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Journal of Sports Sciences
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Match running performance fluctuations in elite
soccer: Indicative of fatigue, pacing or situational
influences?
Paul S. Bradley
a
& Timothy D. Noakes
b
a
Department of Sport & Exercise Sciences , University of Sunderland , Sunderland SR1 3SD ,
UK
b
Department of Human Biology , University of Cape Town , South Africa
Published online: 01 Jul 2013.
To cite this article: Paul S. Bradley & Timothy D. Noakes (2013): Match running performance fluctuations in elite soccer:
Indicative of fatigue, pacing or situational influences?, Journal of Sports Sciences, DOI:10.1080/02640414.2013.796062
To link to this article: http://dx.doi.org/10.1080/02640414.2013.796062
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Match running performance fluctuations in elite soccer: Indicative of
fatigue, pacing or situational influences?
PAUL S. BRADLEY
1
& TIMOTHY D. NOAKES
2
1
Department of Sport & Exercise Sciences, University of Sunderland, Sunderland SR1 3SD, UK, and
2
Department of Human
Biology, University of Cape Town, South Africa
(Accepted 11 April 2013)
Abstract
The aims of this study were to: (1) quantify match running performance in 5-min periods to determine if players fatigue or
modulate high-intensity running according to a pacing strategy, and (2) examine factors impacting high-intensity running
such as score line, match importance and the introduction of substitutes. All players were analysed using a computerised
tracking system. Maintaining ‘high’ levels of activity in the first half resulted in a 12% reduction (P < 0.01) in the second half
for high-intensity running (effect size [ES]: 0.8), while no changes were observed in ‘moderate’ and ‘low’ groups
(ES: 0.0–0.2). The ‘high’ group covered less (P < 0.01) high-intensity running in the initial 10-min of the second versus
first half (ES: 0.6–0.7), but this was not observed in ‘moderate’ and ‘low’ groups (ES: 0.2–0.4). After the most intense
periods, players demonstrated an 8% drop in high-intensity running (P < 0.05) compared to the match average (ES: 0.2)
and this persisted for 5-min before recovering. Players covered similar high-intensity running distances in matches with
differing score lines but position-specific trends indicated central defenders covered 17% less (P < 0.01) and attackers 15%
more high-intensity running during matches that were heavily won versus lost (ES: 0.9). High-intensity running distances
were comparable in matches of differing importance, but between-half trends indicated that only declines (P < 0.01)
occurred in the second half of critical matches (ES: 0.2). Substitutes covered 15% more (P < 0.01) high-intensity running
versus the same time period when completing a full match (ES: 0.5). The data demonstrate that high-intensity running in
the second half is impacted by the activity of the first half and is reduced for 5-min after intense periods. High-intensity
running is also influenced by score line and substitutions but not match importance. More research is warranted to establish
if fluctuations in match running performance are primarily a consequence of fatigue, pacing or tactical and situational
influences.
Keywords: football, pacing, fatigue, score, substitutions, match importance
Introduction
The activity pattern of soccer is intermittent, with
players switching between brief bouts of high-intensity
running and longer periods of low-intensity exercise
(Rampinini, Coutts, Castagna, Sassi, & Impellizzeri,
2007). During elite matches players cover a total
distance of 10–13 km and 1–3kmofhigh-intensity
running (Bangsbo, Norregaard, & Thorso, 1991;
Bradley et al., 2009; Mohr, Krustrup, & Bangsbo,
2003). This results in an average intensity of ~70%
of maximal oxygen uptake and elicits blood lactate
concentrations of 3–6mmol∙ l
−1
(Mohr, Krustrup, &
Bangsbo, 2005). However, expressing match intensity
as an average disguises the unique physiological stress
induced during intense periods (Glaister, 2005).
During such periods, heart rate can exceed 95% of
maximal heart rate and peak blood lactate
concentrations can reach 8–12 mmol ∙ l
−1
(Ali &
Farrelly 1991; Bangsbo, 1994). Such demands
would certainly threaten homeostasis (Edwards &
Noakes, 2009).
Research demonstrates that high-intensity running
declines from the first to the second half of a match
(Di Salvo, Gregson, Atkinson, Tordoff & Drust,
2009; Mohr et al., 2003), although some observe
minimal changes (Di Salvo et al., 2007). Down
regulation of running performance in the second
half could be attributed to fatigue, as studies have
reported depleted muscle glycogen stores at the
end of a match (Bendiksen et al., 2012; Krustrup
et al., 2006). Findings demonstrate that high-intensity
running is also reduced temporarily after the most
intense period of the match (Bradley et al., 2010,
2011; Di Mascio & Bradley, 2013; Mohr et al.,
2003). This could be due to a decline in muscle
Correspondence: Dr Paul S. Bradley, Department of Sport & Exercise Sciences, University of Sunderland, Darwin Building, Chester Road, Sunderland SR1
3SD, UK. Email: paul.s.bradley@sunderland.ac.uk
Journal of Sports Sciences, 2013
http://dx.doi.org/10.1080/02640414.2013.796062
© 2013 Taylor & Francis
Downloaded by [University of Sunderland] at 02:23 01 July 2013
creatine phosphate, intramuscular acidosis or the
accumulation of potassium in the muscle interstitium
(Mohr et al., 2005), but temporary drops also seem to
be related to the time the ball is out of play and the
opportunity to engage in match activities (Carling &
Dupont, 2011). Alternatively, some suggest that
reductions in match running performance could be
due to players employing conscious or subconscious
pacing strategies to enable successful completion of
the match (Drust, Atkinson, & Reilly, 2007; Edwards
& Noakes, 2009). Although this is an attractive
hypothesis, limited data exist to support or reject
such a statement. Carling and Bloomfield (2010)
observed that teams coped with an early player dis-
missal by sparing low-intensity activity in an attempt
to preserve essential high-intensity running, which
may suggest pacing or modified tactics. If players
pace their efforts then covering ‘low’ to ‘moderate’
distances in the first half would enable them to have
the available capacity to maintain match running per-
formances in the second half. However, this has only
been established in elite players across 45-min periods
(Rampinini et al., 2007), which results in a substantial
loss of information when attempting to elucidate fac-
tors associated with pacing or fatigue. Others have
examined match-running performance across 5-min
periods but did not demarcate between positions or
investigate the influence of first half activities on sec-
ond half performances (Weston, Drust & Gregson,
2011a). Moreover, this study did not quantify match
running performance in stoppage time periods, which
could provide additional information as some suggest
that an ‘end spurt’ in the latter stages of the match
could be expected if pacing occurs (Aughey, 2010).
This would be characterised by a ‘reversed J-shape’
pacing strategy (Abbiss & Laursen, 2008), whereby
players are aware of the duration remaining until the
end of the match. Although this is a common scenario
with stoppage time announcements, no evidence
exists of a documented ‘end-spurt’ of activity in soc-
cer. Research using global positioning system (GPS)
technology observed reductions in high-intensity run-
ning towards the end of an Australian football match
but players failed to produce an ‘end spurt’ of activity
(Aughey, 2010). However, this study used a small
sample and the GPS technology had limited resolu-
tion to precisely measure high-intensity running. No
studies have investigated whether and to what extent
pacing strategies occur for players in other football
codes such as soccer. Thus, an in-depth examination
of high-intensity running in 5-min periods of matches
(plus peak and stoppage time periods) specificto
position and the influence of first half activity on
second half match running performances may provide
new insight into pacing or fatigue in soccer.
Although detailed analyses of match running per-
formance could provide information on pacing or
fatigue, this would only demonstrate outcome beha-
viour and not necessarily the motive. Studies have
investigated common factors that dictate physical per-
formance, such as match location, outcome and stan-
dard (Castellano, Blanco-Villasenor, & Alvarez,
2011; Lago, Casais, Dominguez, & Sampaio, 2010),
but not score line or importance, which could present
motives that modulate match running performance.
Furthermore, if pacing or fatigue is evident in soccer
then substitutes introduced in the latter stages of a
match would be expected to cover more high-inten-
sity running than the equivalent time period when
completing the full match. Research has reported
such a trend (Carling, Espié, Le Gall, Bloomfield, &
Jullien, 2010; Mohr et al., 2003) but either used a
separate groups analysis with a small sample or lim-
ited differentiation of player position and time. Thus,
it is clear that additional analyses on score line, match
importance and the introduction of substitutes may
provide an understanding of the factors that influence
high-intensity running. Therefore, the aims of this
study were to: (1) quantify match running perfor-
mance in 5-min periods to determine if players fatigue
or modulate high-intensity running accord ing to
pacing and (2) examine factors impacting high-inten-
sity running in elite soccer matches.
Method
Match analysis
With approval from the institutional ethics commit-
tee, English FA Premier League matches during
successive seasons (2006/07–2008/09) were analysed
using a multiple-camera computerised tracking sys-
tem (Prozone Sports Ltd, Leeds, UK). Players’
movements were captured during matches by cam-
eras positioned at roof level and analysed using pro-
prietary software. The systems reliability and validity
has been quantified to verify the capture process and
data accuracy (Bradley et al., 2009 ; Di Salvo,
Collins, McNeill, & Cardinale, 2006).
Match runn ing performance variables
Players activities were coded into the following
categories and speed thresholds: standing (0–0.6
km · h
−1
), walking (0.7–7.1 km · h
−1
), jogging
(7.2–14.3 km · h
−1
), running (14.4–19.7 km · h
−1
),
high-speed running (19.8–25.1 km · h
−1
) and sprint-
ing (>25.1 km · h
−1
). The speeds for each category
have been previously employed (Bradley et al.,
2009). The absolute distances cov ered by players
(m) were converted to a relative analysis of the dis-
tance covered per unit of time (m ∙ min
−1
). This
enabled comparisons between various periods of
the match including stoppage times. Total distance
2 P. S. Bradley & T. D. Noakes
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represented the summation of distances in all cate-
gories. High-intensity running consisted of the com-
bined distance in running, high-speed running and
sprinting (≥14.4 km · h
−1
). Peak high-intensity run-
ning distance in a 5-min period represented the most
intense period of the match (Mohr et al., 2003).
Comparisons were then made between the average
5-min period (minus the peak) versus the subse-
quent 5-min periods up to a maximum of 15-min
to quantify the time course of recovery.
Part 1. Criteria for evaluating discrete periods of match
running performance
Data were collected in pre-defined 5-min periods
(plus stoppage times), separated into three levels of
activity and classified as ‘high’, ‘moderate’ and ‘low’
based on the total distance covered in the first half
(Rampinini et al., 2007). Data were sorted using
percentiles to produce each level (‘low’: ≤30th per-
centile; ‘moderate’:35–65th percentile and
‘high’: ≥70th percentile). To improve the analytical
approach, boundaries were drawn between percentile
thresholds to ensured demarcation between groups
(e.g. minimising the chance of players in the low-
end of ‘high’ covering a very similar total distance to
players at the high-end of ‘moderate’). From a total
sample of 186 players, 17 were removed to create
these boundaries. Matches were played between
teams of a similar end of season league placing.
Teams were ranked into top, middle and bottom
and only matches played between teams within each
rank were analysed. Moreover, equal distribution of
home and away fixtures was ensured. Based on these
criteria, the match running performances of 169
players (single observations) in three levels and five
positions were profiled (Table I; Part 1).
Part 2. Criteria for evaluating factors influencing match
running performance
Data were analysed in pre-defined periods (plus
stoppage times) to examine the influence of score
line, importance and substitutions on match running
performance. Owing to limited availability of
matches for the above, we were unable to present
data in 5-min periods. Although some players were
included in each subsection of Part 2, some of the
data were comprised of a collection of different
players. On account of the subjectivity involved in
match selection, recommendations were provided
from two UEFA qualified coaches with at least
5 years of elite coaching experience.
Data pertaining to score line were collected from
matches that were competitive (≤1 goal differential)
throughout the entire 90-min compared with those
heavily won or lost (score differential ≥3 goals). All
data from heavily won or lost matches were single
observations versus a median of two competitive
matches. Match running performances of 54 players
in five positions were profiled (Table I; Part 2)
Data relating to match importance were collected
from 55 players during critical matches whereby the
outcome directly impacted upon Championships/
European places or relegation, with local derby
matches also included. This was compared with
matches of lower importance within the same seaso-
nal period. Data from critical matches were single
observations versus a median of two matches for
lower importance. Data were obtained from five
playing pos itions (Table I; Part 2).
Data pertaining to substitutions were collected
from 65 players completing full matches versus
those they were introduced as a substitute.
Substitution and full match comparisons were cor-
rected for exactly the same time period and only
included single observations within a similar seaso-
nal period. Data were collected from players in var-
ious positions (Table I; Part 2). Independent of
position, data were also sub-divided into substitu-
tions that occurred early (45- to 65-min: n = 40) or
late in the second half (65- to 90-min: n = 25). To
further improve the analytical approach adopted, the
average match running performance of all out field
players at the corresponding time-point minus the
substitute was used to account for match tempo
using the procedures described by Westo n et al.
(2011a,b).
Table I. Sample size (n) differentiated into subsection of the study and positional subsets.
Part 1. Discrete Periods Part 2. Influencing Factors
Position/Variable Low Moderate High Total Score Importance Substitution
Central defenders 29 12 1 42 14 14 9
Full-backs 10 20 9 39 14 15 9
Central midfielders 311 233712 11 13
Wide midfielders 1 5 18 24 7 7 20
Attackers 13 9 5 27 7 8 14
All players 56 57 56 169 54 55 65
Match Running Performance Fluctuations in Soccer 3
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Statistical analysis
Analyses were conducted using statistical software
(SPSS Inc., Chicago, USA). Descriptive statistics
were calculated on each variable and z-scores con-
firmed data normality. Factorial analysis of variance
(ANOVA) tests were used to explore the influence of
first half activity and the impact of both score line and
importance on match running performance.
Interactions were examined between measures of phy-
sical performance across playing positions and time
periods. In the event of a difference occurring, univari-
ate analyses using Bonferroni-corrected pairwise com-
parisons were employed. Differences between players
introduced as substitutes versus the identical period of
time when completing the full match or against the
mean match running performances of the remaining
players were determined using Bonferroni-corrected
dependent and independent t-tests. Effect size (ES)
was calculated to determine the meaningfulness of
the difference (Cohen, 1988). The magnitude of the
ES was classified as trivial (<0.2), small (>0.2–0.6),
moderate (>0.6–1.2), large (>1.2–2.0) and very large
(>2.0–4.0) based on guidelines from Batterham and
Hopkins (2006). Statistical significance was set at P <
0.05. Data are presented as means and standard error
of the mean.
Results
Between half match running performance
Reductions of 4–7% (P < 0.01) were observed for
total distance in the second half for players maintain-
ing ‘moderate’ and ‘high’ levels of activity in the first
half (ES: 0.9–1.2), while this did not differ for the
‘low’ group (ES: 0.2). Maintaining ‘high’ levels of
activity in the first half resulted in 12% less
(P < 0.01) high-intensity running in the second half
(ES: 0.8), but no changes were evident for ‘low’ to
‘moderate’ groups (ES: 0.0–0.2). No differences were
observed for sprinting. After collapsing categories,
total distance and high-intensity running were greater
(P < 0.01) in the first versus the second half (ES: 0.2–
0.4) but sprinting remained unchanged (ES: 0.1).
Part 1. Discrete periods of match running performance
The ‘high’ group covered less (P < 0.01) total distance
in the initial 10-min of the second half (ES: 0.9–1.0), in
addition to other periods (70- and 85-min) versus the
same first half period (ES: 0.8–0.9; Figure 1(a)). The
‘low’ and ‘moderate’ groups only covered less (P <
0.01) total distance in the initial 5- and 10-min periods
of the second compared with the first half (ES: 0.7–
0.8). The ‘low’, ‘moderate’ and ‘high’ groups demon-
strated declines (P < 0.01) in total distance between
the first versus last 5-min and stoppage time of the first
half (ES: 1.0–1.3). Although, similar trends were evi-
dent between the first versus last 5-min and stoppage
time of the second half in the ‘low’ group (ES: 0.5–
0.8), the ‘high’ group only illustrated differences (P <
0.05) between the first versus last 5-min but not stop-
page time (ES: 0.5–0.8), with no differences observed
for the ‘moderate’ group (ES: 0.4–0.6). The ‘high’
group covered less (P < 0.05) high-intensity running
in the initial 10-min period of the second versus the
first half (ES: 0.6–0.7), but this was not observed for
‘moderate’ and ‘low’ groups (ES: 0.2–0.4; Figure 1
(b)). The ‘low’, ‘
moderate’ and ‘high’ groups demon-
strated declines (P < 0.01) in high-intensity running
between the first versus last 5-min of the first half (ES:
0.8–0.9), with differences (P < 0.01) only evident in
stoppage time for the ‘low’ group (ES: 0.8). The ‘low’,
‘moderate’ and ‘high’ groups displayed similar high-
intensity running between the first versus last 5-min of
the second half (ES: 0.2–0.5) and stoppage time period
(ES: 0.0–0.3). No differences were observed for sprint-
ing (Figure 1(c)).
After collapsing categories, total distance cov ered
was greater (P < 0.01) in the initial 10-min of the
first versus the second half (ES: 0.5–0.6), with the
only other exception occurring at the end of the half
(40- versus 85-min; ES: 0.4). Players covered more
(P < 0.01) total distance in the first versus final
5-min period of each half (ES: 0.6–1.0) and in first
but not second half stoppage time (ES: 0.2– 0.9).
High-intensity running was greater (P < 0.01) during
the initial 10-min of the first versus second half only
(ES: 0.3–0.4), with the most comparable periods
occurring in stoppage time. High-intensity running
was greater (P < 0.01) in the first versus final 5-min
and stoppage of the first half (ES: 0.5–0.6) but not in
the second half (ES: 0.1– 0.3).
Central defenders and central/wide midfielders
reduced (P < 0.05) their total distance in the
initial 10-min of the second versus
first half (ES:
0.7–0.9), while full-backs were o nly lower for the
second 5-min period (ES: 0.7; Figure 1(d)). Total
distance covered declined (P < 0.01) between the
first versus last 5-min period of both halves for
full-backs and attackers (ES: 0.8–1.0), while cen-
tral defenders, central/wide midfielders only exhib-
ited first half reductions (ES: 1.2–1.4). Only
midfielders demonstrated reductions (P < 0.01)
in high-intensity running in the initial 5-min of
the second versus firsthalf(ES:0.7;Figure1
(e)). Attackers and central/wi de mid fielders
demonstrated declines (P < 0.01) in high-intensity
running between the first versus last 5-min period
of the fi rst half (ES: 0.8–1.2) but this was not
evident in full-bac ks and central defenders (ES:
0.4–0.5). Although, full-backs reduced (P < 0.05)
their distances in first half stoppage time compared
4 P. S. Bradley & T. D. Noakes
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'High' 1
st
half
'Mod' 1
st
half
'Low' 1
st
half
5
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Time (min)
Total Distance (m · min
−1
)
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−1
)
Sprint Distance (m · min
−1
)
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·
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·
min
−1
)
Sprint Distance (m
·
min
−1
)
Figure 1. (a) Total distance, (b) high-intensity running and (c) sprinting in 5-min periods of elite matches when players are separated based on ‘high’, ‘moderate’ and ‘low’ activity in the first half. (d)
Total distance, (e) high-intensity running and (f) sprinting in 5-min periods of elite matches when players are separated into playing positions. Lower than same first half period: (*P < 0.05; **P <
0.01). Lower than first 5-min period of the half (
Δ
P < 0.05;
ΔΔ
P < 0.01). Attacker (ATT), Full-backs (FB), Central defender (CB), Central midfielder (CM), Wide midfielder (WM), High-intensity
running (HIR), Stoppage time (+). Data are presented as means and standard error of the mean.
Match Running Performance Fluctuations in Soccer 5
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with the first 5-min (ES: 0.8). Sprinting was highl y
variable and thus no differences were found for
any position across periods (Figure 1(f)).
Players were tracked in the 15-min after the most
intense period of the match to observe the time
course of recovery. After the most intense period
of the match, players demonstrated an 8% drop in
high-intensity running (P < 0.01) compared with
the match average, but this only persisted for 5-
min before recovering to mean values (ES: 0.2;
Figure 2).
Part 2: Factors influencing match running performance
High-intensity running and sprinting were similar in
matches that were competitive, heavily won or lost
(Table II). Matches that were competitive or won
produced decrements (P < 0.01) in high-intensity
running in the second half but when losing high-
intensity running was maintained (ES: 0.1–0.3).
Central defenders covered 10–17% less high-inten-
sity running (P < 0.01) during matches that were
heavily won versus lost or competitive (ES: 0.6–0.9).
Attackers covered 15% and 54% more high-intensity
running and sprinting in matches won (P < 0.01)
versus defeated (ES: 0.9–1.4).
Match running performance was unchanged
between matches of differing importance (Table III).
Between half trends indicated that total distance
declined (P < 0.01) in the second half of both critical
and less important matches (ES: 0.3–0.4) but high-
intensity running only declined in critical matches
(ES: 0.2). Central defenders and full-backs demon-
strated reductions (P < 0.05) in second half high-
intensity running in critically important matches
only (ES: 0.5–0.6).
Players introduced as substitutes covered more
(P < 0.01) total and high-intensity running distance
compared with the equivalent time period when
completing the full match (ES: 0.5–0.6), although
sprinting did not differ (ES: 0.2; Table IV). Total
distance and high-intensity running were greater
(P < 0.05) in central midfielders when introduced
as substitutes compared with the exact time period
during full matches (ES: 0.7–0.9). Sprinti ng was
only higher (P < 0.05) in central defenders and
full-backs when entering the match as substitutes
(ES: 0.6–0.7). Data separated into early and late
substitutions, produced similar relat ive increases for
high-intensity running (14–16%; ES: 0.5) and total
distance covered (7–8%; ES: 0.6) compared with the
equivalent time period when completing the full
match (Table IV). To account for match tempo,
we compared the mean player match running per-
formances with that of the subs titutes. Data trends
indicated that substitutes also cov ered more
(P < 0.0 1) distance in total (ES: 1.0) and at high-
intensity (ES: 1.1) compared to the mean for all
remaining players at the same time period, although
sprinting did not differ (ES: 0.5).
Discussion
The present study revealed that total distance and
high-intensity running were markedly lower in the
second half for players in a ‘high’ activity group
compared with ‘moderate’ and ‘low’ groups. This
fi
nding agrees with research separating elite players
and referee s into similar categories (D’Ottavio &
Castagna, 2001; Rampinini et al., 2007; Weston,
Castagna, Impellizzeri, Rampinini, & Abt, 2007)
but this is the first study to investigate this trend in
5-min periods of matches. Interestingly, high-inten-
sity running was lower in the initial 10-min of the
second versus first half for the ‘high’ group but not
for ‘moderate’ and ‘low’ groups. The differences
denoted immediately after the half time interval
could indicate a pacing strategy but given the com-
plexity of soccer match-play and the limitations of
the methods used (time-motion analysis), other fac-
tors cannot be discounted. For instance, the decline
in high-intensity running may indicate fatigue occurs
across the course of the second half due to the ‘high’
demands of the first half. This decline was evident
across most equivalent periods of the match but the
lack of statistical significance can be explained by the
stringent Bonferroni correction applied due to the
large array of pairwise comparisons (small-moderate
effect sizes for all). A multitude of mechanisms have
been proposed to explain fatigue development in
soccer, but researchers have failed to identify its
precise cause (Mohr et al., 2005). This is unsurpris-
ing given the complexities of match running perfor-
mance, which is influenced by a myriad of factors
(Drust et al., 2007). Fatigue is not causally linked to
Peak 5-min Post 5-min Post 10-min Post 15-min Average 5-min
0
10
20
30
40
50
*
HIR Distance (m · min
−1
)
Figure 2. The most intense 5-min period of a match and the time
course of recovery in the subsequent 15-min. *Lower (P < 0.05)
than average 5-min period minus the peak value. High-intensity
running (HIR). Data are presented as means and standard error
of the mean.
6 P. S. Bradley & T. D. Noakes
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Table II. Match running performance in competitive matches (1 ≤ goal differential) versus those heavily won or lost (≥3 goal differential).
α
Higher (P < 0.01) in win than loss.
x
Higher (P < 0.05) in
win than loss. ºLower (P < 0.05) in win compared to loss and competitive.
~
Lower (P < 0.01) in win compared to loss and competitive.
+
Lower (P < 0.05) in loss compared to win and competitive.
Δ
Lower (P < 0.01) in second half.
=
Lower (P < 0.05) in second half. Total distance covered (TDC), High-intensity running (HIR) and Sprinting (SPR). Data are presented as means and standard
error of the mean.
Competitive Heavy Win Heavy Loss
Position/Variable First Second Total First Second Total First Second Total
Central defenders (n = 14)
TDC (m ∙ min
−1
) 105.3 ± 1.8 100.1 ± 1.6
Δ
102.6 ± 1.6 103.2 ± 1.8 97.2 ± 1.6
Δ
100.2 ± 1.6 105.0 ± 2.3 101.5 ± 1.9 103.2 ± 2.0
HIR (m ∙ min
−1
) 21.1 ± 0.9 19.2 ± 1.1 20.1 ± 0.9 19.1 ± 1.1 17.0 ± 0.9º
=
18.0 ± 0.9
~
21.5 ± 1.5 21.9 ± 1.27 21.7 ± 1.3
SPR (m ∙ min
−1
) 1.38 ± 0.12 1.69 ± 0.15 1.54 ± 0.12 1.01 ± 0.15º 1.49 ± 0.13 1.21 ± 0.10º 1.67 ± 0.18 1.94 ± 0.25 1.80 ± 0.19
Full-backs (n = 14)
TDC (m ∙ min
−1
) 116.9 ± 1.9 112.2 ± 2.1
=
114.5 ± 1.8 117.7 ± 1.9 111.6 ± 2.1
Δ
114.6 ± 1.9 119.4 ± 3.0 113.5 ± 2.0 116.4 ± 2.1
HIR (m ∙ min
−1
) 31.0 ± 1.5 29.0 ± 1.4 30.0 ± 1.4 32.0 ± 1.6 28.2 ± 1.4
=
30.1 ± 1.4 33.4 ± 2.1 31.9 ± 1.6 32.7 ± 1.6
SPR (m ∙ min
−1
) 3.08 ± 0.34 2.99 ± 0.32 3.02 ± 0.30 3.51 ± 0.30 3.00 ± 0.35 3.26 ± 0.28 3.06 ± 0.34 3.69 ± 0.31 3.38 ± 0.28
Central midfielders (n = 12)
TDC (m ∙ min
−1
) 123.7 ± 1.5 117.6 ± 1.8
Δ
120.6 ± 1.6 125.7 ± 1.8 116.9 ± 1.7
Δ
121.3 ± 1.6 123.1 ± 1.7 120.1 ± 1.5 121.6 ± 1.4
HIR (m ∙ min
−1
) 33.9 ± 1.6 31.3 ± 1.2
=
32.6 ± 1.3 36.1 ± 2.0 31.0 ± 1.5
Δ
33.5 ± 1.6 34.8 ± 1.4 33.1 ± 1.1 33.9 ± 1.1
SPR (m ∙ min
−1
) 2.35 ± 0.25 2.41 ± 0.23 2.39 ± 0.21 2.71 ± 0.39 2.44 ± 0.24 2.57 ± 0.25 2.42 ± 0.24 2.83 ± 0.29 2.64 ± 0.20
Wide midfielders (n =7)
TDC (m ∙ min
−1
) 126.9 ± 3.2 123.9 ± 2.2 125.4 ± 2.2 127.3 ± 3.2 120.6 ± 2.7
Δ
124.0 ± 2.9 121.8 ± 3.9 121.9 ± 4.4 121.8 ± 3.9
HIR (m ∙ min
−1
) 36.6 ± 2.6 35.7 ± 1.9 36.1 ± 1.9 37.5 ± 2.6 35.4 ± 1.8 36.5 ± 2.1 34.2 ± 2.8 34.4 ± 3.5 34.2 ± 2.9
SPR (m ∙ min
−1
) 3.49 ± 0.26 3.51 ± 0.39 3.50 ± 0.23 3.49 ± 0.27 3.37 ± 0.30 3.44 ± 0.12 2.70 ± 0.30 2.53 ± 0.39 2.60 ± 0.35
Attackers (n =7)
TDC (m ∙ min
−1
) 106.3 ± 2.8 103.0 ± 2.8
=
104.6 ± 2.7 106.7 ± 2.1 106.3 ± 2.9
x
106.5 ± 2.4 107.4 ± 2.8 100.4 ± 1.5
=
103.9 ± 2.0
HIR (m ∙ min
−1
) 24.2 ± 1.6 24.3 ± 1.7 24.3 ± 1.6 25.0 ± 1.2 26.2 ± 2.0 25.6 ± 1.5
x
23.8 ± 2.0 20.6 ± 1.0
+
22.2 ± 1.4
SPR (m ∙ min
−1
) 2.99 ± 0.50 3.89 ± 0.42 3.44 ± 0.42 3.73 ± 0.35
x
4.20 ± 0.46
x
3.96 ± 0.38
α
2.60 ± 0.49 2.53 ± 0.28
+
2.57 ± 0.37
All players (n = 54)
TDC (m ∙ min
−1
) 115.3 ± 1.5 110.6 ± 1.5
Δ
112.9 ± 1.4 115.6 ± 1.6 109.5 ± 1.5
Δ
112.5 ± 1.5 115.2 ± 1.6 111.3 ± 1.5
Δ
113.2 ± 1.5
HIR (m ∙ min
−1
) 29.0 ± 1.1 27.2 ± 1.0
Δ
28.1 ± 1.0 29.4 ± 1.23 26.6 ± 1.1
Δ
28.0 ± 1.1 29.5 ± 1.2 28.4 ± 1.1 28.9 ± 1.1
SPR (m ∙ min
−1
) 2.52 ± 0.16 2.71 ± 0.16 2.61 ± 0.15 2.71 ± 0.20 2.69 ± 0.18 2.69 ± 0.17 2.45 ± 0.15 2.74 ± 0.16 2.60 ± 0.14
Match Running Performance Fluctuations in Soccer 7
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Table III. Match running performance in critical versus less important matches.
Δ
Lower (P < 0.01) in second half.
=
Lower ( P < 0.05) in second half. Total distance covered (TDC), High-intensity
running (HIR) and Sprinting (SPR). Data are presented as means and standard error of the mean.
Critical Importance Lower Importance
Position/Variable First Second Total First Second Total
Central defenders (n = 14)
TDC (m ∙ min
−1
) 103.9 ± 1.6 99.6 ± 1.2
Δ
101.7 ± 1.3 103.7 ± 1.6 99.8 ± 1.6
Δ
101.7 ± 1.5
HIR (m ∙ min
−1
) 20.4 ± 0.7 19.0 ± 0.6
=
19.7 ± 0.6 20.1 ± 0.8 19.3 ± 1.3 19.7 ± 1.0
SPR (m ∙ min
−1
) 1.53 ± 0.12 1.59 ± 0.14 1.56 ± 0.10 1.36 ± 0.12 1.60 ± 0.20 1.48 ± 0.13
Full-backs (n = 15)
TDC (m ∙ min
−1
) 117.8 ± 1.7 112.3 ± 1.5
Δ
115.0 ± 1.5 114.7 ± 1.3 113.7 ± 1.7 114.2 ± 1.3
HIR (m ∙ min
−1
) 31.5 ± 1.2 29.3 ± 1.2
=
30.4 ± 1.2 29.5 ± 0.9 30.3 ± 1.2 29.9 ± 0.9
SPR (m ∙ min
−1
) 3.53 ± 0.28 3.28 ± 0.34 3.40 ± 0.29 2.84 ± 0.26 3.43 ± 0.36 3.14 ± 0.28
Central midfielders (n =11)
TDC (m ∙ min
−1
) 123.8 ± 2.5 118.6 ± 2.3
Δ
121.1 ± 2.3 123.8 ± 2.7 117.2 ± 2.8
Δ
120.5 ± 2.7
HIR (m ∙ min
−1
) 33.8 ± 1.6 30.9 ± 1.4 32.3 ± 1.4 33.7 ± 1.9 29.2 ± 1.6
Δ
31.4 ± 1.6
SPR (m ∙ min
−1
) 2.52 ± 0.34 2.62 ± 0.32 2.57 ± 0.28 2.59 ± 0.39 2.25 ± 0.26 2.41 ± 0.25
Wide midfielders (n =7)
TDC (m ∙ min
−1
) 121.9 ± 2.8 120.7 ± 4.4 121.3 ± 3.4 119.5 ± 4.3 115.6 ± 3.5 117.5 ± 3.7
HIR (m ∙ min
−1
) 32.8 ± 1.6 34.1 ± 2.7 33.4 ± 2.0 31.7 ± 3.0 30.0 ± 2.3 30.8 ± 2.6
SPR (m ∙ min
−1
) 3.63 ± 0.48 3.72 ± 0.49 3.67 ± 0.41 3.49 ± 0.46 3.10 ± 0.50 3.29 ± 0.45
Attackers (n =8)
TDC (m ∙ min
−1
) 113.9 ± 3.3 108.4 ± 4.1 111.1 ± 3.5 111.5 ± 3.3 108.6 ± 3.4 110.0 ± 3.3
HIR (m ∙ min
−1
) 27.3 ± 2.3 27.1 ± 2.8 27.2 ± 2.5 26.9 ± 1.9 27.8 ± 2.1 27.4 ± 2.0
SPR (m ∙ min
−1
) 2.93 ± 0.52 3.22 ± 0.37 3.08 ± 0.43 3.06 ± 0.20 3.36 ± 0.36 3.21 ± 0.26
All players (n = 55)
TDC (m ∙ min
−1
) 115.4 ± 1.4 110.8 ± 1.4
Δ
113.1 ± 1.4 113.9 ± 1.4 110.4 ± 1.4
Δ
112.1 ± 1.3
HIR (m ∙ min
−1
) 28.7 ± 0.9 27.3 ± 1.0
Δ
28.0 ± 0.9 27.9 ± 0.9 26.9 ± 0.9 27.3 ± 0.9
SPR (m ∙ min
−1
) 2.74 ± 0.18 2.77 ± 0.17 2.75 ± 0.16 2.53 ± 0.16 2.68 ± 0.18 2.60 ± 0.15
8 P. S. Bradley & T. D. Noakes
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a single factor (Edwards & Noakes, 2009); however
the sustained decline in match running performance
in the second half has been associated with a low-
ering of muscle glycogen (Krustrup et al., 2006;
Saltin, 1973). This could explain the marked reduc-
tions in high-intensity running during the second
half period for the ‘high’ compared with ‘low’ and
‘moderate’ groups. Reductions in match running
performance in the first period of the second half
should be expected and could be attributed to the
fall in muscle temperature during the half-time
break. Research observed a 2°C drop in muscle
temperature after the half-time break and this was
associated with a more pronounced decline in sprint
performance before the second half (Mohr,
Krustrup, Nybo, Nielsen, & Bangsbo, 2004).
Although additional factors could explain the reduc-
tion in running performance at the start of the sec-
ond half, such as the tempo of the match (Weston
et al., 2011b) and the intense nature of the initial
period of the first half which could be an inappropri-
ate benchmark to use as a comparison period
(Lovell, Barrett, Portas, & Weston, 2013).
Alternatively, some suggest that reductions in
match running performance could be due to players
employing a pacing strategy to enable the comple-
tion of a match without any single physiologica l
system failing (Drust et al., 2007). Although limited
data exists to fully explain pacing in team sports, a
model proposed by Edwards and Noakes (2009)
suggests that players may dynamically modulate
their high-intensity efforts in an attempt to avoid
fatigue. The results of the present study and others
(D’Ottavio & Castagna , 2001; Rampinini et al.,
2007; Weston et al., 2007) could provide support
for this model as players covering the lowest total
distances in the first half had the avai lable capacity to
be able to maintain match runn ing parameters in the
second half. Additional complexities are found on
closer inspection of the data in 5-min periods for
various activity groups. Unsurprisingly, the ‘high’
and ‘low’ groups were primarily composed of mid-
fielders and central defenders/attackers, respec-
tively. Rampinini et al. (2007) did not report such
a trend when observing sec ond ha lf declines in
match running performance after ‘high’ first half
activity, despite reporting positional variation in
match running performance. Midfielders perform
more total distance and high-intensity running
than attackers and central defenders (Bradley
et al., 2009, 2010, 2011; Di Salvo et al., 2009)
and this findin g could simply reflect position-speci-
fic decrements or tactical requirements. Thus, cau-
tion is needed before attributing these findings to
fatigue or pacing as the tactical role of the player
could also dictate the amount of high-intensity run-
ning undertaken in the first half and its resultant
impact on the second half. Moreover, the analytical
approach used in this section of the study (percen-
tiles) could have marginally impacted on the data
trends. We separated various groups based on the
total distance covered in the first half but a l imita-
tion of such an approach is that there was limited
separation between some players (e.g. players in the
low-end of ‘high’ could have a total distance close
to players at the high-end of ‘moderate ’). This
could have also been an issue with Rampinini
et al.’s (20 07) analytical approach in which an
order function separated similar groups. Although
we removed 17 players from the analysis to separate
Table IV. Match running performance in various playing positions
and time periods for substitutes introduced in the second half
versus the identical period of time in a full match. *Higher (P <
0.01) than the identical time period in full match.
Δ
Higher (P <
0.05) than the identical time period in full match. Early (45- to 65-
min) or late (65- to 90-min) refers to the time period of the second
half the substitutes were introduced. Total distance covered
(TDC), High-intensity running (HIR) and Sprinting (SPR).
Data are presented as means and standard error of the mean.
Position/Variable
Substitution
Appearance
Equivalent Time in
Full Match
Central defenders (n =9)
TDC (m ∙ min
−1
) 108.8 ± 4.4 99.7 ± 2.5
HIR (m ∙ min
−1
) 25.3 ± 2.7 22.5 ± 2.6
SPR (m ∙ min
−1
) 1.85 ± 0.39
Δ
1.11 ± 0.34
Full-backs (n =9)
TDC (m ∙ min
−1
) 118.3 ± 4.7 105.0 ± 4.8
HIR (m ∙ min
−1
) 32.8 ± 3.1 25.3 ± 1.7
SPR (m ∙ min
−1
) 3.85 ± 0.59
Δ
2.86 ± 0.49
Central midfielders (n = 13)
TDC (m ∙ min
−1
) 123.3 ± 2.6
Δ
111.7 ± 4.3
HIR (m ∙ min
−1
) 35.9 ± 2.4
Δ
30.28 ± 2.3
SPR (m ∙ min
−1
) 2.34 ± 0.31 2.24 ± 0.41
Wide midfielders (n = 20)
TDC (m ∙ min
−1
) 124.4 ± 2.7
Δ
117.9 ± 2.5
HIR (m ∙ min
−1
) 37.5 ± 1.8 33.3 ± 1.8
SPR (m ∙ min
−1
) 4.67 ± 0.58 3.63 ± 0.35
Attackers (n = 14)
TDC (m ∙ min
−1
) 106.0 ± 3.0 103.2 ± 3.3
HIR (m ∙ min
−1
) 26.7 ± 2.3 25.2 ± 1.4
SPR (m ∙ min
−1
) 2.60 ± 0.43 3.37 ± 0.44
All players (n = 65)
TDC (m ∙ min
−1
) 117.2 ± 1.7* 109.2 ± 1.7
HIR (m ∙ min
−1
) 32.5 ± 1.2* 28.3 ± 1.0
SPR (m ∙ min
−1
) 3.25 ± 0.27 2.84 ± 0.21
Early substitution (n = 40)
TDC (m ∙ min
−1
) 114.1 ± 2.1* 106.7 ± 2.0
HIR (m ∙ min
−1
) 30.9 ± 1.5* 27.1 ± 1.2
SPR (m ∙ min
−1
) 3.13 ± 0.31 2.68 ± 0.25
Late substitution (n = 25)
TDC (m ∙ min
−1
) 122.2 ± 2.8
Δ
113.1 ± 3.1
HIR (m ∙ min
−1
) 35.1 ± 2.0
Δ
30.4 ± 1.8
SPR (m ∙ min
−1
) 3.46 ± 0.48 3.10 ± 0.37
Match Running Performance Fluctuations in Soccer 9
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groups,thisisstillafactortoconsiderwhenevalu-
ating the data trends.
Data collapsed independent of activity level or
position highlights some noteworthy trends. High-
intensity running distance was greater in the initial
period of the first versus second half but after this
period no differences were evident, with the most
comparable periods occurring in stoppage time. On
the contrary, total distance declined in the first and
last part of the second half compared with the same
first-half period. If team sports players do pace their
efforts then some suggest an ‘end spurt’ in high-
intensity running in the latter stages of the match
could be expected (Aughey, 2010). Although our
data failed to support the ‘end spurt’ hypothesis,
high-intensity running and sprinting were m ain-
tained throughout the second half compared with
the same first half period. The maintenance of
high-intensity running and sprinting probably
occurred at the expense of a more pronounced
decline in total distance throughout the second
half. Carling and Bloomfield (2010) also observed a
similar finding when teams attempted to cope with
an early player dismissal. This trend could demon-
strate a pacing strategy that spares low-intensity
activity such as walking and jogging in an attempt
to preserve essential high-intensity running (Drust
et al., 2007; Edwards & Noakes, 2009). Studies
have generally observed reductions in total distance
and high-intensity running as the match progresses
(Bradley et al. 2009; Rampinini et al., 2007) and
concluded that fatigue mechanisms are responsible.
However, these studies have tracked match running
performance in 15- and 45-min periods, which
results in a substantial loss of specific informa tion.
Thus, this study illustrates the importance of analys-
ing data in 5-min periods (including stoppage times)
to allow perturbations in match performance to be
observed.
Although others have observed declines in high-
intensity running after the most intense period (Di
Mascio & Bradley, 2013; Mohr et al., 2003) this is
the first study published to date that has quantified
the time course of recovery. We observed that high-
intensity running after the most intense period was
below the match average for 5-min before recover-
ing. Krustrup et al. (2006) found sprint perform ance
declined after intense periods in the first and second
half but this wa s not associated with muscle lactate,
pH, or glycogen content. It has been suggested that
the temporary decline in high-intensity running dur-
ing matches could be related to the accumulation of
potassium in the muscle, which results in electrical
disturbances that impact force development (Mohr
et al., 2005). Alternatively, Edwards and Noakes
(2009) suggest that such high demands would cer-
tainly threaten homeostasis and players thus seek out
extended opportunities to minimise energy expendi-
ture. Thus, this study provides evidence of the need
for recovery after the most intense periods and sports
scientists should design training aimed at coping
with multiple intense actions. However, caution is
needed when attributing these temporary drops to
fatigue or pacing due to the low magnitude of the
effect size statistic and given that they also seem to
be related to the time the ball is out of play and the
opportunity to engage in match activities (Carling &
Dupont, 2011). This was evident in the present
study for players in the same match whereby a large
stoppage between the 20- to 25-min period resulted
in a substantial drop in match running performance
as opposed to fatigue or pacing. Furthermore, the
temporary drop in high-intensity running may have
been underestimated, as our results are based on
pre-defined 5-min periods of matches as opposed
to rolling 1-min periods. Mohr et al. (2003)
observed a 12% decline in high-intensity running
after the most intense period using a rolling method,
indicating that the drop could be even greater and
extend for a longer period (Varley, Elias, & Aughey,
2012). Thus, future studies are advised to use rolling
periods that also account for the degree the ball is
out of play to fully establish the magnitude and time
course of transient fl uctuations in high-intensity
running.
Results demonstrated that players entering the
second half as substitutes covered 15% more high-
intensity running compared with the identical time
period when completing a full match. This was par-
ticularly evident in midfielders. Research reported a
25% greater distance in high-intensity running dur-
ing the final 15-min of matches in substitutes versus
separate players completing the full match (Mohr
et al., 2003). Discrepancies between studies are
due to the separate groups analyses and the low
sample size in the Mohr et al. (2003) study. It
could be argued that the differences are due to the
time periods in which match running performance
was monitored in each study (second half versus
final 15-min). However, this is the first study to
separated data into early and late substitutions, and
this resulted in very similar relative changes in high-
intensity running compared with the same full match
period. Carling et al. (2010) demarcated between
midfielders and attackers but observed that the latter
failed to improve their match running performance
during the first 10-min of being introduced as sub-
stitutes compared with the equivalent full time per-
iod. This trend was evident within the present study
and further supports the assertion that coaches repla-
cing players still need to consider the contribution
that a particular substitute can make based on situa-
tional and positional factors. Our data provide a
more valid expression of full versus partial match
10 P. S. Bradley & T. D. Noakes
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running performance comparisons across all playing
positions and time periods of the second half. This
data support the notion that selected playing posi-
tions completing full matches either experience fati-
gue in the second half or use a more effective pacing
strategy compared with being introduced as an early
or late substitute.
This is the first study to demonstrate that players
are able to maintain their high-intensity running
performances in the second half of heavily defeated
matches, but this was not evident in heavily won
matches. This seems unsurprising as successful
teams cover less distance in total and at high-inten-
sity compared with unsuccessful teams (Di Salvo
et al., 2009; Rampinini et al., 2007). Playing against
strong opposition has been found to be associated
with lower ball possession (Bloomfield, Polman, &
O’Donoghue, 2005; Lago, 2009) and it is possible
that teams that are heavily defeated have to cover
greater high-intensity running distance without the
ball in an attempt to close players down and regain
possession (Bradley, Lago-Peñas, Rey, & Gomez
Diaz, 2013). Team strategies are also influence by
score line as teams will employ different playing
styles when ahead, level or behind (Lago and
Martin, 2007). This is the first study to highlight
positional differences in match running performance
during various score lines, with attackers covering
more and central defenders less high-intensity run-
ning in matches heavily won versus lost. Bradley
et al. (2011) found attackers in offensive formations
covered more high-intensity runn ing than in defen-
sive formations and our findings could be linked to
the tactical characteristics inherent within these sys-
tems. This highlights the complexities of match run-
ning performance and sports scientists must
consider the influence of tactical and situational fac-
tors before attempting to draw inferences. Caution is
also needed when interpreting these findings as a
small sample size was used in some positional sub-
sets, and this is especially relevant given the variable
nature of some match running parameters (Gregson,
Drust, Atkinson, & Di Salvo, 2010). However, this is
an unavoidable drawback given the elite nature of
the players and the rarity of the data set (e.g. heavily
defeated/won matches using a repeated measures
design).
The results indicated that match running perfor-
mance did not change considerably between
matches of differing importance. Altogether, these
findings would suggest that match importance does
not impact on the overall physical demands of elite
soccer match play. However, the aim of any teams’
tactics is to ensure optimal team organisation in
order to best utilise the physical and technical cap-
abilities of its players and thus this trend could sim-
ply reflect tactical discipline nullifying the
importance of the occasion. However, failure to
find an effect for match importance could be due
to the analysis of 45-min periods as oppo sed to
highly sensitive 5-min periods that could have indi-
cated that the initial period of the first half was
higher in critical versus less important matches as
teams attempt to establish their authority through
increased tempo, and more research using such an
approach is warranted.
This study attempted to inve stigate match run-
ning performance fluctuations in elite soccer
matches, but the reader should be aware of certain
limitations. For instance, studies investigating influ-
encing factors in soccer (Lago et al., 2010), gener-
ally use multivariate statistical models on variables
common to the entire sample (match outcome,
location, standard) and adjusted data for effective
playing time (Castellano et al., 2011) but this was
not possible within the present study given the lack
of commonality in the data set (critical/less impor-
tance versus heavily won/lost ma tches). Finally, a
limitation of using distance s covered in variou s
speed thresho lds to determine fatigue or pacing is
that most maximal accelerations do not result in
speeds associated with high-intensity running but
are metabolically taxing (Varley & Aughey, 2013).
Thus, the true energy cost of match running per-
formance cannot be established.
In summary, the data demonstrate that high-
intensity running in the second half is impacted
by the activity of the first half and is reduced for
5-min after intense perio ds. High-in tens ity r unning
is influenced by score line and substitutions but
not match importance. However, the reader must
be aware of the present study’s limitations and the
challenging natu re of usi ng time-motio n data to
determine if pacing or fatigue occurs in complex
sports such as soccer. Thus, more research is wa r-
ranted to establish if perturbations in match run-
ning performance are primarily a consequence of
fatigue, pacing or tactical and situational
influenc es.
References
Abbiss, C.R., & Laursen, P.B. (2008). Describing and under-
standing pacing strategies during athletic competition. Sports
Medicine, 38, 239–252.
Ali, A., & Farrally, M. (1991). Recording soccer players’ heart
rates during matches. Journal of Sports Sciences, 9, 183–189.
Aughey, R.J. (2010). Australian football player work rate: evidence
of fatigue and pacing? International Journal of Sports Physiology
and Performance, 5, 394–405.
Bangsbo, J. (1994). The physiology of soccer – with special refer-
ence to intense intermittent exercise. Acta Physiologica
Scandinavica, 151(suppl 619), 1–155.
Bangsbo, J., Norregaard, L., & Thorso. F. (1991). Activity profile
of competition soccer. Canadian Journal of Sport Sciences, 16,
110–116.
Match Running Performance Fluctuations in Soccer 11
Downloaded by [University of Sunderland] at 02:23 01 July 2013
Batterham, A.M., & Hopkins, W.G. (2006). Making meaningful
inferences about magnitudes. International Journal of Sports
Physiology and Performance, 1,50–57.
Bendiksen, M., Bischoff, R., Randers, M.B., Mohr, M., Rollo, I.,
Suetta, C., Bangsbo, J., & Krustrup, P. (2012). The Copenhagen
soccer test: physiological response and fatigue development.
Medicine and Science in Sport and Exercise, 44, 1595–1603.
Bloomfield, J., Polman, R., & O’Donoghue, P. (2005). Effects of
score-line on team strategies in FA Premier League soccer.
Journal of Sports Sciences, 23, 192–193.
Bradley, P.S., Carling, C., Archer, D., Roberts, J., Dodds, A., Di
Mascio, M., Paul, D., Diaz, A.G., Peart, D., & Krustrup, P.
(2011). The effect of playing formation on high-intensity run-
ning and technical profiles in English FA Premier League
soccer matches. Journal of Sports Sciences, 29, 821–830.
Bradley, P.S., Di Mascio, M., Peart, D., Olsen, P., & Sheldon, B.
(2010). High-intensity activity profiles of elite soccer players at
different performance levels. Journal of Strength and
Conditioning Research, 24, 2343–2351.
Bradley, P.S., Lago-Peñas, C., Rey, E., & Gomez Diaz, A. (2013).
The effect of high and low percentage ball possession on phy-
sical and technical profiles in English FA Premier League
soccer matches. Journal of Sports Sciences, Epub ahead of print.
Bradley, P.S., Sheldon, W., Wooster, B., Olsen, P., Boanas, P., &
Krustrup, P. (2009). High-intensity running in English FA
Premier League soccer matches. Journal of Sports Sciences, 27,
159–168.
Carling, C., & Bloomfield, J. (2010). The effect of an early dis-
missal on player work-rate in a professional soccer match.
Journal of Science and Medicine in Sport, 13, 126–128.
Carling, C., & Dupont, G. (2011). Are declines in physical per-
formance associated with a reduction in skill-related perfor-
mance during professional soccer match-play? Journal of
Sports Sciences, 21,63–67.
Carling, C., Espié, V., Le Gall F., Bloomfield, J., & Jullien, H.
(2010). Work-rate of substitutes in elite soccer: A preliminary
study. Journal of Science and Medicine in Sport, 13, 253–255.
Castellano, J., Blanco-Villasenor, A., & Alvarez, D. (2011).
Contextual variables and time-motion analysis in soccer.
International Journal of Sports Medicine, 32, 415–421.
Cohen, J. (1988). Statistical power analysis for the behavioural
sciences (2nd ed.). Hillsdale: Lawrence Erlbaum.
D’Ottavio, S., & Castagna, C. (2001). Physiological load imposed
on elite soccer referees during actual match play. Journal of
Sports Medicine and Physical Fitness, 41,27–32.
Di Mascio, M., & Bradley, P.S. (2013). Evaluation of the most
intense period of high-intensity running in English FA Premier
League soccer matches. Journal of Strength and Conditioning
Research. 27, 909–915.
Di Salvo, V., Collins, A., McNeill, B., & Cardinale, M. (2006).
Validation of ProZone: A new video-based performance analy-
sis system. International Journal of Performance Analysis in Sport,
6, 108–119.
Di Salvo, V., Baron, R., Tschan, H., Calderon Montero, F.J.,
Bachl, N., & Pigozzi, F. (2007). Performance characteristics
according to playing position in elite soccer. International
Journal of Sports Medicine, 28, 222–227.
Di Salvo, V., Gregson, W., Atkinson, G., Tordoff, P., & Drust, B.
(2009). Analysis of high intensity activity in Premier League
soccer. International Journal of Sports Medicine, 30, 205–212.
Drust, B., Atkinson, G., & Reilly, T. (2007). Future perspectives
in the evaluation of the physiological demands of soccer. Sports
Medicine, 37, 783–805.
Edwards, A.M., & Noakes, T.D. (2009). Dehydration: Cause of
fatigue or sign of pacing in elite soccer? Sports Medicine, 39,
1–13.
Glaister, M. (2005). Multiple sprint work: Physiological
responses, mechanisms of fatigue and the influence of aerobic
fitness. Sports Medicine, 35, 757–777.
Gregson, W., Drust, B., Atkinson, G., & Di Salvo, V. (2010).
Match-to-match variability of high-speed activities in pre-
mier league soccer. International Journal of Sports Medicine,
31, 237–242.
Krustrup, P., Mohr, M., Steensberg, A., Bencke, J., Kjaer, M., &
Bangsbo, J. (2006). Muscle and blood metabolites during a
soccer game: Implications for sprint performance. Medicine
and Science in Sports and Exercise
, 38, 1165–1174.
Lago, C. (2009) . The influence of match location, quality of
opposition, and match status on possession strategies in pro-
fession al associa tion football. Journal of Sports Scie nces, 27,
1463–1469.
Lago, C., & Martin, R. (2007). Determinants of possession of the
ball in soccer. Journal of Sports Sciences, 25, 969–974.
Lago, C., Casais, L., Dominguez, E., & Sampaio, J. (2010). The
effect of situational variables on distance covered at various
speeds in elite soccer. European Journal of Sport Science, 10,
103–109.
Lovell, R., Barrett, S., Portas, M., & Weston, M. (2013). Re-
examination of the post half-time reduction in soccer work-
rate. Journal of Science and Medicine in Sport, 16, 250–254.
Mohr, M., Krustrup, P., & Bangsbo, J. (2003). Match performance
of high-standard soccer players with special reference to develop-
ment of fatigue. Journal of Sports Sciences, 21, 519–528.
Mohr, M., Krustrup, P., & Bangsbo, J. (2005) Fatigue in soccer:
A brief review. Journal of Sports Sciences, 23, 593–599.
Mohr, M., Krustrup, P., Nybo, L., Nielsen, J., & Bangsbo, J. (2004).
Muscle temperature and sprint performance during soccer
matches – beneficial effects of re-warm-up at half time.
Scandinavian Journal of Medicine and Science in Sports, 15, 136–143.
Rampinini, E., Coutts, A.J., C astagna, C., Sassi, R., &
Impe llizzeri, F.M. ( 2007). Variation in to p level soccer
match performance. International Journal o f Sports Medicine,
28,1018–1024.
Saltin, B. (1973). Metabolic fundamentals in exercise. Medicine
and Science in Sports, 5, 137–146.
Varley, M.C., & Aughey, R.J. (2013). Acceleration profiles in elite
Australian soccer. International Journal of Sports Medicine, 34
(1), 34–39.
Varley, M.C., Elias, G.P., & Aughey, R.J. (2012). Current match-
analysis techniques’
underestimation of intense periods of high-
velocity running. International Journal of Sports Physiology and
Performance, 7, 183–185.
Weston, M., Castagna, C., Impellizzeri, F.M., Rampinini, E., &
Abt, G. (2007). Analysis of physical match performance in
English Premier League soccer referees with particular refer-
ence to first half and player work rates. Journal of Science and
Medicine in Sport, 10, 390–397.
Weston, M., Drust, B., & Gregson, W. (2011a). Intensities
of exercise during match-play in FA Premier League
referees and players. Journal of Sports Sciences, 29,527–
532.
Weston, M., Batterham, A.M., Castagna, C., Portas, M.D.,
Barnes, C., Harley, J., & Lovell, R. (2011b). Reduction in
physical match performance at the start of the second half in
elite soccer. International Journal of Sports Physiology and
Performance, 6, 174–182.
12 P. S. Bradley & T. D. Noakes
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