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Sports Medicine (2021) 51:255–273
https://doi.org/10.1007/s40279-020-01359-9
SYSTEMATIC REVIEW
The Effect ofFixture Congestion onPerformance During
Professional Male Soccer Match‑Play: ASystematic Critical Review
withMeta‑Analysis
RossJulian1,2 · RichardMichaelPage3 · LiamDavidHarper4
Published online: 17 October 2020
© The Author(s) 2020
Abstract
Background Fixture congestion (defined as a minimum of two successive bouts of match-play, with an inter-match recovery
period of < 96h) is a frequent and contemporary issue in professional soccer due to increased commercialisation of the sport
and a rise in the number of domestic and international cup competitions. To date, there is no published systematic review or
meta-analysis on the impact of fixture congestion on performance during soccer match play.
Objective We sought to conduct a systematic review and meta-analysis of the literature related to the effects of fixture con-
gestion on physical, technical, and tactical performance in professional soccer match-play.
Methods Adhering to PRISMA guidelines and following pre-registration with the Open Science Framework (https ://osf.io/
fqbuj ), a comprehensive and systematic search of three research databases was conducted to identify articles related to soccer
fixture congestion. For inclusion in the systematic review and meta-analysis, studies had to include male professional soccer
players, a congestion period that contained two matches ≤ 96h, and have outcome measures related to physical, technical or
tactical performance. Exclusion criteria comprised non-male and/or youth players, data that only assessed impact of conges-
tion on injury, used simulated protocols, or were grey literature, such as theses or dissertations.
Results Out of sixteen articles included in the systematic review, only five were eligible for the meta-analysis, and the only
variable that was measured consistently across studies was total distance covered. Fixture congestion had no impact on total
distance covered [p = 0.134; pooled standardized mean difference; Hedge’s G = 0.12 (− 0.04, 0.28)]. Between-study vari-
ance, heterogeneity, and inconsistency across studies were moderate [Cochrane’s Q = 6.7, p = 0.150, I2 = 40.7% (CI 0.00,
93.34)]. Data from articles included in the systematic review suggest fixture congestion has equivocal effects on physical
performance, with variation between studies and low quality of research design in some instances. Tactical performance may
be negatively impacted by fixture congestion; however, only one article was identified that measured this element. Technical
performance is unchanged during fixture congestion; however, again, research design and the sensitivity and relevance of
methods and variables require improvement.
Conclusion Total distance covered is not impacted by fixture congestion. However, some studies observed a negative effect of
fixture congestion on variables such as low- and moderate-intensity distance covered, perhaps suggesting that players employ
pacing strategies to maintain high-intensity actions. There is a lack of data on changes in tactical performance during fixture
congestion. With ever increasing numbers of competitive matches scheduled, more research needs to be conducted using
consistent measures of performance (e.g., movement thresholds) with an integration of physical, technical and tactical aspects.
* Liam David Harper
L.Harper@hud.ac.uk
Extended author information available on the last page of the article
1 Introduction
It is possible for soccer teams to compete in 50–80 matches
during a ~ 40-week competitive season, thus regularly
playing two matches per week, with some teams complet-
ing as many as three matches in a weekly microcycle [1–3].
Contemporary congested match scheduling can be attrib-
uted to a number of factors, such as, but not limited to, the
increased commercialisation of the sport and the subsequent
manipulation of match scheduling in favour of TV revenue,
inclement weather conditions and, thus, the postponement
of matches, and increased numbers of domestic and inter-
national cup competitions.
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256 R.Julian et al.
Key Points
Results of the meta-analysis indicate that fixture conges-
tion has no impact on total distance covered. However,
other physical performance variables, such as low- and
moderate-intensity distance covered, may be negatively
impacted during congested periods.
Tactical performance may be negatively impacted by fix-
ture congestion, with decreased synchronisation between
players. However, these findings are from only one
article; as such, more research is required on this area.
Integration of team behaviour (e.g., team synchrony)
with contemporary measures of technical and physical
performance is warranted.
There is a lack of consistency between studies measuring
the impact of fixture congestion on performance. Fixture
congestion is a contemporary and concerning issue
(including to the players themselves) and more research
is required to elucidate changes in performance.
Although the rotation of squads may prevent some play-
ers from competing in congested schedules, a study con-
ducted with a French Ligue 1 (highest professional league in
France) club identified that ~ 25–40% of players are required
to complete all matches during a two- or three-match mic-
rocycle [13]. However, this may be higher in certain clubs,
particularly in the lower tiers of domestic leagues where
fixture congestion is regularly observed. It is for this reason
that insufficient recovery between successive matches and
the occurrence of congested fixture periods has been pre-
viously suggested as a factor that affects performance. As
such, it is of importance to fully understand the magnitude of
the effect a congested schedule has on match performance.
Although prolonged physical recovery can in turn lead
to residual fatigue and consequently impair physical per-
formance, there has been suggestion that other elements of
performance may be affected. One of the main determinants
of successful soccer performance is technical ability, which
encapsulates, inter alia, passing, shooting and dribbling.
Although it has been suggested that physical fatigue which
occurs throughout a match can lead to a reduction in suc-
cessful technical performance, there has been few studies
to observe the effect of a congested schedule (which may
include residual fatigue) on technical performance [3, 14,
15]. Although the limited literature suggests that a con-
gested schedule does not affect technical performance, it
is important to systematically assess whether the literature
confirms this proposal and to what magnitude. Therefore,
a comprehensive overview of the literature is necessary, to
identify what technical performance parameters might be
affected. Furthermore, previous research has suggested that
congested schedules may affect tactical performance [16].
This may be due to factors such as mental fatigue, with
players attempting to cognitively process multiple different
instructions and events over a relatively short period of time
[17]. Moreover, as mentioned previously, during periods of
congestion, teams are regularly rotated and, therefore, the
tactical cohesion of the team might be disrupted. As such,
further information is required to understand the effect of
congested schedules on tactical performance.
Accordingly, there is a need for research to robustly
assess the current literature and quantify the effect of a con-
gested schedule on physical, technical and tactical perfor-
mance. Although elements of previous literature have been
reviewed in an opinion piece by Carling etal. [18], a system-
atic review has not been conducted in this area. Moreover,
since the publication of Carling etal. [18], there has been
a considerable number of articles published which are spe-
cific to this area. Therefore, the purpose of this systematic
review is to identify whether a congested schedule affects
physical, technical or tactical performance. Moreover, a
meta-analysis will be conducted to identify what physical
performance parameters are affected by congested schedules.
In a recent survey of 543 elite professional players by the
World Players’ Union (FIFPro), 35–40% of players believe
that they are currently competing in too many competitive
matches, and thus are receiving an inadequate number of
days for recovery [4]. In concordance with this perception,
previous research has observed that some players, although
potentially dependent on playing standard, may still not be
100% recovered in the 72h following a competitive match
[5]. For example, measures of sprint and countermovement
jump performance [6–8], thigh muscular isokinetic torque
[6, 8], and biochemical markers, such as creatine kinase and
uric acid [6, 8], remain significantly impaired when com-
pared to baseline levels at ≥ 72h post match. In addition,
Brownstein etal. [9] identified that players’ perceptions
of fatigue persisted 72h post match play. It should also be
acknowledged that as is the case with applied sport and the
completion of congested schedules (a minimum of two suc-
cessive bouts of match-play, with an inter-match recovery
period of < 96h), players who are often not fully recovered
are required to compete in a subsequent match. The physical
and mental demands of these matches can also be further
exacerbated by additional confounding factors, such as trav-
elling to and from away matches [10, 11], with two-thirds
of the players surveyed suggesting that travel is a potential
factor that limits their recovery [4]. Furthermore, during
these congested periods, it is common for matches to be
played during the evening, as such, the timing of matches
may affect indices of sleep which may then further exacer-
bate the recovery time course of a player [12].
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257
Fixture Congestion and Performance in Soccer
Additionally, this review aims to identify areas for future
research and directions in the topic of fixture congestion and
its effects on performance.
2 Methods
A systematic review and meta-analysis were conducted
to evaluate the impact of fixture congestion on in-match
physical, technical and tactical performance. The review
and meta-analysis were conducted and reported in accord-
ance to the PRISMA (Preferred Reporting for Systematic
Reviews and Meta-Analyses) statement (https ://www.prism
a-state ment.org/). The protocol was pre-registered on the
Open Science Framework prior to full searches and analysis
was undertaken (https ://osf.io/fqbuj ).
2.1 Search Strategy
Literature searches of PubMed, MEDLINE, and Scopus
were undertaken to identify suitable journal articles. All
searches were conducted in September 2019 by two of the
authors (LDH and RJ). Searches included the following key-
words as search terms: “soccer”, or “football” in combina-
tion with “fixture congestion”, “congestion”, “congested”,
and “match congestion”. Furthermore, reference lists of
acquired articles were checked for relevant studies and any
articles that were known to the authors were also included.
All articles were saved in a reference manager software
(EndNote X9, Thomson Reuters©, New York, NY, USA).
Following the removal of duplicates, the titles and abstracts
of the remaining articles were independently screened for
relevance. Finally, the remaining full texts were examined
by the two aforementioned authors based upon the inclusion
and exclusion criteria, outlined in Sect.2.2. If there were any
discrepancies between authors, then a third author (RMP)
checked the relevant article and a consensus decision was
reached.
2.2 Selection Criteria
2.2.1 Inclusion
To be considered for the present systematic review article,
papers needed to fulfil the following criteria: (1) origi-
nal article written in English; (2) abstracts available for
screening; (3) relevant data concerning the effect of fixture
congestion on physical and/or technical and tactical per-
formance during soccer match-play; (4) minimum of two
matches ≤ 96h; (5) included male soccer players. There were
no restrictions in terms of publication date.
2.2.2 Exclusion Criteria
Manuscripts were omitted from the review if they violated
any of the following criteria: (1) inclusion of female soc-
cer players; (2) assessed the effects of congestion on youth
soccer players; (3) data that only assessed the impact of
congestion on injury; (4) used protocols which simulate the
demands of soccer match play; (5) published in the follow-
ing formats: grey literature, such as theses and dissertations
(conference proceedings were included if sufficient detail
was reported to enable a full quality assessment), as well as
reviews, systematic reviews and meta-analyses.
2.3 Assessment ofQuality ofMethodologies
ofStudies
The methodological quality of the studies included in this
systematic review was evaluated in accordance with previ-
ously published work [19], based on the original version
developed by Law etal. [20]. The quality of the included
methodologies was assessed using a 16-item assessment tool
created for quantitative studies; the specific items can be
found in Table1.
For each item, quality was rated as 1 (meets criteria), 0
(does not meet criteria) or N/A (not applicable). The final
score of each research paper corresponded to the sum of
every score in a given article divided by the total number of
scored items for that specific research design and expressed
as a percentage. Furthermore, methodology quality thresh-
olds were implemented and classified as follows: (1) low
(≤ 50%); (2) good (51–75%); and (3) excellent (> 75%) as
per [19]. The quality of each methodology was assessed by
two authors (LDH and RJ). To ensure there was an accept-
able level of inter-rater agreement, Cohen’s kappa coefficient
(ĸ) was calculated.
3 Meta‑analysis
A meta-analysis was undertaken to assess the effect of fix-
ture congestion on total distance covered during match-play.
Total distance covered was the only variable included in
the meta-analysis due to it being the only variable that was
measured and reported with enough similarity between
studies (n = 5). All other variables were not measured in a
homogenous way between studies, precluding a meta-anal-
ysis to be undertaken. A meta-analysis using random effects
was conducted using the “metafor” package in R (R Founda-
tion for Statistical Computing, Vienna, Austria. URL: https
://www.r-proje ct.org/ [21]). Standardized mean differences
(SMD; Hedges’ G) for the five studies included in the meta-
analysis were calculated using the inverse variance method,
with statistical heterogeneity calculated using the I2 statistic.
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258 R.Julian et al.
Low, moderate and high risk of heterogeneity thresholds
were set at < 25%, 25–75%, and > 75%. To visualise potential
funnel plot asymmetry, standard errors were plotted against
Hedges’ G values. Furthermore, Duval and Tweedie’s Trim
and Fill method was used to assess funnel plot asymmetry.
Egger’s regression test was not used due to the number of
studies being below 10 [22]. Data used in the meta-analysis
are publicly available at https ://osf.io/2q6aj /.
4 Results
An initial search yielded 527 records, with 406 duplicates
and, thus, 121 individual records. Following title and
abstract inspection, 105 articles were deemed irrelevant,
leaving 16 articles eligible for full-text screening. Following
full-text screening, all 16 articles were included in the sys-
tematic review, five of those included in the meta-analysis.
See Fig.1 for the PRISMA flow diagram. Studies that met
the inclusion criteria for the review are presented in Table2,
alongside their quality assessment ratings. The list of stud-
ies that were excluded is publicly available at https ://osf.io/
pcqu3 /.
4.1 Quality ofStudies
There was good agreement between raters for the quality of
studies (ĸ = 0.718; 95% CI 0.487–0.949, p = 0.0005). The
mean methodological quality score for the 17 articles was
74.9 ± 15.7%, with no articles achieving a score of 100%
(Table2). One article scored below 50%, with seven achiev-
ing a score between 50 and 75% (good methodological qual-
ity) and nine achieving a score over 75% (excellent methodo-
logical quality). The criteria that were not met consistently
were: criterion 16, related to detailing the limitations of the
study; criterion 5, related to justification of sample size;
and criterion 7, description of the reliability of the outcome
measures.
4.2 Pooled Effect Estimate
Results of the meta-analysis revealed no significant effect
of fixture congestion on total distance covered (p = 0.134),
with a trivial effect size [pooled SMD = 0.12 (− 0.04, 0.28);
Fig.2]. Between-study variance, heterogeneity, and incon-
sistency across studies were moderate [Cochrane’s Q = 6.7,
p = 0.150, I2 = 40.7% (CI 0.00, 93.34)]. Visual inspection of
the funnel plot (Fig.3) revealed some asymmetry and Duval
and Tweedie’s Trim and Fill method identified one missing
article from the right side of the plot. When accounting for
this missing article, there was a significant effect of fixture
congestion on total distance covered (p = 0.045) but still with
a trivial effect size [pooled SMD = 0.16 (0.00, 0.32)].
5 Discussion
5.1 Interpretation ofMeta‑analysis Findings
We identified no effect of fixture congestion on total dis-
tance covered during soccer match-play [p = 0.134, pooled
SMD = 0.12 (− 0.04, 0.28); Fig.2]. When all studies were
grouped together, distance covered during a congested
period was 10,565 ± 991m and 10,475 ± 880m during a
non-congested period. There were differences between the
five studies with regard to the method used to measure dis-
tance covered. Three of the studies used semi-automated
tracking systems (Amisco: [23, 24] and ProZone: [16])
Table 1 Quality Criteria from
Sarmento etal. [15], adapted
from Law etal. [16]
Q1 Was the study purpose stated clearly?
Q2 Was relevant background literature reviewed?
Q3 Was the design appropriate for the research question?
Q4 Was the sample described in detail?
Q5 Was sample size justified?
Q6 Was informed consent obtained? (if not described, assume No)
Q7 Were the outcome measures reliable? (if not described, assume No)
Q8 Were the outcome measures valid? (if not described, assume No)
Q9 Was the method described in detail?
Q10 Were results reported in terms of statistical significance?
Q11 Were the analysis methods appropriate?
Q12 Was the importance for practice reported?
Q13 Were any drop-outs reported?
Q14 Were conclusions appropriate given the study methods?
Q15 Are there any implications for practice given the results of the study?
Q16 Were limitations of the study acknowledged and described by the authors?
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259
Fixture Congestion and Performance in Soccer
and two used Micromechanical systems (MEMS) devices
(Catapult Sports Optimeye X4: [25] and Qstarz-1Hz: [26]).
Furthermore, in this meta-analysis, the number of player
observations was used as the method of sampling. The num-
ber of player observations varied between studies, as did the
number of observations within studies between congested
and non-congested periods (although the sum of player
observations between congested and non-congested peri-
ods when all five studies were combined was 836 and 820,
respectively). Therefore, the differences in equipment used
and observation frequency may explain the moderate het-
erogeneity observed (I2 = 40.7%). Indeed, researchers have
demonstrated that there is small-to-moderate differences in
total distance covered when simultaneously measured by
both automated tracking systems and MEMS devices during
soccer match-play [27, 28]. Therefore, although the present
meta-analysis suggests no differences in total distance cov-
ered between congested and non-congested periods, further
Fig. 1 PRISMA flow diagram
of the process used in selection
of the journal articles included
in the systematic review and
meta-analysis
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260 R.Julian et al.
Table 2 Summary of studies accompanied by the quality criteria score, investigating the in-match physical performance response during periods of fixture congestion. ↓ and ↑, denotes signifi-
cant reductions or significant increases in outcome measures, respectively
References Participants Match data collection
methods
Fixture congestion scenario In-match outcome measures Main findings Quality
score (%)
Odetoyinbo etal. [32] 16 elite outfield players from
4 teams in England (FB
n = 3, CB n = 7, CM n = 3,
WM n = 1, FWD n = 2)
Semi-automated video
system (ProZone)
3 successive matches in
5days (2days between
matches 1 and 2, and
3days between matches
2 and 3)
TD and distance covered,
frequency and time spent
in each locomotive activ-
ity. HI distance when the
player’s own team is in
possession, HI distance
when player’s own team
is without possession, HI
distance when the ball is
out of play, recovery time
(average time in between
HI activity), distance cov-
ered per minute of match,
average speed, top speed,
relative intensity (number
of high intensity activities/
time)
Sprint (≥ 7.0m.s−1)
HI (> 5.5m.s−1)
HIR (5.5–6.9m.s−1)
Run (4.0–5.4m.s−1)
Jog (2.0–3.9m.s−1)
Walk (0.2–1.9m.s−1)
Stand (0–0.1m.s−1)
↓ HI distance when team is
in possession and when
ball was out of play during
match 3 vs. match 1
↓ walking distance match 3
vs. match 1
78.6
Dupont etal. [36] 32 elite outfield players
playing for the same Scot-
tish club
Semi-automated video
system (Amisco)
1 match microcycles vs
2 match microcycles
with ≤ 4days between
match 1 and match 2
TD, HI distance, sprinting
distance, frequency of
sprints
Sprint (> 24km·h−1)
HI (19–24km·h−1)
No effect 66.7
Rey etal. [35] 42 elite outfield players from
the same Spanish club
(FB n = 9, CD n = 17, CM
n = 9, WM n = 2, FWD
n = 5)
Semi-automated video
system (Amisco)
2 successive matches with
3days between matches
TD and distance covered in
each locomotive activity.
Frequency of HIR and
sprints, recovery times,
top and average speed
Sprint (> 23km·h−1)
HIR (19.1–23.0km·h−1)
MIR (14.1–19.0km·h−1)
LIR (11.1–14.0km·h−1)
Stand, walk, jog
(0–11km·h−1)
No effect 40.0
Carling and Dupont [1] 7 professional midfield (cen-
tral and wide) players from
the same French club
Semi-automated video
system (Amisco)
3 successive matches
in ≤ 7days
TD, HIR, TD when indi-
vidual in possession of the
ball, peak period HIR
HIR (≥ 14.4km·h−1)
No effect 71.4
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261
Fixture Congestion and Performance in Soccer
Table 2 (continued)
References Participants Match data collection
methods
Fixture congestion scenario In-match outcome measures Main findings Quality
score (%)
Lago-Penas etal. [23] 23 elite outfield players from
the same Spanish club (FB
n = 5, CD n = 5, CM n = 5,
WM n = 4, FWD n = 4)
Semi-automated video
system (Amisco)
1 match vs. 2 match weekly
microcyles
TD, distance covered fre-
quency and time spent in
each locomotive activity
Sprint (> 23km·h−1)
HIR (19.1–23.0km·h−1)
MIR (14.1–19.0km·h−1)
LIR (11.1–14.0km·h−1)
Stand, walk, jog
(0–11km·h−1)
No effect 80.0
Carling etal. [2] 19 elite outfield players from
the same French club
Semi-automated video
system (Amisco)
8 successive matches in a
26-day period
Relative TD, light-intensity,
LIR, MIR, HIR, and TD in
individual ball possession
HIR (> 19.1km·h−1)
MIR (14.1–19.0km·h−1)
LIR (11.1–14.0km·h−1)
Light-intensity
(0–11km·h−1)
Main effect for differences
in TD and light-intensity
↑ TD in matches 4 and 7
compared to 2 and 3
↑ light-intensity in matches
4 and 8 compared to
matches 1, 2, 3, 5 and 6
and 3, respectively
93.3
Dellal etal. [3] 16 elite outfield players from
the same French club
Semi-automated video
system (Amisco)
3 instances of 6 consecu-
tive matches separated by
3days (instance 1, 5 play-
ers; instance 2, 6 players;
instance 3, 5 players)
TD and distance covered in
each locomotive activity
HIR (> 21.0km·h−1)
MIR (18.1–21.0km·h−1)
LIR (12.1–18.0km·h−1)
Walking and light intensity
(0–12.0km·h−1)
No effect 93.3
Andrzejewski etal. [14] 11 professional players from
the same Polish club (FB
n = 2, CD n = 3, CM n = 2,
WM n = 2, FWD n = 2)
Semi-automated video
system (Amisco)
1 vs 2 match weekly micro-
cycles
TD, distance covered in
each locomotive activ-
ity, frequency of HI and
sprinting, recovery time,
average and top speed
Sprint (≥ 24km·h−1)
HIR (21.0–24.0km·h−1)
Fast running (17.0–
21.0km·h−1)
Running (14.0–17.0km·h−1)
Slow running (11.0–14.0)
Stand, walk, jog
(0–11km·h−1)
↑ TD, slow running, run-
ning, fast running in match
3 vs. match 1
↓ standing, walking, jogging
in matches 2 and 3 vs.
match 1
60.0
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262 R.Julian et al.
Table 2 (continued)
References Participants Match data collection
methods
Fixture congestion scenario In-match outcome measures Main findings Quality
score (%)
Djaoui etal. [24] 16 international players from
the same French club (FB
n = 2, CD n = 3, CDM
n = 3, WM n = 3, CAM,
n = 2, FWD n = 3)
Semi-automated video
system (Amisco)
4 periods of 1 vs. 2 match
weekly microcylces
(period 1, 6 matches
in 21days; period 2, 7
matches in 21days; period
3, 7 matches in 22days;
period 4, 6 matches in
24days)
TD and distance covered in
each locomotive activity
Maximal (> 27.0km·h−1)
Sub-maximal (> 25.0–
27.0km·h−1)
VHIR (> 23.0–25.0km·h−1)
HIR (> 21.0–23.0km·h−1)
Sustained cruising (> 18.0–
21.0km·h−1)
Light (< 12km·h−1)
No global effect
↓ light intensity for CB and
CDM during 1 match
microcycles
60.0
Folgado etal. [16] 23 professional players from
the same English club
Semi-automated video
system (ProZone)
3 successive matches with
3days between matches
TD and distance covered in
each locomotive activity
VHIR (> 19.8km·h−1)
HIR (14.4–19.7km·h−1)
MIR (3.6–14.3km·h−1)
LIR (0.0–3.5km·h−1)
No effect 60.0
Mohr etal. [38] 20 players playing in the
top three tiers of soccer
(country and league not
specified)
GPS devices (GPSport
15Hz)
3 successive matches
(3days between matches
1 and 2; 4days between
matches 2 and 3)
TD and distance covered
in HI and sprinting, peak
5-min distance, peak
speed, frequency of ACC,
DEC and impacts
Sprint (> 22km·h−1)
HI (16–22km·h−1)
↓ HI in match 2 compared to
matches 1 and 3
↑ impacts in match 3 com-
pared to matches 1 and 2
81.3
Soroka and Lago-Penas [44] 301 elite players playing in
the 2014 World Cup (FB
n = 59, CD n = 57, CM
n = 61, WM n = 56, FWD
n = 68)
Semi-automated video
system (ProZone)
3 successive matches
(4days between matches 1
and 2 and, 2 and 3)
TD and distance covered in
each locomotive activity
Sprint (> 23.1km·h−1)
HIR (19.1–23.0km·h−1)
MIR (14.1–19.0km·h−1)
Walking and light-intensity
(0.0–14.0km·h−1)
↑ TD in match 3 compared
to matches 1 and 2
↑ walking and light intensity
and MIR in 1st half of
match 3 compared to
matches 1 and 2
↑ TD and HIR in match 1
compared to match 3 for
CM
↑ TD in match 2 compared
to match 1, ↑ MIR in
match 3 compared to
match 2, ↑ HIR in match
3 compared to matches 1
and 2 for WM
↑TD in match 3 compared
to match 2 for FWD
85.7
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263
Fixture Congestion and Performance in Soccer
Table 2 (continued)
References Participants Match data collection
methods
Fixture congestion scenario In-match outcome measures Main findings Quality
score (%)
Penedo-Jamardo etal. [15] 4491 player observations
across
18 German clubs (FB
n = 1079, CD n = 1141,
CM n = 1118, WM
n = 593, FWD n = 560)
Semi-automatic optical
tracking system (VIS-
TRACK)
306 matches with com-
parisons between
recovery cycles < 4, 4–5
and > 5days between
matches during early, mid
and late season macro-
cycles. Plus, microcy-
cles with 3- and 4-days
recovery
TD, frequency of fast runs
and sprints
Sprint (> 4.0m.s−1 for ≥ 2s
and > 6.3m.s−1 for ≥ 1s)
Fast runs (> 5.0m.s−1
for ≥ 1s)
↓ TD with recovery
cycle < 4days
Main effects for positional
role and period of the
season
↓ TD with recovery
cycle < 4days compared
to 4–5 and > 5days
recovery for CD, during
the mid and late season,
respectively
↓ TD with recovery
cycle < 4days compared
to > 5days regardless of
macrocycle and ↓ fast runs
during the late season for
FB. FB also covered less
distance 3days compared
to 4days in mid-and late-
season
↓ TD, HIR and sprints
when < 4days dur ing mid-
season for WM
85.7
Palucci Vieira etal. [26] 40 professional players from
the same Brazilian club
GPS devices (QSTARZ
1Hz)
1 match vs. 2 successive
matches
TD, frequency of HI,
maximal sprinting speed,
average speed
HI (≥ 15km·h−1)
↓ HI for forwards during 2
successive matches
All other parameters no
effect
92.9
Morgans etal. [37] 21 professional players from
the same English club
GPS devices
(STATSports)
5 successive matches in
15days (7 matches in
32days total)
TD, HIR, sprinting distance
Sprint (< 25.0km·h−1)
HIR (> 19.8km·h−1)
No effect 73.3
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264 R.Julian et al.
Table 2 (continued)
References Participants Match data collection
methods
Fixture congestion scenario In-match outcome measures Main findings Quality
score (%)
Jones etal. [25] 37 professional outfield
players from the same
English club
GPS devices (Catapult
10Hz)
79 matches with compari-
sons between three con-
gestion scenarios: 1 match
vs. 2 matches (< 4days
recovery) vs. 3 matches
(< 4days recovery) per
week
TD, distance covered in
each locomotive activ-
ity, 3D PlayerLoad™ per
distance covered (au·m−1),
PlayerLoad™ anterior–
posterior per distance
covered (au·m−1), Player-
Load™ medio-lateral per
distance covered (au·m−1),
PlayerLoad™ vertical per
distance covered (au·m−1)
Further measured in 15-min
epochs
LIR (< 4.0m·s−1)
MIR (4.0–5.5m·s−1)
HIR (5.5–7.0m·s−1)
Sprint (> 7.0m·s−1)
↑ TD in minutes 0–15 and
15–30 during 2 matches
vs. 3 matches per week
↑ TD in the 15–30-min
period in 1 match vs. 3
matches per week
↑ TD during the 30–45-min
period in 2 matches vs. 1
match per week
↓ TD in the 75–90-min
period in 3 matches vs.
both 1 and 2 matches per
week
↑ LIR in the 40–45-min
period of 2 matches vs. 1
match per week
↓ LIR in the 75- to 90-min
period in 3 matches
vs. both 1 match and 2
matches per week
↑MIR during the 0- to
15-min period of 2
matches vs. 3 matches per
week
↑ Sprint distance in the
30- to 45-min epoch in
3 matches vs. 1 and 3
matches per week
93.3
ACC accelerations, CAM center attacking midfielder, CB center back, CDM central defensive midfielder, CM center midfielder, DEC decelerations, FB full back, FWD forward, HI high inten-
sity, HIR high intensity/speed running, LIR low intensity/speed running, MIR moderate intensity/speed running, TD total distance, TD/min total distance per minute, VHIR very high intensity
running, WM wide midfielder
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265
Fixture Congestion and Performance in Soccer
studies should look to use similar methods to measure physi-
cal performance, and use consistent movement velocity
thresholds when measuring distances covered at different
movement intensities. Metrics such as high-intensity dis-
tance covered, sprints, accelerations and decelerations, are
likely to be of greater interest to practitioners and coaches
and, therefore, these measures should be homogeneous
between studies where possible.
The low number of articles eligible for the meta-analysis
is reflective of an inconsistent methodological approach
between studies in this area. We were unable to analyse any
other variables, including arguably more relevant outcome
measures, such as high-intensity running, sprinting, etc.,
as studies employed different thresholds when categoris-
ing different movements. Furthermore, some studies did
not directly compare a congested period to a non-congested
period in the same group of players and instead compared the
first match in a congested schedule to subsequent matches.
This exposed the analysis to the inherent variability evident
in professional soccer match-play, due to the stochastic,
dynamic nature of the sport [29, 30]. However, that is not to
say this same variability may not influence the comparison
between a congested and non-congested period, which is
dependent on the sample size of the individual study. We
identified using Duval and Tweedie’s Trim and Fill method
that there was one missing article on the right side of the
plot. Thus, when accounting for this missing article, there
was a significant effect of fixture congestion on total distance
covered (p = 0.045) but still with a trivial effect size [pooled
SMD = 0.16 (0.00, 0.32)]. This may be due to authors not
publishing data that suggest players cover greater distance
in a congested fixture period. Nonetheless, we stress that
this finding should be interpreted with caution as tests for
funnel plot asymmetry tend to only have power to detect
true effects when there are ≥ 10 or more articles included in
a meta-analysis [22].
Fig. 2 Forest plot of studies
meeting inclusion criteria. CI
confidence interval, RE model
random effects model
Fig. 3 Funnel plot (standard
error vs. Hedges’ G) for studies
meeting inclusion criteria
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266 R.Julian et al.
5.2 Physical Performance
As highlighted in Sect.5.1, there appears to have no negative
effect of fixture congestion on the total running distance cov-
ered by male professional players. However, total distance
covered is but one measure of physical performance, and
whilst arguably a less relevant one than other measures, is
commonly used by practitioners [31]. Notably, the majority
of studies included in this systematic review also measured
a number of other physical performance metrics in conjunc-
tion with total distance covered. However, not only were
there methodological inconsistencies between studies for the
movement velocity thresholds employed, but there were also
differences in how authors compared a congested period to
a non-congested period.
Some studies have attempted to assess the physical
response to three successive elite soccer matches per-
formed over a 6- to 7-day period [1, 16, 32]. These stud-
ies all reported no differences in the total distance covered
and distances covered at high intensities (HID) across the
successive matches. Folgado etal. [16] also identified no
differences in the distances covered in all locomotion cat-
egories across the successive matches. However, Odetoyinbo
etal. [32] did identify that distance covered and duration
of walking, HID whilst in possession of the ball, and HID
when the ball was out of play were all significantly lower
in the third match compared to the first. These data suggest
total distance covered and overall HID are not significantly
impaired when three matches are played over 7days; how-
ever, when three matches are performed over 6days, play-
ers may potentially alter their activity profiles in an attempt
to reduce the volume of activity performed [32]. However,
and critically, it is not known if these observed differences
are a result of contextual factors or reduced physical capac-
ity. In contrast to these investigations, Andrzejewski etal.
[14] observed significantly higher total distance covered and
distances covered in different speed threshold categories up
to 21km·h−1 in the third match of three matches in 7days’
microcycle, with no changes in the number of sprints or
distance covered ≥ 21km·h−1. However, the data were from
11 players playing for the same club, with no indication from
the authors on the quality of the opposition faced in each
match, or the score line. It is possible that the third match
was against superior opposition and/or a closer match score-
wise compared to the other two matches, which may have
influenced the physical response [33, 34].
A strength of Odetoyinbo etal. [32] is that the data col-
lected were from 16 players playing for four different teams,
whereas the players from Folgado etal. [16] and Carling
and Dupont [1] were from the same team (in the English
Premier League and French Ligue 1, respectively). The first
two matches in the study conducted by Odetoyinbo etal.
[32] were interspersed with 48h recovery, whereas each
of the matches in Carling etal. and Folgado etal. [1, 16]
was interspersed by 72h of recovery. Therefore, it seems
feasible that the reduced recovery time associated with the
first two matches in Odetoyinbo etal. [32] may have elicited
the observed fatigue response identified in the third match.
Other authors have compared the physical outputs of players
when two matches were played with 3days’ rest in between
[35]. There was no difference between matches played in
close proximity by elite Spanish players [35]. However, this
study scored 40% on the quality assessment tool (low qual-
ity; Table2) and did not report how many matches were
included in the study, or any contextual factors, such as
match location, quality of opposition, and tactical approach.
Furthermore, Dupont etal. [36] observed no differences in
physical performance when elite French players played two
matches in a week. However, these authors did not report
any data within their manuscript, making comparisons to
other studies difficult.
Studies conducted by Carling etal. [2] and Dellal etal.
[3] assessed the physical response to a period of prolonged
fixture congestion (six–eight matches performed over
18–26days) in elite French soccer. Dellal etal. [3] identi-
fied no significant differences in any of the physical perfor-
mance measures recorded across the six congested matches;
however, any statistically significant differences between
individual matches may have been missed by a lack of an
overall main effect. Although the authors compared the data
collected during the periods of fixture congestion to that
identified during a non-congested schedule, this was only for
injuries not physical performance. Therefore, it would have
been pertinent for the authors not only to compare physi-
cal performance within a congested period (e.g., match 1
compared to match 6), but also compare to a non-congested
period in the same group of players. In contrast, Carling
etal. [2] identified that distances covered at low intensities
and total distance covered differed between some matches
in an eight-match congested schedule. However, this was
not systematic, with one match in particular (match 4) being
significantly different to five other matches, and matches 7
and 8 being different to two matches and one match, respec-
tively. However, when compared with periods of no con-
gestion (although the authors did not define what this was),
there was no difference in any of the physical performance
metrics measured, indicating that this group of elite French
players was able to maintain physical output during a con-
gested schedule. However, it should be noted that the authors
did not report how many of the players who were included in
the congested analysis played in the non-congested matches,
including the number of minutes played. Therefore, caution
should be taken when interpreting the findings of this study.
Morgans etal. [37] followed a similar methodology,
assessing physical performance changes during seven
matches in 29-day microcycle in a group of English
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267
Fixture Congestion and Performance in Soccer
Premier League players. Whilst the authors reported the
overall sample size (n = 21), they did not report how many
players played in all seven matches, or the percentage that
played > 75min. Therefore, the findings may have been
affected by substitutions and players not starting or playing
in all of the matches.
Mohr etal. [38] took a different approach to most of the
other studies reviewed, as instead of using data from pro-
fessional soccer match-play, they created three matches in
one-week scenario in a group of competitive male players
(n = 40; had to have played in the top three divisions of their
country’s league system in the past 5years; the country is
not specified). The authors observed a 7–14% decrement in
high-intensity distance covered in the second match com-
pared to the first (played 3days prior) and third (played
4days after) matches. No other differences were observed
between matches, and this difference in high-intensity dis-
tance is lower than the coefficient of variation previously
reported for this measure [29, 30] and, therefore, may be
reflective of match-to-match variability as opposed to resid-
ual fatigue from the first match. Although beyond the scope
of this systematic review, these authors showed that players
were unable to fully recover physical function between the
three matches, and that there was an increase in muscle sore-
ness and muscular inflammation, particularly following the
second match. This was less pronounced following match
three, which may demonstrate that there is a significant
effect on performance between 3 and 4days of recovery.
All studies included in the meta-analysis also reported
data from other measures of physical performance, not
just total distance covered. Both Folgado etal. [16] and
Lago-Peñas etal. [23] observed no changes in distance
covered at various velocity ranges between a congested
and non-congested period. It should be noted that the six
matches in Folgado etal. [16] were all played (and won)
at home against lower level opposition, which may have
influenced the observed response [33]. Similarly, Djaoui
etal. [24] observed no differences in distance covered at
speeds ≥ 18km·h−1 between congested and non-congested
periods, although they showed central defenders cover more
low-intensity (< 12km·h−1) distance during congested peri-
ods. It is well established that position-specific differences
in physical performance exist during soccer match-play
[34, 39–41] and, as such, match-play analyses should be
considered in relation to player positions. These positional
differences also exist during periods of fixture congestion
[15, 24, 42]. In support of this, Carling etal. [43] identified
that defensive players were more likely to complete > 75min
of match-play compared to other positions, thus exposing
defensive players to congested schedules. Whilst low-inten-
sity distance covered was significantly increased in central
defenders in Djaoui etal. [24], the distance covered by cen-
tral defenders at higher velocities, whilst not statistically
different, was lower in the congested periods. Furthermore,
Penedo-Jamardo etal. [15] reported significantly lower dis-
tances covered and number of fast runs (speed of ≥ 5.0m‧s−2
for ≥ 1s) performed by central defenders during matches
preceded by < 4days recovery from a previous match, com-
pared to > 5days.
Therefore, this may indicate a change in movement inten-
sity by central defenders during fixture congestion, either by
a conscious pacing strategy, or due to match-related fatigue.
However, Jones etal., Palucci Vieira etal. and Soroka and
Lago-Penas [25, 26, 44] did not observe any changes in cen-
tral defensive players’ physical performance in congested
periods. In professional Brazilian football players, fixture
congestion has differential effects on physical performance
[26]. Palucci Vieira etal. [26] observed position, formation,
match location and match outcome-specific effects during
congested periods (defined as two matches a week vs. one
match a week) on some physical performance parameters.
In particular, they showed that forwards perform less high-
intensity activity in congested periods and there is less high-
intensity activity in drawn matches and when using a 4-3-3
formation as opposed to 4-4-2. Furthermore, total distance
and average velocities were reduced during congested fix-
tures played away compared to at home. However, it must
be noted that all effect sizes for these reported differences
were trivial or small [26].
Soroka and Lago-Penas [44] analysed players who com-
pleted 90min of three matches each separated by 4days of
rest in the group stage of the 2014 FIFA World Cup. They
found that players covered more distance in the third match
than the second match (and the first match compared to the
second match), with concomitant increases in the amount
of light-intensity and moderate-intensity running in the first
half of the third match compared to both the first and sec-
ond matches. This may be reflective of the importance of
the final group stage match, although no differences were
observed for high-intensity distance or number of sprints.
These authors also observed position-specific changes in
physical performance during the three group stage matches,
with central midfielders covering less total distance and
high-intensity running distance during the third match com-
pared to first match, whereas wide midfielders and forwards
covered more total distance and wide midfielders also cov-
ered more distance at moderate and high intensities. Without
contextual data, such as the formations employed by teams
in the final group stage matches, or the permutations regard-
ing qualification to the knockout stage, it is difficult to fully
interpret these findings.
Penedo-Jamardo et al. [15] observed significantly
lower distance covered by full-backs and wide midfielders
(dependent on season phase) when matches were separated
by < 4days compared to ≥ 5days. Furthermore, these authors
observed reduced total distance covered in the early- and
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268 R.Julian et al.
mid-season phase of the 2011/12 German Bundesliga sea-
son when there were < 4days of recovery between matches
compared to > 5days recovery, irrespective of playing posi-
tion. With the high number of matches (n = 306) and player
observations (n = 4491) in this study, the findings may indi-
cate that less than 4days of recovery between matches are
insufficient for players to be able to maintain some aspects of
physical performance (see Table2). However, the number of
fast runs and sprints was not affected by fixture congestion.
The findings of this study are in contrast to the findings of
the meta-analysis (Sect.4.2), and indicate fixture congestion
does indeed have a negative impact on performance.
Whilst Jones etal. [25] did not observe any differences in
physical performance during fixture congestion when play-
ers were separated by position, they did observe reductions
in total, low-intensity, and moderate-intensity distance cov-
ered in specific 15-min epochs in the final match of three
matches in a week’s microcycle compared to when matches
were played in one match per week or two matches per
week microcycle. This is particularly relevant as when they
compared whole match averages, there were no differences
between matches in a congested vs. non-congested period.
The findings from Penedo-Jamardo etal. and Jones etal.
[15, 25] seem to suggest that reductions in low-intensity dis-
tance covered when there is limited recovery time between
matches may be due to conscious or unconscious pacing
strategies employed by the players to preserve their ability
to perform high-intensity movements [25, 45].
5.3 Technical andTactical Performance
In comparison to the larger body of literature that has inves-
tigated changes in physical performance during periods of
fixture congestion, there is a paucity of research that has
examined changes in technical (i.e., skill) and tactical per-
formance. Within our searches, we identified five published
journal articles that have analysed the impact of fixture
congestion on technical (four) or tactical (one) performance
(Table3). Technical performance is well maintained dur-
ing periods of fixture congestion, with no changes in per-
formance during a microcycle when players are exposed to
three matches in 7days or less [14], or when six consecu-
tive matches are played with 3days’ rest in between [1].
The findings of these two studies should be interpreted with
caution, as the matches may have been influenced by con-
textual factors (e.g., match location, quality of opposition,
and score line) and the small, homogenous sample sizes.
Indeed, Andrzejewski etal. [14] investigated 11 players from
the same Polish Ekstralasa (highest professional division
in Poland) club, and Carling & Dupont [1] assessed seven
midfield players who either played in one (four players) or
two (three players) sequences of three matches in 7days
during 1month of the French Ligue 1 season. However,
two studies with larger sample sizes and a higher number of
instances of fixture congestion have also identified no effect
of fixture congestion on technical performance [3, 15]. Nev-
ertheless, Penedo-Jamardo [15] only investigated the effect
of time between matches on one variable (pass accuracy),
with no indication of how this was measured, including
the validity and reliability of the measure. Furthermore, in
the three instances of fixture congestion analysed in Dellal
etal. [3], only five or six players’ technical performance was
assessed in each instance, with all players representing the
same French Ligue 1 club. Again, performances may have
been influenced by contextual variables and be reflective of
this club only (as acknowledged by the authors). As such,
whilst the current evidence suggests that fixture congestion
has no effect on technical performance, further investiga-
tions utilising data from multiple clubs with an analysis of
position-specific differences and a broader range of more
meaningful measures (e.g., expected goals for and against,
expected assists, pass/cross accuracy in the final third of the
pitch, and loss or gain of possession due to interceptions). As
technical performance between matches has been shown to
be more variable than physical performance [46], large data-
sets are required to ensure any differences during congested
schedules are meaningful and reflective of actual changes.
Only one published research investigation has assessed
changes in tactical performance during a period of fixture
congestion. Folgado etal. [16] assessed dyadic synchronisa-
tion of pairs of players in an English Premier League team
during a period of congested (three matches with 3days
recovery between each match) vs. non-congested fixtures
(three matches with 6 or more days recovery between each
match). The authors observed reduced synchronisation
between dyads [in particular between wide players (i.e.,
full-backs and wingers) and other positions] during the
congested period vs. the non-congested period at low/mod-
erate movement intensities (0.0–14.3 km·h−1), but not at
high/very high movement intensities (> 14.4 km·h−1). They
postulated that the reduced synchronisation at low/moder-
ate intensities may have been due to mental fatigue, and
players deliberately adopting pacing strategies to preserve
energy [17, 45]. Nevertheless, these changes in synchroni-
sation during a congested period may also be due to the
lower amount of available time to train between matches,
with likely greater emphasis placed on rest and regeneration
protocols. With less time to train, there is less opportunity
for teams to train together and improve tactical behaviours.
It should be noted that all matches were played (and won)
against lower level opposition, which may have influenced
the observed response (e.g., players ‘switching off’ when
leading or playing against perceived lower level opposition).
Nonetheless, the de-synchronisation between specific dyads
may expose teams to counterattacks, where the suboptimal
spatial and temporal relationship between players allows
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269
Fixture Congestion and Performance in Soccer
Table 3 Summary of studies investigating technical and/or tactical performance during periods of fixture congestion
↓ and ↑ denote significant reductions and significant increases in measures respectively
References Participants Match data collection
methods
Fixture congestion scenario Outcome measures Main findings Quality
score (%)
Carling and Dupont [1] 7 professional midfield
(central and wide) play-
ers playing for the same
French Ligue 1 club
Semi-automated video sys-
tem (Amisco)
3 successive matches in
7days or less
Total number of passes,
percentage of completed
or uncompleted passes,
number of ball possessions
and possessions gained or
lost, number of touches
per possession, number of
duels and percentage of
duels won or lost
No effect 71.4
Andrzejewski etal. [10] 11 professional players
playing for the same Polish
Ekstralasa (highest tier)
club
Semi-automated video sys-
tem (Amisco)
1 vs 2 match weekly micro-
cycles
Total individual ball posses-
sion, contacts with the ball,
passes, ground challenges
and aerial challenges
No effect 60.0
Dellal etal. [3] 16 professional outfield play-
ers from the same French
Ligue 1 club
Semi-automated video sys-
tem (Amisco)
3 instances of six consecu-
tive matches separated by
3days. Five players in the
first instance, six in the
second instance and five in
the third instance
Percentage of successful
passes, number of balls
lost, total number of
touches per possession and
percentage of duels won
No effect 93.3
Folgado etal. [12] 23 professional outfield play-
ers from the same English
Premier League club
Semi-automated video sys-
tem (ProZone) and Hilbert
Transform
3 successive matches with
3days between matches
Space–time synchronisation
between pairs of players
and player displacement
on horizontal and vertical
axes
↓ synchronisation dur-
ing periods of fixture
congestion at low and
moderate movement
intensities (0–3.5 km·h−1
and 3.6–14.3 km·h−1). No
differences at high move-
ment intensities (> 14.4
km·h−1)
60.0
Penedo-Jamardo etal. [11] 4491 player observations
across
18 German clubs (Bundes-
liga) (fullbacks n = 1079,
central defenders n = 1141,
central midfielders
n = 1118, wide midfielders
n = 593, attackers n = 560)
Semi-automatic optical
tracking system (VIS-
TRACK)
306 matches with com-
parisons between
recovery cycles < 4, 4–5
and > 5days between
matches during early, mid
and late season macrocy-
cles. Plus microcycles with
3 and 4days recovery
Percentage of successful
passes
No effect 85.7
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270 R.Julian et al.
opponents opportunities to attack, particularly through wide
areas. However, further research on tactical performance
changes during fixture congestion is required, with larger
sample sizes (e.g., multiple teams) and a greater number of
instances of congestion.
5.4 Future Research Directions
andRecommendations
Whilst the journal articles discussed provide somewhat of
an overview of the effect of fixture congestion on perfor-
mance, there is scope for future research to improve methods
employed and expand the currently available data. Studies
that do not compare a congested period to a non-congested
period in the same group of players should be avoided, as
comparing within a single congested microcycle only elicits
a high risk of bias due to match-to-match variability, and
leaves the measured variables open to contextual factors.
Furthermore, to allow for future meta-analyses on other per-
formance variables, such as high-intensity running, sprint
speed, and the number of accelerations and decelerations,
studies should aim to employ the same threshold definitions
to allow for data to be accurately analysed and compared
across studies, as well as report temporal changes across
matches (e.g., across 15-min periods; [25]). Additionally,
and in line with a call for more transparent research practices
[47], we encourage authors to make their data available for
analysis (whilst accounting for participant anonymity) on
platforms such as the Open Science Framework (osf.io), as
we have done in this article.
Assessing the types of movement performed would also
provide a clearer picture of the effect of fixture congestion.
For example, are players changing how much they press
the opposition during congested periods, and how much of
their movement contributes to overall attacking sequences?
A recent mixed-method study [48] used a combination of
network analysis and qualitative content analysis to assess
the attacking behaviour of AS Monaco players during the
2016/17 French Ligue 1 season. Through interviews with
the head coach and performance analyst, the authors were
able to identify why certain players performed the way they
did during the season. This type of collaboration within the
context of fixture congestion would provide a robust over-
view of how performance changes during congestion, and
how coaches potentially manipulate their tactics in the face
of a high number of matches in a short duration [49].
The most recent paper assessing the frequency of expo-
sure to fixture congestion was published in 2015 and only
analysed players from one club [13]. In the context of con-
temporary fixture scheduling and statistical power, this arti-
cle requires an update, with more than one club’s exposure
to fixture congestion assessed. Furthermore, no studies
have investigated the impact of fixture congestion in female
soccer players; whilst this may not be a particularly preva-
lent issue during domestic competition schedules, the FIFA
Women’s World Cup and the UEFA Women’s Championship
may expose female players to congested periods that they
are not accustomed to. Therefore, assessing the impact of
fixture congestion on female players is required, especially
as physical performance and markers of inflammation have
been shown to change negatively following match-play in
elite female soccer players [50, 51].
As players cover more high-intensity distance when
playing superior opposition [52], if a team is to play three
matches in 6–7 days all against better-ranked teams, there
may be an exacerbated fatigue response in the recovery
phase as players will have a higher physical output. This
may then influence potential injury risk. Therefore, practi-
tioners should aim to assess recovery daily during periods
of fixture congestion to assess which players may be at high-
est risk of reduced performance and injury. Additionally,
matches that require extra-time are typically played during
congested periods (e.g., on a midweek evening between two
weekend league matches, or during the knockout phase of
international tournaments). Case studies have shown that
ET may have an additional negative impact on recovery [53,
54]; however, studies in controlled environments (i.e., using
laboratory-based simulations) are required.
In support of Page etal. [55], laboratory-based soccer
simulations may also help identify the mechanisms that
potentially explain reductions in performance during fixture
congestion. Likewise, the use of protocols, such as the Inter-
mittent Soccer Performance Test [56], that are performed
on non-motorised treadmills and, therefore, can identify
changes in running distance/speed could further enhance
our understanding of congested match schedules. Mohr etal.
[38] assessed the impact of three matches in 1week and
were able to measure recovery every day during that period.
However, the design was susceptible to inherent match-to-
match variation and, therefore, the use of validated and reli-
able simulations can increase the robustness of the data [55,
57]. Moreover, studies can then also use such designs to
investigate the effectiveness of interventions that accelerate
recovery and improve performance during congested periods
[18].
5.5 Practical Applications
Coaches and practitioners should be aware that congested
fixture periods may have an impact on the physical, tech-
nical and tactical performance of players. Whilst tactical
performance has only been assessed in one study, there was
reduced synchronisation between players, which could neg-
atively impact the tactical strategy implemented. Further-
more, during fixture congestion, there is less high-intensity
activity when employing a 4-3-3 formation compared to a
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
271
Fixture Congestion and Performance in Soccer
4-4-2 formation [26]. Therefore, coaches may want to iden-
tify systems and players that are particularly susceptible to
fixture congestion, and adapt their strategies accordingly.
For example, as Folgado etal. [16] identified increased sus-
ceptibly to counterattacks in wide areas, coaches may want
to ensure that defensive midfield players are able to cover
and prevent counterattacks in these areas when their team
is attacking. However, it should be noted that time to work
on tactical behaviours is limited during congested periods,
and players may not be able to process complex informa-
tion in close proximity to matches due to match-induced
mental fatigue [58]. The data reported in this review sug-
gest that central defenders in particular are the positional
group most exposed to periods of fixture congestion, with
attacking players the least exposed due to substitutions and
rotation. Whilst central defenders typically have the lowest
external workload during matches [34, 40, 59], practitioners
should ensure that recovery protocols for these players are
optimised and adapted to reflect their greater exposure to
match-play compared to some of their teammates. Neverthe-
less, regardless of playing position, if a player is exposed to
repeated match-play during congestion, then it is likely that
they will have an increased risk of injury [60] and modu-
late the intensity of their movements, potentially impacting
overall performance.
6 Conclusion
The results of the meta-analysis suggest that total distance
covered is not impacted by fixture congestion. However, no
other variables were assessed quantitatively due to methodo-
logical differences between studies, and there was a moder-
ate level of heterogeneity between the five included stud-
ies. Nevertheless, some studies have identified a negative
effect of fixture congestion on variables, such as low- and
moderate-intensity distance covered; this may suggest that
players consciously employ pacing strategies to maintain
high-intensity actions. Furthermore, this may be position-
specific and related to the time in a match. Whilst physical
performance is crucial to overall success in soccer, technical
and tactical performance are perhaps even more important,
and there is a lack of data on these two elements of per-
formance during fixture congestion. In conclusion, fixture
congestion is a very contemporary issue, one that players are
particularly conscious of [4]. With ever increasing numbers
of competitive matches scheduled, more research needs to
be conducted using consistent, sensitive measures of per-
formance, including physical, technical and tactical aspects.
Declarations
Funding No funding was obtained to support the current manuscript.
Conflict of interest Ross Julian, Richard M Page and Liam D Harper
declare no conflicts of interest.
Ethics approval Not applicable.
Consent to participate Not applicable.
Consent for publication Not applicable.
Availability of data and material Data used in the meta-analysis can be
found publicly at the following link: https ://osf.io/2q6aj /
Code availability Not applicable.
Author contributions All authors (RJ, RMP, LDH) made substantial
contributions to the conception of this work, the methods employed,
and the analysis and interpretation of the data. All authors have drafted
and revised the manuscript and approve the version to be published. All
authors agree to be accountable for all aspects of the work.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
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need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.
References
1. Carling C, Dupont G. Are declines in physical performance asso-
ciated with a reduction in skill-related performance during profes-
sional soccer match-play? J Sports Sci. 2011;29:63–71.
2. Carling C, Le Gall F, Dupont G. Are physical performance and
injury risk in a professional soccer team in match-play affected
over a prolonged period of fixture congestion? Int J Sports Med.
2012;33:36–42.
3. Dellal A, Lago-Peñas C, Rey E, Chamari K, Orhant E. The effects
of a congested fixture period on physical performance, technical
activity and injury rate during matches in a professional soccer
team. Br J Sports Med. 2015;49:390–4.
4. Gouttebarge V, Brink MS, Kerkhoffs GMMJ. The perceptions of
elite professional footballers on the International Match Calendar:
a cross-sectional study. Sci Med Footb. 2019;3:339–42.
5. Nedelec M, McCall A, Carling C, Legall F, Berthoin S, Dupont
G. Part I—post-match fatigue and time course of recovery. Sports
Med. 2012;42(12):997–1015.
6. Ispirlidis I, Fatouros IG, Jamurtas AZ, Nikolaidis MG, Michailidis
I, Douroudos I, etal. Time-course of changes in inflammatory and
performance responses following a Soccer game. Clin J Sport
Med. 2008;18:9.
7. Rampinini E, Bosio A, Ferraresi I, Petruolo A, Morelli A, Sassi
A. Match-related fatigue in soccer players. Med Sci Sports Exerc.
2011;43:2161–70.
8. Magalhães J, Rebelo A, Oliveira E, Silva JR, Marques F, Ascen-
são A. Impact of Loughborough Intermittent Shuttle Test versus
soccer match on physiological, biochemical and neuromuscular
parameters. Eur J Appl Physiol. 2010;108:39–48.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
272 R.Julian et al.
9. Brownstein CG, Dent JP, Parker P, Hicks KM, Howatson G,
Goodall S, etal. Etiology and recovery of neuromuscular
fatigue following competitive soccer match-play. Front Physiol.
2017;8:831.
10. Abbott W, Brownlee TE, Harper LD, Naughton RJ, Clifford T.
The independent effects of match location, match result and the
quality of opposition on subjective wellbeing in under 23 soccer
players: a case study. Res Sports Med. 2018;26:262–75.
11. Fowler P, Duffield R, Vaile J. Effects of simulated domestic and
international air travel on sleep, performance, and recovery for
team sports: travel and recovery of physical performance. Scand
J Med Sci Sports. 2015;25:441–51.
12. Fullagar HHK, Skorski S, Duffield R, Julian R, Bartlett J, Meyer
T. Impaired sleep and recovery after night matches in elite football
players. J Sports Sci. 2016;34:1333–9.
13. Carling C, McCall A, Le Gall F, Dupont G. What is the extent of
exposure to periods of match congestion in professional soccer
players? J Sports Sci. 2015;33:2116–244.
14. Andrzejewski M, Konarski MJ, Chmura J, Pluta B. Changes in
the activity profiles of soccer players over a three-match training
micro cycle. Int J Perform Anal Sport. 2014;14:814–28.
15. Penedo-Jamardo E, Rey E, Padrón-Cabo A, Kalén A. The impact
of different recovery times between matches on physical and tech-
nical performance according to playing positions. Int J Perform
Anal Sport. 2017;17:271–82.
16. Folgado H, Duarte R, Marques P, Sampaio J. The effects of con-
gested fixtures period on tactical and physical performance in elite
football. J Sports Sci. 2015;33:1238–47.
17. Smith MR, Thompson C, Marcora SM, Skorski S, Meyer T,
Coutts AJ. Mental fatigue and soccer: current knowledge and
future directions. Sports Med. 2018;48:1525–32.
18. Carling C, Gregson W, McCall A, Moreira A, Wong DP, Brad-
ley PS. Match running performance during fixture congestion in
elite soccer: research issues and future directions. Sports Med.
2015;45:605–13.
19. Sarmento H, Clemente FM, Araújo D, Davids K, McRobert A,
Figueiredo A. What performance analysts need to know about
research trends in association football (2012–2016): a systematic
review. Sports Med. 2018;48:799–836.
20. Law M, Stewart D, Pollock N, Letts L, Bosch J, Westmorland M.
Critical review form-quantitative studies. Hamilton: Macmaster
University; 1998.
21. Viechtbauer W. Conducting meta-analyses in R with the metafor
Package. J Stat Soft [Internet]. 2010;36. https ://www .js tat soft.org/
v36/i03/. Accessed 17 Feb 2020.
22. Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M,
etal. Cochrane handbook for systematic reviews of interventions
version 6.0 (updated July 2019). [Internet]. Cochrane; 2019. www.
train ing.cochr ane.org/handb ook. Accessed Oct 2019.
23. Lago-Peñas C, Rey E, Lago-Ballesteros J, Casáis L, Domínguez
E. The influence of a congested calendar on physical performance
in Elite Soccer. J Strength Cond Res. 2011;25:2111–7.
24. Djaoui L, Wong DP, Pialoux V, Hautier C, Da Silva CD, Chamari
K, etal. Physical activity during a prolonged congested period in
a top-class European Football Team. Asian J Sports Med [Inter-
net]. 2013;5. https ://asjsm .com/en/artic les/73546 .html. Accessed
17 Feb 2020.
25. Jones RN, Greig M, Mawéné Y, Barrow J, Page RM. The influ-
ence of short-term fixture congestion on position specific match
running performance and external loading patterns in English
professional soccer. J Sports Sci. 2019;37:1338–466.
26. Palucci Vieira LH, Aquino R, Lago-Peñas C, Munhoz Martins
GH, Puggina EF, Barbieri FA. Running performance in Brazilian
Professional Football Players during a congested match schedule.
J Strength Cond Res. 2018;32:313–25.
27. Harley JA, Lovell RJ, Barnes CA, Portas MD, Weston M. The
interchangeability of global positioning system and semiauto-
mated video-based performance data during Elite Soccer Match
Play. J Strength Cond Res. 2011;25:2334–6.
28. Buchheit M, Allen A, Poon TK, Modonutti M, Gregson W, Di
Salvo V. Integrating different tracking systems in football: mul-
tiple camera semi-automatic system, local position measurement
and GPS technologies. J Sports Sci. 2014;32:1844–57.
29. Gregson W, Drust B, Atkinson G, Salvo V. Match-to-match vari-
ability of high-speed activities in Premier League Soccer. Int J
Sports Med. 2010;31:237–42.
30. Carling C, Bradley P, McCall A, Dupont G. Match-to-match
variability in high-speed running activity in a professional soccer
team. J Sports Sci. 2016;34:2215–23.
31. Akenhead R, Nassis GP. Training load and player monitoring in
high-level football: current practice and perceptions. Int J Sports
Physiol Perform. 2016;11:587–93.
32. Odetoyinbo K, Wooster B, Lane A. The effect of a succession of
matches on the activity profiles of professional soccer players.
Science and Football VI. London: Routledge; 2007. p. 105–108.
33. Folgado H, Duarte R, Fernandes O, Sampaio J. Competing with
lower level opponents decreases intra-team movement synchro-
nization and time-motion demands during pre-season soccer
matches. PLoS One. 2014;9:e97145.
34. Rampinini E, Coutts A, Castagna C, Sassi R, Impellizzeri F. Vari-
ation in top level soccer match performance. Int J Sports Med.
2007;28:1018–24.
35. Rey E, Lago-Peñas C, Lago-Ballesteros J, Casais L, Dellal A. The
effect of a congested fixture period on the activity of elite soccer
players. Biol Sport. 2010;27:181–5.
36. Dupont G, Nedelec M, McCall A, McCormack D, Berthoin S,
Wisløff U. Effect of 2 soccer matches in a week on physical per-
formance and injury rate. Am J Sports Med. 2010;38:1752–8.
37. Morgans R, Orme P, Anderson L, Drust B, Morton JP. An Inten-
sive winter fixture schedule induces a transient fall in salivary
iga in English Premier League soccer players. Res Sports Med.
2014;22:346–54.
38. Mohr M, Draganidis D, Chatzinikolaou A, Barbero-Álvarez
JC, Castagna C, Douroudos I, etal. Muscle damage, inflam-
matory, immune and performance responses to three football
games in 1 week in competitive male players. Eur J Appl Physiol.
2016;116:179–93.
39. Di Salvo V, Pigozzi F, González-Haro C, Laughlin M, De Witt J.
Match performance comparison in top English soccer leagues. Int
J Sports Med. 2012;34:526–32.
40. Di Salvo V, Baron R, Tschan H, Calderon Montero F, Bachl N,
Pigozzi F. Performance characteristics according to playing posi-
tion in Elite Soccer. Int J Sports Med. 2007;28:222–7.
41. Mohr M, Krustrup P, Bangsbo J. Match performance of high-
standard soccer players with special reference to development of
fatigue. J Sports Sci. 2003;21:519–28.
42. Soroka A. The locomotor activity of soccer players based on play-
ing positions during the 2010 World Cup. J Sports Med Phys Fit.
2018;58:837–42.
43. Carling C, Le Gall F, McCall A, Nédélec M, Dupont G. Squad
management, injury and match performance in a professional soc-
cer team over a championship-winning season. Eur J Sport Sci.
2015;15:573–82.
44. Soroka A, Lago-Peñas C. The effect of a succession of matches on
the physical performance of elite football players during the World
Cup Brazil 2014. Int J Perform Anal Sport. 2016;16:434–41.
45. Waldron M, Highton J. Fatigue and pacing in high-intensity inter-
mittent team sport: an update. Sports Med. 2014;44:1645–58.
46. Bush MD, Archer DT, Hogg R, Bradley PS. Factors influencing
physical and technical variability in the English Premier League.
Int J Sports Physiol Perform. 2015;10:865–72.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
273
Fixture Congestion and Performance in Soccer
47. Consortium for Transparency in Exercise Science (COTES) Col-
laborators, Caldwell AR, Vigotsky AD, Tenan MS, Radel R, Mel-
lor DT, etal. Moving sport and exercise science forward: a call for
the adoption of more transparent research practices. Sports Med.
2020;50:449–59.
48. Sarmento H. Analysis of the offensive process of AS Monaco pro-
fessional soccer team: a mixed-method approach. Chaos Fractals
Solicitons. 2020;7:109676.
49. Harper LD, McCunn R. “Hand in Glove”: using qualitative meth-
ods to connect research and practice. Int J Sports Phys Perform.
2017;12:990–3.
50. Andersson H, Raastad T, Nilsson J, Paulsen G, Garthe I, Kadi
F. Neuromuscular fatigue and recovery in Elite Female Soccer:
effects of active recovery. Med Sci Sports Exerc. 2008;40:372–80.
51. Andersson H, Bøhn SK, Raastad T, Paulsen G, Blomhoff R, Kadi
F. Differences in the inflammatory plasma cytokine response fol-
lowing two elite female soccer games separated by a 72-h recov-
ery: cytokine response after female soccer games. Scand J Med
Sci Sports. 2010;20:740–7.
52. Lago-Peñas C, Lago-Ballesteros J. Game location and team qual-
ity effects on performance profiles in professional soccer. Sports
Sci Med. 2011;10:465–71.
53. Russell M, Sparkes W, Northeast J, Kilduff LP. Responses to a
120 min reserve team soccer match: a case study focusing on the
demands of extra time. J Sports Sci. 2015;33:2133–9.
54. Winder N, Russell M, Naughton R, Harper L. The impact of
120 minutes of match-play on recovery and subsequent match
performance: a case report in professional soccer players. Sports.
2018;6:22.
55. Page RM, Marrin K, Brogden CM, Greig M. Biomechanical and
physiological response to a contemporary soccer match-play
simulation. J Strength Cond Res. 2015;29:2860–6.
56. Aldous JWF, Akubat I, Chrismas BCR, Watkins SL, Mauger AR,
Midgley AW, etal. The reliability and validity of a soccer-specific
nonmotorised treadmill simulation (Intermittent Soccer Perfor-
mance Test). J Strength Cond Res. 2014;28:1971–80.
57. Harper LD, Hunter R, Parker P, Goodall S, Thomas K, Howat-
son G, etal. Test–retest reliability of physiological and perfor-
mance responses to 120 minutes of simulated soccer match play.
J Strength Cond Res. 2016;30:3178–86.
58. Russell S, Jenkins D, Rynne S, Halson SL, Kelly V. What is men-
tal fatigue in elite sport? Perceptions from athletes and staff. Eur
J Sport Sci. 2019;19:1367–76.
59. Di Salvo V, Baron R, González-Haro C, Gormasz C, Pigozzi F,
Bachl N. Sprinting analysis of elite soccer players during Euro-
pean Champions League and UEFA Cup matches. J Sports Sci.
2010;28:1489–94.
60. Bengtsson H, Ekstrand J, Hägglund M. Muscle injury rates in
professional football increase with fixture congestion: an 11-year
follow-up of the UEFA Champions League injury study. Br J
Sports Med. 2013;47:743–7.
Aliations
RossJulian1,2 · RichardMichaelPage3 · LiamDavidHarper4
1 Department ofNeuromotor Behavior andExercise, Institute
ofSport andExercise Sciences, University ofMuenster,
48149Muenster, Germany
2 School ofSport andExercise, University ofGloucestershire,
GloucestershireGL502RH, UK
3 Department ofSport andPhysical Activity, Edge Hill
University, St. Helens Road, Ormskirk, LancashireL394QP,
UK
4 School ofHuman andHealth Sciences, University
ofHuddersfield, HuddersfieldHD13DH, UK
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
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