Journal of Science and Medicine in Sport 16 (2013) 556–561
Contents lists available at ScienceDirect
Journal of Science and Medicine in Sport
journal homepage: www.elsevier.com/locate/jsams
Original research
Diminutions of acceleration and deceleration output during professional football
match play
Richard Akenhead a,b,∗ , Philip R. Hayes a , Kevin G. Thompson c , Duncan French a
a
b
c
School of Life Sciences, Northumbria University, Newcastle Upon Tyne, United Kingdom
Newcastle United Football Club, Newcastle Upon Tyne, United Kingdom
Faculty of Health, University of Canberra, ACT, Australia
a r t i c l e
i n f o
Article history:
Received 4 May 2012
Received in revised form 17 August 2012
Accepted 13 December 2012
Keywords:
Acceleration
Time-motion analysis
GPS
a b s t r a c t
Objectives: This study examined distances covered at low (1–2 m s−2 ), moderate (2–3 m s−2 ) and high
(>3 m s−2 ) acceleration (LACC , MACC and HACC respectively) and deceleration (LDEC , MDEC , and HDEC
respectively) during competitive football games. Temporal and transient patterns of acceleration and
deceleration were also examined.
Design: Observational, repeated measures.
Methods: Thirty-six professional male professional footballers were monitored using a 10 Hz nondifferential global positioning system (NdGPS). Match data was organised into six 15 min periods (P1:
1–15 min, P2: 16–30 min, P3: 31–45 min, P4: 46–60 min, P5: 61–75 min, and P6: 76–90 min) for analysis
of temporal patterns, and into eighteen 5 min periods for analysis of transient patterns. ANOVA with
Bonferroni post hoc tests were used to identify significant (p < 0.05) differences between periods.
Results: Distance covered at LACC , MACC , HACC , LDEC , MDEC , and HDEC was 424 ± 75 m, 242 ± 25 m, 178 ± 38 m,
365 ± 54 m, 210 ± 23 m and 162 ± 29 m respectively. Between period decrements ranged from 8.0% to
13.2% from P1 to P3, 9.2% to 16.3% from P4 to P6, and from 14.9% to 21.0% from P1 to P6. Following PEAK
HACC (148% of mean 5 min HACC ), HACC at 5POST was 10.4% lower than mean (p < 0.01).
Conclusions: Time-dependent reductions in distances covered suggest that acceleration and deceleration
capability are acutely compromised during match play. Further, the occurrence of transient fatigue may
be supported by the findings that HACC and HDEC performance following PEAK was approximately 10%
lower than mean values.
© 2012 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
1. Introduction
Professional association football is a high-intensity intermittent team sport.1,2 Out-field players cover approximately 9–12 km
at a mean intensity of approximately 80–90% maximal heart rate
and 70% VO2 max 3–5 Players also perform numerous high-intensity
activities including sprinting, jumping, kicking and changing
direction.2 When studying the physical demands of match play,
gross locomotion is often categorised using several speed “thresholds”, ranging from motionless standing to maximal sprinting.6,7
This method however, neglects frequent actions such as acceleration and deceleration.8 Speed threshold measurements in isolation
ignore that, even at low absolute speeds, acceleration, and therefore
metabolic demand can still be maximal.
Elite and non-elite players have been suggested to exhibit
signs of temporary fatigue during match play.1,9 Following the
∗ Corresponding author.
E-mail address: r.akenhead@northumbria.ac.uk (R. Akenhead).
peak 5 min period of high speed running (>15 km h−1 ), physical performance in the subsequent 5 min period was found to
be reduced by 12% (p < 0.05) compared to mean 5 min values.9
Furthermore, Bradley et al.10 reported a 6–8% (p < 0.05) reduction in high-intensity (>19.8 km h) distance following a peak 5 min
period. However the same trend cannot be assumed for accelerations and decelerations during football as this has not previously
been studied. Certainly, acceleration is a pre-cursor to high speed
running11,12 and requires high rates of force development. It is however a distinct quality11 requiring greater neural activation to the
working muscles compared to constant speed sprinting.13
To date only a study by Osgnach et al.12 has described
the sum total of accelerations and decelerations during football
match play. However, temporal patterns were not reported in
this study. Recently, the use of non-differential global positioning satellite technology (NdGPS) based time-motion analysis has
become commonplace within team sports. Although early 1- and
5 Hz systems may have been unsuitable for the measurement of
changes of speed,8 recent advances in chipset technology and
an increased sampling rate (10 Hz) allow for a greater level of
1440-2440/$ – see front matter © 2012 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.jsams.2012.12.005
R. Akenhead et al. / Journal of Science and Medicine in Sport 16 (2013) 556–561
accuracy and reliability when measuring discrete movements over
short distances.8,14
To the best of our knowledge there are no publications describing the effect of playing time on accelerations and decelerations
in football, or whether acceleration or deceleration output is compromised following the peak period of activity. The primary aim
of this study was to describe the distances covered at total, low,
moderate and high acceleration (TACC , >1 m s−2 ; LACC , 1–2 m s−2 ;
MACC , 2–3 m s−2 ; HACC , >3 m s−2 ) and deceleration (TDEC , <−1 m s−2 ;
LDEC , −1 to −2 m s−2 ; MDEC , −2 to −3 m s−2 ; HDEC , <−3 m s−2 ) during competitive games. The current classifications were chosen as
appropriate arbitrary demarcations during match play based on
previous data (unpublished). Although maximal accelerations may
be as high as 6 m s−2 ,11 such rates are fleeting and only achieved
from stationary or low speed starts, which occur less infrequently
than higher speed rolling starts in football match play. A secondary
aim was to examine the temporal patterns of acceleration and
deceleration during match play. It was hypothesised that there
would be a time-dependent reduction in acceleration and deceleration variables throughout a match. Furthermore it was also
considered that following the peak period of HACC and HDEC , performance in the subsequent 5 min period would be reduced.
2. Methods
NdGPS sampling at 10 Hz (MinimaxX, Catapult Innovations,
Canberra, ACT, Australia) was used to conduct time-motion analysis
during 18 competitive English Premier Reserve League matches in
the 2010–2011 season. All matches were played on outdoor natural grass pitches (105 m × 68 m) in accordance with English Football
Association rules. All teams utilised a 4-4-2 formation.
Thirty-six outfield professional male football players participated in the study, including players with under-21 and
full-international caps. Their age, height, body mass and body fat
percentage were 19.3 ± 0.5 years, 77.9 ± 7.4 kg, 1.83 ± 0.05 m, and
8.6 ± 1.7%, respectively. Participants were given full details of the
study procedures and provided written consent to participate. All
experimental procedures received approval from the Institutional
Ethics Review Board at Northumbria University.
Fifteen minutes prior to the commencement of warm-up NdGPS
units were switched on and placed outdoors. Immediately prior to
warm-up each starting player (n = 10) was fitted with an NdGPS
unit worn in tight-fitting vests to reduce movement artefact.15
Data were downloaded and each 45 min half was split into nine
5 min periods using the manufacturers’ software (Logan Plus v4.5,
Catapult Innovations, Canberra, ACT, Australia). Only the first fortyfive minutes of each half was included for analysis. Raw data for
LACC , MACC , HACC , TDEC , LDEC , MDEC and HDEC for each 5 min period
557
were exported to Microsoft Excel. Total distance (m−1 ), high speed
(>5.8 m s−2 , HSR) and sprint (>6.78 m s−2 , SPD) distance data were
also exported.
Player data were included for analysis provided they met the
following criteria: (i) the player completed the full duration of the
game; (ii) they did not suffer an injury during the game; (iii) they
played the same position throughout the game; (iv) they were
familiar with their playing position; and (v) the first half of the
game had no more than two added minutes of playing time. Application of the inclusion criteria yielded data sets for 36 separate
players; central defenders [CD] (n = 8), wide defenders [WD] (n = 8),
central midfielders [CM] (n = 8), wide midfielders [WM] (n = 6) and
forwards [F] (n = 6).
To investigate whole-game temporal patterns, data were
organised into three 15 min periods per half (0–15 min [P1],
16–30 min [P2], 31–45 min [P3], 46–60 min [P4], 61–75 min [P5],
and 76–90 min [P6]). The peak 5 min value (PEAK) for HACC and
HDEC was identified and recorded. Distance covered in the preceding 5 min (5PRE ), subsequent 5 min (5POST ) and following 10 min
(10POST ) were also recorded. Values for 5PRE , 5POST and 10POST were
then expressed as a percentage relative to the mean 5 min value
of the half in which it occurred (x̄45). Total ball-in-play (BIP) time
was also analysed for each 15 min period. Video based analysis was
conducted by an experienced video analyst.
Data were tested for normality using a Shapiro–Wilk test, with
all data shown to be normally distributed (p > 0.05). Betweenperiod differences in TACC , LACC , MACC , HACC , TDEC , LDEC , MDEC , HDEC
and BIP were tested for using a repeated-measures one-way ANOVA
with a post hoc Bonferroni test (significance accepted at p < 0.05).
Between-half differences were tested for using a t-test. Statistical analyses were conducted in SPSS 15.0 for Windows (SPSS, Inc.,
Chicago, IL). Cohen’s d effect sizes (ES) and 95% confidence intervals
(CI) were calculated for all between-period differences. Effects were
classified as trivial (0.0–0.2), small (0.2–0.5), moderate (0.5–0.8),
and large (>0.8). Descriptive statistics are mean ± SD unless otherwise stated
3. Results
Mean ± SD and ranges for time-motion parameters during the
first and second halves of match play are presented in Table 1. A
significant main time effect was found for TACC (df = 5, F2 = 10.3,
p < 0.01), LACC (df = 5, F2 = 8.1, p < 0.01) and MACC (df = 5, F2 = 8.1,
p < 0.01). Post hoc comparison (Fig. 1A–D) showed P1 to be greater
than P3, P5 and P6 (p < 0.01; CI: 6.0, 49.6 m; ES: 0.67–1.51). P2 and
P4 were greater than P6 (p < 0.01, CI: 3.2, 34.9 m, ES: 0.60–0.94).
For LACC P1 was greater than P3, P5 and P6 (p < 0.01, CI: 2.3, 30.1 m,
ES: 0.47–1.31). P2 was greater than P6 (p < 0.01, CI: 1.9, 23.0 m, ES:
Table 1
Distance covered in defined speed, acceleration and deceleration thresholds during elite match play.
Parameter (m)
First half (45-min)
Second half (45-min)
Match total (90-min)
TD
HSR
SPD
TACC
LACC
MACC
HACC
TDEC
LDEC
MDEC
HDEC
5345 ± 413 [4638–6383]
248 ± 119 [74–481]
94 ± 58 [0–221]
532 ± 47 [398–632]
223 ± 41 [150–303]
126 ± 15 [90–160]
91 ± 23 [48–150]
466 ± 42 [351–546]
191 ± 30 [118–245]
108 ± 13 [75–130]
84 ± 17 [54–125]
5106 ± 408 [4556–5988]*
257 ± 110 [24–542]
100 ± 56 [0–262]
492 ± 51 [409–596]*
201 ± 39 [135–276]*
116 ± 14 [83–151]*
88 ± 18 [56–138]
434 ± 46 [351–516]*
175 ± 29 [110–244]*
102 ± 14 [81–125]*
78 ± 15 [46–118]*
10,451 ± 760 [9376–12,247]
505 ± 209 [116–973]
194 ± 101 [0–450]
1022 ± 89 [813–1163]
424 ± 75 [312–570]
242 ± 25 [189–281]
178 ± 38 [114–267]
899 ± 80 [734–1018]
365 ± 54 [231–479]
210 ± 23 [157–250]
162 ± 29 [103–225]
Values are expressed as mean ± SD [range]. TD, total distance, HSR, high speed running distance (≥5.8 m s−1 ); SPD, sprint distance (≥6.7 m s−1 ); TACC , total acceleration
(>1 m s−2 ); LACC , low acceleration (1–2 m s−2 ); MACC , moderate acceleration (2–3 m s−2 ); HACC , high acceleration (>3 m s−2 ); TDEC , total deceleration (>−1 m s−2 ); LDEC , low
deceleration (−1 to −2 m s−2 ); MDEC , moderate deceleration (−2 to −3 m s−2 ); HDEC , high deceleration (>−3 m s−2 ).
*
Significantly lower than first half (p < 0.05).
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R. Akenhead et al. / Journal of Science and Medicine in Sport 16 (2013) 556–561
Fig. 1. Mean ± SD distance covered for TACC (A), LACC (B), MACC (C), HACC (D), TDEC (E), LDEC (F), MDEC (G), and HDEC (H). Data were analysed with ANOVA followed by Bonferroni
post hoc tests. Significantly (p < 0.05) different from P1 (*), P2 (#), and P5 (‡). (P1: 1–15 min, P2: 16–30 min, P3: 31–45 min, P4: 46–60 min, P5: 61–75 min, P6: 76–90 min.)
0.66–0.86). For MACC P1 was greater than P3, P5 and P6 (p < 0.01,
CI: 1.2, 14.5 m, ES: 0.81–1.36). P2 and P4 were greater than P6
(p = 0.04, CI: 0.1, 9.9 m, ES: 0.73–0.77). HACC did not exhibit a significant main time effect (df = 5, F2 = 2.0, p = 0.08). However P1 and P4
were greater than P6 with a moderate effect (CI: −1.3, 10.0 m, ES:
0.61).
Similar results were found for deceleration (Fig. 1E–H). Significant main time effects were found for TDEC (df = 5, F2 = 8.5, p < 0.01),
R. Akenhead et al. / Journal of Science and Medicine in Sport 16 (2013) 556–561
559
were lower than x̄45 (p < 0.02, CI: 3.6, 19.2 m, ES: 0.59–0.87). Additionally, 10POST was shown to be significantly greater than 5POST
(p = 0.03, CI: 3.4, 23.6 m, ES: 0.36).
Mean ± SD number of connected satellites during data collection
was 13 ± 1, and horizontal dilution of position was 0.8 ± 0.1, both
of which are within the required ranges for accurate measurement.
Analysis of BIP demonstrated no significant time-dependent reductions in ball-in-play time, with the greatest decrease being 6% from
P1 to P6 (p = 1.0).
4. Discussion
Fig. 2. Mean (±SD) percentage change from x̄45 at 5PRE , PEAK, 5POST and 10POST for
HACC (A), and HDEC (B). Significantly (p < 0.05) different from x̄45 (*), PEAK (␪), 5PRE
(␶) and +5 min (#).
LDEC (df = 5, F2 = 7.7, p < 0.01), MDEC (df = 5, F2 = 3.4, p = 0.006) and
HDEC (df = 5, F2 = 3.6, p = 0.004). Post hoc analysis for TDEC showed
P1 was greater than P3, P5 and P6 (p < 0.01, CI: 2.5, 42.0 m, ES:
0.77–1.37). P2 and P4 were greater than P6 (p = 0.03, CI: 0.6, 31.0 m,
ES: 0.73–0.89). For LDEC P1 was significantly greater than P3, P5
and P6 (p = 0.01, CI: 1.4, 25.3 m, ES: 0.75–1.22). P2 and P4 tended
towards being greater than P6 (p = 0.10, CI: −0.7, 17.0 m, ES: 0.65)
and (p = 0.095, CI: −0.6, 17.0 m, ES: 0.68), respectively. For MDEC P1
was greater than P5 and P6 (p < 0.42, CI: 0.1, 9.5 m, ES: 0.70–0.95). P4
tended towards being greater than P6 (p = 0.20, CI: −0.8, 7.9 m, ES:
0.60). For HDEC P1 was greater than P6 (p = 0.002, CI: 1.4, 10.8 m, ES:
0.92) and tended towards being greater than P5 (p = 0.16, CI: −0.6,
8.7 m, ES: 0.70). P4 tended towards being greater than P6 with a
moderate effect size (p < 0.06, CI: −0.1, 9.3 m, ES: 0.75).
Analysis of transient acceleration revealed a main effect for HACC
(df = 4, F2 = 37.14, p < 0.01) (Fig. 2A). PEAK was greater than x̄45,
5PRE , 5POST and 10POST (p < 0.00, CI: 39.6, 69.9 m, ES: 2.18–3.01). Also,
5POST was lower than x̄45 (p < 0.01, CI: 5.0, 16.4 m, ES: 0.74). For HDEC
(df = 4, F2 = 93.2, p < 0.01) PEAK was greater than x̄45, 5PRE , 5POST
and 10POST (p < 0.01, CI: 46.5, 77.6 m, ES: 2.57–4.82). 5PRE and 5POST
The current study is the first to use 10 Hz GPS technology to
describe temporal patterns of acceleration and deceleration distance during football match play. On average 18% of total distance
covered is done so whilst accelerating or decelerating at a rate
greater than 1 m s−2 . Further, 7.5%, 4.3% and 3.3% of total distance is
covered at 1–2 m s−2 , 2–3 m s−2 and >3 m s−2 respectively. In agreement with our hypothesis, our data also provide support for the
occurrence of time-dependent and transient reductions in acceleration and deceleration output (Figs. 1 and 2).
Observed diminutions in acceleration and deceleration distance
ranged from 8.0% to 13.2% from P1 to P3, 9.2% to 16.3% from P4 to
P6, and from 14.9% to 21.0% from P1 to P6 (ES: 0.62–1.51). P1 consistently contained the highest values, which may be due to tactical
enforcements made by coaches resulting in game tempo being elevated in the initial stages of the game.16 It may also be contributed
to by the preceding period of rest following pre-game warmup. This period of approximately ten minutes of passive recovery
facilitates re-synthesis of ATP, clearance of inorganic phosphates,
restoration of intracellular pH, conversion of lactate to pyruvate,
and restoration of efferent nerve membrane potential.17,18 Combined with the possible occurrence of post-activation potentiation,
a residual elevation in VO2 kinetics,19 intact glycogen reserves,1
and the motivation to gain an initial advantage, the initial minutes
of a match may be predisposed to a higher work rate.
Despite between-half reductions in acceleration and deceleration performance, there was little difference between first and
second half HSR and SPD (Table 1) which may be attributed to a pacing strategy. However it may also be reasonable to assume that if
acceleration output is reduced, a player would take longer to reach
any given speed, and may therefore be required to run further, thus
covering a greater distance at high speed. This may also explain the
often reported second half increase in distanced covered at low
speed such as walking, as greater rest periods may be required.
Following PEAK HACC (148% of mean), HACC performed at 5POST
was on average 10.4% lower than mean values. At 10POST HACC
had recovered to 99.9% of the mean value. This trend is similar to
that reported for high speed running by Mohr et al.9 and Bradley
et al.10 Krustrup et al.20 argued that following 5 min of intense
football match play perturbations in creatine phosphate, inosine
monophosphate, hydrogen ion and intracellular potassium concentration compromise excitation–contraction coupling resulting
in peripheral fatigue.
It may be reasonable to suggest that exercise-induced homeostatic perturbations are partially responsible for the significant
11.4% reduction of HDEC at 5POST following PEAK HDEC . However
the possible consequences for performance may be different for
deceleration. Eccentric force production and regulation is required
for deceleration as the hip, knee and ankle extensors work eccentrically to increase braking forces.21 Failure of the working muscles
to produce the required force at the appropriate times may lead
to compromised physical performance during change of direction
and an increased risk of injury.21 Rahnama et al.17 showed that
concentric and eccentric peak torque of the knee extensors was
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R. Akenhead et al. / Journal of Science and Medicine in Sport 16 (2013) 556–561
significantly reduced following football match-play, suggesting
that acceleration and deceleration performance would be compromised as a result. Fatigue characteristics are however muscle
specific22 and the current understanding of fatigue characteristics
of the synergistic muscle groups responsible for decelerating is
limited. The current study provides a strong rationale for future
research to examine such responses.
Observed reductions in HACC and HDEC following PEAK appear
to persist for approximately 5 min, although predictors of the magnitude and duration of any temporary fatigue remain to be fully
elucidated and are likely highly individual. The large range of values for PEAK HACC and PEAK HDEC across the sample (121–221% of
mean) is likely due to the intermittent nature of the game, playing
position and individual differences. Individual variations in muscle
fibre-type distribution, strength, training status and proportional
contribution from metabolic pathways are likely factors. Further
research is needed in quantifying the determinants of magnitude
of this fatigue. Profiling athletes’ maximum voluntary activation
or rate of force development, repeat sprint ability and aerobic
capacity may provide useful measures in determining an athletes’
predisposition to transient peripheral fatigue. However, our data
(unpublished) suggest that intra-reliability of HACC and HDEC during
match play (CV = 12–25%) may be a more sensitive measure than
high speed (CV = 25–45%) and sprint (CV = 30–47.5%) distance.
Our finding that acceleration and deceleration output exhibited
time-dependent reductions from the start to the end of a game
(ES > 1.0) provides football-specific support for previous studies
which have demonstrated reductions in determinants of acceleration output following football-specific activity17,23–26 and repeat
sprint activity.27,28 A recent study by Bradley et al.29 suggested
that acceleration capacity was not reduced by match play in
the same way as distance covered during high-intensity running
(>14.4 km h−1 , 3.88 m s−1 ). This suggestion was made following
analysis of the number of discrete acceleration efforts in the first
vs. second half, and in the first compared to final 15 min period
of the game. Although the exact mechanisms are not fully understood, reductions in central neural drive and increases in peripheral
fatigue have been shown to reduce the rate of force development (RFD) and maximal force production compared to baseline
levels.17,24 Indeed, as a consequence of football match-play and
repeat-sprint protocols, sprint performance, jump performance
and RFD have all been shown to be reduced.17,30 The current study
provides further evidence that acceleration and deceleration capacity are attenuated during football match-play.
When interpreting the current findings, a number of limitations
should be considered. Physical demands of professional match play
are known to be position specific.4 Despite using participants from
two football clubs, sample size was insufficient for differentiation of
position specific acceleration and deceleration demands. This situation is difficult to overcome as clubs often have only 3–5 players in
each playing position, with yet fewer playing regularly. A further
consideration is the validity and accuracy of 10 Hz GPS technology for measuring peak effort changes in speed. Recently however,
it has been established that the 10 Hz system used provides sufficient accuracy to quantify the acceleration and deceleration phases
in team sports.8
5. Conclusion
In conclusion, this study is the first to describe the temporal and transient patterns of acceleration and deceleration during
professional football match play. Time dependent reductions in
acceleration and deceleration output may provide further support
for the occurrence of fatigue during match play. The presence of
transient reductions in HACC and HDEC following periods of peak
activity suggest that a players’ capacity to perform required actions
may be transiently compromised during match play.
6. Practical implications
• With 18% of total distance covered being whilst accelerating or
decelerating at >1 m s−2 , the current data highlights the importance of both eccentric and concentric conditioning to football
performance.
• Practitioners may use the published data to design and prescribe
appropriate match-specific training stimuli, ensuring a sufficient
acceleration and deceleration stimulus is included.
• As the reliability of acceleration and deceleration output appears
to be greater than that of high speed and sprint running, practitioners may wish to explore the use of these measures as
performance monitoring variables.
Conflicts of interest
None declared.
Funding
Funding for this study was provided by Newcastle United Football Club and the School of Life Sciences, Northumbria University.
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