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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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Science and Medicine in Football
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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
Published online: 06 Jun 2017.
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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
Womens 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 (7.125%). 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;
timemotion 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,
timemotion 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 68% (Osgnach et al. 2010; Özgünen et al. 2010;
Gaudino et al. 2013), whilst not accounting for accelerations.
Temperatures was defined as; cold (010°C), moderate (10
20°C), warm (2030°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 (0500 m), low
altitude (5002000 m), moderate altitude (20003000 m) and
high altitude (30005500 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 Castellanos 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 Mens 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 Womens 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 Mens World Cup N.S. 1 1 1 0 1 0 0 1 1 6
Nassis (2015) Fifa Mens 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 19.837.1% 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 1.94.3% 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 (5166%) or low (3450%) 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*
14.419.7 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
14.419.7 km · h
1
1270 ± 209 1375 ± 228
19.825.1 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
14.419.7 km · h
1
1702 ± 271 1777 ± 243
19.825.1 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
14.419.7 km · h
1
2069 ± 334 2000 ± 330
19.825.1 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
14.419.7 km · h
1
2118 ± 412 1977 ± 413
19.825.1 km · h
1
884 ± 182 885 ± 173
>25.1 km · h
1
326 ± 119 341 ± 106
Attackers Total distance 10,778 ± 865 9988 ± 1002
14.419.7 km · h
1
1685 ± 422 1422 ± 344
19.825.1 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
1114 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
1114 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
1114 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
2124 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
2124 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 3239% (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 (1125 in the world rank-
ings; 17%), compared to playing against teams ranked within
the top 10 of FIFAs Womens 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, Womens 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 17.121.0 km · h
1
417 ± 143
21.124.0 km · h
1
144 ± 59
>24 km · h
1
115 ± 72
Middle 7 17.121.0 km · h
1
411 ± 135
21.124.0 km · h
1
137 ± 57
>24 km · h
1
117 ± 75
Bottom 7 17.121.0 km · h
1
386 ± 124
21.124.0 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 (110)
b
17.121.0 km · h
1
1625
21.124.0 km · h
1
2950
% >24 km · h
1
3.5
Group B (1125)
b
17.121.0 km · h
1
1475
21.124.0 km · h
1
3450
% >24 km · h
1
4.2
Group C (25+)
b
17.121.0 km · h
1
1550 ± 25
21.124.0 km · h
1
3250 ± 50
% >24 km · h
1
3.6 ± 0.3
Rampinini 2007 MajorEuropean 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
(15 final ranking)
Total distance 11,647
>14 km · h
1
3787
>19 km · h
1
1196
Less successful
(1520 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 Womens 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= 0.120.96) 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 (925% 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%
0.014.9 km.h
1
~79 m · min
1
~3.8%
15.036.0 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%
0.014.9 km · h
1
~80 m · min
1
~12.5%
15.036.0 km · h
1
~14 m · min
1
~7.1%
>10.0 km · h
2
~2.0 accel ·min 5.0%
Bohner (2015) NCAA womens 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 1120°C and >21°C Total distance 123.4 m · min
1
3.8%
14.419.7 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%
0.014.9 km · h
1
~98 m · min
1
~8.2%
15.036.0 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 14.619.5 km · h
1
934 ± 227 m 25.7%
19.625.5 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 womenssoccerrequired.
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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12 J. TREWIN ET AL.
... There have been some consistent findings from previous research, demonstrating that wide players (WM or FB) cover the greatest high-intensity distance with CB the lowest. That said, numerous factors such as formations/playing styles, locations (Home or Away), score-line and opponent levels influence match performance, especially in high-intensity running (Trewin et al., 2017). This could be further supported by the match-tomatch variability produced by players. ...
... Regrading position-specific trends, attackers ran 71% more distance in high-speed running when their team was in possession of the ball compared to out of possession whilst defenders performed 156% more distance when out of possession compared to in possession (Di Salvo et al., 2009). However, due to a lack of research using repeated measures designs, the effect of both formation and ball possession is still largely unclear (Trewin et al., 2017). ...
... According to a systematic review by Castellano, Alvarez-Pastor and , only ~30% of papers considered tactical or technical variables alongside physical metrics. As players' performance can be influenced by all of the factors above in isolation or collectively Carling et al., 2008;Trewin et al., 2017), physical metrics should be integrated with tactical and technical factors to obtain a more holistic understanding of football performance. ...
Thesis
Full-text available
The traditional approach to quantifying football (soccer) match physical demands in isolation from tactical and technical performances has been used over the last 45 years. An integrated approach for the contextualisation of match physical performance with key tactical actions has been devised. However, scientific examinations into match physical-tactical profiles are sparse. The aim of Study 1 (Chapter 3) was to improve the original integrated approach to quantifying match physical-tactical performances (e.g., the lack of objectivity within the coding process and limited information regarding the actual tactical purpose of the action), and then verify the validity and reliability of the newly developed integrated approach. The new integrated approach demonstrated a high degree of validity and strong inter- (κ=0.81) and almost perfect intra-observer (κ=0.94) reliability. Hence, unique high-intensity profiles of elite players/teams in relation to key tactical actions can be validly and reliably generated. Study 2 (Chapter 4) determined differences in match physical-tactical performances between various tactical roles to provide better insights into match performance in football. Physical-tactical profiles during match-play were position-specific. Analysing positions with specific tactical roles (e.g., central defensive and attacking midfielders) was found to be more sensitive in detecting match performance of players compared to a general positional analysis (e.g., central midfielders) that could over or underestimate physical-tactical demands. This suggests that coaches and practitioners should account for specific playing styles of their players within the team when designing position- or player-specific training programmes. The Study 3 (Chapter 5) sought to establish the physical-tactical profiles of elite football teams and individual players with special reference to final league rankings alongside technical metrics to better understand associations between success in football and match performance. Higher-ranked teams not only performed more physical-tactical activities when in possession of the ball during a competitive match (e.g., ‘Move to Receive/Exploit Space’, ‘Run with Ball’, etc.) but also demonstrated better technical performances (e.g., greater number of shots on target, passes, etc.) compared to lower-ranked teams. The contextualised data can improve our understanding of a team’s playing style according to their competitive standard. The Study 4 (Chapter 6) analysed the physical-tactical trends of elite players and teams during peak 1-, 3- and 5-min (i.e., the most 1-, 3- and 5-min intense period of play) and the following periods during matches to provide better insights into match peak physical demands of players in relation to tactical actions and transient decrements in high-intensity running after intensified periods of play. The contextualised data showed that during the most demanding passage of play, players/teams covered the largest high-intensity distance for ‘Recovery Run’ out of possession and ‘Support Play’ in possession. After peak periods players/teams covered less high-intensity distance compared to the match average, especially when out of possession performing less high-intensity ‘Covering’ and ‘Recovery Run’ distance. However, some physical-tactical actions showed inconsistency in different time durations of the next periods with these physical-tactical data being position-specific (e.g., central offensive players covered ~80-100% less ‘Break into Box’ high-intensity distance in the next 1- and 5-min periods compared to the match average with performing ~20% more during the next 3-min period). Such data can help practitioners prescribe position- or player-specific drills whilst replicating peak physical-tactical demands of play and better understand transient decrements in high-intensity running after intense periods. This research programme provides novel data through investigating match physical-tactical profiles of players and teams. The studies reported above have demonstrated much clearer insights into match performance due to the fusion of physical metrics alongside their tactical context. Therefore, it is hoped that the contextualised data from the present research programme can help coaches and applied practitioners not only better understand match demands but also apply these into training sessions more effectively.
... Specifically, football teams are performing higher numbers of goal scoring, passing and organising related technical actions whilst committing fewer fouls and receiving fewer cards in home games comparing to away games (Sarmento et al., 2014;Liu et al., 2015). Higher ranked teams generally involved more in actions with ball possession (Liu et al., 2015;Trewin et al., 2017), and covered more distance and high-speed-running distance whilst in ball possession (Hoppe et al., 2015;Trewin et al., 2017). Playing against stronger opposition demanded a higher level of technical, tactical and physical performance (Taylor et al., 2008;Castellano et al., 2011;Liu et al., 2015). ...
... Specifically, football teams are performing higher numbers of goal scoring, passing and organising related technical actions whilst committing fewer fouls and receiving fewer cards in home games comparing to away games (Sarmento et al., 2014;Liu et al., 2015). Higher ranked teams generally involved more in actions with ball possession (Liu et al., 2015;Trewin et al., 2017), and covered more distance and high-speed-running distance whilst in ball possession (Hoppe et al., 2015;Trewin et al., 2017). Playing against stronger opposition demanded a higher level of technical, tactical and physical performance (Taylor et al., 2008;Castellano et al., 2011;Liu et al., 2015). ...
Article
The study aims to quantify the variation in the physical and technical match performance of football teams in different months of a season in the Chinese Super League (CSL). Data of 1,899 matches in the seasons 2012–2019 of CSL collected by Amisco Pro® were analysed. The generalised mixed modelling was employed to estimate the per match mean values of six physical performance-related parameters and 16 technical performance-related parameters of CSL teams in every month of all the eight seasons. Results showed that: (1) the mean values of all the analysed physical performance-related parameters (total/sprint/HSR/MSR distance, sprint/HSR efforts) of CSL teams through a season were characterised like a ‘U’ shape, the highest value was observed in the beginning of season (March), then decreased gradually, reaching the lowest in August, and rebounded progressively from September to November; (2) the mean values of eight technical performance-related parameters (goals, shots, shot accuracy, individual possession, individual possession in the last third, crosses, cross accuracy and yellow cards) presented trivial changes through the whole season; (3) the number of passes, passes per shot, forward passes, and time in individual possession showed trivial changes from March to October, but showed a substantially increase in November (the last month of season); (4) Pass accuracy, forward pass accuracy, and the number of mean ball touches per individual possession substantially increased in June, July and August, whilst the number of challenges, ground challenges, air challenges, tackles and fouls all substantially decreased in these 3 months. These results could provide detailed information to help the practitioners choose the best training and match preparation strategy in the means of periodisation in different season phases.
... Traditionally the physical demands of professional football match play have been quantified as absolute match running volumes using either video match analysis systems or global positioning systems (GPS) [1,2]. As such, the physical demands of football and the intra-and inter-match differences in physical match demands (total distance (TD), high-speed distance [> 19.8 km · h −1 ] (HSD) and acceleration profiles) have become routinely reported [2][3][4]. ...
... Traditionally the physical demands of professional football match play have been quantified as absolute match running volumes using either video match analysis systems or global positioning systems (GPS) [1,2]. As such, the physical demands of football and the intra-and inter-match differences in physical match demands (total distance (TD), high-speed distance [> 19.8 km · h −1 ] (HSD) and acceleration profiles) have become routinely reported [2][3][4]. Separately, research has explored the effects of acute reductions in physical performance during match-play, as represented by temporal changes in physical demands throughout a match [3]. ...
Article
Temporal changes in the total running demands of professional football competition have been well documented, with absolute running demands decreasing in the second half. However, it is unclear whether the peak match running demands demonstrate a similar decline. A total of 508 GPS files were collected from 44 players, across 68 matches of the Australian A-League. GPS files were split into the 1st and 2nd half, with the peak running demands of each half quantified across 10 moving average durations (1-10 min) for three measures of running performance (total distance, high-speed distance [> 19.8 km · h-1] and average acceleration). Players were categorised based on positional groups: attacking midfielder (AM), central defender (CD), defensive midfielder (DM), striker (STR), wide defender (WD) and winger (WNG). Linear mixed models and effect sizes were used to identify differences between positional groups and halves. Peak running demands were lower in the second half for STR across all three reported metrics (ES = 0.60-0.84), with peak average acceleration lower in the second half for DM, WD and WNG (ES = 0.60-0.70). Irrespective of match half, AM covered greater peak total distances than CD, STR, WD and WIN (ES = 0.60-2.08). Peak high-speed distances were greater across both halves for WIN than CD, DM and STR (ES = 0.78-1.61). Finally, STR had lower peak average acceleration than all positional groups across both halves (ES = 0.60-1.12). These results may help evaluate implemented strategies that attempt to mitigate reductions in second half running performance and inform position specific training practices.
... Several studies [3,6,7,11,12] have described various context situations and their effects on the wearable-derived metrics. Nevertheless, there were no attempts to increase the level of data sampling and examine physical demand change in a game on a minute-byminute basis. ...
... Every game is unique and, therefore, should be treated independently. In the literature, a lot of work has analyzed the intensity concerning the scoreline [9,11,12,20]. This was done by comparing relevant metrics (e.g., HSR, distance, and sprint distance) depending on the score and also the quality of the opponent. ...
Article
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Every soccer game influences each player’s performance differently. Many studies have tried to explain the influence of different parameters on the game; however, none went deeper into the core and examined it minute-by-minute. The goal of this study is to use data derived from GPS wearable devices to present a new framework for performance analysis. A player’s energy expenditure is analyzed using data analytics and K-means clustering of low-, middle-, and high-intensity periods distributed in 1 min segments. Our framework exhibits a higher explanatory power compared to usual game metrics (e.g., high-speed running and sprinting), explaining 45.91% of the coefficient of variation vs. 21.32% for high-, 30.66% vs. 16.82% for middle-, and 24.41% vs. 19.12% for low-intensity periods. The proposed methods enable deeper game analysis, which can help strength and conditioning coaches and managers in gaining better insights into the players’ responses to various game situations.
... Research on possible detraining effects during the COVID-19 break in matches and soccer-specific training indicate reduced performance in sprint and jump tests which can be associated with the decrease in soccer-specific (group) training during lockdown [6,7]. Regarding congested match schedules, research unrelated to the COVID-19 situation points out a potential increasing impact on injury occurrence and decreasing effect on physical match performance due to congested match schedules [8][9][10][11]. Numerous studies examined physical match performance and injury numbers before and after the Covid-19 induced break in matches and training in different European professional male soccer leagues [3,4]. However, the topic is currently not systematically reviewed. ...
... In most studies investigating injury rates, there seems to be no difference from pre to post COVID-19 break across all analyzed European soccer leagues (3 out of 4) [3,23,25]. These results contradict previous findings indicating an increase in injuries after an interruption of matches (i.e., winter break) and thus a short soccer-specific preparation time before the restart of matches [33] and during a congested match schedule [8,10,30]. ...
Article
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Background Due to the COVID-19 pandemic, matches and soccer-specific training were suspended for several weeks, matches after resumption were congested, and substitutions per team and game increased from three to five. Objective The aim of this review was to examine possible differences in physical match performance and injuries between before and after the COVID-19 induced break of matches and training in professional male European soccer leagues during the 2019/2020 season. Methods A systematic search identified all scientifically peer-reviewed publications involving elite male soccer players competing in the European leagues which reported physical match performance variables such as total running distance and running distance at different speed zones and/or injury parameters pre- and post-COVID-19 induced break. Results In total, 11 articles were included, which were coming from German Bundesliga, Polish Ekstraklasa, Croatian HNL, Spanish La Liga, and Italian Serie A. In all studies investigating the German Bundesliga, most parameters of physical match performance remained unaffected (0.08 ≤ p ≤ 0.82; − 0.15 ≤ ES 0.15), while studies investigating the Polish Ekstraklasa (p ≤ 0.03; − 0.27 ≤ ES − 0.18), Croatian HNL (p ≤ 0.04; − 1.42 ≤ ES ≤ 1.44), Spanish La Liga (p ≤ 0.017; − 0.32 ≤ ES ≤ 5.5), and Italian Serie A (p ≤ 0.014; − 1.01 ≤ ES 0.24) showed a decrease in most parameters of physical match performance after the COVID-19 break. Injury rates were only investigated by studies targeting the German Bundesliga and Italian Serie A. In the majority of studies (3 out of 4 studies), there occurred no difference in injuries between pre- and post-COVID-19 break (p > 0.05; ES = N/A). Conclusion Results indicate that Bundesliga teams maintained physical match performance during the 9-weeks break in matches and 3-weeks break in group training, whereas a longer match and group training interruption up to 15 weeks and 8 weeks, respectively, in the other leagues appeared to lead to a decreased physical match performance. Regarding injuries, we speculate that the increase in substitutions from 3 to 5 substitutions per game might prevent an increase in injury occurrence during matches. The underlying studies’ results provide hints for possible upcoming unexpected interruptions with respect to optimal physical preparations for the resumption of matches and a congested schedule to maintain physical match performance, or for possible rule changes such as 5 instead of 3 substitutions to avoid physical overload during congested match schedules.
... These studies reported that injuries in males occurred more frequently during the attacking phase than during the defensive phase. It has been observed that players' physical performance changes depending on ball possession [71], which may explain the higher number of injuries occurring during the attacking phase. However, this information has been reported only in a few studies and usually in a generic way, and therefore needs to be further investigated. ...
Article
Full-text available
Background A comprehensive examination of the sport-specific activities performed around the time of injury is important to hypothesise injury mechanisms, develop prevention strategies, improve management, and inform future investigations. The aim of this systematic review is to summarise the current literature describing the activities performed around the time of injury in football (soccer). Methods A systematic search was carried out in PubMed, Web of Science, SPORTDiscus, and OpenGrey. Studies were included if participants were football players aged > 13 years old and the activities performed at the time of injury were reported together with the total number of injuries. Risk of bias was assessed using an adapted version of checklists developed for prevalence studies. The activities reported by the studies were grouped to account for inconsistent reporting, and the proportion of each injury activity was calculated. Data were not meta-analysed due to high heterogeneity of methods and classification criteria. Results We included 64 studies reporting on 56,740 injuries in total. ACL injures were analysed by 12 studies, ankle/foot and knee injuries were analysed by five studies, thigh injuries were analysed by four studies, hip/groin injuries were analysed by three studies, and hamstring injuries were analysed by two studies. Five studies analysed more than one type of injury and 38 studies did not specify the type of injuries analysed. Running and kicking were the predominant activities leading to thigh and hamstring injuries. Changing direction and kicking were the predominant activities leading to hip and groin injuries and duels were the predominant activities leading to ankle injuries. Duels and pressing seem the predominant activities leading to ACL injuries, while results for other knee and general injuries were inconsistent. Conclusions A qualitative summary of the activities performed at the time of injury has been reported. The results need to be interpreted carefully due to the risk of bias observed in the included studies. If we are to meaningfully progress our knowledge in this area, it is paramount that future research uses consistent methods to record and classify injuries and activities leading up to and performed at the time of injury. Registration The protocol of this systematic review was registered at the Open Science Framework (https://doi.org/10.17605/OSF.IO/U96KV).
... BMC Sports Science, Medicine and Rehabilitation (2022) 14:179 soccer [5,6], physical performance (i.e., quantified by running performance [RP] such as total distance covered and distances covered in various speed zones) are more commonly investigated [3,7,8]. Such growing interest in RP has led to a large body of published research [3,9,10]. Although this has forcibly shaped contemporary opinions, with researchers and practitioners frequently emphasizing the importance of RP, particularly high-intensity running, in professional soccer [9,11], current research remains equivocal regarding the high-intensity distance covered and success in soccer. ...
Article
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Abstract Background To clarify does physical performance affect success in highest-level soccer, the purpose of the present study was to identify differences in technical-tactical performance (TP) between teams covering high and low running performance (RP) during the UEFA Champions League (UCL) matches. Methods The RP and TP data were collected from UCL group stage matches in the 2020/21 season. RP variables included total distance covered (TD), high intensity running (HIR), total distance when in ball possession (TDB), and high intensity running when in ball possession (HIRB). TP variables included goal chances, shots, shots on target, passes, accurate passes, key passes, key passes accurate, crosses, crosses accurate, counter attacks, counter attacks with a shot, high pressing, high pressing successful, low pressing, low pressing successful, tackles, tackles successful, entrances to the opponent’s box, total actions, and successful actions. K-means cluster analysis method was used to classify teams covering (i) low and high TD, (ii) low and high HIR, (iii) low and high TDB, (iv) low and high and HIRB. Linear mixed models were used to identify differences in teams’ TP according to their RP. Pearson’s correlations were used to establish direct association between team TP and RP. Results Similar TP were observed whether teams covering high or low TD/HIR. Teams covering greater TDB/HIRB had more goal chances, shots, shots on target, passes, accurate passes, key passes, accurate key passes, crosses, successful high pressing, entrances to the opponent’s box, total actions, and successful actions were observed (all moderate to very large effect sizes. Significant association between specific TP variables and TDB/HIRB were evidenced (Pearson’s r = 0.35–0.96, all p
... The first step to clarify this issue was to analyze differences in MRP across different 15-min match periods while controlling the influence of various situational factors. As previous research demonstrated that the most influential situational factors in elite soccer are match outcome, match location, team and opponent quality [51][52][53], these factors were considered in current study. Results indicated no effect of match outcome, match location, team, and opponent quality on TD and HIR for players in all playing positions. ...
Article
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It is widely recognized that there is a decline in match running performance (MRP) towards the end of matches. To clarify whether it is primarily a consequence of fatigue, pacing or situational influences, this study aimed to examine MRP across 15-min match periods for players on different playing positions. Players’ MRP (n = 244) were examined from the UEFA Champions League matches (n = 20) using a semiautomatic optical tracking system. Linear mixed models for repeated measures were adjusted to analyze MRP over the six 15-min match periods while controlling the influence of situational factors. No effects of match outcome, match location, team, and opponent quality on total distance (TD) and high-intensity running (HIR) for players in all playing positions were found (F = 0.03–2.75; all p > 0.05). Significant differences in TD (F = 17.57–53.01; η2 = 0.39–0.52, all large effect sizes) and HIR (F = 3.67–7.64; η2 = 0.05–0.19, small to medium effect sizes) among six 15-minute match periods were found for players in all playing positions. In addition, players in all playing positions covered less TD (d = 1.41–2.15, large to very large effect sizes) and HIR (d = 0.16–0.6, trivial to medium effect sizes) in the last compared to the first 15-min match period. No differences in TD and HIR between the last two match periods in the second half were observed. This study confirmed that soccer players reduce MRP towards the end of matches, and suggest that the decline of MPR in highest-level soccer may be a consequence of pacing strategies.
... Moreover, this study considered only main playing positions (e.g., defenders, midfielders or attackers), what may also limit practical application. Finally, it has been reported that the effect of team formation on MRP using a repeated-measures analysis has also not been undertaken, emphasizing the importance of future research (Trewin et al. 2017). ...
Article
This study aimed to determine the effect of team formation on position-specific match running performance (MRP) at highest-level football. Players’ MRP (n = 226) was observed in four team formations: 3-5-2 (n = 24), 4-4-2 (n = 44), 4-2-3-1 (n = 77) and 4-3-3 (n = 81). Central defenders in the 3-5-2 formation achieved a greater amount of high-intensity running distance than in the 4-3-3 formation (mean difference (MD) [95% confidence interval] = 144 m [12, 267], medium ES). Fullbacks in the 4-4-2 formation covered less total distance than in 3-5-2 (MD = −762 m [−1431, −94], large ES) and 4-2-3-1 (MD = −662 m [−1055, −269], medium ES). Central midfielders’ total distance in the 4-4-2 formation was lower than that in the 3-5-2 (MD = −645 m [−79, −1211], medium ES) and 4-3-3 (MD = −656 m [−1181, −132], medium ES) formations. Wide midfielders’ walking distance in the 4-4-2 formation was lower than that in the 4-3-3 (MD = −484 m [-742, -226], very large ES) and 4-2-3-1 (MD = −535 m [−789, −282], very large ES) formations. Forwards’ high-intensity running in the 4-2-3-1 formation was lower than that in the 4-3-3 (MD = −363 m [−613, −112], large ES) and 4-4-2 (MD = −396 m, [−688, −103], large ES) formations. These findings show that conditioning programs for players on all playing positions should be tailored according to the formations of their teams.
Article
Soccer, as the most popular sport in the world, is characterized by complex performance requirements and is influenced by many external factors. In order to record and systematize the scientific findings of the effects of weather factors and altitude on physical and technical performance in professional male and female soccer a systematic literature search was conducted in the relevant databases from 8th to 15th of February 2022. From 2.396 records, 150 were selected for detailed screening. 21 studies were included in this review that met the following inclusion criteria: professional male or female soccer players over 18 years of age; field study under real-life conditions; effects on physical and/or technical performance, influence of at least one weather-related factor. The selected articles considered different research objects, periods of time, technologies, or methods. Most publications investigated the factors of temperature, humidity and altitude and showed some significant effects on physical performance, while technical performance often did not change significantly. For all analysed environmental factors, it can be summarized that in different environmental conditions, professional soccer players may consciously adjust certain performance parameters to maintain key match characteristics throughout the whole game. This pacing strategy allows them to keep the influence of environmental factors in check as far as possible.
Article
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Investigations finding that soccer players perform more work when the score is level than when leading or trailing have not considered hat the significant effects were due to fatigue rather than score-line. For example, two teams may be level for the early part of a game and the score diverges later on. The current study established a typical fatigue pattern using data from 79 player performances during five 0-0 drawn English FA Premier League matches. This typical fatigue pattern was used to adjust the work-rate of 90 player performances in five English FA Premier League matches where both teams were level, ahead and behind for at least 15 minutes each. There was a significant interaction between player position and score-line (p = .010) with forwards spending a greater percentage of time moving at 4 m.s⁻¹ or faster when their team was leading than when level while defenders spent a greater percentage of time moving at 4 m.s⁻¹ or faster when their team was trailing than when level. An explanation for this interaction effect is that forwards feel encouraged to work harder when their team has earned a lead with the work-rate of opposing defenders also increasing as a result.
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Athlete tracking devices that include global positioning system (GPS) and micro electrical mechanical system (MEMS) components are now commonplace in sport research and practice. These devices provide large amounts of data that are used to inform decision-making on athlete training and performance. However, the data obtained from these devices are often provided without clear explanation of how these metrics are obtained. At present, there is no clear consensus regarding how these data should be handled and reported in a sport context. Therefore, the aim of this review was to examine the factors that affect the data produced by these athlete tracking devices to provide guidelines for collecting, processing, and reporting of data. Many factors including device sampling rate, positioning and fitting of devices, satellite signal and data filtering methods can affect the measures obtained from GPS and MEMS devices. Therefore researchers are encouraged to report device brand/model, sampling frequency, number of satellites, horizontal dilution of precision (HDOP) and software/firmware versions in any published research. Additionally, details of data inclusion/exclusion criteria for data obtained from these devices are also recommended. Considerations for the application of speed zones to evaluate the magnitude and distribution of different locomotor activities recorded by GPS are also presented, alongside recommendations for both industry practice and future research directions. Through a standard approach to data collection and procedure reporting, researchers and practitioners will be able to make more confident comparisons from their data, which will improve the understanding and impact these devices can have on athlete performance.
Article
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The aims of the current study were to examine the external validity of inertial based parameters (inertial movement analysis; IMA) to detect multi-planar explosive actions during maximal sprinting, change of direction (COD) and to further determine its reliability, set appropriate magnitude bands for match analysis and assess its variability during international women's soccer matches. Twenty U20 female soccer players, wearing GPS units with a built-in accelerometer, completed three trials of a 40-m sprint and a 20-m sprint with a change of direction to the right or left at 10-m. Further, thirteen women's national team players (157 files; 4-27 matches per player) were analyzed to ascertain match-to-match variability. Video synchronization indicated IMA signal was instantaneous with explosive movement (acceleration/deceleration/COD). Peak GPS velocity during the 40-m sprint showed similar reliability (CV = 2.1%) to timing gates, but increased pre- and post-COD (CV = 4.5-13%). IMA variability was greater at the start of sprints (CV = 16-21%) compared to pre- and post-COD (CV = 13-16%). IMA threshold for match analysis was set at 2.5m.s-2 by subtracting one standard deviation from the mean IMA during sprint trials. IMA match variability (CV = 14%) differed from high-speed GPS metrics (35-60%). Practitioners are advised that timing lights should remain the gold standard for monitoring sprint and acceleration capabilities of athletes. However, IMA indicates a reliable method to monitor between match explosive actions and assess changes due to various factors such as congested schedule, tactics, heat or altitude.
Article
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The effect of altitude on soccer game activity profiles was retrospectively examined in six NCAA Division I female soccer players. Comparisons were made between two matches played at sea level (SL) and one match played at a moderate altitude (1839 m). A 10-Hz global positioning system device was used to measure distance and velocity. The rate of total distance capacity (TDC) and high intensity running (HIR) as well as percent of time at HIR were evaluated. Significant differences were seen in the distance rate (120.55 ± 8.26 m·min-1 versus 105.77 ± 10.19 m·min-1) and the HIR rate (27.65 ± 9.25 m·min-1 versus 25.07 ± 7.66 m·min-1) between SL and altitude, respectively. The percent of time at HIR was not significantly different (p = 0.064), yet tended to be greater at SL (10.4 ± 3.3%) than at altitude (9.1 ± 2.2%). Results indicate that teams residing at SL and competing at a moderate altitude may have a reduced ability in distance covered and a high intensity run rate.
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This study examined the effect of high (HPBPT) and low percentage ball possession (LPBPT) on physical and technical indicators during 2014 FIFA World Cup matches. This would enable a regression model to be constructed to further understand the impact of different ball possession (BP) strategies on match performance. Data were collected from 346 international soccer players using a multiple-camera computerised tracking system. Although players in HPBPT covered lower distances (P < 0.01) in total and at low speed compared to LPBPT this produced a trivial Effect Size (ES). However, they covered similar distances (P > 0.05) at medium and high speeds. Players in LPBPT covered more distance without ball possession but less with ball possession than HPBPT (P < 0.01; ES large). All positions in LPBPT spent less time in the opposing half and attacking third than the players in HPBPT (P < 0.01; ES small-moderate), but all positions in HPBPT completed more short and medium passes than LPBPT (P < 0.01; ES moderate). Players in HPBPT produced more solo runs into the attacking third and penalty area than LPBPT (P < 0.05, ES small). The equation to predict BP from physical and technical indicators highlighted the importance of distances covered (total, with and without ball possession), time spent in the attacking third and successful short passes during matches. In practical terms, high or low BP does not influence the activity patterns of international matches although HPBPT spend more time in offensive areas of the pitch.
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Exercising in the heat induces thermoregulatory and other physiological strain that can lead to impairments in endurance exercise capacity. The purpose of this consensus statement is to provide up-to-date recommendations to optimise performance during sporting activities undertaken in hot ambient conditions. The most important intervention one can adopt to reduce physiological strain and optimise performance is to heat acclimatise. Heat acclimatisation should comprise repeated exercise-heat exposures over 1-2 weeks. In addition, athletes should initiate competition and training in a euhydrated state and minimise dehydration during exercise. Following the development of commercial cooling systems (eg, cooling-vest), athletes can implement cooling strategies to facilitate heat loss or increase heat storage capacity before training or competing in the heat. Moreover, event organisers should plan for large shaded areas, along with cooling and rehydration facilities, and schedule events in accordance with minimising the health risks of athletes, especially in mass participation events and during the first hot days of the year. Following the recent examples of the 2008 Olympics and the 2014 FIFA World Cup, sport governing bodies should consider allowing additional (or longer) recovery periods between and during events, for hydration and body cooling opportunities, when competitions are held in the heat.
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Full-text available
Exercising in the heat induces thermoregulatory and other physiological strain that can lead to impairments in endurance exercise capacity. The purpose of this consensus statement is to provide up-to-date recommendations to optimise performance during sporting activities undertaken in hot ambient conditions. The most important intervention one can adopt to reduce physiological strain and optimise performance is to heat acclimatise. Heat acclimatisation should comprise repeated exercise-heat exposures over 1-2 weeks. In addition, athletes should initiate competition and training in a euhydrated state and minimise dehydration during exercise. Following the development of commercial cooling systems (eg, cooling-vest), athletes can implement cooling strategies to facilitate heat loss or increase heat storage capacity before training or competing in the heat. Moreover, event organisers should plan for large shaded areas, along with cooling and rehydration facilities, and schedule events in accordance with minimising the health risks of athletes, especially in mass participation events and during the first hot days of the year. Following the recent examples of the 2008 Olympics and the 2014 FIFA World Cup, sport governing bodies should consider allowing additional (or longer) recovery periods between and during events, for hydration and body cooling opportunities, when competitions are held in the heat.
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