Content uploaded by Paul S Bradley
Author content
All content in this area was uploaded by Paul S Bradley on May 21, 2018
Content may be subject to copyright.
Are Current Physical Match Performance Metrics in Elite Soccer
Fit for Purpose or Is the Adoption of an Integrated Approach Needed?
Paul S. Bradley and Jack D. Ade
Time–motion analysis is a valuable data-collection technique used to quantify the physical match performance of elite soccer players.
For over 40 years, researchers have adopted a “traditional”approach when evaluating match demands by simply reporting the distance
covered or time spent along a motion continuum of walking through to sprinting. This methodology quantifies physical metrics in
isolation without integrating other factors, and this ultimately leads to a 1-dimensional insight into match performance. Thus, this
commentary proposes a novel “integrated”approach that focuses on a sensitive physical metric such as high-intensity running but
contextualizes this in relation to key tactical activities for each position and collectively for the team. In the example presented, the
integrated model clearly unveils the unique high-intensity profile that exists due to distinct tactical roles, rather than 1-dimensional
“blind”distances produced by traditional models. Intuitively, this innovative concept may aid the coaches’understanding of the
physical performance in relation to the tactical roles and instructions given to the players. In addition, it will enable practitioners to
effectively translate match metrics into training and testing protocols. This innovative model may well aid advances in other team sports
that incorporate similar intermittent movements with tactical purpose. Evidence of the merits and application of this new concept are
needed before the scientific community accepts this model as it may well add complexity to an area that conceivably needs simplicity.
Keywords:match analysis, football, tactics, physical performance
Soccer is a complex sport with unpredictable movement
patterns during matches.
1
Players regularly transition between
short multidirectional high-intensity efforts and longer periods
of low-intensity activity.
2
The “traditional”approach to quantify-
ing demands in the absence of physiological and mechanical
measures during match play is to determine the distance covered
or the time spent at different speeds.
3
While not accounting for
metabolically taxing accelerations and directional changes,
4
it still
crudely provides an indirect energetics’measure. Studies reveal
that elite players cover 9 to 14 km in total during a game with high-
intensity running accounting for 5% to 15% of this distance.
5–7
Although only a small proportion is covered at high intensity, it is
assumed that this is related to important phases of play and critical
to game outcome,
8
but this remains to be elucidated scientifically.
9
Using the traditional approach, physical match performances
have been quantified across competitions such as the English
Premier League,
10,11
Italian Serie A,
6,12
Spanish La Liga,
13
French
Ligue 1,
14
and German Bundesliga
15
in addition to the European
Champions League
16,17
and international tournaments.
18,19
Research demonstrates high-intensity running during matches has
increased by a third in some leagues across the last decade.
20–22
Thus, preparing players who are robust enough to cope with modern
game requirements has received increasing attention.
23–25
But
despite hundreds of publications centering on the physical match
demands, little progress has been made regarding optimizing the
array of metrics used by applied staff within clubs. Thefirst in-depth
study on this subject was published more than 40 years ago by the
pioneer Professor Tom Reilly,
26
and since then, researchers have
adopted this traditional approach of reporting distance covered and
time spent along a motion continuum of walking through to
sprinting. Acceleration and metabolic cost indices have been pro-
gressively introduced alongside this approach, with the former a
welcome addition,
4,27
whereas the latter remains controversial.
28
Despite the simplistic nature of the traditional approach, researchers
have still been able to reveal the rudimental demands of various
positions,
10,11
competitive standards,
6,29,30
sex,
31–33
formations,
34
and match-related fatigue patterns.
5,6
However, at present, a new
“integrated”approach that contextualizes match physical perfor-
mance would surely progress the field’s understanding of the global
demands and assimilate the physical and tactical data more effec-
tively. Intuitively, this may aid the coaches’understanding of the
physical performancein relation to the tactical roles and instructions
given to the players and enable practitioners to effectively translate
match metrics into training and testing.
35
Alternatively, this con-
temporary approach may well add complexity to an area that
conceivably needs more simplicity regarding the quantification and
interpretation of match exertion.
Therefore, this commentary specifies the advantages of such
an integrative model by demonstrating the concept using current
computerized tracking technology. An example will demonstrate
an alternative or complimentary way of analyzing and interpreting
physical match performances. At the very least, this piece should
generate constructive dialogue within the academic and applied
domains. The feasibility and challenges associated with such
multifaceted match data will also be discussed given the infancy
of the proposed approach.
Defining the Approaches to Quantifying
Match Physical Performance
The Traditional Approach
In the last 4 decades, the traditional approach has quantified the
relative or absolute distance covered and time spent along a motion
continuum of walking through to sprinting (Figure 1). This has been
Bradley and Ade are with the Research Inst for Sport and Exercise Sciences,
Liverpool John Moores University, Liverpool, United Kingdom. Ade is also with
the Medical and Sports Science Dept, Liverpool Football Club, Liverpool, United
Kingdom. Bradley (P.S.Bradley@ljmu.ac.uk) is corresponding author.
1
International Journal of Sports Physiology and Performance, (Ahead of Print)
https://doi.org/10.1123/ijspp.2017-0433
© 2018 Human Kinetics, Inc. INVITED COMMENTARY
accomplished with the aid of validated computerized tracking or
global positioning technology.
11,36,37
Although researchers have used
generic descriptors for movement categories (jogging, etc), they have
assigned a wide range of speed thresholds to these activities. This is
due to variations in player sex,
16,38
maturation,
39
competitive stan-
dard,
6
and physical capacity.
40
To complicate matters, technologies
use different algorithms and dwell times to classify high-intensity
actions, and this limits comparability between studies.
41
Studies using this traditional approach are reductionist,
whereby the physical metrics are explored without consideration
for the technical and tactical indices.
4,5,10,11,27,36,42,43
One could
argue that this enables an in-depth physical analysis, with the
inclusion of other factors diluting this, especially if the study aims
do not include a technical–tactical element. Moreover, it is difficult
for researchers to gain access to technical analyses,
44
and the
tactical aspects of the game are a challenge to quantify at present.
34
Despite shortcomings, the demands using this approach are well
understood and have been for some time now. So, is it wise to keep
going over “old ground”or produce similar research questions with
slight permutations! The question that begs an answer is: Will this
approach progress this field from both a fundamental or applied
perspective? With a saturated research area that boasts hundreds of
papers that have varying degrees of originality and application, the
inconvenient and uncomfortable answer to this question is proba-
bly “no.”Studies have attempted to expand on this reductionism by
incorporating technical, tactical, and physical metrics within their
methodology.
20–22
However, data are still reported separately
within the results with limited synthesis, and consequently, our
understanding of the global game demands still remains superficial.
Some tracking systems do provide a basic physical–tactical
perspective by categorizing high-intensity running with/without
ball possession and when the ball is out of play.
45
It is debatable as
to the benefits of this information in isolation as it simply reflects
ball possession status. Regarding possession-based running me-
trics, teams that employ defensive formations with a direct style of
play have comparable overall high-intensity performances to
offensive formations that dominate possession. But the former
covers the majority of the distance without the ball, whereas the
latter does it with the ball.
18,46
In fact, only a small proportion of
high-intensity running (∼5%–10%) is covered when the ball is out
of play (eg, corners and throw-ins).
11,20,21,29,45,46
No study to date
has highlighted its sensitivity or application; thus, this could be
removed, otherwise, reclassified as effective playing time/distance
or “in play”activity.
13
This may shed light on match performance
fluctuations as effective playing time/distance decreases as a
product of more game interruptions rather than fatigue.
14
There-
fore, this approach does not seem to be the solution as it provides
negligible insight regarding physical efforts with a tactical purpose
(eg, recovery running). The scarcity of research merging physical,
technical, and tactical components is even more surprising when
evidence suggests that the last 2 aspects are notable discriminators
between competitive standards.
29,47
Consequently, they should be
considered when contextualizing match performance.
Arguably, this approach has provided some insight into fatigue,
context, and positional demands to name just a few.
10,11,13,17,35,36,48–50
However, the application of this data into practice is limited as most
simply report game or half-by-half averages for general categories
such as sprinting. Few studies have translated discrete actions into
usable metrics such as angles of turns, technical sequences, and
tactical actions associated with physical data that could be used
within the club setting.
35,51
To progress this field and to advance the
application of physical match data, it is imperative that scientists
examine updated methodologies that develop our understanding of
contextualizing game demands or at the very least generate construc-
tive dialogue within the literature.
The Integrated Approach
Soccer is a multifaceted sport with the physical, tactical, and
technical factors amalgamating to influence performance with
each factor not mutually exclusive of another.
52
Hence, this article
proposes a novel integrated approach that focuses on a sensitive
0%
20%
40%
60%
80%
100%
Total (%)
High Intensity running
Sprinting
High speed running
Running
Jogging
Walking
Standing
de
r
evocecnatsiD
em
itgn
iy
alP
Figure 1 —The traditional approach has been used for the last 4 decades to detail the match physical performance of players by quantifying the relative
or absolute distance covered, frequency of occurrence, and time spent along a motion continuum of walking through to sprinting. Data derived from
Bradley et al.
10
(Ahead of Print)
2Bradley and Ade
metric such as high-intensity running
32,53
but contextualizes this in
relation to key tactical activities for each position (eg, overlapping
for a full back) and collectively for the team (eg, closing down
opposition players).
Figure 2depicts the generalized model using a Venn format.
Three performance factors are represented in isolation and combi-
nation as circles. The regions in which factors overlap are the
intersections. The area whereby all factors overlay is called the
union (black dot) and denotes innovation in match analysis as full
integration occurs (considered beyond the realms of technology
and expertise at present). This commentary will focus on the
intersection of the Venn between physical and tactical factors.
The variables listed within this intersection were adapted from a
recently developed High Intensity Movement Programme.
35
This
data set was used in the example below and comprised of a single
team tracked across 3 consecutive English Premier League seasons
using a computerized tracking system (Amisco Pro; Sport Univer-
sal Process, Nice, France). High-intensity efforts were activities
reaching speeds ≥21 km·h
−1
for a minimal dwell time of 1 second.
To synchronize data, the tactical actions associated with each effort
were manually coded from video recordings viewed using com-
puterized tracking software. Definitions for the physical–tactical
actions are provided in Table 1, and zonal areas are depicted in
Figure 3.
Example of the Integrated Approach
Using Current Match Analysis Technology
Practitioners tend to use a “one-size-fits-all”approach when mea-
suring the work rate profiles of various positions, as the same
categories are uniformly used.
6,10,11,13,14,17,22,29,34,36,50,54–56
To
make sense of this information, some advocate individualized
rather than arbitrary speed thresholds that are founded on a player’s
physical fitness indices.
38–40
This is centered on the premise that
positional variation has consistently been found for fitness attri-
butes.
1,7,53,57,58
This provides a more representative indicator of a
player’s physical match exertion rather than the use of arbitrary
thresholds that are likely to over or underestimate demands.
40
Irrespective of speed thresholds, players in selected positions
will only be able to exert themselves based on match scenarios
as a result of tactical, contextual, and physical factors.
56
Accord-
ingly, some suggest that “in game”running performance should be
used to assign such thresholds.
19
This is a particularly pertinent
point given the games submaximal nature, which results in some
positions working well within their physical capabilities, particu-
larly if constrained by tactical rather than physical factors.
56
As
such, the tactical role of a player seems to be a powerful determi-
nant of their match physical performance. Thus, a one-size-fits-all
approach even with optimal speed thresholds could provide tacti-
cally constrained physical data for selected positions that is chal-
lenging to interpret given the lack contextualization.
A more customized approach that is derived from physical
actions with a tactical purpose could be advantageous. Even if
tactics or context are the main physical modulators, then practi-
tioners could still establish if crucial roles were fulfilled or not
using this new model. Figure 4presents the integrated approach
specialized to the position of each player. The nodal size (circle)
denotes the high-intensity distance covered by each position/
activity, and the edge thickness (line) represents the frequency
of actions (data derived from Ade et al
35
). Ten individual variables
are presented, with 6 occurring in possession and 4 out of posses-
sion. Defensive positions have a lower ratio of in-possession/
out-of-possession variables (center backs: ⅕), whereas offensive
positions are assigned a higher ratio (center forwards: ⅘). Covering
and recovery running are common for all positions except center
forwards, while closing down/intercepting is the only collective
variable. The inclusion of specialist variables enables key actions
to be contextualized (eg, running in behind for center forwards).
The diversity of actions makes its challenging to catalog each
player’s unique physical–tactical profile using 5 variables; thus,
a sixth variable entitled “other”was created to amass additional
activities.
Match physical performance data for each position are dis-
played in Figures 5using both models. Central midfielders, full
backs, and center forwards covered similar high-intensity distances
(∼600 m), so using the traditional approach, one could argue that
these performances are comparable. As match physical perfor-
mances are complex,
52,58,59
this does not infer that the demands are
similar (ie, a multitude of physiological and mechanical factors
impact this). The integrated method compartmentalizes data more
clearly by unveiling the unique high-intensity profile that exists
due to distinct tactical roles, rather than 1-dimensional “blind”
distances produced by existing models. This purposeful distance
could be valuable to practitioners, as they do not necessarily want
to determine which positions are the most demanding or cover the
most distance. But, rather how each performs their duties in relation
to a specific opponent and team philosophy. The traditional model
cannot provide this insight, and thus, the subsequent section will
detail the sensitivity of this integrative methodology.
Out of possession, positions with a major defensive role in the
team such as center backs, full backs, and central midfielders
Table 1 The High Intensity Movement Programme
Physical–tactical
variable Description
In possession
Break into box Player enters the opposition penalty box.
Overlap Player runs from behind to in front of or
parallel to the player on the ball.
Push-up pitch Player moves up the pitch to support the play
(defensive and middle third of the pitch only).
Run the channel Player runs with or without the ball down to
one of the external areas of the pitch.
Run-in behind Player aims to beat the opposition offside trap
to run through onto the opposition goal.
Drive inside/through
the middle
Player runs with/without ball through the
middle of the pitch or from external flank into
the central area.
Out of possession
Closing down/
interception
Player runs directly toward opposition player
on the ball or cuts out pass from opposition
player.
Covering Player moves to cover space or a player on the
pitch while remaining goal side.
Recovery run Player runs back toward own goal when out of
position to be goal side.
Ball over the
top/down side
Opposition plays a pass over the defense
through the center or down the side of pitch.
Other All other variables that could not be
categorized by the above.
Note: Definitions are adapted from Ade et al
35
but some variables have been
merged to simplify the model.
(Ahead of Print)
Integrative Match Analysis 3
(26%–31%) cover a greater proportion of their distance at high-
intensity covering space or teammates compared with wide mid-
fielders (13%). This innovative approach provides defensive
insight to practitioners on how players cover one another at
high-intensity and their propensity to remain compact to limit
space for the opposition during defensive phases of play.
60
The
proportion of high-intensity distance covered in defensive activities
such as closing down/intercepting was similar for central (16%–
19%) and wide positions (14%–16%) but greatest for the most
offensive position in the team (center forwards: 23%). Center
forwards frequently perform arc runs out of possession
35
to channel
an opponent with the ball one way while closing them down in
order to delay their attack and enable teammates to support the
press.
61
This assimilated information could conceivably verify if
players are adhering to tactical directives during phases of play that
require high-intensity efforts. This may well be a particularly
powerful communication tool to coaches if combined with zonal
data and translated into informative graphics. The position cover-
ing the greatest relative high-intensity distance in the category of
recovery running was center backs (20%) with full backs, central
midfielders, and wide midfielders producing similar proportions
(15%–17%). Full backs typically preceded efforts with a 90° to
180° turn as they transition from offensive into defensive roles,
executing more tackles post effort than other positions.
35
Ball over
the top/down side contributed to 20% of the total high-intensity
distance covered by center backs. This position performed more 0°
to 90° turns compared with other defensive players with most
efforts anticipated with players already on a half turn as sudden
directional changes are necessary to react to opposition move-
ment.
35
The physiological and mechanical consequences of direc-
tional changes during matches remain to be elucidated, but some
have quantified them in isolation.
62,63
Obtaining true match de-
mands should incorporate accelerations, but such data have yet to
be validated using optical tracking systems. Although including
accelerometer indices is more representative of current practices, it
must be noted that these are typically presented blind and without
context. Thus, this new approach could be used to contextualize
accelerations. As the previously mentioned variables are consid-
ered notable defensive attributes in the literature,
64
this approach
could add real-world value by detailing the physical–tactical match
behavior across position.
In possession, center forwards covered more high-intensity
distance in the offensive third of the pitch,
35
while driving inside/
through the middle (32%), running in behind (12%), breaking into
the box (10%), and running the channel (11%). These tactics
exploit space in order to score and create opportunities for team-
mates,
65
so they provide data to practitioners concerning purpose-
ful offensive running. Wide players like full backs and wide
midfielders covered a greater proportion of high-intensity distance
running the channel than other positions (20%–24%). They per-
form more crosses after these runs than other positions due to more
efforts finishing in wide attacking pitch areas.
64
Strategies that
employ offensive wide players means that specialist variables
within this model could provide confirmation that players are
abiding to the tactical philosophy. For example, full backs cover
9% of their total high-intensity distance overlapping players to
Physical
Total distance
High-intensity running distance
Sprinting distance
Accelerations/decelerations
Technical
Passes
Tackles
Shots
Headers
Dribbling
Crosses
Tactical
Playing style
Phase of play
Formation
Coaching philosophy
Positional role
Physical activities with
Tactical purpose
Recovery run
Covering
Overlapping
Closing down/interception
Push up pitch
Run in behind
Break into box
Physical activities with
Technical purpose
Dribbling ball
Run to cross ball/tackle
Jumping to head ball
Technical activities with tactical purpose
Technical events during transitions/phases of play
Technical events during set pieces
Full integration
Figure 2 —A Venn diagram depicting a generalized integrated approach to quantifying and interpreting the physical match performance of players.
This focuses on high-intensity running efforts across the game but contextualizes these actions in relation to key technical and tactical activities. Note this
diagram is a simplification of the sport and is not an exhaustive list of factors.
(Ahead of Print)
4Bradley and Ade
deliver a cross.
35
High-intensity running by full backs has
increased by ∼40% in this league in the last decade
22
as a duel
role requires them to be defensive out of possession but conduct
offensive in-possession actions such as overlapping to cross. The
previously mentioned actions are meaningful offensive attributes
for the relevant positions within the literature
22,64,65
highlighting
the importance of amalgamating physical–tactical actions. Activi-
ties consigned to the variable “other”contributed to ∼10% of the
high-intensity distance covered by each position. These actions are
certainly not redundant, but to simplify this innovative concept, it
was imperative that some actions were reclassified.
Feasibility and Challenges of the Integrated
Approach
Scientists have a duty of care to provide a balanced view of
contemporary methodologies including their practicalities and short-
comings. The integrated approach is manually coded within com-
puterized tracking software by time stamping each high-intensity
effort before then observing associated video footage to derive its
tactical purpose. Although time consuming at present, algorithms
could be incorporated within such technologies, so this becomes part
of the normal coding process. This manual technique limits the
proposed model, and at this moment in time, it is more applicable to
the research setting. Thus, it could be difficult to analyze the
reference team and the opposition when multiple games are played
in a congested period. As the levels of complexity increase, the
ability to clearly define actions and scenarios becomes more diffi-
cult. It may be possible in future through supervised machine
learning to have a more automated system; however, there would
be an extensive period of filtering to refine the data. In an effort to
minimize uninteresting actions, such as a center back running up for
a set play or a central midfielder moving up the pitch supporting
the play. The analyst could consider reducing the number of efforts
by modifying the minimum duration above the high-intensity speed
threshold required to register a high-intensity effort (≥3 s) or only
analyze sprint efforts as it is more likely those actions are of greater
importance to the outcome of the match.
9
The categorization of
actions can also be problematic. Although most are straightforward
to classify, on occasions, some cross-over is evident between
variables. For instance, a player may initially produce an effort
to cover space but then transition into closing down the opposition.
This could be coded as different activities depending on the start or
end of the effort. One must decide the primary nature of the action
to enable this approach to work; thus, operational definitions must
be clear for repeatability. Although a major concern, reasonable
interobserver and intraobserver agreement was reported for this
approach,
35
but this needs to be replicated by others to verify if
issues exist.
The High Intensity Movement Programme is a starting point
for the proposed integrated model, but additional factors should
be considered in future when contextualizing physical perfor-
mance. Quantifying physical data relevant to the tactical actions
in and out of possession are beneficial but would be more
informative if condensed into phases of play. These could be
classified as in-possession construction, in-possession counterat-
tack, out-of-possession low/medium block, and out-of-possession
counter-defending. This is particularly important, as success in
transition moments has been shown to be critical to match
outcome.
66
Are the technical and tactical actions associated
Figure 3 —Pitch zone areas that were used to code physical–tactical actions. The pitch location of a high-intensity effort was calculated using a grid
generated from the semiautomated systems software. Pitch length was divided into thirds to establish defensive, middle, and attacking zones while central
areas of the pitch were equal to the width of the penalty box with the remaining areas considered wide. Descriptions adapted from Ade et al.
35
(Ahead of Print)
Integrative Match Analysis 5
with high-intensity efforts performed by players during these
moments successful? An overall value score could be placed on
the action based on its success, area of the pitch, and impact on the
game (eg, assist, goal). Therefore, each player would have an
impact rating on the match. There are caveats associated with each
model but another drawback relates to information overload.
Scientists can easily drown themselves and coaching staff with
considerable data outputs,
44
which used ineffectively could lead
Figure 4 —Position-specific application of the integrated approach in relation to physical–tactical activities. Note the node size has been adjusted to
represent the distance covered in each position/activity and the edge thickness for the frequency of efforts. Data derived from Ade et al
35
but some
variables have been merged
Figure 5 —Purposeful high-intensity distance covered during matches for: centre backs (CB; n = 4; observations = 20), full backs (FB; n = 4;
observations = 20), central midfielders (CM; n = 4; observations = 20), wide midfielders (WM; n = 4; observations = 20), and centre forwards (CF; n = 4;
observations = 20). The bottom of each stack includes out of possession variables, whereas the top includes in possession variables for each position. The
relative trends differ somewhat from Ade et al
35
study as variables have been merged in certain instances and the above data present the distance instead of
the frequency of high-intensity actions.
(Ahead of Print)
6Bradley and Ade
to the rejection of this approach. However, as this concept merges
physical with tactical actions, it should intuitively interest coa-
ches as opposed to overwhelming them.
Conclusions
The traditional approach has been used for 4 decades to quantify
match physical performances. However, the integrated approach
contextualizes match demands by assimilating physical and tactical
data effectively. In the example presented, the contemporary model
unveiled the unique high-intensity profile that exists due to distinct
tactical roles, rather than the 1-dimensional blind distance covered
produced by existing models. This model may well aid advances
in other team sports (eg, rugby, hockey) that incorporate similar
intermittent movements with tactical purpose. Evidence of the
merits and application of this new concept are needed before
the scientific community accepts it as it may well add complexity
to an area that conceivably needs simplicity. Finally, it imperative
that the reader focusses more on the overall concept of this new
approach as opposed to the intricacy of each variable and trend,
especially given the infancy of the model and that the data are
generated from a single team.
Acknowledgments
The authors would like to thank Ian Graham (Liverpool Football Club)
for kind suggestions during manuscript preparation. The authors have
no potential conflicts of interest, and no funding was obtained for the
preparation of this article.
References
1. Di Mascio M, Ade J, Bradley PS. The reliability, validity and
sensitivity of a novel soccer-specific reactive repeated-sprint test
(RRST). Eur J Appl Physiol. 2015;115(12):2531–2542. PubMed
doi:10.1007/s00421-015-3247-0
2. Bangsbo J, Mohr M, Krustrup P. Physical and metabolic demands
of training and match-play in the elite football player. J Sports Sci.
2006;24(7):665–674. PubMed doi:10.1s080/02640410500482529
3. Castellano J, Alvarez-Pastor D, Bradley PS. Evaluation of research
using computerised tracking systems (Amisco® and Prozone®) to
analyse physical performance in elite soccer: a systematic review.
Sports Med. 2014;44(5):701–712. PubMed doi:10.1007/s40279-014-
0144-3
4. Varley MC, Aughey RJ. Acceleration profiles in elite Australian
soccer. Int J Sports Med. 2013;34(1):34–39. PubMed doi:10.1055/s-
0032-1316315
5. Di Mascio M, Bradley PS. Evaluation of the most intense high-
intensity running period in English FA Premier League soccer
matches. J Strength Cond Res. 2013;27(4):909–915. PubMed
doi:10.1519/JSC.0b013e31825ff099
6. 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(7):519–528. PubMed doi:10.1080/026404103
1000071182
7. Stolen T, Chamari K, Castagna C, Wisloff U. Physiology of soccer:
an update. Sports Med. 2005;35(6):501–536. PubMed doi:10.2165/
00007256-200535060-00004
8. Reilly T, Williams AM. Science and Soccer. 2nd ed. London, UK:
Routledge; 2003.
9. Faude O, Koch T, Meyer T. Straight sprinting is the most frequent
action in goal situations in professional football. J Sports Sci. 2012;
30(7):625–631. PubMed doi:10.1080/02640414.2012.665940
10. Bradley PS, Sheldon W, Wooster B, Olsen P, Boanas P, Krustrup P.
High-intensity running in English FA Premier League soccer
matches. J Sports Sci. 2009;27(2):159–168. PubMed doi:10.1080/
02640410802512775
11. Di Salvo V, Gregson W, Atkinson G, Tordoff P, Drust B. Analysis of
high intensity activity in Premier League soccer. Int J Sports Med.
2009;30(3):205–212. PubMed doi:10.1055/s-0028-1105950
12. Vigne G, Gaudino C, Rogowski I, Alloatti G, Hautier C. Activity
profile in elite Italian soccer team. Int J Sports Med. 2010;31(5):
304–310. PubMed doi:10.1055/s-0030-1248320
13. Castellano J, Blanco-Villasenor A, Alvarez D. Contextual variables
and time-motion analysis in soccer. Int J Sports Med. 2011;32(6):
415–421. PubMed doi:10.1055/s-0031-1271771
14. Carling C, Dupont G. Are declines in physical performance associ-
ated with a reduction in skill-related performance during professional
soccer match-play? J Sports Sci. 2011;29(1):63–71. PubMed doi:
10.1080/02640414.2010.521945
15. Hoppe MW, Slomka M, Baumgart C, Weber H, Freiwald J. Match
running performance and success across a season in German
Bundesliga Soccer Teams. Int J Sports Med. 2015;36(7):563–566.
PubMed doi:10.1055/s-0034-1398578
16. Bradley PS, Dellal A, Mohr M, Castellano J, Wilkie A. Gender
differences in match performance characteristics of soccer players
competing in the UEFA Champions League. Hum Mov Sci. 2014;
33:159–171. PubMed doi:10.1016/j.humov.2013.07.024
17. Di Salvo V, Baron R, Gonzalez-Haro C, Gormasz C, Pigozzi F,
Bachl N. Sprinting analysis of elite soccer players during European
ChampionsLeague and UEFA Cup matches. J Sports Sci. 2010;28(14):
1489–1494. PubMed doi:10.1080/02640414.2010.521166
18. da Mota GR, Thiengo CR, Gimenes SV, Bradley PS. The effects of
ball possession status on physical and technical indicators during the
2014 FIFA World Cup Finals. J Sports Sci. 2016;34(6):493–500.
PubMed doi:10.1080/02640414.2015.1114660
19. Schimpchen J, Skorski S, Nopp S, Meyer T. Are “classical”tests of
repeated-sprint ability in football externally valid? A new approach
to determine in-game sprinting behaviour in elite football players.
J Sports Sci. 2016;34(6):519–526. PubMed doi:10.1080/02640414.
2015.1112023
20. Barnes C, Archer DT, Hogg B, Bush M, Bradley PS. The evolution of
physical and technical performance parameters in the English Premier
League. Int J Sports Med. 2014;35(13):1095–1100. PubMed doi:
10.1055/s-0034-1375695
21. Bradley PS, Archer DT, Hogg B, et al. Tier-specific evolution of
match performance characteristics in the English Premier League:
it’s getting tougher at the top. J Sports Sci. 2016;34(10):980–987.
PubMed doi:10.1080/02640414.2015.1082614
22. Bush M, Barnes C, Archer DT, Hogg B, Bradley PS. Evolution of
match performance parameters for various playing positions in the
English Premier League. Hum Mov Sci. 2015;39:1–11. PubMed
doi:10.1016/j.humov.2014.10.003
23. Ade JD, Harley JA, Bradley PS. Physiological response, time-motion
characteristics, and reproducibility of various speed-endurance drills
in elite youth soccer players: small-sided games versus generic run-
ning. Int J Sports Physiol Perform. 2014;9(3):471–479. PubMed doi:
10.1123/ijspp.2013-0390
24. Iaia FM, Fiorenza M, Perri E, Alberti G, Millet GP, Bangsbo J.
The effect of two speed endurance training regimes on performance
of soccer players. PLoS ONE. 2015;10(9):e0138096. PubMed doi:
10.1371/journal.pone.0138096
(Ahead of Print)
Integrative Match Analysis 7
25. Iaia FM, Rampinini E, Bangsbo J. High-intensity training in football.
Int J Sports Physiol Perform. 2009;4(3):291–306. PubMed doi:10.
1123/ijspp.4.3.291
26. Thomas V, Reilly T. Application of motion analysis to assess
performance in competitive football. Ergonomics. 1976;19(4):530.
27. Akenhead R, Hayes PR, Thompson KG, French D. Diminutions of
acceleration and deceleration output during professional football
match play. J Sci Med Sport. 2013;16(6):556–561. PubMed doi:10.
1016/j.jsams.2012.12.005
28. Osgnach C, Poser S, Bernardini R, Rinaldo R, di Prampero PE.
Energy cost and metabolic power in elite soccer: a new match analysis
approach. Med Sci Sports Exerc. 2010;42(1):170–178. PubMed
doi:10.1249/MSS.0b013e3181ae5cfd
29. Bradley PS, Carling C, Gomez Diaz A, et al. Match performance and
physical capacity of players in the top three competitive standards of
English professional soccer. Hum Mov Sci. 2013;32(4):808–821.
PubMed doi:10.1016/j.humov.2013.06.002
30. Di Salvo V, Pigozzi F, Gonzalez-Haro C, Laughlin MS, De Witt JK.
Match performance comparison in top English soccer leagues. Int J
Sports Med. 2013;34(6):526–532. PubMed doi:10.1055/s-0032-
1327660
31. Datson N, Drust B, Weston M, Jarman IH, Lisboa PJ, Gregson W.
Match physical performance of elite female soccer players during
international competition. J Strength Cond Res. 2017;31(9):
2379–2387. PubMed doi:10.1519/JSC.0000000000001575
32. Krustrup P, Mohr M, Ellingsgaard H, Bangsbo J. Physical demands
during an elite female soccer game: importance of training status. Med
Sci Sports Exerc. 2005;37(7):1242–1248. PubMed doi:10.1249/01.
mss.0000170062.73981.94
33. Vescovi JD, Favero TJ. Motion characteristics of women’s college
soccer matches: Female Athletes in Motion (FAiM) study. Int J
Sports Physiol Perform. 2014;9(3):405–414. PubMed doi:10.1123/
ijspp.2013-0526
34. Bradley PS, Carling C, Archer D, et al. The effect of playing
formation on high-intensity running and technical profiles in English
FA Premier League soccer matches. J Sports Sci. 2011;29(8):
821–830. PubMed doi:10.1080/02640414.2011.561868
35. Ade J, Fitzpatrick J, Bradley PS. High-intensity efforts in elite soccer
matches and associated movement patterns, technical skills and tacti-
cal actions. Information for position-specific training drills. J Sports
Sci. 2016;34(24):2205–2214. PubMed doi:10.1080/02640414.2016.
1217343
36. Bangsbo J, Norregaard L, Thorso F. Activity profile of competition
soccer. Can J Sport Sci. 1991;16(2):110–116. PubMed
37. Buchheit M, Allen A, Poon TK, Modonutti M, Gregson W, Di Salvo
V. Integrating different tracking systems in football: multiple camera
semi-automatic system, local position measurement and GPS tech-
nologies. J Sports Sci. 2014;32(20):1844–1857. PubMed doi:10.
1080/02640414.2014.942687
38. Bradley PS, Vescovi JD. Velocity thresholds for women’s soccer
matches: sex specificity dictates high-speed running and sprinting
thresholds—Female Athletes in Motion (FAiM). Int J Sports Physiol
Perform. 2015;10(1):112–116. PubMed doi:10.1123/ijspp.2014-
0212
39. Harley JA, Barnes CA, Portas M, et al. Motion analysis of match-
play in elite U12 to U16 age-group soccer players. J Sports Sci.
2010;28(13):1391–1397. PubMed doi:10.1080/02640414.2010.
510142
40. Abt G, Lovell R. The use of individualized speed and intensity
thresholds for determining the distance run at high-intensity in
professional soccer. J Sports Sci. 2009;27(9):893–898. PubMed
doi:10.1080/02640410902998239
41. Varley MC, Jaspers A, Helsen WF, Malone JJ. Methodological
considerations when quantifying high-intensity efforts in team sport
using global positioning system technology. Int J Sports Physiol
Perform. 2017;12(8):1059–1068. PubMed doi:10.1123/ijspp.2016-
0534
42. Aughey RJ, Varley MC. Acceleration profiles in elite Australian
soccer. Int J Sports Med. 2013;34(3):282. PubMed doi:10.1055/
s-0032-1331776
43. Carling C, Bloomfield J. The effect of an early dismissal on player
work-rate in a professional soccer match. J Sci Med Sport. 2010;
13(1):126–128. PubMed doi:10.1016/j.jsams.2008.09.004
44. Carling C, Wright C, Nelson LJ, Bradley PS. Comment on ‘Perfor-
mance analysis in football: a critical review and implications for
future research’.J Sports Sci. 2014;32(1):2–7. PubMed doi:10.1080/
02640414.2013.807352
45. Gregson W, Drust B, Atkinson G, Salvo VD. Match-to-match
variability of high-speed activities in premier league soccer. Int J
Sports Med. 2010;31(4):237–242. PubMed doi:10.1055/s-0030-
1247546
46. Bradley PS, Lago-Penas C, Rey E, Gomez Diaz A. The effect of high
and low percentage ball possession on physical and technical profiles
in English FA Premier League soccer matches. J Sports Sci. 2013;
31(12):1261–1270. PubMed doi:10.1080/02640414.2013.786185
47. Collet C. The possession game? A comparative analysis of ball
retention and team success in European and international football,
2007–2010. J Sports Sci. 2013;31(2):123–136. PubMed doi:10.1080/
02640414.2012.727455
48. Bradley PS, Di Mascio M, Peart D, Olsen P, Sheldon B. High-
intensity activity profiles of elite soccer players at different perfor-
mance levels. J Strength Cond Res. 2010;24(9):2343–2351. PubMed
doi:10.1519/JSC.0b013e3181aeb1b3
49. Carling C. Analysis of physical activity profiles when running with
the ball in a professional soccer team. J Sports Sci. 2010;28(3):
319–326. PubMed doi:10.1080/02640410903473851
50. Lago C, Casais L, Dominguez E, Sampaio J. The effects of situational
variables on distance covered at various speeds in elite soccer. Eur J
of Sport Sci. 2010;10(2):103–109. doi:10.1080/17461390903273994
51. Bloomfield J, Polman R, O’Donogue P. The ‘Bloomfield Movement
Classification’: motion analysis of individual players in dynamic
movement sports. Int J Perform Anal Sport. 2004;4(2):20–31. doi:
10.1080/24748668.2004.11868300
52. Paul DJ, Bradley PS, Nassis GP. Factors affecting match running
performance of elite soccer players: shedding some light on the
complexity. Int J Sports Physiol Perform. 2015;10(4):516–519.
PubMed doi:10.1123/ijspp.2015-0029
53. Krustrup P, Mohr M, Nybo L, Jensen JM, Nielsen JJ, Bangsbo J. The
Yo-Yo IR2 test: physiological response, reliability, and application to
elite soccer. Med Sci Sports Exerc. 2006;38(9):1666–1673. PubMed
doi:10.1249/01.mss.0000227538.20799.08
54. Bradley PS, Noakes TD. Match running performance fluctuations in
elite soccer: indicative of fatigue, pacing or situational influences?
J Sports Sci. 2013;31(15):1627–1638. PubMed doi:10.1080/
02640414.2013.796062
55. Carling C, Le Gall F, Dupont G. Analysis of repeated high-intensity
running performance in professional soccer. J Sports Sci. 2012;30(4):
325–336. PubMed doi:10.1080/02640414.2011.652655
56. Schuth G, Carr G, Barnes C, Carling C, Bradley P. Positional
interchanges influence the physical and technical match performance
variables of elite soccer players. J Sports Sci. 2016;34(6):501–508.
PubMed doi:10.1080/02640414.2015.1127402
57. Bradley PS, Mohr M, Bendiksen M, et al. Sub-maximal and
maximal Yo-Yo intermittent endurance test level 2: heart rate
(Ahead of Print)
8Bradley and Ade
response, reproducibility and application to elite soccer. Eur J Appl
Physiol. 2011;111(6):969–978. PubMed doi:10.1007/s00421-010-
1721-2
58. Bradley PS, Di Mascio M, Bangsbo J, et al. The maximal and
sub-maximal versions of the Yo-Yo intermittent endurance test
level 2 are simply reproducible, sensitive and valid. Eur J Appl
Physiol. 2011;112(5):1973–1975. PubMed doi:10.1007/s00421-
011-2155-1
59. Carling C. Interpreting physical performance in professional soccer
match-play: should we be more pragmatic in our approach? Sports
Med. 2013;43(8):655–663. PubMed doi:10.1007/s40279-013-
0055-8
60. Bangsbo J, Peitersen B. Defensive Soccer Tactics. Champaign, IL:
Human Kinetics; 2002.
61. Michels R. Team Building: The Road to Success. Spring City, PA:
Reedswain; 2001.
62. Akenhead R, French D, Thompson KG, Hayes PR. The physiological
consequences of acceleration during shuttle running. Int J Sports
Med. 2015;36(4):302–307. PubMed doi:10.1055/s-0034-1389968
63. Hader K, Mendez-Villanueva A, Palazzi D, Ahmaidi S, Buchheit M.
Metabolic power requirement of change of direction speed in young
soccer players: not all is what it seems. PLoS ONE. 2016;11(3):
e0149839. PubMed doi:10.1371/journal.pone.0149839
64. Hughes M, Caudrelier T, James N, Donnelly I, Kirkbride A,
Duschesne C. Moneyball and soccer: an analysis of the key perfor-
mance indicators of elite male soccer players by position. J Hum Sport
Exerc. 2012;7(2):402–412. doi:10.4100/jhse.2012.72.06
65. Bangsbo J, Peitersen B. Offensive Soccer Tactics. Champaign, IL:
Human Kinetics; 2004.
66. Tenga A, Holme I, Tore Ronglan L, Bahr R. Effect of playing tactics
on goal scoring in Norwegian professional soccer. J Sports Sci.
2010;28(3):237–244. PubMed doi:10.1080/02640410903502774
(Ahead of Print)
Integrative Match Analysis 9
