RESEARCH ARTICLE
Player load in male elite soccer: Comparisons
of patterns between matches and positions
Terje DalenID1*, Tore Kristian Aune1, Geir Håvard Hjelde2, Gertjan EttemaID3,
Øyvind SandbakkID3, David McGhie3
1 Department of Physical Education and Sport Science, Nord University, Levanger, Norway, 2 Rosenborg
FC, Trondheim, Norway, 3 Centre for Elite Sports Research, Department of Neuromedicine and Movement
Science, Norwegian University of Science and Technology, Trondheim, Norway
* terje.dalen@nord.no
Abstract
Our primary aim was to explore the development of player load throughout match time (i.e.,
the pattern) using moving 5-min windows in an elite soccer team and our secondary aim
was to compare player load patterns between different positions within the same team. The
dataset included domestic home matches (n = 34) over three seasons for a Norwegian Elite
League team. Player movements (mean ± SD age 25.5 ± 4.2 years, height 183.6 ± 6.6 cm,
body mass 78.9 ± 7.4 kg) were recorded at 20 Hz using body-worn sensors. Data for each
variable (player load, player load per meter, total distance, accelerations, decelerations,
sprint distance, high-intensity running distance) were averaged within positions in each
match, converted to z-scores and averaged across all matches, yielding one time series for
each variable for each position. Pattern similarity between positions was assessed with
cross-correlations. Overall, we observed a distinct pattern in player load throughout match
time, which also occurred in the majority of individual matches. The pattern shows peaks at
regular intervals (~15 min), each followed by a period of lower load, declining until the next
peak. The same pattern was evident in player load per meter. The cross-correlation analy-
ses support the visual evidence, with correlations ranging 0.88–0.97 (p < .001) in all position
pairs. In contrast, no specific patterns were discernible in total distance, accelerations,
decelerations, sprint distance and high-intensity running distance, with cross-correlations
ranging 0.65–0.89 (p < .001), 0.32–0.64 (p < .005), 0.18–0.65 (p < .005 in nine position
pairs), 0.02–0.38 (p < .05 in three pairs) and 0.01–0.52 (p < .05 in three pairs), respectively.
This study demonstrated similarity in player load patterns between both matches and posi-
tions in elite soccer competition, which could indicate a physical “pacing pattern” employed
by the team.
Introduction
For optimal performance in team sports like soccer (association football), players are required
to maximize their technical, tactical, and physical abilities. The physical demands of soccer
matches are characterized by a constant variation between low- (e.g., standing and walking),
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OPEN ACCESS
Citation: Dalen T, Aune TK, Hjelde GH, Ettema G,
Sandbakk Ø, McGhie D (2020) Player load in male
elite soccer: Comparisons of patterns between
matches and positions. PLoS ONE 15(9):
e0239162. https://doi.org/10.1371/journal.
pone.0239162
Editor: Laurent Mourot, University of Bourgogne
France Comte´, FRANCE
Received: June 2, 2020
Accepted: August 31, 2020
Published: September 21, 2020
Peer Review History: PLOS recognizes the
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https://doi.org/10.1371/journal.pone.0239162
Copyright: © 2020 Dalen et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
high- (e.g., running), and very high-intensity (e.g., accelerations, decelerations and sprinting)
activities [1–3]. Along with additional sport-specific activities (e.g., tackles, turns, headers,
dribbles), these locomotor activities constitute the total physical load of a player during train-
ing and matches [4]. However, the total physical load of the players is determined by a combi-
nation of direct involvement in play, responding to movements of attacking players, tactical
restrictions, and willingness to support team-mates [5]. These variations are likely to result in
a relatively large match to match variability in physical performance [6, 7].
Time-motion analyses have provided accurate and objective quantification of the players’
activities, and therefore improved our understanding of the physical demands in soccer [8–
11]. However, measurements of different locomotor classifications or speed zones may be
insensitive to the totality of mechanical stresses common to team sports. Tri-axial accelerome-
ters provide complementary information to time-motion analysis for understanding player
load during matches and training [12, 13] as they record the acceleration of body movement
in three dimensions, which better estimates the players’ physical exertion. Therefore, manufac-
turers of global positioning systems (GPS) and local positioning measurements (LPM) have
incorporated high-resolution triaxial accelerometers as a measure of player load. Such analyses
are shown useful for validly quantifying the physical demands in soccer [12, 14–16], in which
various estimations of player load are regarded as acceptable measures of external load and
largely correlated to players’ physiological and perceptual responses to training [17, 18]
To date, monitoring external training and match load measures in soccer has tended to rely
on results based on locomotor activities. In previous analyses of soccer matches, considerable
heterogeneity has been observed in the within-match development of locomotor activities
(total distance, HiR, sprint, accelerations and decelerations) throughout match time (i.e., the
pattern) across studies [6, 9, 19–25]. Some studies report a reduction in total and high-inten-
sity running (HiR) distances toward the end of each half [9, 26], whereas others do not find
such changes [20, 21]. These contradictory results are likely caused by different measurement
systems, different tactical elements, opponents’ playing style, pacing strategies, score line, and
team formation, which would all affect the players’ ability to regulate and maintain their physi-
cal effort [22]. However, previous studies show high variability in high-speed activities within
matches and that individual players show inconsistency in high-speed activity (i.e., HiR and
sprinting) across matches [6, 23]. A component of soccer matches that has received relatively
less attention is the players’ number of accelerations and decelerations [19], although some
previous studies suggest that inter- and intra-individual variability is smaller for accelerations
compared to distance-related measures [6, 24]. Additionally, a recent study found a continu-
ous reductional pattern in accelerations over the course of a match and after peak working
periods of a match, which was consistent across positions [25].
In the existing literature, the within-match player load based on three-dimensional move-
ment analyses has been investigated using a standardised soccer simulation with 15-min stan-
dardised activity blocks [27]. Here, the authors found that player load increased over time in
each half, likely due to a change in movement strategy and/or a reduced locomotor efficiency
[27]. In contrast to this, reductions in player load were identified in the latter stages of each
half in the analyses of 86 matches in U-21 English Championship teams [14]. However, in the
same 15-min time periods, the player load per total distance covered increased, suggesting an
increased loading for every given meter covered on the pitch [14]. These investigations have
allowed a general determination of player load patterns during soccer matches and soccer-spe-
cific intermittent exercises. However, to understand more in detail how teams and individual
players distribute their player load and related locomotor activities throughout soccer matches,
the same factors need to be analyzed over shorter time-periods than 15-min blocks. More
instantaneous analyses of player load and the corresponding activities during soccer matches
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Funding: The funder (Rosenborg FC) provided
support in the form of salaries for author [G.H.H.],
but did not have any additional role in the study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing interests: The commercial affiliation
(Rosenborg FC) does not alter our adherence to all
PLOS ONE policies on sharing data and materials
by including the following statement: "This does
not alter our adherence to PLOS ONE policies on
sharing data and materials.”
would logically show a variable “pacing” influenced by e.g., tactical elements, player position,
and the level of the opponents. In order to quantify this across e.g., positions, the similarity of
patterns throughout the duration of matches must be analyzed. Long term analyses of such
data and the relationships to changes in tactics, different opponents, and match outcome have
the potential to provide imperative understanding of how the team and the players in different
positions distribute the load (i.e., “pacing strategies”) during different types of matches.
Since analyses based on predefined periods cannot provide information about the “real”
peaks and valleys in the analysis of patterns throughout a match, moving windows is a poten-
tial solution, providing more accurate information about player load and locomotor variables
(total distance, acceleration, deceleration, HiR and sprint). Our primary aim was to explore
the patterns of player load, as well as locomotor variables for comparison, with analyses from
moving 5-min windows in an elite soccer team. Our secondary aim was to compare these pat-
terns between different positions within the same team.
Methods
Participants
The dataset includes domestic home matches (n = 34) over three full seasons for a team in the
Norwegian Elite League. In one of the seasons, the team participated in the Europe League
group stages. All matches were played on a grass surface. Movements of all players (mean ± SD
age 25.5 ± 4.2 years, height 183.6 ± 6.6 cm, body mass 78.9 ± 7.4 kg) were observed, and only
data from the 39 players completing an entire match were used (n = 212: complete match data
of players, goalkeepers excluded). The sample included eight central defenders (CD, n = 47),
six external defenders (ED, n = 52), six central midfielders (CM, n = 46), 11 external midfield-
ers (EM, n = 40), and eight attackers (ATT, n = 27). Some players participated in different posi-
tions across, but not within, the matches included in the data material. Following an
explanation of the procedures, all participants gave verbal and written informed consent to
participate in the study. The study was conducted according to the Declaration of Helsinki
and has been approved by the Norwegian Social Science Data Services (reference number
468065).
Study design and methodology
This study used a fully automatic sport tracking system to evaluate match performances of pro-
fessional soccer players at the elite level over three full seasons. Player movement was captured
by small, body-worn sensors located at the lumbar region, continuously recording the players’
actions. Data were transferred by microwave radio channel to 10 RadioEyeTM sensors (ZXY
SportTracking, ChyronHego, Trondheim, Norway) mounted in the team’s home arena. Player
movement was registered at 20 Hz. Accelerations and decelerations were recorded when they
reached limits of 2 m.s-2 and -2 m.s-2, respectively, and a HiR category of >19.8 km.h-1 and
sprint category of >25.2 km.h-1 were selected for this study. The thresholds for accelerations,
HiR, and sprint were similar to those reported in previous studies [12, 28]. In this study, the
player load is calculated as a downscaled (by a factor of 800) value of the sum of the squared,
high pass-filtered accelerometer values for the respective axes (X, Y, and Z): (X2 + Y2 + Z2) /
800 [12]. Test-retest reliability of the sport tracking system is reported earlier, indicating good
reliability [12, 28].
Evaluation of 5-minute periods throughout match time.
To construct an analysis cap-
turing the immediate, dynamic nature of a match for all players, mean values were calculated
over consecutive (i.e., moving) 5-min periods for player load and player load per meter, as well
as time-motion variables (total distance, accelerations, decelerations, sprint distance, HiR
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distance) for comparison, beginning with the first five minutes of the match [25, 29]. The sec-
ond 5-min period lasts from the second to the sixth minute, and so on. This method is argued
to provide a more accurate representation of the distances covered by players [29]. These
5-min periods were used to investigate patterns of player load and locomotor variables
throughout match time. The similarities of patterns were then quantified between positions
and patterns were evaluated across variables.
Statistical analysis
All data processing and statistical analysis was performed in Matlab R2019b version
9.7.0.1190202 (Mathworks, Natick, MA, USA). For each match, data for each variable (player
load, player load per meter, total distance, accelerations, decelerations, sprint distance, HiR
distance) were averaged within positions if there was data from multiple players at the same
position, yielding a single time series per variable for each position measured in that match.
These data were then converted to z-scores, to facilitate the direct comparison of patterns, dis-
regarding absolute magnitudes. Finally, the z-scores were averaged across all matches for each
position, resulting in one time series for each position for each variable. The degree of similar-
ity of patterns between positions was assessed with cross-correlations. For statistical purposes,
the break in the time series caused by halftime was disregarded (i.e., the data were treated as
continuous for the duration of playing time). Linearity was assessed visually using scatter
plots. Cross-correlations were calculated for every position pair for n-1 lags at either side of
zero, where n = 82, the number of moving 5-min windows in a 90-min match (41 5-min win-
dows in each 45-min half). To best represent the development of player load and time-motion
variables across positions throughout match time, the correlation at zero lag (with 95% confi-
dence interval and p-value) is presented. For comparison, maximum correlations and corre-
sponding lags are also reported. A negative lag means that the first time series (player position
in table columns) shifts to the left relative to the second time series (player position in table
rows). The level of statistical significance was set at α = .05. Correlation values were interpreted
categorically as trivial (0–0.1), low (0.1–0.3), moderate (0.3–0.5), high (0.5–0.7), very high
(0.7–0.9), or nearly perfect (0.9–1) using the scale presented by Hopkins et al. [30].
Results
Overall, we observed a distinct pattern in player load throughout match time (Fig 1, black
line). The pattern shows peaks at seemingly regular intervals (~15 min), each followed by a
period of lower load, typically declining until the next peak. This pattern was clear in all posi-
tions (Fig 1A, colored lines), and could also generally be observed in the majority of individual
matches (Fig 2). The cross-correlation analysis (Table 1) supports the visual evidence, indicat-
ing very high to nearly perfect correlations (range 0.88–0.95, all p < .001) in all position pairs,
all having the highest correlation at zero lag. The same pattern was evident for player load per
meter, both overall (Fig 1B, black line) and in all positions (Fig 1B, colored lines), with nearly
perfect correlation values (range 0.93–0.97, all p < .001; Table 1) in all position pairs, all having
the highest correlation at zero lag.
For total distance, no distinct pattern throughout match time was evident (Fig 3A, black
line). However, the patterns for all positions appear to follow each other reasonably well (Fig
3A, colored lines), which is reflected in high to very high correlation values (range 0.65–0.89,
all p < .001; Table 1), with all position pairs again having the highest correlation at zero lag.
For accelerations, no specific pattern was evident throughout match time (Fig 3B, black
line), but the different positions appear to follow roughly similar patterns (Fig 3B, colored
lines). Further, correlation values were moderate to high (range 0.32–0.64, all p  .005; S1
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Table), with all but one position pair having the highest correlation at zero lag (EM vs. CD
highest absolute correlation 0.56, lag -27; S2 Table). For decelerations, again no specific pattern
was evident throughout match time (Fig 3C, black line), but the different positions sporadically
follow roughly similar patterns (Fig 3C, colored lines). Correlation values were low to high
(range 0.18–0.65, all but one p  .005; S1 Table), with more than half of all position pairs hav-
ing the highest correlation at zero lag (highest correlation absolute range 0.32–0.65, lag -4–44;
S2 Table).
For sprint distance and HiR distance, no specific pattern throughout match time could be
discerned in either variable (Fig 3D and 3E, black lines). Further, the patterns for the different
positions do not follow each other well (Fig 3 and 3E, colored lines). In line with this, trivial to
moderate correlation values were found for sprint distance (absolute range 0.02–0.38, p < .05
in three position pairs, two having the highest correlation at zero lag; highest correlation abso-
lute range 0.29–0.53, lag -29–54 [S1 and S2 Tables]), whereas trivial to moderate (one high)
correlation values were found for HiR distance (absolute range 0.01–0.52, p < .05 in three
position pairs, two having the highest correlation at zero lag; highest correlation absolute
range 0.32–0.52, lag -28–32 [S1 and S2 Tables]).
Discussion
The primary aim of this study was to explore the patterns of player load with analyses from
moving 5-min windows in an elite soccer team. Further, the secondary aim was to compare
the player load patterns between different positions within the same team. The main finding
was the distinct player load pattern with three “high-load periods” in each half, separated by
“lower-load periods”. The player load patterns were relatively similar between positions and
Fig 1. Mean values (z-scores) of player load and player load per meter in 5-min moving windows throughout match time across all matches (n = 34) for each
position (colored lines) and for all positions combined (black line). A: player load; B: player load per meter.
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occurred at approximately the same time points during the majority of matches. These novel
findings will be discussed with two points of departure: the team’s pacing strategy from a phys-
ical viewpoint and from a perspective based on interpersonal coordination between player
positions.
Player load patterns and pacing
The use of 5-min moving averages to analyze within-match player load patterns in this study
allowed us to study the players’ “pacing strategies” (i.e., distribution of player load and related
locomotor activities) in more detail than in previous studies evaluating simulated soccer
matches [27] and English championship matches [16] by dissection into 15-min periods. The
present results show distinct player load patterns with three “high-load periods” in each half of
the match (Fig 1), separated by “lower-load periods”, in most of the matches (Fig 2), which dif-
fers from patterns found in research on English championship players [16]. Although the new
methodology for analyzing player load used in the present study provides novel information
about high- and lower-load periods of the soccer matches, these distinct patterns found in
almost all matches were rather surprising since differences between the opponents’ level and
tactics should rationally have influenced player load patterns between matches. In addition,
the player load would also largely be determined by the players’ decision-making about oppor-
tunities to become engaged in play. One likely explanation of this apparent player load pattern
is that this study investigated one of the top-ranked clubs in the Norwegian top division at
Fig 2. Mean player load (z-scores) in 5-min moving windows throughout match time for all measured positions per match. M: match number.
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their home arena, where they had the opportunity to “control the match” in most of the
matches. Thus, it seems reasonable to ask whether these similar positional fluctuations in
player load are typical for this team at their home arena matches where they normally were the
dominant team. Therefore, an interesting approach for future studies would be to investigate
these patterns with the same moving average-method in teams at different performance levels
(i.e., if the investigated team or the opposition controls the match or in teams with different
overall tactical dispositions).
Since locomotor actions in soccer are not performed in isolation, consideration of player
load as a proxy for “overall external load” might be useful. A previous investigation of player
load found high to very high associations between player load and measures of internal train-
ing load (TRIMP and sRPE) [18], with internal load being especially related to the volume of
accelerations. Barrett et al. [18] found nearly perfect within-subject correlation between player
load and heart rate/VO2, but trivial to moderate association for the between-subject correla-
tion on the same variable [19]. Overall, this suggests that the fluctuations in player load found
in the present study are also associated with fluctuations in internal load, thereby indicating a
physical “pacing pattern” (pattern in distribution of load) employed by the investigated team
(Fig 1). These “pacing patterns” were relatively similar between positions and occurred at the
same time point during the matches (Fig 2), even though the different positions have different
roles during attacks and defense; one single attack gives higher intensities on attacking players,
but not for the defending players, and vice versa. However, the time scale with 5-min moving
averages is too long to differentiate between high-intensity periods based on one single attack
or one defensive stand and normally contain several attacking and defensive actions. More-
over, player load patterns based on moving 5-min windows will give more information about
Table 1. Cross-correlations [95% CI] of mean position values (z-scores) across all matches (n = 34) at zero lag for
player load, player load per meter, and total distance.
CD
ED
CM
EM
ATT
Player load
CD
---
ED
0.95 [0.92, 0.96]
---
CM
0.93 [0.89, 0.95]
0.94 [0.91, 0.96]
---
EM
0.93 [0.89, 0.95]
0.94 [0.91, 0.96]
0.93 [0.90, 0.96]
---
ATT
0.91 [0.87, 0.94]
0.95 [0.93, 0.97]
0.88 [0.83, 0.92]
0.89 [0.84, 0.93]
---
Player load per meter
CD
---
ED
0.93 [0.89, 0.95]
---
CM
0.95 [0.93, 0.97]
0.95 [0.92, 0.97]
---
EM
0.95 [0.92, 0.97]
0.94 [0.91, 0.96]
0.95 [0.92, 0.97]
---
ATT
0.93 [0.90, 0.96]
0.97 [0.96, 0.98]
0.96 [0.94, 0.97]
0.95 [0.92, 0.97]
---
Total distance
CD
---
ED
0.88 [0.81, 0.92]
---
CM
0.80 [0.71, 0.87]
0.84 [0.76, 0.89]
---
EM
0.89 [0.84, 0.93]
0.87 [0.81, 0.92]
0.86 [0.79, 0.91]
---
ATT
0.76 [0.65, 0.84]
0.71 [0.59, 0.81]
0.65 [0.51, 0.76]
0.72 [0.59, 0.81]
---
CD = central defender; ED = external defender; CM = central midfielder; EM = external midfielder; ATT = attacker.
All correlations p < .001. For all correlations, the maximum value occurred at zero lag.
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the overall load of the match, instead of detailed information about when the team is attacking
(more load on offensive players) or defending (more load on defensive players).
The present study shows very high to nearly perfect associations between positional pat-
terns of player load and player load per meter (Table 1). Hence, the periods of high player load
are associated with movements on the field that increases the player load per meter, which is
shown to be associated with unorthodox movements such as jumping, tackling, collisions,
passing, accelerations, decelerations etc., movement which are common for soccer and
detected when triaxial accelerometers are employed [12, 13]. Although this study found differ-
ences in the absolute values of the highest and lowest player load periods in the presented
results, there were no positional differences in the pattern of increase and decrease of player
load throughout the matches (Fig 1). Thus, the present study is the first to report similarity
Fig 3. Mean values (z-scores) of time-motion variables in 5-min moving windows throughout match time across all matches (n = 34) for each position (colored
lines) and for all positions combined (black lines). A: total distance; B: accelerations; C: decelerations; D: sprint distance; E: high-intensity running distance (HiR).
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across playing positions in the player load patterns throughout matches in male elite soccer
players. The use of this approach and the findings from this study may contribute to new
hypotheses concerning the patterns of player load and intensity throughout a soccer match.
Therefore, before one can conceptualize more in-field applications, different aspects of player
load patterns should be investigated further.
Whereas other investigations show a considerable heterogeneity in the within-match pat-
tern of total distance, HiR and sprint across studies [9, 20, 21], in this study, total distance was
the variable besides player load and player load per meter which displayed the highest correla-
tion between positions, with no lag between positional patterns (Table 1). Regardless of this,
the patterns of total distance for the different positions do not follow the same distinct pattern
as the player load variables. For accelerations and deceleration, no specific pattern was evident
throughout match time (Fig 3B and 3C), but the different positions appear to follow roughly
similar patterns with correlations ranging from low to high (S1 Table). For sprint and HiR dis-
tance, the present study shows no meaningful similarities between positions, with negligible to
moderate cross-correlations (S1 Table). These findings are similar to those from other studies
investigating high-intensity patterns [6, 7]. In the present results, the patterns of the different
HiR and sprint distance throughout match time show heterogeneity; patterns of sprint and
HiR distance show that high-intensity periods occur at different times both between matches
and between positions. These differences could be caused by different tactical elements, oppo-
nents playing style, pacing strategies, score line, and team formation, which would all affect the
players’ ability to regulate their physical effort and maintain work rates at appropriate levels
[22].
Player load and interpersonal coordination patterns
The observed in-phase pattern for player load in this study is also interesting from perspectives
of interpersonal coordination patterns, and it demonstrates that the interaction in player load
between the team’s subunits probably is more complex than the behavior of each individual
player considered separately [31, 32]. Specifically related to soccer, the actions of one player or
a player subunit (e.g., attackers, midfielders, defenders) cause re-actions and adjustments from
other players or player subunits to stabilize performance, and these adjustments interact and
influence player load collectively. The emergence of the synchronized player load patterns
between subunits is likely self-organized to improve team performance and is a result of the
interactions of a player’s constraints and information exchange within their own team and
those imposed by the opponent. What type of constraints and information that evolves in
spontaneous self-organization and synchronization of player load is not easy to identify, but
might be easily understood intuitively. Examples of such constraints in soccer could be other
players’ positions and movements, position and speed of the ball, tactical decisions, fatigue,
etc. According to the rationale by Haken and Portugali [33], if the meaning of a player’s action
is understood (information exchange), it triggers action and changes the structure or behavior
(player load) in the whole team. E.g., the reaction of players on the action of another depends
on the success or failure (information) of that action. The interesting finding of the present
study is that, even though each action’s success or failure may occur randomly, the player load
pattern that evolves seems very stable. Thus, the interpersonal patterns of coordination of
player load in a soccer team might be modelled as an open complex dynamical system at a
behavioral level of analysis, as suggested in evolutionary game theory [34]. Given the stable
player load pattern over various matches, even though a soccer match is the complex combina-
tion of actions by individuals, no individual player (or subunit) seems to initiate or control the
behavior of the match. In other words, each player is enslaved in a self-organized system that
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at the same time consists of all these same players. This self-organized system could be affected
by the fact that this study investigated one of the top-ranked clubs at their home arena, which
could have produced a more consistent player load pattern due to typically being the dominant
team.
Limitations
Since this study investigated one of the top-ranked clubs at their home arena, it is possible a
more consistent player load pattern was produced due to typically being the dominant team. It
is unclear to what extent the results will replicate across teams or if they are particular to either
the investigated team or e.g., teams sharing certain characteristics. This study did not investigate
differences between various tactical elements, opponents’ playing styles, ball in versus out of
play, score line, or team formations, which could all affect the players’ ability to regulate their
physical effort and maintain work rate profiles. Differences in measurement technology makes
it difficult to compare player load variable between different tracking systems (or even different
versions of the same system), since differences in measurement technology could partly account
for eventual discrepancies between the values registered in this study and other studies. Hence
caution is required when comparing analyses of football match activities across studies.
Conclusion
This study demonstrated similarity in player load patterns between positions in elite soccer
matches. The novelty is the clear pattern which consists of three high-load periods in both
halves, where these “high load” periods are followed by periods with reduced load. The present
study did not find similar unambiguous patterns on any of the locomotor variables. The evi-
dent pattern in player load indicates a physical “pacing pattern” employed by the team. These
“pacing patterns” were relatively similar between positions and occurred at the same time
points during the matches over three successive seasons. From the perspective of interpersonal
coordination patterns, these synchronized player load patterns between positions are likely
self-organized to improve team performance and are a result of the interactions of the players’
constraints and information exchange within their own team and those imposed by the oppo-
nents. It should be noted that a more consistent player load pattern might have been produced
due to the investigated team being a top-ranked club playing home matches.
Practical applications
Since this study is the first to report this distinct pattern of player load it is important that
more studies of player load patterns are conducted, in teams at different performance levels
before in-field applications can be firmly conceptualized. Considering the previously reported
high association between player load and internal training load, it could be argued that coaches
might want to regulate player load in training for an overreaching effect. This could eventually
allow for a more aggressive pacing strategy, shortening the lower-load periods and hence put-
ting more pressure on the opposition. However, an approach like this must be cautious against
overloading. During matches, coaches can also use the method proposed here in real-time to
monitor if certain players or position groups appear to be “out of sync” with the rest of the
team.
Supporting information
S1 Table. Cross-correlations [95% CI] of mean position values (z-scores) across all matches
(n = 34) at zero lag for accelerations, decelerations, sprint distance, and high-intensity
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running distance. CD: central defender; ED: external defender; CM: central midfielder; EM:
external midfielder; ATT: attacker. For p-values, bold text indicates significance at α = .05.
(DOCX)
S2 Table. Maximum cross-correlations (corresponding lag) of mean position values (z-
scores) across all matches (n = 34) for accelerations, decelerations, sprint distance, and
high-intensity running distance. CD: central defender; ED: external defender; CM: central
midfielder; EM: external midfielder; ATT: attacker. A negative lag means that the first time
series (player position in table columns) shifts to the left relative to the second time series
(player position in table rows).
(DOCX)
S1 Dataset.
(XLSX)
Acknowledgments
We thank the players for their efforts throughout the period.
Author Contributions
Conceptualization: Terje Dalen, Tore Kristian Aune, Geir Håvard Hjelde, Øyvind Sandbakk,
David McGhie.
Data curation: Terje Dalen, David McGhie.
Formal analysis: Terje Dalen, Øyvind Sandbakk, David McGhie.
Investigation: Terje Dalen, Geir Håvard Hjelde, David McGhie.
Methodology: Terje Dalen, Øyvind Sandbakk, David McGhie.
Project administration: Terje Dalen, David McGhie.
Resources: Terje Dalen.
Supervision: Terje Dalen.
Validation: Terje Dalen.
Visualization: Terje Dalen, David McGhie.
Writing – original draft: Terje Dalen, Tore Kristian Aune, Øyvind Sandbakk, David McGhie.
Writing – review & editing: Terje Dalen, Tore Kristian Aune, Gertjan Ettema, Øyvind Sand-
bakk, David McGhie.
References
1.
Ingebrigtsen J.; Bendiksen M.; Randers M.B.; Castagna C.; Krustrup P.; Holtermann A. Yo-Yo IR2 test-
ing of elite and sub-elite soccer players: Performance, heart rate response and correlations to other
interval tests. J Sports Sci 2012, 30, 1337–1345 https://doi.org/10.1080/02640414.2012.711484
PMID: 22867048
2.
Bloomfield J.; Polman R.; O’Donoghue P. Physical demands of different positions in FA Premier League
soccer. J Sports Sci Med 2007, 6, 63–70. PMID: 24149226
3.
Krustrup P.; Andersson H.; Mohr M.; Randers M.B.; Jensen J.M.; Zebis M. et al. Match activities and
fatigue development of elite female soccer players at different levels of competition. Science and Foot-
ball VI: the proceedings of the Sixth World Congress on Science and Football 2009, 205–211.
4.
Mallo J.; Navarro E. Physical load imposed on soccer players during small-sided training games. J
Sports Med Phys Fitness 2008, 48, 166–171. PMID: 18427410
PLOS ONE
Player load pattern in soccer matches
PLOS ONE | https://doi.org/10.1371/journal.pone.0239162
September 21, 2020
11 / 13
5.
Drust B.; Atkinson G.; Reilly T. Future Perspectives in the Evaluation of the Physiological Demands of
Soccer. Sports Med 2007, 37, 783–805. https://doi.org/10.2165/00007256-200737090-00003 PMID:
17722949
6.
Gregson W.; Drust B.; Atkinson G.; Salvo V.D. Match-to-match variability of high-speed activities in pre-
mier league soccer. Int J Sports Med 2010, 31, 237–242. https://doi.org/10.1055/s-0030-1247546
PMID: 20157871
7.
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, 2215–2223. https://doi.org/10.1080/02640414.
2016.1176228 PMID: 27144879
8.
Rebelo A.; Brito J.; Seabra A.; Oliveira J.; Drust B.; Krustrup P. A New Tool to Measure Training Load in
Soccer Training and Match Play. Int J Sports Med 2012, 297–304. https://doi.org/10.1055/s-0031-
1297952 PMID: 22290322
9.
Bradley P.S.; Di Mascio M.; Peart D.; Olsen P.; Sheldon B. High-intensity activity profiles of elite soccer
players at different performance levels. J Strength Cond Res 2010, 24, 2343–2351. https://doi.org/10.
1519/JSC.0b013e3181aeb1b3 PMID: 19918194
10.
Mohr M.; Krustrup P.; Bangsbo J. Fatigue in soccer: a brief review. J Sports Sci 2005, 23, 593–599.
https://doi.org/10.1080/02640410400021286 PMID: 16195008
11.
Carling C.; Bloomfield J.; Nelsen L.; Reilly T. The Role of Motion Analysis in Elite Soccer: Contemporary
Performance Measurement Techniques and Work Rate Data. Sports Med 2008, 38, 839–862. https://
doi.org/10.2165/00007256-200838100-00004 PMID: 18803436
12.
Dalen T.; Ingebrigtsen J.; Ettema G.; Hjelde G.H.; Wisløff U. Player load, acceleration, and deceleration
during forty-five competitive matches of elite soccer. J Strength Cond Res 2016, 30, 351–359. https://
doi.org/10.1519/JSC.0000000000001063 PMID: 26057190
13.
Dalen T.; Overas O.; van den Tillaar R.; Welde B.; von Heimburg E.D. Influence of different soccer-spe-
cific maximal actions on physiological, perceptual and accelerometer measurement loads. Open
Access J Sports Med 2018, 9, 107–114, https://doi.org/10.2147/OAJSM.S167347 PMID: 29942166
14.
Barrett S.; Midgley A.; Reeves M.; Joel T.; Franklin E.; Heyworth R.et al. The within-match patterns of
locomotor efficiency during professional soccer match play: Implications for injury risk? J Sci Med Sport
2016, 19, 810–815. https://doi.org/10.1016/j.jsams.2015.12.514 PMID: 26787341
15.
Scott B.R.; Lockie R.G.; Knight T.J.; Clark A.C.; De Jonge X.A.K.J. A Comparison of Methods to Quan-
tify the In-Season Training Load of Professional Soccer Players. Int J Sports Physiol Perform 2013, 8,
195–202. https://doi.org/10.1123/ijspp.8.2.195 PMID: 23428492
16.
Gaudino P.; Iaia F.M.; Strudwick A.J.; Hawkins R.D.; Alberti G.; Atkinson G. et al. Factors Influencing
Perception of Effort (Session Rating of Perceived Exertion) During Elite Soccer Training. Int J Sports
Physiol Perform 2015, 10, 860–864. https://doi.org/10.1123/ijspp.2014-0518 PMID: 25671338
17.
Casamichana D.; Castellano J.; Calleja-Gonzalez J.; San Roman J.; Castagna C. Relationship between
indicators of training load in soccer players. J Strength Cond Res 2013, 27, 369–374. https://doi.org/10.
1519/JSC.0b013e3182548af1 PMID: 22465992
18.
Barrett S.; Midgley A.; Lovell R. PlayerLoad™: Reliability, Convergent Validity, and Influence of Unit
Position During Treadmill Running. Int J Sports Physiol Perform 2014, 9, 945–952. https://doi.org/10.
1123/ijspp.2013-0418 PMID: 24622625
19.
Harper D. J.; Carling C.; Kiely J. (2019). High-Intensity Acceleration and Deceleration Demands in Elite
Team Sports Competitive Match Play: A Systematic Review and Meta-Analysis of Observational Stud-
ies. Sports Med 2019 49(12): 1923–1947. https://doi.org/10.1007/s40279-019-01170-1 PMID:
31506901
20.
Wehbe G.M.; Hartwig T.B.; Duncan C.S. Movement Analysis of Australian National League Soccer
Players Using Global Positioning System Technology. J Strength Cond Res 2014, 28, 834–842.
https://doi.org/10.1519/JSC.0b013e3182a35dd1 PMID: 23897019
21.
Rampinini E.; Couts A.J.; Castagna C.; Sassi R.; Impellizzeri F.M. Variation in Top Level Soccer Match
Performance. Int J Sports Med 2007, 28, 1018–1024. https://doi.org/10.1055/s-2007-965158 PMID:
17497575
22.
Waldron M.; Highton J. Fatigue and Pacing in High-Intensity Intermittent Team Sport: An Update.
Sports Med 2014, 44, 1645–1658. https://doi.org/10.1007/s40279-014-0230-6 PMID: 25047854
23.
Akenhead R.; Hayes P.R.; Thompson K.G.; French D. Diminutions of acceleration and deceleration out-
put during professional football match play. J Sci Med Sport 2013, 16, 556–561. https://doi.org/10.
1016/j.jsams.2012.12.005 PMID: 23333009
24.
Carling C. Interpreting Physical Performance in Professional Soccer Match-Play: Should We be More
Pragmatic in Our Approach? Sports Med 2013, 43, 655–663. https://doi.org/10.1007/s40279-013-
0055-8 PMID: 23661303
PLOS ONE
Player load pattern in soccer matches
PLOS ONE | https://doi.org/10.1371/journal.pone.0239162
September 21, 2020
12 / 13
25.
Dalen T.; Lorås H.; Hjelde G.H.; Kjøsnes T.N.; Wisløff U. Accelerations–a new approach to quantify
physical performance decline in male elite soccer? Eur J Sport Sci 2019, 19(8), 1015–1023. https://doi.
org/10.1080/17461391.2019.1566403 PMID: 30632940
26.
Bradley P. S.; Sheldon W.; Wooster B.; Olsen P.; Boanas P.; Krustrup P. (2009). High-intensity running
in English FA Premier League soccer matches. J Sports Sci 2009, 27(2): 159–168.
27.
Barrett S.; Midgley A.W.; Towlson C.; Garrett A.; Portas M.; Lovell R. Within-Match PlayerLoad™ Pat-
terns During a Simulated Soccer Match: Potential Implications for Unit Positioning and Fatigue Manage-
ment. Int J Sports Physiol Perform 2016, 11, 135–140. https://doi.org/10.1123/ijspp.2014-0582 PMID:
26114855
28.
Ingebrigtsen J, Dalen T, Hjelde GH, Drust B, Wisloff U. Acceleration and sprint profiles of a professional
elite football team in match play. Eur J Sport Sci 2015, 15(2), 101–110. https://doi.org/10.1080/
17461391.2014.933879 PMID: 25005777
29.
Varley M.C.; Elias G.P.; Aughey R.J. Current Match-Analysis Techniques’ Underestimation of Intense
Periods of High-Velocity Running. Int J Sports Physiol Perform 2012, 7, 183–185. https://doi.org/10.
1123/ijspp.7.2.183 PMID: 22634968
30.
Hopkins W.; Marshall S.; Batterham A.; Hanin J. Progressive statistics for studies in sports medicine
and exercise science. Med Sci Sports Exerc 2009, 41, 3–13. https://doi.org/10.1249/MSS.
0b013e31818cb278 PMID: 19092709
31.
Schmidt R.C.; Fitzpatrick P. Dynamical perspective on motor learning. In In, Zelaznik H.N. (ed.),
Advances in motor learning and control, Champaign, Ill., Human Kinetics Publishers, 1996, p. 195–
223; 1996.
32.
Turvey M.T. Coordination. Am Psychol 1990, 45, 938. https://doi.org/10.1037//0003-066x.45.8.938
PMID: 2221565
33.
Haken H.; Portigali J. Information and self-organization. Entropy 2017, 19, 18.
34.
Santos F.; Encarnacão S.; Santos F.; Portugali J.; Pacheco J. An evolutionary game theoretic approach
to multi-sector coordination and self-organization. Entropy 2016, 18, 152.
PLOS ONE
Player load pattern in soccer matches
PLOS ONE | https://doi.org/10.1371/journal.pone.0239162
September 21, 2020
13 / 13

Player load in male elite soccer: Comparisons of patterns between matches and positions.
Dalen, Terje,Aune, Tore Kristian,Hjelde, Geir Håvard,Ettema, Gertjan,Sandbakk, Øyvind,McGhie, David