Table 2. Study participant characteristics and quality ratings (total out of 10).
First Author (year) Level Participants (Files) System Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Total
Arruda (2014) Elite Male U15 Youth 15 (N.S.) GPS 1 1 1 0 1 0 1 1 1 7
Aughey (2013) Male Elite age group National 27 (104) GPS 1 0 1 1 1 0 0 1 1 6
Bohner (2015) NCAA Women 6 (18) GPS 1 0 1 0 1 0 0 0 1 4
Bradley (2011) Male English Premier League 153 ProZone 1 1 1 1 1 1 1 1 1 9
Bradley (2013) Male English Premier League 810 ProZone 1 1 1 1 1 1 1 1 1 9
Carling (2013) Male French League 1 28 (228) Amisco 1 1 1 1 1 1 0 1 1 8
Carling (2011)
a
Male French League 1 9 (339) Amisco 1 0 1 1 1 1 0 1 1 7
Carling (2011)
b
Male French League 1 21 (297) Amisco 1 1 1 1 1 1 0 1 1 8
da Mota (2016) Fifa Men’s World Cup 346 (792) Optical Tracking 1 1 1 1 1 1 0 0 1 7
Dellal (2011) Male European Premier Leagues (5938) Amisco 1 1 1 1 0 1 0 1 1 7
Dellal (2013) Male French League 1 16 Amisco 1 0 1 0 1 0 1 1 1 6
Di Salvo (2009) Male English Premier League 563 (7355) ProZone 1 0 1 1 1 1 1 1 1 8
Djaoui (2014) Male Elite European 16 (132) Amisco 1 1 1 1 1 1 0 1 1 8
Dupont (2010) Male Scottish Premier League 32 (3696) Amisco 1 1 1 1 1 1 2 1 1 10
Folgado (2015) Male English Premier League 23 ProZone 1 1 1 0 0 0 1 1 1 6
Garvican (2014) Male Australian U20 National Team 12 GPS 1 0 1 0 1 0 1 1 1 6
Hewitt (2014) Australian Women’s National Team 15 (58) GPS 1 1 1 1 1 0 1 1 1 8
Hoppe (2015) Male German Bundesliga N.S. Vis.Track 1 0 1 0 0 0 0 1 1 4
Lago (2010) Male Spanish Premier League 19 (182) Amisco 1 1 1 1 1 0 1 1 1 8
Lago (2011) Male Spanish Premier League 23 (172) Amisco 1 1 1 1 0 1 2 1 1 9
Mohr (2010) Male Spanish Second and Third Divisions 20 Amisco 1 0 1 1 1 0 1 1 1 7
Nassis (2013) Fifa Men’s World Cup N.S. 1 1 1 0 1 0 0 1 1 6
Nassis (2015) Fifa Men’s World Cup N.S. 1 0 1 0 0 0 0 1 1 4
Özgünen (2010) Male Semi-Professional 11 (19) GPS Watch 1 0 1 1 0 0 0 1 1 5
Rampinini (2007) Male European National League 20 ProZone 1 0 1 0 0 1 1 1 1 6
Rampinini (2009) Male Italian Serie A 186 SICS 1 0 1 0 0 0 1 1 1 5
Redwood-Brown (2012) Male English Premier League (169) ProZone 1 0 1 1 1 1 0 1 1 7
Rey (2010) Male Spanish Premier League 42 (84) Amisco 1 0 1 1 1 0 1 1 1 7
N.S. = not stated within the article cited.
SCIENCE AND MEDICINE IN FOOTBALL 3

analysing GPS data. To appropriately detect changes, research-
ers must account for match-to-match variation and device
reliability. Population-specific match-to-match variation, repre-
sented by a coefficient of variation (CV), has been reported to
vary by 19.8–37.1% for high-speed running and sprinting
(Carling et al. 2016). However, the interpretation of variation
can be further complicated by different players, positions,
different technology and the lack of consensus on speed
thresholds amongst researchers (Abt & Lovell 2009; Carling
et al. 2016). If not accounted for within an appropriate statis-
tical model, match-to-match variation should be reported to
avoid misinterpretation of findings. Inter-unit GPS variability
should also be considered, with CV ranging from 1.5% to 6.0%
for running and sprinting activities in a linear and non-linear
fashion with a 1-Hz sampling rate (Gray et al. 2010). Inter-unit
reliability for velocity whilst accelerating has improved with
10-Hz sampling rates, with CVs of 1.9–4.3% reported when
accelerating from a range of constant velocities (Varley et al.
2012). When examining factors affecting performance, it is
crucial to take reliability into account to detect changes out-
side the standard inter-unit variability.
Discussion
Situational factors influencing match-running
Formation
The effect of different formations on match-running for both
opposition and of multiple reference teams have been investi-
gated in the English Premier League and French Ligue 1 (Table 3)
(Bradley et al. 2011;Carling2011). Whilst cognizant that very few
studies have investigated this metric, there seems to be no
meaningful effects of formation on match-running, suggesting
team formations have little to no impact on how a player moves
globally throughout a match. The use of multiple reference
teams also complicates interpreting the effect of formation on
match-running (Bradley et al. 2011). Furthermore, the evolution
of tactics within a game results in teams rarely staying in one
formation for a full game (Bloomfield et al. 2007), adapting to
current match situations such as scorelines and opposition stra-
tegies (Lago & Martín 2007). These factors make examining the
effect of team formations onmatch-running challenging (Carling
2011). Studies have tended to examine team formations using 4
defenders (e.g., 4-4-2, 4-5-1 or 4-3-3), possibly limiting the find-
ings and subsequent implications. Different formations (e.g., 5-4-
1 or 3-5-2) are likely to result in different positional changes in
match-running. The effect of different formations on reference
team match-running using a repeated measures analysis has also
not been undertaken and provides a focus for future research.
The effect of ball possession, both high/low and in/out of pos-
session, on match-running may provide a better insight as to
how match-running changes in relation to opposing team for-
mations (Di Salvo et al. 2009; Dellal et al. 2011; Bradley et al. 2013;
Da Mota et al. 2016).
The effect of possession on position-specific demands
Globally, having either a high (51–66%) or low (34–50%) per-
centage of ball possession results in trivial differences in
match-running (Table 4) (Bradley et al. 2013; Da Mota et al.
Table 3. The effect of reference/opposing team formation on player movements (mean ± SD) of male soccer players.
Games Formation
First author (year) Level (# files/players) Capture method Team Thresholds 4-4-2 4-3-3 4-5-1 4-2-3-1
Bradley (2011) English Premier League 20 (n/a/153) Prozone Reference Total distance 10,697 ± 945 10,786 ± 1041 10,613 ± 1104
>14.4 km ·h
−1
2633 ± 671 2649 ± 706 2585 ± 734
>19.8 km · h
−1
956 ± 302 924 ± 316 901 ± 305
Carling (2011)
a
French Ligue 1 45 (297/21) Amisco Pro Opposing Total distance 10,594 ± 681 10,795 ± 624 10,808 ± 661*
14.4–19.7 km · h
−1
1577 ± 373 1630 ± 376 1608 ± 374
>19.7 km · h
−1
704 ± 219 741 ± 236 721 ± 222
a
Considered 4-3-3 and 4-5-1 as a dynamic, interchangeable, formation.
*Significant difference compared to 4-4-2 (P< 0.05)
SD: standard deviation.
4J. TREWIN ET AL.

Table 4. The effect of high/low and in/out of possession on player movements (mean ± SD) of male soccer players.
First author (year) Level Games (# files/players) Capture method Positions Thresholds High/in possession Low/out of possession
Bradley (2013)
a
English Premier League 54 (N.S./810) Prozone Central defenders Total distance 9739 ± 525 9943 ± 567
14.4–19.7 km · h
−1
1270 ± 209 1375 ± 228
19.8–25.1 km · h
−1
451 ± 104 480 ± 115
>25.1 km · h
−1
153 ± 70 159 ± 61
Fullbacks Total distance 10,610 ± 623 10,856 ± 614
14.4–19.7 km · h
−1
1702 ± 271 1777 ± 243
19.8–25.1 km · h
−1
701 ± 174 748 ± 169
>25.1 km · h
−1
275 ± 104 300 ± 113
Central midfielders Total distance 11,458 ± 625 11,457 ± 726
14.4–19.7 km · h
−1
2069 ± 334 2000 ± 330
19.8–25.1 km · h
−1
764 ± 178 699 ± 194
>25.1 km · h
−1
226 ± 95 207 ± 88
Wide midfielders Total distance 11,669 ± 828 11,489 ± 859
14.4–19.7 km · h
−1
2118 ± 412 1977 ± 413
19.8–25.1 km · h
−1
884 ± 182 885 ± 173
>25.1 km · h
−1
326 ± 119 341 ± 106
Attackers Total distance 10,778 ± 865 9988 ± 1002
14.4–19.7 km · h
−1
1685 ± 422 1422 ± 344
19.8–25.1 km · h
−1
783 ± 198 682 ± 173
>25.1 km · h
−1
317 ± 114 30 ± 125
da Mota (2016)
a, c
Forwards Total distance 10,011 ± 1042 10,405 ± 1011
≤11 km · h
−1
6016 ± 426 6158 ± 426
11–14 km · h
−1
1611 ± 347 1832 ± 347
>14 km · h
−1
2842 ± 474 3000 ± 474
Midfielders Total distance 10,776 ± 845 10,919 ± 861
≤11 km · h
−1
6121 ± 383 6169 ± 271
11–14 km · h
−1
2009 ± 335 2056 ± 319
>14 km · h
−1
3092 ± 558 3156 ± 606
Defenders Total distance 9850 ± 853 9930 ± 692
≤11 km · h
−1
6116 ± 274 6180 ± 306
11–14 km · h
−1
1738 ± 241 1803 ± 241
>14 km · h
−1
2543 ± 515 2543 ± 515
Dellal (2011)
b
EPL and La Liga, 600 (5938/N.S.) Amisco Pro Defensive Total distance
(in/out of possession)
10,890 ± 417
21–24 km · h
−1
95 ± 32 158 ± 11
>24.1 km · h
−1
92 ± 32 127 ± 14
Offensive Total distance
(in/out of possession)
11,098 ± 382
21–24 km · h
−1
164 ± 15 123 ± 31
>24.1 km · h
−1
155 ± 26 90 ± 22
Di Salvo (2009)
b
EPL (7355/563) Central defenders >19.8 km · h
−1
179 ± 93 459 ± 74
Wide defenders >19.8 km · h
−1
364 ± 89 498 ± 71
Central midfielders >19.8 km · h
−1
394 ± 91 489 ± 71
Wide midfielders >19.8 km · h
−1
505 ± 76 484 ± 62
Attackers >19.8 km · h
−1
566 ± 104 331 ± 83
a
Compared high and low possession.
b
Compared in and out of possession.
c
Data were extrapolated from figures.
SD: standard deviation. N.S. = not stated.
SCIENCE AND MEDICINE IN FOOTBALL 5

2016). Positional changes are more apparent when examining
performance with respect to being in or out of ball possession
(Di Salvo et al. 2009; Dellal et al. 2011). Attacking players
appear to cover greater high-speed running distance (71%)
when the team is in possession compared to out of possession
(Di Salvo et al. 2009). Whilst defenders cover greater high-
speed running distance (156%) when out of possession com-
pared to in possession. This may be explained by forwards
attempting to create space for scoring opportunities when the
team is in possession, whilst defenders are required to cover
these movements and regain ball possession (Di Salvo et al.
2009). For instance, players from a high percentage ball pos-
session team were found spending more time in the opposi-
tion half and attacking third to create goal scoring
opportunities (Da Mota et al. 2016), probably requiring for-
wards to move off the ball and defenders tracking them.
Furthermore, match-running changes as a team were
observed whilst in or out of possession against different oppo-
sition playing formations (Bradley et al. 2011). Very high-speed
running was 32–39% (P< 0.01) greater when in possession
against a 4-4-2 compared to a 4-5-1; however, total distance
was similar against all formations (Bradley et al. 2011). Authors
noted the inherent attacking and defensive characteristics of
different formations as a possible reason for the changes they
observed (Bradley et al. 2011). Due to the lack of repeated
measures designs, the effect of ball possession and formation
is still largely unclear.
The effect of scoreline on match-running
An evolving factor, such as scoreline, can also alter the work
rate of players (Castellano et al. 2011; Redwood-Brown et al.
2012). It was suggested that in the hope of getting back into
the game, very high-speed running increased (11.3%) when
Spanish Premier League teams were losing a match compared
to winning, when either total or effective playing time (i.e.,
total time minus stoppages) was considered (Castellano et al.
2011). Lago et al. (2010) also demonstrated that for every
minute losing, an extra 1 m of distance was covered at sprint
speeds (>19.1 km · h
−1
) compared to winning. Alternatively,
winning increased low-speed movement (<11 km · h
−1
)by2m
compared to losing. This finding in particular supports the
suggestion that players do not always use their maximal phy-
sical capacity for an entire game (Lago et al. 2010). Further,
Redwood-Brown et al. (2012) observed a greater percentage
of time spent at >14.4 km · h
−1
was completed by attackers in
the English Premier League when winning a game (1.3%),
whilst defenders performed less (−0.7%). Further analysis is
needed to better understand the role of scoreline on match-
running. Losing has been observed to increase possession in
comparison to winning (11%) (Lago 2009); however, pass
accuracy of losing teams has been observed to be lower
compared to the winning teams (Collet 2013). The inclusion
of technical information and team success would therefore
appear important when examining the match-running profile
of reference teams.
Team success
The effect of opposition ranking on high-speed (Table 5), very
high-speed and sprinting profiles has recently received
attention (Rampinini et al. 2007,2009; Di Salvo et al. 2009;
Castellano et al. 2011; Hewitt et al. 2014; Hoppe et al. 2015).
Total match-running alone does not relate to winning games,
but Hoppe et al. (2015) reported total distance in ball posses-
sion as the strongest predictor of point accumulation across a
season. Players from more successful teams had greater ball
involvement, with greater total distance and high-speed run-
ning performed whilst in possession. However, less successful
teams have been reported to cover greater high- and very
high-speed running distance compared to more successful
teams in the Italian Serie A (Rampinini et al. 2009). Also,
teams who finished in the bottom 5 of the English Premier
League were observed to cover more distance at high- (3.8%)
and sprint-speeds (5.4%) compared to those in the top 5 (Di
Salvo et al. 2009). Success therefore might be characterised by
greater movement whilst in possession, to create space and
maintain possession.
Researchers found after quantifying match-running perfor-
mance of a highly ranked European reference team (Rampinini
et al. 2007), that their match-running was greater against more
successful teams. Additionally, both technical and physical
performance may be at their greatest against similarly ranked
opponents due to a greater perceived chance of winning
(Castellano et al. 2011; Collet 2013). Alternatively, players
from a reference team performed greater high-speed running
against similarly ranked opponents (11–25 in the world rank-
ings; 17%), compared to playing against teams ranked within
the top 10 of FIFA’s Women’s World Rankings (Hewitt et al.
2014). Less successful teams may play a more defensive style
against higher ranked teams, increasing the player density
within their defensive half to minimise attacking threats and
opportunities (shots and crosses), impacting movement at
higher speeds (Hewitt et al. 2014). However, the findings of
Hewitt et al. (2014) are limited due to a very small sample
available (n= 15, files = 58). Therefore, further examination of
the influence of team success on match-running is required to
better understand player performance against higher or lower
ranked oppositions, with particular attention to playing style.
In addition, with respect to physical performance against simi-
larly ranked teams, researchers should look to characterise
their reference team success to improve interpretation of
findings presented.
Congested schedule
Many top European Club teams are required to cope with
periods of congestion (Dupont et al. 2010; Lago et al. 2011;
Dellal et al. 2013; Djaoui et al. 2014; Carling et al. 2015a;
Folgado et al. 2015), although the extent to which players
are exposed to these congested periods has been questioned
recently (Carling et al. 2015b). However, it appears appropriate
to examine how match-running is affected during these per-
iods, with consideration to international soccer tournaments
(such as the Olympics, Women’s Algarve and Cyprus Cup)
where recovery periods are ~72 h throughout the group
stage. Peak sprint speed, hamstring strength and counter-
movement jump height are understood to be compromised
for up to 72 h post-match (Nedelec et al. 2014), indicating
changes in performance could occur. A recent opinion article
highlighted the current issues with research protocols for
6J. TREWIN ET AL.

Table 5. The influence of team success and opposition rankings on distance covered at high intensities (mean ± SD).
First author (year) Level Games (# files/players) Capture method Opposition/reference quality Threshold Distance
Castellano (2011) Spanish Premier League (N.S./434) Amisco Pro Top 6 17.1–21.0 km · h
−1
417 ± 143
21.1–24.0 km · h
−1
144 ± 59
>24 km · h
−1
115 ± 72
Middle 7 17.1–21.0 km · h
−1
411 ± 135
21.1–24.0 km · h
−1
137 ± 57
>24 km · h
−1
117 ± 75
Bottom 7 17.1–21.0 km · h
−1
386 ± 124
21.1–24.0 km · h
−1
128 ± 61
>24 km · h
−1
103 ± 72
Di Salvo (2009) English Premier League Prozone Top 5 >19.8 km · h
−1
885 ± 113
>25.2 km · h
−1
222 ± 41
Middle 10 >19.8 km · h
−1
917 ± 143
>25.2 km · h
−1
230 ± 51
Bottom 5 >19.8 km · h
−1
919 ± 128
>25.2 km · h
−1
234 ± 53
Hewitt (2014)
a
Elite female soccer 13 (58/15) GPS Group A (1–10)
b
17.1–21.0 km · h
−1
1625
21.1–24.0 km · h
−1
2950
% >24 km · h
−1
3.5
Group B (11–25)
b
17.1–21.0 km · h
−1
1475
21.1–24.0 km · h
−1
3450
% >24 km · h
−1
4.2
Group C (25+)
b
17.1–21.0 km · h
−1
1550 ± 25
21.1–24.0 km · h
−1
3250 ± 50
% >24 km · h
−1
3.6 ± 0.3
Rampinini 2007 “Major”European League and
Champions League
34 (N.S./20) Best (Champions League or Top 8 in National League) Total distance 11,097 ± 778
>14.4 km · h
−1
2770 ± 528
>19.8 km · h
−1
902 ± 237
Worst (Bottom 12 of National League) Total distance 10,827 ± 760
>14.4 km · h
−1
2630 ± 536*
>19.8 km · h
−1
883 ± 268*
Rampinini 2009 Italian Serie A 416 (327/186) SICS Successful
(1–5 final ranking)
Total distance 11,647
>14 km · h
−1
3787
>19 km · h
−1
1196
Less successful
(15–20 final ranking)
Total distance 12,190
>14 km · h
−1
4263
>19 km · h
−1
1309
a
Data extrapolated from figures.
b
ased on FIFA Women’s World rankings.
*Significantly lower than best group P< 0.05. N.S. = not stated.
SCIENCE AND MEDICINE IN FOOTBALL 7

examining congested schedules (Carling et al. 2015a); there-
fore, the current review only provides a brief overview of
current knowledge which should be interpreted with caution.
Examination of Spanish first division players who played 2
matches in a week, observed as mean distance covered across
2 games, with small changes in match-running observed (−8%
to 1%, P= 0.12–0.96) when compared to 1 match per week
(Lago et al. 2011). A weekly mean was presented, with match-
to-match changes not presented when 2 games were played
in a week, severely limiting the findings and generalisability of
this study. Furthermore, examination of half to half changes
has resulted in no statistical differences in total distance or
high-speed running distance during the second match in
comparison to the first (Rey et al. 2010). The major limitations
of current literature assessing congested schedules are the
small number of files available for analysis (n= 172 and 42,
respectively). Although changes do not appear meaningful,
injury rates, and time loss from these injuries, have been
shown to increase in game 2 of a congested schedule, com-
pared to game 1 (Dupont et al. 2010; Dellal et al. 2013).
Although common match-running variables appear unaf-
fected, alternative variables might be more sensitive to a
period of match congestion (Arruda et al., 2014). An examina-
tion of youth soccer players who played 5 games, shortened in
length, over 3 days reported a decrease in accelerations
(−34%) (Arruda et al., 2014). It has been suggested that accel-
eration data should be examined through periods of conges-
tion given its association with neuromuscular fatigue (Nedelec
et al. 2014; Carling et al. 2015a). A decreased rate of power
development, due to neuromuscular fatigue, has also shown a
small meaningful change (effect size = −0.25) at 72 h post
fatiguing exercise during a jump analysis (Gathercole et al.
2015). Therefore, it is plausible that accelerations could be
affected for greater than 72-h post-match. However, GPS is
associated with high match-to-match variation (18%) when
quantifying the number of maximal accelerations (Meylan
et al. 2016). This could limit the utility of acceleration as a
measure for making informed decisions with absolute cer-
tainty, based on the possible large change required. Further
analysis is required to determine the effect of a congested
match schedule (<72 h) on accelerations utilising more robust
study designs (Carling et al. 2015a). Researchers should also
look to better define congested schedules as successive
matches, with players, rather than teams, involved in multiple
games. This is important to properly identify the changes that
might occur.
Environmental factors
The effect of temperature on match-running
Soccer is played in a wide variety of environments (Table 6),
with temperature being a consistent factor which may affect
match outcome. For instance, the likelihood of a visiting team
winning in the Gulf Region decreases by 3% for every 1°C
increase in temperature as compared to home baseline con-
ditions (Brocherie et al. 2015). The home team might have
been more acclimatised to the heat. Playing in the heat
increases sweat rate and peripheral vasodilation in an attempt
to dissipate heat, which can result in dehydration and
competition between metabolic demands and heat loss
requirements (Corbett et al. 2014; Racinais et al. 2015). These
acute requirements can be offset by heat acclimation, with
increased plasma volume and sweat rates to facilitate cooling
to attenuate the rise in core temperature and heart rate
(Périard et al. 2015). However, it remains unknown if the
match outcome was influenced by a reduced match-running
for the away team compared to the home team. Recent
research has observed declines in match-running in tempera-
tures greater than 21°C (Carling et al. 2011), with French Ligue
1 midfielders completing 4% less total distance. Furthermore,
total distance and high-speed running decreased (7% and
26%, respectively) whilst playing in the heat (43°C) compared
to control conditions (21°C) in elite Scandinavian soccer
players (Mohr et al. 2012). The non-randomised controlled
design used by Mohr et al. (2012) makes the application of
these findings to a larger population challenging.
A decrease (−2.4%, P< 0.001) in percentage of total dis-
tance covered at low to moderate-speed running has been
observed when playing in 41°C compared to 35°C (Özgünen
et al. 2010). An analysis of the 2014 FIFA World Cup Brazil
(Nassis et al. 2015) noted that high-speed activity was also
decreased in matches played under high heat stress (8.5%,
P= 0.020), classified using wet bulb globe temperature. This
decrease in high-speed actions was suggested to allow players
to maintain a similar rate of successful passes (3%), with a
similar number of passes performed under high heat stress
compared to low heat stress. Perception may account in the
change of performance, with subconscious changes in move-
ment patterns in response to thermal comfort (Edwards &
Noakes 2009; Waldron & Highton 2014; Périard et al. 2015;
Schulze et al. 2015) and players may subconsciously also
modify movement patterns to preserve technical actions
(Nassis et al. 2015). Additionally, Link and Weber (2015)
reported players in the 1. Bundesliga reduced their total dis-
tance when playing in the heat, whilst preserving their ability
to perform high-speed actions when required. Future analyses
are advised to include technical data, where possible, to iden-
tify if changes in match-running are to preserve technical
ability, whilst also including all match-running thresholds for
analysis. Further inclusion of player hydration status and core
temperature may provide a better understanding with regards
to the effect of these physiological markers on match-running
in the heat.
The effect of altitude on match-running
The effect of altitude on match-running is presented in Table 6.
High-speed activity and accelerations (9–25% decrease) appear
to be most susceptible to changes when matches at an altitude
between 1600 and 3600 m are examined in comparison to sea
level (Aughey et al. 2013;Garvicanetal.2014; Bohner et al. 2015).
Despite the fact that altitude facilitates high-speed running, due
to a decrease in the partial pressure of oxygen (Levine et al.
2008), negative changes occur due to a decrement in the pro-
duction of adenosine triphosphate (ATP) at altitude. A slowing of
ATP re-synthesis following fatiguing exercise has been observed
in hypoxic conditions (Haseler et al. 1999), possibly limiting high-
speed efforts, especially during short recovery periods (Brosnan
et al. 2000). Total distance covered at the 2010 FIFA WorldCup in
8J. TREWIN ET AL.

Table 6. The effect of temperature and altitude on distance covered, heart rate (bpm) and physiological markers.
First author (year) Level
Games
(# files/players) Capture method Environment
Team
(where relevant) Variables Control
Environmental
change
Aughey (2013) Australian and Bolivian
male age group
national teams.
4 (122/27) GPS Sea level vs. altitude (3600 m) Australia Total distance ~92 m · min
−1
~9.8% ↓
0.0–14.9 km.h
−1
~79 m · min
−1
~3.8% ↓
15.0–36.0 km · h
−1
~12 m · min
−1
~25.0% ↓
>10.0 km · h
−2
~2.2 accel · min ~4.3% ↓
Bolivia Total distance ~100 m · min
−1
~9.0% ↓
0.0–14.9 km · h
−1
~80 m · min
−1
~12.5% ↓
15.0–36.0 km · h
−1
~14 m · min
−1
~7.1% ↓
>10.0 km · h
−2
~2.0 accel ·min 5.0% ↑
Bohner (2015) NCAA women’s soccer
players
3 (18/6) GPS Sea level vs. altitude (1839 m) Total distance ~120 m · min
−1
~10.0% ↓
>13.0 km · h
−1
~27 m · min
−1
~7.4% ↓
>13.0 km · h
−1
% 10.4 % 1.3 % ↓
Carling (2011) Elite male soccer
players
80 (339/9) Amisco Pro 11–20°C and >21°C Total distance 123.4 m · min
−1
3.8% ↓
14.4–19.7 km · h
−1
21.3 m · min
−1
15.0% ↓
>19.8 km · h
−1
8.2 m · min
−1
8.5% ↓
First half >19.8 km · h
−1
8.1 m · min
−1
9.5% ↓
Second half >19.8 km · h
−1
8.3 m · min
−1
8.4% ↓
Garvican (2014) Australian male age
group national team
3 (36/12) GPS Sea level vs.
altitude (1600 m)
Total distance ~114 m · min
−1
~9.6% ↓
0.0–14.9 km · h
−1
~98 m · min
−1
~8.2% ↓
15.0–36.0 km · h
−1
~16 m · min
−1
~18.8% ↓
>10.0 km · h
−2
~2.9 accel ·min ~3.4% ↓
Mohr (2010) Elite male soccer
players
2 (34/17) Amisco Pro 21°C and 43°C Average/peak heart rate 160/183 bpm 1.3% ↓/1.1% ↑
First/second half core temperature 38.7/38.3°C 2.3% ↑/3.4% ↑
Post-match plasma lactate 3.3 mmol · L 48.5% ↑
Total distance ~ 10,100 m 7.0% ↓
>14 km · h
−1
~2250 m 26.0% ↓
Özgünen (2010) Semi-professional
male soccer players
2 (15/11) GPS Heat index 35°C and 41°C 14.6–19.5 km · h
−1
934 ± 227 m 25.7% ↓
19.6–25.5 km · h
−1
382 ± 99 m 12.6% ↓
>25.6 km · h
−1
102 ± 44 m 5.9% ↑
SCIENCE AND MEDICINE IN FOOTBALL 9

South Africa was also reduced (−2%, P< 0.05) above 1200 m
(Nassis 2013). However, given the small sample sizes and limited
games analysed by previous research (Aughey et al. 2013;
Garvican et al. 2014; Bohner et al. 2015), application of inferences
to larger populations is challenging. Furthermore, these studies
were subject to different conditions, such as non-regulation
match-lengths (Garvican et al. 2014), coach instructions to play
conservatively (Aughey et al. 2013) and inappropriate player
inclusion criteria (Bohner et al. 2015) amongst others. The study
of Nassis (2013) was also limited as only total distance was
analysed and summed for each team, therefore, not accounting
for the match-running metrics most sensitive to environmental
conditions or positional differences identified in this review.
Findings would suggest that analysis of high-speed and accel-
eration metrics should be included in future studies due to their
sensitivity to playing at altitude.
Conclusion
From the studies reviewed, it would appear that environmental
factors play a strong role in the variability and differences
observed in the match-running of soccer players (Table 7).
Caution however needs to be exercised due to the limitations
of the studies presented, such as small sample sizes and minimal
control. Alternatively, the proposed situational factors that may
affect performance showed trivial to small changes, which were
often within the match-to-match variation typically observed on
a global level (full match). Further research is required to fully
examine factors deemed to have a meaningful impact on per-
formance, utilising repeated measure designs to better identify
any changes that occur within player, with a particular focus on
youth a women’ssoccerrequired.
Recommendations
It is recommended that researchers identify within-player
match-to-match variability and assess changes within players.
Changes are entirely individual based on a variety of factors
(such as position, physiological capacity) and therefore the use
of linear mixed modelling is suggested. Particularly when asses-
sing environmental changes where some athletes may respond
differently to their environment than others. This will improve
the understanding of these factors, but also allow for stronger
justifications to be made with respect to the changes observed.
Eventually, interventions to mitigate these changes, such as heat
acclimation, should be assessed against match-related data such
as actual games, small-sided games or 11v11 scrimmages.
The physical implications of specific tactics should be profiled
in realistic training situations to assess the possible effect during
matches. Coding in-game tactical shifts from reference team
and/or oppositions and ensuring changes in physical work rate
may also provide more insight into the impact of specific factors.
Finally, peak periods should also be examined to identify the
worst-case scenarios that players could encounter. The identifi-
cation of these periods will be more informative for practitioners
who should be preparing players for these scenarios. This will
inform training to a greater extent, with particular relevance for
small-sided games and high-intensity interval training and their
combined use for conditioning purposes.
Acknowledgement
No sources of funding were used to assist in the preparation of this
review.
Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Joshua Trewin http://orcid.org/0000-0002-2129-9158
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