Position specific player load during match-play in a professional football club
Position specific player load during match- play in a professional football club
Ivan Baptista 2 3
Dag Johansen 1 3
AndreÂ Seabra 0 3
Svein Arne Pettersen 2 3
0 Research Centre in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sport, University of Porto , Porto , Portugal
1 Computer Science Department, University of Tromsø, the Arctic University of Norway , Tromsø , Norway
2 School of Sport Sciences, University of Tromsø, the Arctic University of Norway , Tromsø , Norway
3 Editor: Riccardo Di Giminiani, University of L'Aquila , ITALY
There is a rapid growing body of knowledge regarding physical aspects of a football match due to studies using computer-assisted motion analysis. The present study used timemotion analysis and triaxial-accelerometers to obtain new insights about differences in physical profiles of elite football players across playing-positions. Player performance data in 23 official home matches from a professional football club, during two seasons were collected for analysis. Eighteen players from five different playing positions (central backs: n = 3; full-backs: n = 5; central midfielders: n = 6; wide midfielders: n = 3; and central forwards: n = 4), performing a total of 138 observations. A novel finding was that central backs and central midfielders had significantly lower work-rate in sprints, decelerations and accelerations than full-backs, wide midfielders and central forwards (p<0.001). Furthermore, wide midfielders and full-backs performed significantly more turns (>90Ê) than central backs. The most common distance covered in high-intensity runs ( 19.8 km h−1) for central backs, central midfielders, wide midfielders and central forwards was 1±5 m, but for full-backs was 6±10 m. This may help coaches in developing individualized training programs to meet the demands of each position in match-play.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
To understand physical demands of match-play in football objective data is essential andsuch
data could be important for practitioners in designing training programs [
]. Of particular
importance is the potential value objective data provide for personalized prescription of
training load in a cohort of players following the same overall training regime.
Time motion analysis is commonly used in elite football to analyse player and team
performance in training and match as it allows quantification of player running activities and
indirect verification of the energetics of match-play [
], creating a rapid growing body of
knowledge regarding the physical aspects of football training and match-play [
Football has a high-intensity intermittent nature [
], characterised by prolonged
intermittent exercise interspersed by periods of maximal or close to maximal effort [
]. Players may be
required to repeat sprints, accelerations and turns of short duration interspersed by brief
recovery periods over an extended period of time, and these activities have been reported as
crucial factors for team performance [6±9].
Previous research has focused on the influence of different factors in the players' match
running profiles, such as the tactical systems [
], possession status [
standard , seasonal fluctuations [
], environment [
], opponent [
] and playing positions
Based on robust findings within the research literature, it is evident that specific playing
positions have an influence on total match-load. Midfielders appear to cover the greatest
overall distances (~11.5 km) while defenders and forwards cover lower distance (10±10.5 km) [4,
19±21]. Regarding high-intensity runs (HIR), the literature shows that, typically, wide
midfielders (WM) and full-backs (FB) display superior HIR profiles [
20, 22, 23
] and central backs
(CB) perform a significantly less amount of time sprinting and running with high intensity
compared with other positions [
The use of only distance and speed may underestimate the calculation of external player
workload since this type of time-motion analysis has neglected some essential and specific
movements of football (turns, accelerations, decelerations, etc.) that together appear numerous
times during every match and may cause significant physical stress on the players [
A previous study, with a Norwegian elite football team [
], combined data from triaxial
accelerometer and time-motion analysis and experienced that player load was accumulated in
a variety of ways across the different playing positions with accelerations and decelerations
contributing 7±10% and 5±7%, respectively. Previous research has shown that players in lateral
positions (FB and WM) accelerate more often, whereas CB and central midfielders (CM)
decelerate less compared to other positions [24±26].
Therefore, the aims of the present study were to establish and compare the physical
demands during official match-play in five different playing positions (CB, FB, CM, WM and
central forwards [CF]) in a Norwegian elite football team using time-motion and
Subjects and match analysis
With approval from UiT The Arctic University of Norway Institutional Review Board, written
informed consent from players and approval from Norwegian Centre for Research Data, data
on performance in 23 official home matches from the first team (highest level) in a Norwegian
elite football club, during two seasons (2016 and 2017), were collected for analysis. The
matches were all played on artificial grass surface (Alfheim Stadium, Tromsø, length = 110m;
width = 68m). The sample included 18 players (25.2 ± 4.4 years; 76.2 ± 6.4 kg; 181.6 ± 5.6 cm;
in age, body mass and height, respectively) across five different playing positions: CB (n = 3,
observations[obs] = 35), FB (n = 5, obs = 34), CM (n = 6, obs = 38), WM (n = 3, obs = 18) and
CF (n = 4, obs = 13), making a total of 138 observations. These positions were chosen
according to team's main tactic formation and previous research [
8, 18, 20, 24, 26, 27
Data was analysed only if: (1) players completed the entire match, (2) the player played in
the same position during all the match and (3) the team used 4-5-1 or 4-3-3 tactic formations.
To ensure players confidentiality, all data was anonymized before analyses.
A stationary radio-based tracking system (ZXY Sport Tracking System, Trondheim Norway)
was used to characterize match activity profiles in the team. Each player wore a specially
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designed belt, wrapped tightly around the waist, with an electronic sensor system at the
player's lumbar spine [
]. The accuracy and reliability of the system in measuring player
movements in elite soccer competitions have been described in more detail in previous studies [
Physical performance variables
Physical parameters analysed included: number of accelerations (acccounts), acceleration
distance per minuteÐwork-rateÐ(accwr), number of decelerations (deccounts), deceleration
work-rate (decwr), HIR work-rate (HIRwr), HIR distance (HIRdist), sprint work-rate (sprintwr),
sprint distance (sprintdist) and turns.
The following locomotor categories were selected: HIR ( 19.8 km h−1) and sprinting
( 25.2 km h−1). The speed thresholds applied for each locomotor categories are similar to
those reported in previous research [
16, 20, 24, 26
According to the ZXY Sport Tracking system, accelerations are defined by four event
markers: (1) the start of the acceleration event is marked by the acceleration reaching the minimum
limit of 1 m s −2, (2) the acceleration reaches the acceleration limit of 2 m s −2, (3) the
acceleration remains above the 2 m s −2 for at least 0.5 seconds and (4) the duration of the acceleration
ends when it decreases below the minimum acceleration limit (1 m s −2).
A turn was defined as a continuous and significant rotation of the body in one direction
(derived from gyroscope and compass data). When a rotation in the opposite direction is
measured, that will be the end of the previous turn and the start of the next turn. Due to the angle
threshold used by ZXY Sport Tracking system only turns 90 degrees were analysed.
Descriptive statistics (means and standard deviations) were calculated for the total sample and
Differences in match performance measures by field position were tested with a one-way
analysis of variance (ANOVA). When significance was found, a Bonferroni post-hoc test was
Effect sizes (ES), using Cohen`s d, was calculated and interpreted as trivial ( 0.2), small
(>0.2±0.6), moderate (>0.6±1.2) and large (>1.2). Significance level was set at 0.05 [
Statistical analyses were conducted using SPSS version 24.0.
Acceleration and deceleration profiles
There were similar patterns in accwr and decwr with CB and CM performing less than FB, WM
and CF, with the most significant difference being between CB (3.5 ± 0.7) and CF (5.3 ± 1.0) in
In relation to acccounts and deccounts WM presented higher values (76.7 ± 12.1; 86.1 ± 14.7)
than CB (64.9 ± 9.7; 61.5 ± 10.8) and CM (65.8 ± 15.6; 71.5 ± 20.6) (p<0.001), respectively.
Furthermore, all positions, except CB, performed less acccounts than deccounts during the
entire match (Table 1).
HIR and sprint profiles
Differences were observed in HIRWR and Sprintwr between CB and the other positions. CB
had the lowest values of all positions in both variables but especially pronounced in Sprintwr
(0.9 ± 0.5 m/min) when compared with CF (2.5 ± 1.0 m/min) (p<0.001).
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Regarding HIRdist, CF presented higher values in 26±30 m than all the other positions,
while distances of 36±40 and 46±50 m were covered more times by FB (1.7 ± 1.4; 0.9 ± 1.0). CB
(0.8 ± 0.9; 0.2 ± 0.6) were the players with lowest values in these longer distances (36±40 and
46±50). Furthermore, distances of 1±5 m were the distances covered more often by CB, CM,
WM and CF, whereas FB had higher values in distances of 6±10 m (Table 2).
In relation to sprintdist CB, FB, CM and WM performed higher number of 1±5 m, while CF
covered higher number of 6±10 m sprints. (Table 3).
Furthermore, there was a pattern of covariance in the work-rates analysed (acc, dec, HIR
and sprint) across playing positions (Fig 1).
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The present study shows that the physical demands in official match-play, in elite football, vary
greatly across playing positions. As previously mentioned, a novel finding from this study was
that the work-rates in HIR, sprints, accelerations and decelerations change in the same pattern
across playing positions. Although further research is needed to verify the correlation between
these variables, our results demonstrate that CB and CM had significantly lower work-rate in
sprints, accelerations and decelerations than FB, WM and CF with CB also having lower
HIRwr than these three playing positions (p<0.001). These findings are in line with the
research literature regarding FB covering greater high-intensity and sprinting distances during
matches compared to CB. [
13, 18, 20, 31
Previous studies have reported greater distances in HIR and sprint covered by wide players
(FB and WM) compared with more central positions (CB, CM and CF) [
13, 20, 24, 31
however the present study shows significant higher work-rate for wide positions only in acc, dec
and sprints but not in HIR, even though the values for wide positions are slightly, though
insignificantly, higher than for central positions (excluding CF). No significant differences were
observed between CF and WM in HIRwr which is in line with previous research , but in
opposition to others [
11, 20, 31
]. Furthermore, our data show that CF is the most physical
demanding position with longer distances covered in HIR, sprints, accelerating and
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Fig 1. Work-rate profiles across playing position. Mean work-rate in sprints, HIR, acc and dec.
decelerating than the other positions. It has been speculated within the research literature that
these differences between wide and more central positions are due to a lack of space for
reaching sprinting velocity and the playing style (different roles for different positions) [
24, 25, 32
Taking into consideration the specific context of the club where our data was collected, it
seems evident that the style of play (playing many times with low defence and in
counterattacking) had a crucial influence on position's specific physical demands.
Table 2 illustrates that player position had a significant influence on the different distances
covered in HIR. To the best of our knowledge, no previous research has characterized players'
HIR profiles regarding specific distances covered per HIR in official match-play across
different playing positions. Our data show that while the most common distance covered in HIR for
CB, CM, WM and CF was 1±5 m, for FB it was 6±10 m. An aspect to consider is that we also
observed some HIR longer than the ones presented in Table 2 but with no significant
differences between positions.
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Different patterns appear in sprintdist with CB, FB, CM and WM covering more often
shorter distances (1±5 m) in sprint while CF had higher values in longer distances (6±10 m).
Another important finding is that CF and WM accelerated more often compared with
players in the other positions, which differs from a previous study with another Norwegian
professional football club [
]. However, some similar trends were observed between these studies,
with CB being the players who decelerated the least times compared with other playing
positions. Furthermore, when comparing our data with results from previous research [
4, 24, 25
we observed slightly lower values of acccounts in almost all the positions (CB, FB, CM and
WM). The inverse trend was observed in deccounts with all positions presenting higher values
in our study, probably due to style of play.
A main finding of the present study refers to the number of turns observed across playing
positions. In fact, even though our study has taken into consideration only turns >90Ê (angle
threshold defined by ZXY Sport Tracking), total different values were obtained compared with
previous research [
]. One difference is related to the total number of turns per match with
our study presenting a mean of ~42 ± 13 to attackers (CF), ~39 ± 13 to midfielders (CM and
WM) and ~37 ± 12 to defenders (CB and FB), while previous research [
] presented mean
values significantly higher for each position: attackers (~101), midfielders (~107) and
defenders (~97) in turns >90Ê. They observed that midfielders performed significantly fewer turns
during a match than defenders and strikers. Our data show that CM did not perform
significantly different compared to the other positions while WM performed more turns than CB.
These differences may be caused by the different sampling technology used.
Both turns, acceleration and deceleration activities add substantial load in addition to
highintensity running and must be taken into consideration when analysing physical demands of
It should be noted that different measurement technologies could cause the discrepancy in
results between the present study and previous research [
]. Also, different playing styles,
cultural and competitive contexts may account for differences observed.
In summary, our data show that speed and distance measures only to some extent predict
the physical demands of a football player and that these demands vary greatly across playing
positions. Taking into consideration the law of training specificity [
] and the idea that the
physical loading of the training session should be individually designed to improve
performance and avoid excess of fatigue and overtraining [
] the coaches need a clear view how
different playing positions achieve load.
The present results may provide useful and novel insight regarding positional differences in
physical profiles of elite football players during match-play. The positional differences in
workload and work pattern need to be taken into consideration when designing and implementing
training program cycles, according to the team's style of play. As for the team explored in the
present study, lateral players should perform some longer sprints 30 m in normal training
weeks to be prepared for these actions that appear during match. Performing sprints in
addition to small sided games must be taken into consideration when planning the trainings since
small and medium sided games do not provide enough space to elicit these actions.
Apart from providing valuable information to coaches about the activity profiles of
different positions, the results may also provide the foundation for a real-time personalization
computerized coach toolkit based on our whole-field video analysis system [
] that integrates
with positional data in real-time. We are currently developing such a mobile system to
customize individual training load to player positions while the practice is unfolding.
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S1 File. Data review.
Conceptualization: Ivan Baptista.
Data curation: AndreÂ Seabra.
Formal analysis: AndreÂ Seabra.
Investigation: Ivan Baptista.
Methodology: Ivan Baptista.
Project administration: Svein Arne Pettersen.
Supervision: Dag Johansen, Svein Arne Pettersen.
Writing ± original draft: Ivan Baptista.
Writing ± review & editing: Dag Johansen, Svein Arne Pettersen.
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