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Anthropometric Characteristics, Physical Fitness, and Throwing Velocity in Elite women's Handball Teams

Saavedra, Jose M.1; Kristjánsdóttir, Hafrún1; Einarsson, Ingi Þ.1; Guðmundsdóttir, Margrét L.1; Þorgeirsson, Sveinn1; Stefansson, Axel2,3

Author Information
Journal of Strength and Conditioning Research: August 2018 - Volume 32 - Issue 8 - p 2294-2301
doi: 10.1519/JSC.0000000000002412
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Team handball (handball) is a high-intensity body contact team sport in which the players have to be able to sprint, jump, throw, hit, block, and push (9), inter alia. Handball performance is a combination of many factors: constitution-disposition, coordination, strength, endurance, constitution-disposition, nutrition, cognition, tactics, and social and external influences (38). Measuring performance is one of the main subjects of sports science studies because it plays a key role in planning training and competition (4). Sport performance is of course influenced by the sex and age of the players, but, relative to those of male players, there have been few studies of female players in different age groups.

Anthropometric studies of women's handball players have generally shown that stature is important for throwing and blocking, and a relatively high body mass for one-on-one actions (38), but some studies (11,25) found no difference in stature and mass between elite and nonelite female players, and another study (10) found a difference in stature but not body mass between elite and amateur female players. It has also been reported (40) that the better youth players as defined by selection were taller than “nonselected” players, but that there was no difference in mass between the 2 groups. It is therefore clear that any relationship between female handball players' performance and their stature and mass is at best ambiguous. Given that handball is a particularly physical sport, power, strength, speed, and endurance are believed to be important factors for performance (9). Leg power as measured by jumps has, however, yet to be related to adult female handball players' performance (20). Neither has any difference in jumping height been found between female players of different levels (10,11). Another study found that young female “selected” players jumped farther from standing possession than their “nonselected” counterparts (40). In regard to speed, female elite players ran 5- and 15-m sprints faster than amateur players (10). However, no difference was found in running speed between female elite national and international handball players (11). In young female handball players, there is a relationship between speed and performance (18). Research on the relationship between endurance and handball performance has given inconsistent results. Although elite female handball players had greater aerobic capacity than amateur players (10), another study by the same authors (11) found no difference in this capacity between national and international elite female handball players. Although throwing speed is greater in elite female players than amateur players (10), no difference is found between national and international elite female players (11). On the contrary, youth category female “selected” players had a greater throwing speed than their “nonselected” counterparts (40). Only a few studies have analyzed in the same work the differences in the anthropometric profile and physical condition of young players according to age (14,26).

The findings of the few studies of elite and young female handball players' anthropometric, physical, and technical (throwing speed) characteristics tend to be inconsistent. Also, no previous study has evaluated multidimensional factors in elite female handball players or studied the difference in those factors between 4 age groups of these players. In particular, the objectives of this study were as follows: (a) to analyze anthropometric parameters, physical fitness, and throwing speed in women elite handball players of different ages and (b) to develop a multivariate model explaining handball performance from a multidimensional perspective. We hypothesize that (a) the A Team achieves the highest performance in all the variables studied, whereas the lowest performance corresponds to the U15 team; (b) all the variables studied can predict handball performance; and (c) all the variables studied are correlated with (7 and 9 m) throwing speed, with the strongest correlations corresponding to height, countermovement jump (CMJ), medicine ball throw, and 10-m sprint.


Experimental Approach to the Problem

The study was cross-sectional in nature. We analyzed the relationship between different women national teams (A Team, U19, U17, and U15) as the independent variable, and anthropometric and fitness parameters and handball throwing speed as dependent variables. Also, we developed multidimensional models (through discriminant analysis) to determine the predictor variables of handball performance and the relationships between variables. There have previously only been analyses of various parameters between different teams or age categories for men players (18,20,22,23,30), so that, to the best of our knowledge, this is the first study performed for women. The tests selected have been used in previous handball studies (18,22,23).


Eighty women handball players (mean ± SD 18.2 ± 4.0 years in age), components of the Icelandic national teams, participated in the study. The participants were classified into the A Team (n = 23; 23.7 ± 3.9 years), under-19 national team (n = 16; 18.3 ± 0.7 years), under-17 national team (n = 20; 16.1 ± 0.6 years), and under-15 national team (n = 21; 14.5 ± 0.5 years). The A Team played matches in the qualifying stages of the European Championship in late September 2017, U19 took part in the Scandinavian Open, and U17 in the European Championship in the Former Yugoslav Republic of Macedonia. The U15 team has not yet played in any international tournaments. In 2015, the A, U19, and U17 teams were ranked 25th of 50 teams. There was no ranking for the U15 team ( The study was approved by the Ethics Committee of Reykjavik University and respected the principles of the Declaration of Helsinki. It was performed inside the national teams' training camp. The players and their parents or legal guardians if they were younger than 18 signed an informed written consent before participation.


All subjects took a comprehensive battery of tests, which included anthropometry, physical fitness tests, and throwing speed. The anthropometric measurements were taken in accordance with the International Society for the Advancement of Kinanthropometry standardized procedures (15): stature, mass, body mass index (BMI) (16), and reciprocal ponderal index (RPI) (27). Reciprocal ponderal index power was calculated as RPI = height (cm)/mass (kg) 0.333, using a Seca model 769 scale. The physical fitness tests used were as follows: CMJ, medicine ball throw, hand dynamometry, 10- and 30-m sprint, and yo-yo intermittent recovery level 2 (Yo-Yo IR2) test. All these tests have been used in previous handball studies. Countermovement jump with hand on hips (5) was evaluated by measurements of high-speed (EX-F1, 300 fps and 1,920 × 1,080 pixels; Casio) video recordings using the open-license software package Kinovea (Kinovea 0.8.15 for Windows; available at, Tokyo, Japan). The camera was then placed on a tripod at a distance of 1.5 m perpendicular to the players' sagittal plane and the filming zone. The jumping zone was marked out on the floor. Once the jumps made by the players had been filmed, the jump time was calculated using the Kinovea software, and the jump height was estimated (2). Jump height (cm) and peak power (W) were evaluated, with the latter calculated as CMJ (W) = (60.7 × stature [cm]) + (45.3 × mass [kg]) − 2,055 (32). Medicine ball (3 kg) throw with one knee on the floor (as adapted by Ref. 18) was scored as the distance (m). Hand dynamometry of the dominant hand (7) was evaluated with a Vernier hand dynamometer (Vernier, Orlando, FL, USA), with the subject seated and the elbow at 90°. The 10- and 30-m sprints (as adapted by Ref. 18) were evaluated with photo cells. The Yo-Yo IR2 (3,17) was scored as the speed (km·h−1). All except the hand dynamometry and yo-yo tests were performed twice, recording only the better of the 2 scores. Throwing speed was evaluated with a radar gun (Perform Better, Warwick, United Kingdom) located behind the goal, measuring 2 types of shot made without opposition: 7 m standing (9) and 9 m after 3 steps and a jump (35). Each of these throws was performed twice, recording only the better score in each case. The measurements were made in the order of the above descriptions. After the anthropometry measurements, the subjects performed a standardized warm-up procedure consisting of a stretching exercise, 4–6 repetitions of 30 m doing different exercises (knees up, lunge walk, etc.), 5–7 accelerations of 30 m building up the speed, and 10 minutes of passing. Full recovery was ensured between each of the trials.

The performance level was evaluated relative to that of the 6 starter players (field players without goalkeeper). In particular, each team was divided into 2 subgroups (starters and nonstarters) based on the coaches' opinions. The choice of the 6 starter players was made by each of the teams' (A Team, U19, U17, and U15) head coaches. Although this choice could be considered to have a certain level of subjectivity, it is the usual mode of selection of starter players in team sports. Furthermore, this methodological approach is similar to that of previous studies (18,40). Goalkeepers were excluded from the analysis because of their very different characteristics in comparison with field players (39).

Statistical Analyses

All the variables satisfied the tests of homoskedasticity (Levene homogeneity test) and normality (Kolmogorov-Smirnov test). The basic descriptive statistics (mean and SD) were calculated. A 1-way analysis of variance (ANOVA) was used to examine differences between teams (A Team, U19, U17, and U15). The Bonferroni post hoc test was used to compare mean values. The confidence intervals were also calculated.

A discriminant analysis was performed for each team. Subjects were classified by the sample-splitting method into 2 groups according to their performance level (starters and nonstarters). The criterion used to determine whether a variable entered the model (i.e., discriminant function) was Wilks lambda, a measure of the deviations within each group with respect to the total deviations. The sample-splitting method included initially the variable that most minimized the value of Wilks lambda, provided that the corresponding F value was greater than a certain threshold (i.e., F = 3.84 to enter). The next step was a pairwise combination of the variables with one of them being the variable included in the previous step. Successive steps were performed in the same way, always with the condition that the F value corresponding to the Wilks lambda of the variable to select has to be greater than the aforementioned entry threshold. If this condition was not met, the process was halted and no further variables were selected. Before including a new variable, an attempt was made to eliminate some of those already selected if the increase in the value of Wilks lambda was minimal, and the corresponding F value was below a critical value (i.e., F = 2.71 to remove). As indicators of performance, Wilks lambda, the canonical correlation index, and the percentage of subjects correctly classified for the whole sample and for each category group were computed.

Finally, Pearson simple correlation coefficients were calculated between each of the variables using chronological age as control variable or covariate (partial correlation). The values of this statistic were assigned linguistic labels: >0.1 small, >0.3 moderate, >0.5 large, >0.7 very large, and >0.9 nearly perfect (13). The level of significance for all statistical tests was set at p ≤ 0.05. All calculations were performed using SPSS version 20.0.


Table 1 lists the mean and SD of each variable and the results of the 1-way ANOVA. The values of mass and the yo-yo test were greater in the A Team than in all the other groups. There were only differences between the A Team and U19 team in mass, CMJ, medicine ball throw, and yo-yo test. No differences were found between U19 and U17. The U15 results were lower than U19 and U17 in CMJ and medicine ball throw.

Table 1.:
Mean and SD of each variable.*†

Table 2 presents the results of the discriminant analysis. The A Team and U19 predictive models correctly classified 76.2 and 90% of the sample, respectively, with only 2 pairs of variables—mass and BMI (A Team) and 30-m sprint and 7-m throwing speed (U19 team). The procedure did not select any variable for the U17 and U15 teams.

Table 2.:
Discriminant analysis models by performance (starters vs. nonstarters), giving the percentage correctly classified, Wilks lambda, canonical correlation index, and variables included in the model by order of selection.*

Table 3 lists the partial correlations between variables. Countermovement jump is correlated with all the variables (−0.473 ≤ r ≤ 0.529; 0.001 ≤ p ≤ 0.05) except stature and BMI. The 7- and 9-m throwing speeds were correlated with each other and with stature, mass, CMJ, and medicine ball throw (0.367 ≤ r ≤ 0.533; 0.001 ≤ p ≤ 0.05).

Table 3.:
Pearson's linear partial correlation for each variable.*


This particular study has analyzed the basic anthropometry, physical fitness, and throwing speed in elite women handball players of different age categories of this particular country, developing a multivariate model to explain handball performance and the relationships between variables. Although there have been studies of this type for men, to the best of our knowledge, this is the first such study for women. Our first hypothesis was not confirmed because the A Team did not obtain the best results in all the variables studied. The A Team's mass and yo-yo test scores were better than all the other groups (U19, U17, and U15). There were small differences between the A Team and the U19 team (mass, CMJ, medicine ball throw, and yo-yo test) but no differences between the U19 and U17 teams. Neither was our second hypothesis confirmed because not all the variables were predictors of handball performance. Thus, the A Team and U19, the discriminant analysis model selected just 2 variables: mass and BMI (A Team) and 30-m sprint and 7-m throwing speed (U19). The model selected no variables for the U17 and U15 teams. In particular, for the A Team and U19, there were in each case 2 variables predictive of handball performance—mass and BMI in the A Team, and 30-m sprint and 7-m throwing speed in the U19 team. Finally, our third hypothesis was not confirmed because the 7- and 9-m throwing speeds were correlated with stature, CMJ, and medicine ball throw but not with 10-m sprint. Nonetheless, CMJ was correlated with all the variables except stature and BMI.

The basic anthropometry measurements made were stature, mass, and BMI. The A Team was taller than the U17 team and heavier than all the other teams. Body mass index was greater in the A Team and the U19 team than in the U15 team. Previous studies have shown that elite teams are taller than amateurs (10), and also that stature is a relevant factor in throwing and blocking (38). The stature of the A Team (1.74 ± 0.06 m) is similar to that of other countries' national teams, which are ranked higher: Germany 1.76 ± 0.07 m (19), Norway 1.76 ± 0.06 m (31), and Spain 1.75 ± 0.08 m (11). In regard to physical fitness, 6 variables were analyzed. The CMJ and medicine ball throw scores were higher in the A Team than the U19 and U17 teams and in these 2 teams than in the U15 team. These results are similar to those of 2 previous studies, one of men (30) and one of women (14), in which there were no differences in CMJ between U18 and U16 teams. In this sense, it is relevant to note that lower and upper limb power are important for performance in the movements specific to handball (38). In the hand dynamometry, the A Team achieved a higher score than the U17 and U15 teams, but there was no such difference with the U19 team. All these teams play with the same ball (size and mass). Perhaps therefore, after 4 years of playing (U17 and U15), the players become adapted, and any differences in their hand dynamometry disappear. Hand dynamometry is a good, simple test with which to evaluate handball players' strength, and, in female players, it has been shown to have a good correlation with the isokinetic strength of shoulder rotator muscles (1). In the sprint tests (10 and 30 m), the A Team achieved the best scores, whereas there were no clear differences between the youth teams. This is unlike a study (14) in which U18 players were faster than U16 players. For the Yo-Yo IR2 test, the A Team had a higher score than the other 3 groups, among whom there were no differences. Although endurance capacity is not a limitation for handball performance (9), the duration of a match is two 30-minute halves, in which the player covers approximately 4,000 m of which some 1,600 are at a jog and running (24). So, given the match duration, the distance covered, and the number of explosive actions (e.g., jumps), a strong aerobic system will facilitate recovery between bouts of strong effort (34). With regard to throwing speed, there were only differences between the A Team and the U17 (7-m standing) and U15 (9 m after 3 steps and jump) teams. None of the younger teams demonstrated better performance than any older age group. The A Team and U19 speeds measured in this study are similar to those of the Spanish National Team (10,11).

A discriminant analysis was performed to obtain a multivariate model explaining handball performance. Only in the A Team and U19 cases did the corresponding model show differences between starters and nonstarters. The variables identified were mass and BMI for the selection of the A Team (76.2% correctly classified), and 30-m sprint and 7-m throwing speed for the U19 team (90% correctly classified). There were no differences in the 2 youth teams (U17 and U15), indicating that, at the “formation age,” these indicators are irrelevant to predicting handball performance. The variables selected in the A Team case suggest that heavier players are important in a handball team, and that this mass does not compromise their performance. This could be because handball is a sport involving a lot of body contact in actions, such as block, push, or pull, among others (9). Nevertheless, elite players should have a good balance between mass or BMI and other capacities, such as jumping and running, for example. The present results differ from those of a study of elite men players in which the selected variables for the model were CMJ (power) and stature (28). That height was selected as a discriminatory variable in that study (28) that could have been due to the fact that the analysis involved 3 teams of different levels in the same league, rather than between players of the same team as in this study. The U19-selected variables (30-m sprint and 7-m throwing speed) suggest that the starting line-up has a considerable speed advantage over their non-starting team mates. The stronger 30-m sprint performance of the starters could be reflected positively in the team's tactical ability to run fast breaks, especially because fast breaks often begin with an up to 30-m run from the defensive 9-m area to the opponent's goal zone, and finish with a jump throw from within the 6-m zone. However, it is necessary to note that this variable (30-m sprint) could be influenced by the tactics that this particular team uses (priority given to attack in fast-break situations). With respect to the U17 and U15 teams, it is surprising that the model did not select any variable. This seems to indicate that the players' characteristics are homogeneous, even in height, mass, and BMI, although this last variable, the BMI, can not be considered valid as an indicator of the mass/height ratio until a person's definitive height has been reached, and most players in youth teams have yet to reach that height. Indeed, the absolute value of the BMI is not used as an indicator of the height/mass ratio in young people, but rather the percentile relative to the reference population (6). Finally, it might seem that grip strength (hand dynamometry) should be relevant when it comes to catching the ball and being able to manipulate it mainly for passing and throwing. This study did not find that this type of variable was discriminant for performance, in coherence with the findings of other previous studies. For example, a study conducted with men of different levels (21) included a discriminant analysis for each of the game positions (goalkeeper, wing, back, center, and pivot) and found that grip strength did not discriminate between the 2 performance groups for any of these 5 positions. However, another study (36) did find differences in this test between men's teams of different levels (national and regional leagues). All this would seem to indicate that, although hand dynamometry may be relevant when considering teams of different levels, this is not so within a given team as was the case in this study.

Finally, CMJ showed a relationship with all the other variables (−0.473 ≤ r ≤ 0.529; 0.001 ≤ p ≤ 0.05) except stature and BMI (r = 0.257, p = 0.131). The lack of any relationship with stature and BMI is contrary to the finding of a study of adolescent male players (29), in which CMJ was related to BMI. But our results are similar to those of previous studies of male players in which CMJ was correlated with medicine ball throw (8), yo-yo test (12), and standing and jump throws (30). Throwing speeds (7 and 9 m) were correlated (0.367 ≤ r ≤ 0.533; 0.001 ≤ p ≤ 0.05) with stature, mass, CMJ, and medicine ball throws. A study of male players showed that taller players with greater body mass can make faster jump throws (37). Also, another study of men (33) reported correlations of stature, mass, and BMI with the speeds of 3 types of throws (set shot, low arm shot, and jump shot, each of the 3 independently of whether the placement on goal was high or low). The 10- and 30-m sprint tests were positively (r = 0.849; p < 0.01) correlated with each other and negatively (−0.645 ≤ r ≤ 0.656; 0.001 ≤ p ≤ 0.05) with the Yo-Yo IR2 test. This seems to indicate that faster players have lower aerobic levels. This could be related to player position. For instance, wing players spend more time sprinting than backcourt players (24). This is relevant information for coaches because different player positions have different requirements on the field. Thus, wing players spend more time sprinting than backcourt players (24). Finally, hand dynamometry was only correlated with CMJ (r = 0.462; p < 0.01) and medicine ball throws (r = 0.361; p ≤ 0.05).

This study has several limitations. First, no body composition data were collected. These could have provided insight into the muscle mass development of players of different ages. Second, sexual maturation was not accounted for. This can influence somatic variables and hence the performance on physical tests. Third, we have no conclusive data on each participant's real training volume. Fourth, the comparisons were made between the teams treated as a whole, not according to player position which has been shown to condition performance characteristics because of the game's demands. Fifth, the choice made by each of the head coaches of their starter or substitute players could be regarded as a subjective criterion with which to group the players in the discriminant analysis and hence could have led to some bias in the investigation (another coach might choose other starters). However, this methodological approach is similar to that of other studies (18,40). And sixth, this study refers to a particular country, so that this fact must be taken into account when drawing conclusions. However, the study may be specifically useful for this particular country and for other countries with similar characteristics and generally useful for a better knowledge of women's handball.

In summary, in this particular study, we have found small differences between the A Team and the U19 team (CMJ, medicine ball throw, and yo-yo test), but no differences between U19 and U17. Between the U17 and U15 teams, we only found differences in CMJ (height) and medicine ball throws. The multidimensional model selected 2 pairs of variables—mass and BMI (A Team) and 30-m sprint and 7-m throwing speed (U19 team)—as handball performance predictors. No variable was selected by the model for the U17 and U15 teams. Finally, countermovement jump was correlated with all the other variables except stature and BMI, and the 7- and 9-m throwing speeds were correlated with each other and with stature, mass, CMJ, and medicine ball throw.

Practical Applications

These results could help improve coaches' knowledge of elite female teams, in particular, in the country where the study was conducted and in other countries in which the level of handball and its characteristics are similar. In these countries in particular, coaches could pay particular attention to looking for taller and heavier players, especially in an A Team. This difference in stature between the A Team and all the youth teams seems to indicate that, for a given stature, the A Team players have greater mass and BMI probably because of their greater muscle mass. This would thus seem to be an interesting area for potential development (14). Also, in this study in particular, CMJ would seem to be a good test with which to determine a player's physical fitness because it presented moderate correlations with the rest of the physical condition variables. In the youth teams (U17 and U15), there were no predictor variables of handball performance. It would seem logical to expect some differences between these last 2 groups, but perhaps the lack of any predictor variables for them is because of their nonspecialized training in this particular case. Finally, in this study in particular, throwing speeds (7-m standing and 9 m after 3 steps and jump) were correlated with stature, mass, CMJ, and medicine ball throw. This latter group of simple measurements could thus be used to select a player who could attain faster throwing speeds and to work with players in training for them to throw faster and more technically correctly.


This study was partially granted by the Icelandic Handball Federation (Handknattleikssamband Íslands—HSÍ). The authors thank Robert A. Chatwin, PhD for revision of the English and every one of the participants in the study. Also, The authors thank Kristján Halldórsson (PAPESH Research Center—Reykjavik University) for his excellent coordination of the measurements and MSc students that collaborated with him: Dean Martin, Matthias Hinz, Arna V. Erlingsdóttir.


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performance; power; endurance; strength

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