International  Journal  of
Environmental Research
and Public Health
Article
High-Performance Handball Player’s Time-Motion
Analysis by Playing Positions
Carmen Manchado 1
, Juan Tortosa Martínez 1
, Basilio Pueo 1
,
Juan Manuel Cortell Tormo 1
, Helena Vila 2,*, Carmen Ferragut 3,
Francisco Sánchez Sánchez 4, Sonia Busquier 5, Sergio Amat 5 and Luis Javier Chirosa Ríos 6
1
Faculty of Education, University of Alicante, 03690 San Vicente del Raspeig, Spain;
carmen.manchado@ua.es (C.M.); juan.tortosa@gcloud.ua.es (J.T.M.); basilio@gcloud.ua.es (B.P.);
jm.cortell@gcloud.ua.es (J.M.C.T.)
2
Faculty of Education, University of Vigo, 36905 Pontevedra, Spain
3
Faculty of Medicine and Health Sciences, University of Alcalá, 28871 Alcalá de Henares, Spain;
cferragutﬁol@gmail.com
4
Faculty of Sport Science, University of Castilla La Mancha, 45071 Toledo, Spain; Fco.Sanchez@uclm.es
5
Department of Applied Mathematics and Statistics, University of Cartagena, 30203 Cartagena, Spain;
sonia.busquier@upct.es (S.B.); sergio.amat@upct.es (S.A.)
6
Department of Physical Education and Sports, University of Granada, 18011 Granada, Spain; lchirosa@ugr.es
*
Correspondence: evila@uvigo.es
Received: 31 July 2020; Accepted: 13 September 2020; Published: 17 September 2020


Abstract: The purpose of this study was to analyze the on-court demands of handball players during
the European Handball Federation Champions League Final Four (VELUX EHF FINAL4) 2019 to deﬁne
time–motion characteristics (played time; covered distances) both in oﬀense and defense. Furthermore;
we aimed to deﬁne position-speciﬁc demands and diﬀerences among them. Forty players from three
teams were analyzed during the tournament using a local positioning system (LPS) for the ﬁrst time
in top handball. Players covered similar distances both in oﬀense (1388.28 ± 2627.08 m), and in
defense (1305.47 ± 5059.64 m) and remained on court for a similar average time (15.69 ± 8.02 min
and 15.40 ± 8.94 min respectively). When locomotion activities were normalized according to
the time they spent on court; signiﬁcant diﬀerences were found for defense compared to oﬀense in
walking (+20%; p < 0.000; Cohen’s eﬀect size (ES) = 1.01) and jogging (−29.6%; p = 0.000; ES = 0.90),
as well as a tendency for high-intensity running (+ 25.2%; p = 0.077; ES = 0.31). Per playing
position; center and left back (CB = 94.86 ± 10.98 m·min−1; LB = 96.55 ± 24.65 m·min−1) showed
the highest running pace in oﬀense and mid-left; front center defender and outside right for
the defense (ML = 90.38 ± 30.16 m·min−1; FCD = 87.04 ± 14.94 m·min−1; OR = 89.64 ± 34.93 m·min−1).
In conclusion; proﬁle diﬀerences existed among players’ position activity; both in oﬀense and defense;
which should be taken into account when designing speciﬁc physical training programs
Keywords: running pace; running distance; competition load; LPS
1. Introduction
Handball is an Olympic sport, belonging to so-called team sports. It is characterized by fast
transitions between oﬀensive and defensive actions during the game with the ultimate objective of
scoring a goal [1,2]. To this end, oﬀensive players (six ﬁeld players and one goalkeeper) attempt to
create spaces that allow them to throw the ball towards the goal in advantageous conditions, while
the defense tries to avoid it, causing a great amount of physical confrontations between players [2].
These attack phases in handball are dynamic, characterized by fast movements and a high frequency
of fast passes, so physical demands are important [1]. Furthermore, these physical demands are not
Int. J. Environ. Res. Public Health 2020, 17, 6768; doi:10.3390/ijerph17186768
www.mdpi.com/journal/ijerph
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only the same if the team is in the oﬀensive or defensive phase and even if the player plays in one
position or another [3–5].
The very nature of the game implies that players must be physically trained to maintain the game’s
speed and intensity throughout a match [4,6–8], whether they play in oﬀense or defense. Therefore,
knowing and understanding the sport’s physical demands (distances, speeds, intensities) [1], as well
as technical–tactical actions [4,7] (passes, throws, jumps, marking, change of direction, etc.) is essential
to correctly plan players’ training. [1,8]. All these elements are of great importance in handball and are
also closely related to each other, which makes handball a particularly complex sport [4,5,9].
Likewise, it is important to note that the playing position, the game phase (oﬀense or defense),
as well as the team’s playing style can lead to big diﬀerences in each player’s physical demands.
Therefore, the physical load cannot only be determined generally, but according to each player’s
speciﬁc position on the court both in oﬀense and defense [4,6,10]. All this information could help
coaches to better individualize training loads and thereby improve performance [4,6].
This necessity to understand handball’s physical characteristics has raised great interest among
researchers who have studied these demands using diﬀerent methodologies [1]. The most widely used
method has been time–motion analysis, based on observing players in the competition followed by an
analysis of a video, taken with one camera [11,12] or two cameras [13]. The video-recorded matches
are analyzed and the actions encoded. However, this method is time-consuming and depends on a
subjective analysis of the observer, thus not being an objective or precise method when determining
the diﬀerent locomotion speeds.
Notwithstanding, no method exists to date that allow one to accurately measure the physical
and physiological demands of handball players during the competition. In order to overcome this gap,
the European Handball Federation (EHF), Select® and Kinexon® jointly developed the Kinexon®
tracking system for handball players (Kinexon: München, Germany; Select Sport 1947: Glostrup,
Denmark) in addition to a monitored ball, the iball, which has been recently validated [14] and used in
studies on handball [15] and other team sports [16]. This technology provides us with values regarding
movements, accelerations, changes of direction, jumps, as well as data on the speed at which the ball
is transferred (game speed) and the speed and position of the throws in real time, opening up new
possibilities in the study of handball competition requirements [16]. With this fully automatic tracking
system, the inconveniences mentioned for the conventional time–motion analysis are solved.
Despite a great interest in understanding the requirements of high-level players, only a few
studies have focused on analyzing the real demands of an elite handball competition in male
handball [3,13,17,18]. Cardinale, Whiteley, Hosny, and Popovic [3], studied players’ movements during
the men’s world championship using three cameras, and provided new data on players’ movements
(distances and intensities) during the match. They concluded that there was no signiﬁcant diﬀerence in
terms of distance covered in diﬀerent locomotion categories, but they did not distinguish between
oﬀense—and defense—speciﬁc playing positions. In the same line, González de Haro [17] reported
the analysis of only one match with Global Positioning System devices (WIMU PRO™, Realtrack
Systems S.L.: Almeria, Spain). These researchers [3,17] concluded that speciﬁc physical conditioning is
necessary to maximize performance of handball players and minimize the occurrence of fatigue.
To the best of our knowledge, no study has been conducted considering in detail the two phases
of the game, oﬀense and defense, and analyzing all the playing positions by using a technology that
allows load individualization and automation, a local positioning system (LPS). Better knowledge of
on-court demands of handball players at the highest level is necessary to improve the individualization
of physical preparation [3,6,7,17,18].
Thus, the aim of this study was to analyze on-court demands of handball players during the VELUX
EHF FINAL4 to deﬁne time–motion characteristics (played time, covered distances) both in oﬀense
and defense, including position-speciﬁc demands and diﬀerences among them.
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2. Materials and Methods
2.1. Subjects
Data were obtained from players participating in the VELUX European Handball Federation
(EHF) Champions League Final Four 2019/20, held in Cologne (Germany). The teams that participated
in the Final Four were FC Barcelona (Spain), Telekom Veszprém (Hungary), HC Vardar (The Republic of
North Macedonia), and KS Kielce (Poland). Barcelona’s players were not included in the study because
their sensors were not placed properly, causing interferences in the signal and thus unreliable data.
Dainis Krištop¯ans (HC Vardar) did not wear the sensors during the games so his data were not available
for the analysis either. Finally, 40 players were analyzed during both semiﬁnals, the ﬁnal championship
game and the bronze medal game. Goalkeepers were excluded from the analysis as distance and motion
characteristics do not reﬂect their performance needs. Anthropometric characteristics and the age of
the players are presented in Table 1. This information was collected from the oﬃcial statistical data
provided by the EHF.
Table 1. Physical characteristics of the players (Mean ± Standard Deviation).
Teams
n
Height (cm)
Body Mass (kg)
BMI (kg/m2)
Age (Years)
TELEKOM
VESZPRÉM
14
193.0 ± 8.8
92.9 ± 13.6
24.8 ± 1.8
31.0 ± 4.2
HC VARDAR
13
190.2 ± 10.4
90.5 ± 14.3
24.9 ± 2.4
29.7 ± 4.2
KS KIELCE
13
190.1 ± 6.4
90.1 ± 9.9
24.9 ± 2.1
28.2 ± 6.1
Total
40
191.1 ± 8.6
91.2 ± 12.5
24.8 ± 2.1
29.7 ± 4.9
Legend: BMI = Body mass Index.
2.2. Instruments
The players’ position data were collected through a Local Positioning System (LPS) (Kinexon
Precision Technologies, Munich, Germany), which has been recently validated [7] and used in studies
on team sports [8,9], showing adequate between-device reliability (coeﬃcient of variation around 5%)
when compared to well-known systems such as GPS. Firmware versions and application software
versions corresponded to the latest releases on the testing date (August 2019). Figure 1 shows the setting
of the 9 antennae around the playing ﬁeld, connected via ethernet to the main server, and 10 anchor
antennae distributed at 3 diﬀerent levels above the ground in the Lanxess Arena.
The LPS system was installed, calibrated, and checked for accuracy by a technician who worked
for the manufacturer as follows: The exact position of the anchors in reference to the playing ﬁeld was
measured (blue numbered positions in Figure 1). Then, the anchor positions and the playing ﬁeld
position and size were transmitted to the Kinexon application. The location of one sensor at pre-deﬁned
positions (corner, penalty line, center point) was checked. In addition, two paths were followed to
test the data quality and calculated distance—walking on the sideline and walking on a meander
inside the ﬁeld (black discontinued line in Figure 1). The devices worn by players comprised a sensor
(player tag) positioned between the player’s shoulder blades using a pouch sewn onto the player’s
jersey. The functionality of the sensors was tested in the venue by randomly walking and checking
if signals were received from all units with adequate signal strength. These sensors transmit time
signals via radio-technology to the antennae, which send signals via a wide local area network (WLAN)
to local static base stations at known locations. A player’s momentary position is determined via
20 Hz frequency by calculating the time-of-ﬂight (TOF) of ultra-wide-band radio signals traveling
from the transmitter to the base stations, which calculate the actual 2D position of the devices within
the playing ﬁeld. Subsequently, instantaneous speed, i.e., scalar magnitude of velocity, as per the rate
of change in horizontal x, y positions, and acceleration, as per the rate of change in speed, are derived
by calculating the diﬀerence between two consecutive positions, i.e., approximating the derivative of
the player’s position. The raw position and speed data are then ﬁltered and smoothed by means of a
Int. J. Environ. Res. Public Health 2020, 17, 6768
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Kalman ﬁlter for position data and an exponential moving average with a window length of 1 s for
speed and position data. Data were split into oﬀensive and defensive moments of play automatically.
To this end, there was automatically a change from oﬀensive to defensive for the team and vice versa
at the moment where the ball possession changed. The respective oﬀensive shift started with the ball
possession of the team. Moreover, the system also checked if the players and the ball were moving
in the direction of the opponent’s goal. In the event the ball was outside the court, the shift was
interrupted. All data were analyzed using the system software (Kinexon Web Application, version
3.2.6, Munich, Germany).
 
TELEKOM VESZPRÉM 
14 
193.0 ± 8.8 
92.9 ± 13.6 
24.8 ± 1.8 
31.0 ± 4.2 
HC VARDAR 
13 
190.2 ± 10.4 
90.5 ± 14.3 
24.9 ± 2.4 
29.7 ± 4.2 
KS KIELCE 
13 
190.1 ± 6.4 
90.1 ± 9.9 
24.9 ± 2.1 
28.2 ± 6.1 
Total 
40 
191.1 ± 8.6 
91.2 ± 12.5 
24.8 ± 2.1 
29.7 ± 4.9 
Legend: BMI = Body mass Index. 
2.2. Instruments 
The players’ position data were collected through a Local Positioning System (LPS) (Kinexon 
Precision Technologies, Munich, Germany), which has been recently validated [7] and used in studies 
on team sports [8,9], showing adequate between-device reliability (coefficient of variation around 
5%) when compared to well-known systems such as GPS. Firmware versions and application 
software versions corresponded to the latest releases on the testing date (August 2019). Figure 1 
shows the setting of the 9 antennae around the playing field, connected via ethernet to the main 
server, and 10 anchor antennae distributed at 3 different levels above the ground in the Lanxess 
Arena. 
 
Figure 1. Local positioning system (LPS) setting: nine antennae connected to the server in red 
locations; ten reference antennae (anchors) in blue locations; meander path inside the field followed 
to check calibration accuracy (black discontinued line). 
Figure 1. Local positioning system (LPS) setting: nine antennae connected to the server in red locations;
ten reference antennae (anchors) in blue locations; meander path inside the ﬁeld followed to check
calibration accuracy (black discontinued line).
2.3. Procedure
In this study, a descriptive observational cross-sectional study was used to examine the physical
demands according to playing positions during competitive matches. This time–motion analysis is
used with team [5,19] and beach handball [19], as well as with other team sport studies [20,21].
The study was approved by the EHF. The clubs signed an informed consent in the initial contract
with the EHF to take part in the competition, where they accepted the rules and norms of the EHF,
including their participation in diﬀerent studies. The players’ data were anonymized for the purpose of
this study. The players were informed of the purposes, procedures, and risks of the study and provided
informed consent before the beginning of the study. All the procedures were conducted in accordance
with the Declaration of Helsinki and approved by the Ethics Committee of the University of Vigo
(registration number 04-719).
The variables described next were measured based on position and speed data. The distances
covered during the entire match (total distance/duration of play), distances per minute during play
and relative distance in established speed zones were computed. These zones were set as zone
1: standing (≤0.9 m/s), zone 2: walking (1.0–1.9 m/s), jogging (2.0–3.9 m/s), running (4.0–5.4 m/s),
high-intensity running (5.5–6.9 m/s) and sprinting (≥7 m/s), in accordance with similar handball
studies [3,5,18,19].
We also considered the distinction between oﬀense (when the team was in possession of the ball)
and defense (not in possession of the ball), and classiﬁed the players by their positions according to
handball nomenclature in oﬀense (left wing = LW, left back = LB, center back = CB; line player = LP;
right back = RB; and right wing = RW) and defense (center back = CB; mid right = MR; mid left = ML;
Int. J. Environ. Res. Public Health 2020, 17, 6768
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outside right = OR; outside left = OL; and front center defender = FCD). The descriptive analysis of
the data included the mean, the range, the variance and the standard deviation.
2.4. Data Analysis
Graphical, analytical and numerical studies were performed using our own developed programs.
The Shapiro–Wilk test was performed in order to verify the normality of the data. Group diﬀerences
were determined by variance analysis (ANOVA) followed by Games–Howell or Tukey post hoc testing,
or Student’s t-tests for independent samples, where appropriate. To determine the magnitude of each
relationship, Cohen’s eﬀect size (ES) was used with a modiﬁed classiﬁcation (trivial <0.2, small 0.21–0.6,
moderate 0.61–1.2, large 1.21–1.99, and very large >2.0) proposed for sports sciences [22] and used
in other similar handball studies [3]. The precision of population estimates was reported as 95%
conﬁdence intervals, and statistical signiﬁcance was set at p < 0.05.
3. Results
3.1. Time on Court, Distance Covered in Oﬀense and Defense
The average time on court in oﬀense (n = 66) and defense (n = 67) during the VELUX EHF Final
4 was 15.69 min (±8.02 min) and 15.40 min (±8.94 min), respectively. The total average distance
covered per player during each game in oﬀense was 1388.28 ± 2627.08 and 1305.47 ± 5059.64 m in
defense. When comparing oﬀense and defense with regard to the absolute distances covered (Figure 2),
signiﬁcant diﬀerences were found in walking (p = 0.017; ES = 0.61) and jogging (p = 0.03; ES = 0.77),
as well as a tendency towards high-intensity running (p = 0.075; ES = 0.45).
Int. J. Environ. Res. Public Health 2020, 17, x  
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2.4. Data Analysis 
Graphical, analytical and numerical studies were performed using our own developed 
programs. The Shapiro–Wilk test was performed in order to verify the normality of the data. Group 
differences were determined by variance analysis (ANOVA) followed by Games–Howell or Tukey 
post hoc testing, or Student’s t-tests for independent samples, where appropriate. To determine the 
magnitude of each relationship, Cohen´s effect size (ES) was used with a modified classification 
(trivial <0.2, small 0.21–0.6, moderate 0.61–1.2, large 1.21–1.99, and very large >2.0) proposed for 
sports sciences [22] and used in other similar handball studies [3]. The precision of population 
estimates was reported as 95% confidence intervals, and statistical significance was set at p < 0.05. 
3. Results 
3.1. Time on Court, Distance Covered in Offense and Defense 
The average time on court in offense (n = 66) and defense (n = 67) during the VELUX EHF Final4 
was 15.69 min (±8.02 min) and 15.40 min (±8.94 min), respectively. The total average distance covered 
per player during each game in offense was 1388.28 ± 2627.08 and 1305.47 ± 5059.64 m in defense. 
When comparing offense and defense with regard to the absolute distances covered (Figure 2), 
significant differences were found in walking (p = 0.017; ES = 0.61) and jogging (p = 0.03; ES = 0.77), as 
well as a tendency towards high-intensity running (p = 0.075; ES = 0.45). 
Walking
Jogging
Running
HIRunning
Sprinting
Distance covered (m)
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
Offense
Defense
16.27%
23.47%
47.06%
37.65%
24.28%
23.83%
9.27%
13.84%
0.35% 0.85%
*
*
 
Figure 2. Differences in distance covered in offense and defense during different locomotion 
characteristics. * Statistical differences; p ≤ 0.05.; HIRunning: High-Intensity running 
Locomotion activities were then normalized for each player according to the time they spent on 
court to obtain a true reflection of these demands, both for offense and defense. The running pace per 
game showed by the complete team in offense was 88.45 ± 20.72 m·min−1, walking: 13.89 ± 2.98 m·min−1, 
jogging: 40.55 ± 10.12 m·min−1, running: 23.65 ± 12.53 m·min−1, high-intensity running: 9.70 ± 9.39 
m·min−1 and sprinting: 0.42 ± 0.94 m·min−1. 
The running pace per game showed by the complete team in defense was 80.83 ± 27.11 m·min−1, 
walking: 17.53 ± 4.18 m·min−1, jogging: 28.56 ± 4.18 m·min−1, running: 20.49 ± 11.47 m·min−1, high-
intensity running: 12.96 ± 11.54 m·min−1 and sprinting: 0.56 ± 1.29 m·min−1. 
Figure 2.
Diﬀerences in distance covered in oﬀense and defense during diﬀerent locomotion
characteristics. * Statistical diﬀerences; p ≤ 0.05.; HIRunning: High-Intensity running
Locomotion activities were then normalized for each player according to the time they spent on
court to obtain a true reﬂection of these demands, both for oﬀense and defense.
The running
pace per game showed by the complete team in oﬀense was 88.45 ± 20.72 m·min−1, walking:
13.89 ± 2.98 m·min−1, jogging: 40.55 ± 10.12 m·min−1, running: 23.65 ± 12.53 m·min−1, high-intensity
running: 9.70 ± 9.39 m·min−1 and sprinting: 0.42 ± 0.94 m·min−1.
Int. J. Environ. Res. Public Health 2020, 17, 6768
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The running pace per game showed by the complete team in defense was 80.83 ± 27.11 m·min−1,
walking: 17.53 ± 4.18 m·min−1, jogging: 28.56 ± 4.18 m·min−1, running: 20.49 ± 11.47 m·min−1,
high-intensity running: 12.96 ± 11.54 m·min−1 and sprinting: 0.56 ± 1.29 m·min−1.
When comparing oﬀense and defense, signiﬁcant diﬀerences were found in walking (p < 0.000;
ES = 1.01) and jogging (p = 0.000; ES = 0.90), as well as a tendency for total distance (p = 0.71; ES = 0.32)
and high-intensity running (p = 0.077; ES = 0.31).
3.2. Positional Diﬀerences in Distance Covered and Speeds
The distances covered in oﬀense by playing position for each locomotion category are shown
in Figure 3.
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When comparing offense and defense, significant differences were found in walking (p < 0.000; 
ES = 1.01) and jogging (p = 0.000; ES = 0.90), as well as a tendency for total distance (p = 0.71; ES = 0.32) 
and high-intensity running (p = 0.077; ES = 0.31). 
3.2. Positional Differences in Distance Covered and Speeds 
The distances covered in offense by playing position for each locomotion category are shown in 
Figure 3. 
Walking
LP
CB
RB
LB
RW
LW
Distance covered (m)
0
100
200
300
400
500
 
Jogging
LP
CB
RB
LB
RW
LW
Distance covered (m)
0
200
400
600
800
1000
1200
1400
 
Running
LP
CB
RB
LB
RW
LW
Distance covered (m)
0
100
200
300
400
500
600
 
HI running
LP
CB
RB
LB
RW
LW
Distance covered (m)
0
50
100
150
200
250
300
350
*
*
*
¥
 
Sprinting
LP
CB
RB
LB
RW
LW
Distance covered (m)
0
10
20
30
40
*
*
*
*
 
Figure 3. Distance covered in different locomotion category by playing position in offense. * Statistical 
differences with the left wing p ≤ 0.05; ¥ = statistical differences with the right wing p ≤ 0.05. Legend: 
left wing = LW; left back = LB; center back = CB; line player = LP; right back = RB; right wing = RW. 
Although not many significant differences were found, high effect size values were obtained 
(Table 2). 
 
 
Figure 3. Distance covered in diﬀerent locomotion category by playing position in oﬀense. * Statistical
diﬀerences with the left wing p ≤ 0.05; ¥ = statistical diﬀerences with the right wing p ≤ 0.05. Legend:
left wing = LW; left back = LB; center back = CB; line player = LP; right back = RB; right wing = RW.
Int. J. Environ. Res. Public Health 2020, 17, 6768
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Although not many signiﬁcant diﬀerences were found, high eﬀect size values were obtained
(Table 2).
Table 2. Eﬀect sizes in diﬀerent locomotion categories by playing position in oﬀense.
Playing Positions
Walking
Jogging
Running
High Running
Sprinting
LP
CB 0.80
RB 0.86
LB 0.61
RW 0.76
LW 0.76
CB
LP 0.63
LB 0.54
LW 0.57
RB 0.75
RB
LP 0.75
LB 0.54
LB
RW
LP 0.93
LP 1.19
LB 1.23
CB 0.90
RB 1.34
RB 0.79
CB 0.69
LW
LP 1.25
LP 1.44
CB 1.07
CB 1.24
LB 1.81
RB 1.11
RB 2.04
LB 0.95
Legend: left wing = LW; left back = LB; center back = CB; line player = LP; right back = RB; right wing = RW.
The distances covered in defense for each locomotion category by playing position are shown
in Figure 4.
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Table 2. Effect sizes in different locomotion categories by playing position in offense. 
Playing Positions 
Walking 
Jogging 
Running 
High Running 
Sprinting 
LP 
 
CB 0.80 
 
 
 
RB 0.86 
LB 0.61 
RW 0.76 
LW 0.76 
CB 
 
 
LP 0.63 
LB 0.54 
 
LW 0.57 
RB 0.75 
RB 
LP 0.75 
 
 
 
 
LB 0.54 
LB 
 
 
 
 
 
RW 
 
 
 
LP 0.93 
LP 1.19 
LB 1.23 
CB 0.90 
RB 1.34 
RB 0.79 
CB 0.69 
 
LW 
 
 
 
LP 1.25 
LP 1.44 
CB 1.07 
CB 1.24 
LB 1.81 
RB 1.11 
RB 2.04 
LB 0.95 
Legend: left wing = LW; left back = LB; center back = CB; line player = LP; right back = RB; right wing = RW. 
The distances covered in defense for each locomotion category by playing position are shown in 
Figure 4. 
Walking
CB
MR
ML
OR
OL
FD
Distance covered (m)
0
100
200
300
400
500
600
700
§
§
 
Jogging
CB
MR
ML
OR
OL
FD
Distance covered (m)
0
200
400
600
800
1000
1200
1400
 
Running
CB
MR
ML
OR
OL
FD
Distance covered (m)
0
100
200
300
400
500
600
700
 
HI running
CB
MR
ML
OR
OL
FD
Distance covered (m)
0
100
200
300
400
500
 
Figure 4. Cont.
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Sprinting
CB
MR
ML
OR
OL
FD
Distance covered (m)
0
10
20
30
40
50
#
#
#
 
Figure 4. Distance covered in different locomotion categories by playing position in defense. # 
Statistical differences with front defender p ≤ 0.05; § statistical differences with center back p ≤ 0.05. 
Legend: center back = CB; mid right = MR; mid left = ML; outside right = OR; outside left = OL; front 
center defender = FD. 
Furthermore, moderate, large and very large effect sizes were found in the different locomotion 
characteristics by playing positions in defense (Table 3). 
Table 3. Effect sizes in different locomotion categories by playing position in defense. 
Playing positions 
Walking 
Jogging 
Running 
High Running 
Sprinting 
CB 
MR 1.31 
OR 0.77 
 
ML 0.68 
 
ML 1.38 
MR 1.05 
OR 1.19 
OR 1 
ML 0.93 
OL 1.26 
OL 0.90 
FD 1.11 
FD 2.65 
FD 0.51 
 
 
MR 
OL 0.61 
OL 0.52. 
OR 0.65 
 
 
FD 0.77 
OL 0.84 
ML 
OL 0.65  
 
 
 
 
FD 0.84 
OR 
 
 
 
 
 
OL 
 
 
 
 
CB 1.07 
ML 0.85 
MR 0.64 
FD 
 
MR 1.74 
MR 1.46 
OR 0.50 
CB 4.21 
ML 1.89 
CB 1.17 
MR 1.62 
OR 1.45 
ML 1.04 
ML 2.46 
OL 0.71 
OR 0.71 
OR 1.80 
 
OL 0.56 
OL 0.57 
Legend: center back = CB; mid right = MR; mid left = ML; outside right = OR; outside left = OL; front 
center defender = FD. 
3.3. Running Pace by Playing Positions 
When the distance covered in each locomotion category normalized according to the time spent 
on court in different playing positions during offense were analyzed, the ANOVA showed significant 
differences for jogging (p = 0.029) and sprint distances (p = 0.045) between the different playing 
Figure 4. Distance covered in diﬀerent locomotion categories by playing position in defense. # Statistical
diﬀerences with front defender p ≤ 0.05; § statistical diﬀerences with center back p ≤ 0.05. Legend:
center back = CB; mid right = MR; mid left = ML; outside right = OR; outside left = OL; front center
defender = FD.
Furthermore, moderate, large and very large eﬀect sizes were found in the diﬀerent locomotion
characteristics by playing positions in defense (Table 3).
Table 3. Eﬀect sizes in diﬀerent locomotion categories by playing position in defense.
Playing Positions
Walking
Jogging
Running
High Running
Sprinting
CB
MR 1.31
OR 0.77
ML 0.68
ML 1.38
MR 1.05
OR 1.19
OR 1
ML 0.93
OL 1.26
OL 0.90
FD 1.11
FD 2.65
FD 0.51
MR
OL 0.61
OL 0.52.
OR 0.65
FD 0.77
OL 0.84
ML
OL 0.65
FD 0.84
OR
OL
CB 1.07
ML 0.85
MR 0.64
FD
MR 1.74
MR 1.46
OR 0.50
CB 4.21
ML 1.89
CB 1.17
MR 1.62
OR 1.45
ML 1.04
ML 2.46
OL 0.71
OR 0.71
OR 1.80
OL 0.56
OL 0.57
Legend: center back = CB; mid right = MR; mid left = ML; outside right = OR; outside left = OL; front center
defender = FD.
Int. J. Environ. Res. Public Health 2020, 17, 6768
9 of 15
3.3. Running Pace by Playing Positions
When the distance covered in each locomotion category normalized according to the time spent
on court in diﬀerent playing positions during oﬀense were analyzed, the ANOVA showed signiﬁcant
diﬀerences for jogging (p = 0.029) and sprint distances (p = 0.045) between the diﬀerent playing positions.
However, the post hoc analysis did not show any statistically signiﬁcant diﬀerences (Figure 5).
Int. J. Environ. Res. Public Health 2020, 17, x  
9 of 14 
outside right (p = 0.016; ES = 1.19) and the front defender (p = 0.003; ES = 0.37), as well as a tendency 
between the central back and the mid left (p = 0.074; ES = 1.20). 
Offense
LP
CB
RB
LB
RW
LW
Normalized distance  (m.min
-1)
0
10
20
30
40
50
60
70
Walking
Jogging
Running
High Running
Sprinting
 
Figure 5. Distance covered in each locomotion category normalized according to the time spent in 
court in different playing positions during offense. Legend: left wing = LW, left back = LB, center back 
= CB; line player = LP; right back = RB; right wing = RW. 
Defense
Normalized distance (m.min
-1)
10
20
30
40
50
60
Walking
Jogging
Running
High Running
Sprinting
§
§
Figure 5. Distance covered in each locomotion category normalized according to the time spent in court
in diﬀerent playing positions during oﬀense. Legend: left wing = LW, left back = LB, center back = CB;
line player = LP; right back = RB; right wing = RW.
When the distance covered in each locomotion category normalized according to the time spent
on court in diﬀerent playing positions during defense was analyzed, the ANOVA showed signiﬁcant
diﬀerences only for high-intensity running (p = 0.038) between the diﬀerent playing positions (Figure 6).
Post hoc analysis showed signiﬁcant diﬀerences in this category between the central back and the outside
right (p = 0.016; ES = 1.19) and the front defender (p = 0.003; ES = 0.37), as well as a tendency between
the central back and the mid left (p = 0.074; ES = 1.20).
Int. J. Environ. Res. Public Health 2020, 17, 6768
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LP
CB
RB
LB
RW
LW
 
Figure 5. Distance covered in each locomotion category normalized according to the time spent in 
court in different playing positions during offense. Legend: left wing = LW, left back = LB, center back 
= CB; line player = LP; right back = RB; right wing = RW. 
Defense
CB
MR
ML
OR
OL
FD
Normalized distance (m.min
-1)
0
10
20
30
40
50
60
Walking
Jogging
Running
High Running
Sprinting
§
§
 
Figure 6. Distance covered in each locomotion category normalized according to the time spent in 
court in different playing positions during defense. § Statistical differences with center back p ≤ 0.05. 
Legend: center back = CB; mid right = MR; mid left = ML; outside right = OR; outside left = OL; front 
center defender = FD. 
Figure 6. Distance covered in each locomotion category normalized according to the time spent in court
in diﬀerent playing positions during defense. § Statistical diﬀerences with center back p ≤ 0.05. Legend:
center back = CB; mid right = MR; mid left = ML; outside right = OR; outside left = OL; front center
defender = FD.
4. Discussion
The aim of this study was to analyze on-court demands of handball players during the VELUX
EHF FINAL4 to deﬁne time–motion characteristics (played time, covered distances) both in oﬀense
and defense, including position-speciﬁc demands and diﬀerences among them. Signiﬁcant diﬀerences
were found between oﬀense and defense in the walking and jogging categories. In oﬀense, signiﬁcant
diﬀerences were established in the high-intensity running category between LW and other playing
positions. In defense, diﬀerences were also identiﬁed for CB in walking, and the FCD in sprinting
when compared to other playing positions.
Several studies have analyzed handball games differentiating intensity categories [3,5,11,17,19,23,24],
although they have taken into account neither all playing positions nor the different phases of the game.
In this regard, there is a broad consensus among researchers on the need to establish certain categories when
analyzing players’ movements, ranging from low-intensity (standing, walking, jogging), medium-intensity
(running) and high-intensity (HIrunning, sprinting) situations. However, little consensus exists on
the speed ranges for defining the different categories, which makes it difficult to compare between
the different studies.
For the analysis of the intensity at which the player moves across the ﬁeld, the categorization
proposed by Cardinale et al. [3] used for the Qatar WCh 2015 study was applied. Analyzing the results
by locomotion categories, we found that for the oﬀense, the longest distance was performed in jogging
(47.06%), while defenders covered a greater distance in the walking category. When comparing oﬀense
and defense, results showed a trend for signiﬁcance in the HIrunning category. These results highlight
the needs to diﬀerentiate the characteristics of the game phases. As we do not have more studies
Int. J. Environ. Res. Public Health 2020, 17, 6768
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to compare these results, we present an analysis of the general data with respect to other research
carried out.
Globally, players covered similar distances both in oﬀense and defense. These results are in line
with the study reported by Michalsik et al. [11], which also analyzed distances by phases of the game.
Other studies [3,13] have analyzed these variables without diﬀerentiating the game phases. They were
conducted during the ﬁnal phase of top level international men´s competition, the Men’s World
Handball Championships of Germany (2007) and Qatar (2015), respectively. Our results are consistent
with these studies regarding the total average distances covered, being 8% higher in the Germany
WCh and 1% smaller in the Qatar WCh 2015.
Other studies that analyzed men´s top national leagues showed greater covered distances than
ours, ranging from 3157 m on average in the analysis of the German ﬁrst division [25] to 3627 m
in the main Danish league [4], or 4370 m in the main Portuguese league [12]. The problem is that,
in most of these studies, standard deviations from the average are very high, thus complicating the use
of this criterion as a performance control measure. A better criterion, with a practical application
for coaches, is to normalize the data according to a players’ time on court [3,6]. When the data are
normalized according to the time spent on court, which in the case of national leagues is larger (above
40 min) than in international competitions, the average running pace does not diﬀer too much between
the studies, that is, by about 10%. According to this criterion, at the highest level, a handball player
covered between 70 and 90 m·min−1. This data normalization facilitates the comparison between
studies and allows the coaches to dispose of a workload reference regarding locomotion activities.
Regarding time spent on court, oﬀense and defense presented average times of 15.69 min
and 15.40 min, respectively. Summing up both phases, values were similar to those presented by
Luig et al. [13] and Cardinale et al. [3], being equal to 32 min and 37 min respectively, but lower that
those described in national league studies [3,4,14,15], where values over 40 min were found.
Regarding the average running pace values in oﬀense and defense (89 m·min−1 and 81 m·min−1,
respectively), our results did not match those presented by Michalsik et al. [11], although the diﬀerences
were less than 10%. In this line, the results showed in the diﬀerent studies are very heterogeneous,
possibly due to the methodology used, the instrument of control, level of play, the category
and the competition analyzed.
Regarding speciﬁc positions, our main ﬁnding was that there were diﬀerences among players’
locomotion categories in each playing position, both in oﬀense and defense, which implies a need
for greater diﬀerentiation and individualization in the training load according to the diﬀerent
playing positions.
In the case of the oﬀense, left wing players showed the highest covered distances in
the high-intensity running and sprinting categories in relation to the other speciﬁc positions,
and the right wings covered longer distances than right back players for the HIrunning category.
We should bear in mind that it is possible that some statistical comparisons in our study may show no
statistical signiﬁcance even when the means of the two groups are quite diﬀerent, because the sample
is small and the SD are high. In this context, ES might be a good indicator not only of the magnitude of
the changes but also of the associations that are likely to present signiﬁcant diﬀerences in larger samples.
The ES in the HIrunning and sprinting categories reinforces the observed statistical diﬀerences,
since they present a large and very large ES. Therefore, we can conclude that the wings have diﬀerent
demands for high-intensity activities than the rest of the players in the oﬀensive phase. These results
are reinforced by the idea that these players are those who are responsible for performing most of
the counter-attacks or to reach position in the ﬁrst wave of the fastbreak, which are the fastest actions
of the oﬀensive phase. These data are in line with previously reported data by Michalsik et al. [4]
and Povoas et al. [5] for the oﬀensive phase.
For the defensive phase, the results showed a similar behavior to that in the oﬀense.
It is the defense-speciﬁc playing positions (CBs and FCDs) that have the highest values during
the defensive phase, showing high ES values. CBs have higher values in the covered distance for
Int. J. Environ. Res. Public Health 2020, 17, 6768
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the walking category than those found for mid defenders. These results are consistent with the work
performed by CBs during this phase, as they are the players who move depending on the area
where the ball is directed, and these displacements are usually of low intensity. In the same line,
when the locomotion categories were normalized according to the time spent on court, we observed
that in the HIrunning category, CBs covered less distance than the ORs and FCDs, which indicates that
CBs carried out most of their activities in the low-intensity categories.
In the sprinting category, the highest covered distances correspond to the FCDs, which showed
signiﬁcant diﬀerences with the CBs and Mid defenders, with a large and very large ES. These data are
consistent with the speciﬁc role of this player, who carries out his activities mainly in the front defense
line, covering the central area of the defense, moving from side to side. Again here, as an application
to training, coaches should diﬀerentiate training by speciﬁc positions, for example, by creating very
intense tasks for FCDs and wings.
To our knowledge, this is the ﬁrst time that speciﬁc defensive playing positions have been analyzed,
so these data cannot be compared. It provides a novel and in-depth knowledge of the real needs of
this phase of the game.
The total distances covered by each playing position are smaller, for both phases of the game,
when compared to those reported by Michalsik et al. [4]. Variations in the methodology and the diﬀerent
technology used, as well as the diﬀerent competitions analyzed in both studies, may account for these
diﬀerences. On the one hand, LPS technology that allows load individualization and automation
through micro sensors [16] was used for this study. However, Michalsik et al. [4] performed a manual
estimation of intensities based on distance references on the court, following the player’s individual
monitoring with a camera. Diﬀerences also existed between these two studies regarding the time
spent on the court. Danish players stayed clearly longer on court in all cases. This may be because
we are comparing a national league with a European ﬁnal league tournament. The game-sharing
times can be altered by the number and quality of players taking part in the Velux EHF Final 4, which
gathers the best teams and players in the world, as well as the nature of the competition (ﬁnal phase of
the biggest club tournament). Therefore, the time sharing, a larger use of rolling substitutions as well
as more rotations to maintain the intensity of the game may be greater than in a national league.
Other studies have also analyzed the playing positions in men’s championships with senior high
level players, but have neither diﬀerentiated the phases of the game nor analyzed each individual
playing position. Some of these studies have also shown greater high intensity values by the wing
players compared to the rest of the playing positions [13,25].What seems to be clear is that, in general,
studies that analyze the maximum distance covered per playing position varies widely, making a
comparison with our study diﬃcult because of the diﬀerent procedures used [3,12,13,25].
A trend that can be observed in the available studies is that regardless of the category, procedure
used, level of play or gender, all studies analyzing locomotion activities in handball have in common
the diﬀerentiation of loads according to positions [3,5,6,10,19,25]. For this reason, given the great
variability according to playing position, we propose to diﬀerentiate the physical work according to
the role adopted in the game, in line with the conclusions of most studies.
Several limitations were found that made it diﬃcult to discuss this study and compare it to
others. The ﬁrst is the small number of works focusing on the playing load in high-level men’s
professional handball in ﬁnal tournaments, such as the Velux EHF Final 4. Moreover, it is complicated
to compare studies because of the lack of uniﬁed criteria to determine locomotion categories. It would
be necessary and essential to standardize criteria so that they could be taken into consideration in future
studies. Other limitations present in the work are related to the lack of development of multivariate
analysis techniques. Additionally, only one championship has been analyzed, corresponding to a high
performance level in the senior men’s category.
Further studies will be needed to deepen our knowledge of handball’s total load through
the individualized use of sensor technology (EPTS), which allows us to learn about the other previously
mentioned parameters. In addition, it would be advisable to combine the advances in the physical
Int. J. Environ. Res. Public Health 2020, 17, 6768
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understanding of the game with its impact on the game’s technical–tactical component. Furthermore,
there is also a need for extending the analysis to other competitions, categories and gender.
5. Conclusions
Oﬀensive players covered longer distances in the jogging category and defensive players in
the walking category. Proﬁle diﬀerences existed among players’ position activity, both in oﬀense
and defense. In fact, more activity in high-intensity categories was found for wing players in oﬀense.
In the case of defense, it was the CB that covered the largest distances in low-intensity categories,
and the FCD covered most of the distance in high-speed categories.
Practical Applications
Our ﬁndings suggest the need to diﬀerentiate the training load speciﬁcally for each position,
and diﬀerentiate between the phases of the game, creating speciﬁc exercises, that is, very short work
(less than 2 m of displacement) involving high-intensity movements (above 5 m−1) and repeated in a
random way over time, with high active rest time between sets. For example, you can do integrated
training with simulated game situations, where FCDs in oﬀense and LWs in defense have greater
involvement. Another possibility is to design tasks that raise the fatigue threshold at each position
and phase to check their impact on the game. These integrated exercises can also include explosive
resistance training that improves performance in decisive ﬁnal actions such as 1v1, blocks, etc.
In addition, knowing the speciﬁc load of a top-level tournament will allow coaches to determine
the maximum levels of physical requirements in elite handball and set them as references based
on the category. Furthermore, knowing that the diﬀerent demands for the playing positions are
diﬀerentiated will allow coaches to individualize and plan their workouts accordingly, as well as
consider it in the match load dosing and in players’ substitutions, for example, if possible, giving more
rest to the LWs and the FCDs to maintain the level of intensity.
At a high performance level, coaches should work to improve training control. The normalization
of locomotion activity data allows disposing of a workload reference, in addition to facilitating
the comparison between sessions. Very little information is available about the demands of the game
in the diﬀerent national leagues. Currently, the system is only being used in the German Bundesliga.
The use of the system in the VELUX EHF Champions league would undoubtedly provide us with new
relevant information about the highest competition among European clubs.
In the future, studies could be carried out to analyze players’ rotations in oﬀense and defense.
In addition, future research should relate workload on the court to the workload outside the court,
such as in the ﬁtness room. Finally, it is also possible that the results obtained in this study are useful
for the future design of more speciﬁc physical tests related to the demands of the game.
Author Contributions: Conceptualization, C.M., H.V. and F.S.S.; data curation, B.P.; formal analysis, J.T.M., S.B.
and S.A.; investigation, C.M.; methodology, J.T.M. and B.P.; resources, J.M.C.T.; supervision, H.V., F.S.S. and L.J.C.R.;
writing—original draft, C.M., H.V. and L.J.C.R.; writing—review & editing, J.T.M. and C.F. All authors have read
and agreed to the published version of the manuscript.
Funding: This research was partially funded by Séneca-CARM—grant number 20928/PI/18, by MINECO/FEDER
grant number PID2019-108336GB-100 and by Consejo Superior de Deportes, grant number 24/UPB/19
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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High-Performance Handball Player's Time-Motion Analysis by Playing Positions.
Manchado, Carmen,Tortosa Martínez, Juan,Pueo, Basilio,Cortell Tormo, Juan Manuel,Vila, Helena,Ferragut, Carmen,Sánchez Sánchez, Francisco,Busquier, Sonia,Amat, Sergio,Chirosa Ríos, Luis Javier