Researchers in the field of excellence in sport performance are becoming increasingly focused on the study of sport-specific characteristics and requirements. Nevertheless, the research on expertise has tended to use a monodisciplinary approach (i.e., focusing in only 1 type of attributes, e.g., morphologic, physiological, specific skills, psychological, or biosocial).
The literature has shown that in team handball (i.e., “game that is played between 2 teams, each comprising 6 court players and a goalkeeper” ), the morphologic profile of top elite handball players has attracted much research interest. To date, studies have centered on (a) the morphologic differences between players with different performance levels (16) and (b) on the differences between athletes with the same playing position but at different performance levels (3,17).
Furthermore, some methodological guidelines were suggested in the literature as being relevant to the study of the body composition in team-handball players. For example, Reilly et al. (38) have suggested the use of the Durnin and Womersley (6) equation for the estimation of body fat (16). However, most studies on adult male team-handball players used the Jackson and Pollock (21) equation (11). Moreover, a small number of studies have also focused on the potential importance of accessing the muscle mass (16,28,30).
It is true that modern handball involves high-intensity short duration exercise (37), in addition to well-developed aerobic fitness, velocity, and strength (47). In fact, the ability to repeatedly perform intermittent high-intensity actions throughout the game seems to be important in team-handball players (36,43). However, very little work (most of which is in non-ISI indexed publications, and therefore with limited accessibility, e.g., Lam HP, unpublished observation ) has been focused on the intermittent endurance capacity of handball players (36,43).
Is it correct to consider only the morphologic and fitness profiles in the study of team-handball players?
It seems relevant to note that other factors (e.g., handball-specific skills profile, psychological profile, and biosocial profile) can be determinants to predict sports success.
In fact, proficiency in team handball, usually attributed to a combination of technical and tactical skills (35), is currently analyzed by the completion of evaluation sheets (14,22,35) during the match(es), or by retrospective analysis of videotapes. However, technical-tactical efficiency is both team and match specific (14,35). It is therefore difficult to obtain an objective measure of each performer's efficiency in a game (46). Notwithstanding its disadvantages, coaches rely heavily on such a judgment of each player's individual contributions in terms of handball-specific skill to team performance (35,46).
The literature also reports that psychological attributes and mental skills contribute to athletic success (24). In fact, motivation is one of the components of athletic success that has been most studied in sports psychology and, to access goal orientation, researchers have employed a range of self-report measures (e.g., Task and Ego Orientation in Sport Questionnaire [TEOSQ] and Perception of Success Questionnaire). The TEOSQ, in particular, was supported by one of the most popular and well-researched theories of motivation in sport psychology—the achievement goal theory (34). According to Hodge et al. (20), researchers employing this theory have made a substantial contribution to the understanding of individual motivation in sports. In fact, studies comparing successful and less successful athletes' have reported significant differences in their achievement goal orientations (7,12,39).
Furthermore, the study of the influence of environment and living conditions on the success of an athlete seems to be of irrefutable importance. According to Mensink et al. (33), leisure time activity can be influenced by socioeconomic status (SES), level of urbanization, and occupation. In fact, it seems that low SES (in samples of the young) may be a disadvantage with regard to their ability to participate in organized sports (9).
Massuça and Fragoso (29) observed that SES was significantly associated with team-handball success (i.e., a higher SES is related to a higher performance level). They also noted that top elite team-handball players have higher weekly energy expenditures in organized team-handball activity than do nontop elite players. In fact, top elite players spent twice as much time training than the nontop elite players did (29).
It seems that the identification of individual characteristics of the top elite player, and the determination of the extent to which they may differentiate the top elite player from the nontop elite player, will allow us to have a better understanding of a team-handball player's success, and to adjust a reduced battery of tests for evaluation and training control.
In accordance, the purposes of this study were (a) to examine the morphologic-, fitness-, handball-specific skills and psychological and “biosocial” differences between top elite and nontop elite team-handball players and (b) to investigate the extent to which they may be used to identify top elite team-handball players.
Experimental Approach to the Problem
This study was designed to report the basic descriptors and benchmarks of attributes, from 5 individual categories (i.e., morphologic-, fitness-, handball-specific skills and psychological and “biosocial”), of 2 performance groups of team-handball players (i.e., top elite and nontop elite). A sample of 167 team-handball players was used. Independent samples t-test was used to evaluate the differences between the 2 groups of athletes (top elite vs. nontop elite) for 28 morphologic (20 anthropometric measures; absolute and relative fat and muscle mass), 9 fitness (30-m time; difference between dominant and nondominant handgrip; height and power in squat jump (SJ) and countermovement jump (CMJ), abdominal strength, distance, and position achieved in the Yo-Yo Intermittent Endurance Test—level 2 [YYIE2]), 1 handball-specific skill (offensive power), 2 psychological (task and ego orientation), and 2 “biosocial” (SES and energy spent, per week, on regular handball activities) variables. In continuation, 5 binary logistic regression models were adjusted, using a forward variable selection method, to identify which variables significantly contributed to predict the probability of an athlete to be a top elite player (the variables of each individual categories were the independent variables, and the performance group was the dependent variable for the binary logistic regression models). Moreover, this preliminary study can be an important “key” to perform (in a near future) a multidisciplinary approach to talent selection in team handball.
Before their inclusion in the study, the experimental objectives and procedures were explained to the participants, from whom written informed consent was obtained. The experimental protocol was approved by the Scientific and Ethical University committees.
One hundred sixty-seven male team-handball players (n = 167; age: 23.6 ± 5.3 years) participated in this study. The participants were divided into 2 groups: (a) top elite (n = 41; age: 26.2 ± 4.9 years), players of first Portuguese Handball Division—Portuguese Handball Federation—PO.01 (top elite players can be considered as one of the Portuguese leading professional handball teams because they were the Portuguese Champions and vice champions), and (b) nontop elite (n = 126; age: 25.2 ± 4.8 years), players from second or third Portuguese Handball Division (Portuguese Handball Federation, PO.02 and PO.03). All the participants were tested during a competitive period of the 2008–2009 Portuguese team-handball season (February and March 2009).
Twenty anthropometric dimensions were determined. We obtained 2 basic measures, stature (centimeters) and body mass (kilograms), 8 skinfolds (millimeters), 6 girths (centimeters), 1 breadth (centimeters), and 3 lengths (centimeters) measure. The 8 skinfolds were taken at the subscapular, triceps, biceps, chest, supraspinale, abdominal, front thigh, and medial calf location. The 6 girths were arm (relaxed), forearm (maximum), chest (mesosternale), waist (minimum), thigh (mid-troch-tib. lat.), and calf (maximum). To adjust the measure of thigh girth (mid-troch-tib. lat.), to the original measure used by Lee et al. (25) (i.e., inguinal-patellar), a random subsample (n = 31) was used to calculate the coefficient of adjustment (R = 0.986) (30). The bone breadth measure that was obtained was the biacromial. The 3 lengths were the acromiale dactylion, the radiale dactylion, and the midstylion dactylion. The measurements were obtained following the protocol in Marfell-Jones et al. (27), with the exception of chest skinfold (the skinfold measurement was taken obliquely in the mean distance between the breast nipple and the axilla fold), acromiale-dactylion length (the linear distance between the acromiale and dactylion sites), and radiale-dactylion length (the linear distance between the radiale and dactylion sites). All such measurements were obtained using portable measurement devices. Stature and heights were measured using a portable anthropometer (GPM; Siber-Hegner, Zurich, Switzerland). Body mass was measured, to the nearest 0.5 kg, using a scale (Secca model 761 7019009; Vogel & Halke, Hamburg, Germany). Skinfold thickness was obtained using a skinfold callipers (Slim Guide; Rosscraft, Surrey, CA, USA) and the breadth and lengths were measured using a large sliding calliper (GPM, Siber-Hegner), and girths using a flexible, nonstretching steel tape (Model W606PM, Lufkin, TX, USA). All the measurements were taken by 4 technicians accredited by International Society for the Advancement of Kinanthropometry (ISAK) and under the supervision of an ISAK Level 4. Practitioner individual measurements were always collected, in all the subjects, by the same ISAK evaluators (intraobserver technical error of measurements—TEM: stature, R ≥ 0.98; skinfolds, between R = 0.90 and R = 0.98; breadth and girths, between R = 0.92 and R= 0.98). In continuation, the body density was estimated using the equations of Durnin and Womersley (6) and Jackson and Pollock (21), and the equation of Siri (42) was used to convert the body density to body fat percentage. Also, muscle mass was calculated according to the Heymsfield et al. (19) and Lee et al. (25) equations.
Before the fitness tests, all the participants performed a 20-minute warm-up (involving a slow jog followed by static and dynamic stretching). They were also allowed 10 minutes of passive rest between tests. Water breaks and extra rest time were allowed. Each player was instructed and verbally encouraged to give his maximal effort. Six tests were performed by the participants (following the order established in the description), and 9 variables were recorded for the analysis. The tests included a 30-m speed test. The participants completed 3 trials, and the best score (time in seconds) was recorded for analysis (26). All sprint times were recorded using electronic timing lights (Wireless Sprint System; Brower Timing Systems, Salt Lake City, UT, USA). Handgrip was assessed using a grip strength dynamometer (Grip Takei Physical Fitness Test; TKK 5001, Tokyo, Japan). Again, the participants completed 3 trials, with each hand. The best scores (in kilograms) were recorded (13), and the difference between dominant and nondominant handgrip (Handgrip, D-ND) was calculated. The players performed 2 vertical jump tests, on an Ergojump (Bosco System, Globus, Treviso, Italy) using the Bosco protocol (4), to determine lower body explosive strength. Three trials of each test (SJ and CMJ) were performed, and the best trial result of each test was recorded (height, centimeters). Leg power was also assessed (Pavg, watts), using a modified version of the Lewis formula (8). Abdominal strength (i.e., endurance) was assessed using the 60-second sit-up test (41). The participants completed 1 trial, and the number of repetitions (#) was recorded. Finally, to study the intermittent endurance capacity, the YYIE2 was used (2). The distance (meters) and the position achieved (using a 4-point scale where 1, 2, 3, and 4 represent, respectively, <1,000 m; ≥1,000 m; and ≤1,300 m; ≥1,300; and <1,600 m; ≥1,600 m) were recorded (2).
Handball-Specific Skills Profiling
Despite the growing popularity and professionalization of handball, the scientific literature, produced until this moment, does not include validated tools to assess a technical and tactical proficiency. This consideration justifies the development of a rating scale to evaluate the technical and tactical aspects of team handball. Therefore, a total of 17 expert team-handball coaches evaluated the technical and tactical proficiency of 235 male team-handball players (age: 23.46 ± 5.25 years), excluding goalkeepers, using a 10-item Likert-type scale. The coefficient of internal consistency measured by Cronbach's alpha coefficient was 0.936, and the technical-tactical tool was empirically validated via factor analysis. Just 1 factor with 6 items was highlighted as important by the results (Cronbach's alpha = 0.934). This measurement model allowed us to find values of 0.997 for the goodness of fit index (GFI), 0.993 for the adjusted GFI (AGFI) and of 0.022 for the modified root mean square residual (RMSR*). The scale was built so that the higher the score, the better the offensive power of the handball player. This tool of 5-point Likert-type scale evaluated all the athletes, with scores ranging from 1 (very poor) to 5 (excellent). The Likert-type scale comprised 6 items: (a) pass and reception, (b) different types of shooting, (c) the 1 vs. 1 actions, (d) the ability to create and to occupy spaces, (e) tactical skills, and (f) the reactive ability. Scores on all 6 items were summed up to obtain individual handball-specific skills score, which we named offensive power.
To assess task and ego achievement goal orientations, the Portuguese version (Fernandes A and Serpa S, unpublished observation) of the TEOSQ questionnaire was used. In a previous study (31), the Portuguese translation of the TEOSQ questionnaire was validated, using a sample of adult male team-handball players (n = 203), from the same cultural context as in this study. The exploratory factor analysis (employing the extraction method of Principal Component Analysis and the Varimax rotation with Kaiser Normalization) supported that (a) the hypothesized theoretical model of 2 factors (task and ego orientation) (Bartlett's test of sphericity: χ2 = 628.992, df = 78, p < 0.001; KMO = 0.754; GFI = 0.927; AGFI = 0.874; RMSR* = 0.040), and (b) the satisfactory internal consistency (Cronbach's alpha coefficients being 0.70 and 0.77 for the task and ego orientation subscales, respectively) (30). To fill in the scale, the subjects always took <20 minutes. They must respond to 13 items concerning success in sport that are preceded by the statement “I feel most successful in sport when.” Responses to each item are measured on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). The intensity of agreement or disagreement with each item reflects a possible task orientation (e.g., “I learn a new skill by trying hard”) or an ego orientation (e.g., “I can do better than my team mates”). Both task and ego orientations scores were calculated.
Recently, Massuça and Fragoso (29) used a questionnaire to assess biosocial variables and the lifestyle of national level handball players. They identified that SES, and the energy spent on regular team-handball activities (per week), were able to discriminate team-handball players of different performance levels. The same methodology was adopted in this study. The athlete was assigned to 1 of 5 SES classes (where a higher score means a higher SES), and to calculate the energy spent, the participants recorded the frequency and the duration of their team-handball activities (during all 1 week of normal training). The rate of energy expenditure was estimated according to Massuça and Fragoso (29).
Descriptive and comparative summary data are presented, and group data are expressed as means and SD for all dependent variables. Intraobserver technical error of measurements were presented in morphologic profiling subtitle, and the ICCR reliability of procedures was adopted in fitness (2,4,8,13,26,41), handball-specific skills (validation test); psychological (31), and biosocial (29) evaluations were supported by the literature as cited in procedures. Top elite and nontop elite groups were compared on each variable of interest using independent samples t-test, with the Welch correction if necessary or the Mann-Whitney-Wilcoxon test. The normality and homogeneity of variance assumptions were tested using the Shapiro-Wilk and the Levene tests, respectively. Five logistic regression analyses, using a stepwise method for variable selection (Forward: LR), were performed with the performance level as the dependent variable (Top elite = 1, if the team-handball player is a top elite athlete; Top elite = 0, if the team-handball player is a nontop elite athlete) and the variables of each category (i.e., morphologic-, fitness-, handball-specific skills, psychological, and “biosocial”) as candidate predictors. All statistical analyses were performed, at the 5% significance level, using Microsoft Excel (Microsoft, Seattle, WA, USA) and the Statistical Package for the Social Sciences (SPSS Inc.; version 17.0, Chicago, IL, USA).
Relatively to the morphological variables, top elite handball players were heavier (+7.09 kg, p < 0.01) and taller (+7.05 cm, p < 0.001), had a higher biacromial girth (p < 0.01), and higher upper limb lengths (acromiale dactylion, p < 0.05; radiale dactylion, p < 0.001) than did the lower level players. Top elite players also had a significantly higher absolute muscle mass (p < 0.001) and absolute fat mass (: p < 0.01; : p < 0.001). Relative muscle mass was also significantly (p < 0.001) different between groups when the Heymsfield et al. (19) equation was used, and relative fat mass was significantly (p < 0.05) different when the Jackson and Pollock (21) equation was used.
The top elite handball players scored better on all fitness evaluations, that is, they were faster, stronger, more powerful, and had a superior aerobic capacity than did nontop elite handball players. In fact, significant differences were observed (all in favor of the top elite athletes) in speed time (−0.18 seconds, p < 0.001), between dominant and nondominant handgrip (+2.15 kg, p < 0.05), average power in booth jump tests (SJ, +103.91 W; CMJ, +111.99 W; both p < 0.01), sit-ups (+9 #, p < 0.001) and in the YYIE2 (performance, +940.36 m, and class; both, p < 0.001).
Regarding the handball-specific skills, the top elite players scored significantly better (+3.61; p < 0.001) than nontop elite athletes did.
In what concerns the psychological variables, despite the lower scores of top elite handball players, when compared with nontop elite athletes, on the goal dimensions (task orientation, −0.01; ego orientation, −0.29), the differences were not statistically significant.
Relatively to the “biosocial” variables, the results of the Mann-Whitney-Wilcoxon test showed that the top elite handball players came from a higher SES Class, and the t-test results revealed that top elite players spent more energy per week in handball practice than did nontop elite players (+107.62 MET·wk−1, p < 0.001). All these findings are presented in Table 1.
Considering the performance level as the response variable, 5 binary logistic regression models were adjusted for each group of independent variables. The predictors that significantly contributed to predict the probability of an athlete being a top elite player were (a) body mass, waist (minimum) girth, radiale-dactylion length, midstylion-dactylion length, and absolute muscle mass (G2 = 93.158, p < 0.001; correct classification of 84.2%), for the morphological profiling, (b) 30-m sprint time, CMJ height, and average power, abdominal strength (endurance), and the class of performance in the YYIE2 (G2 = 121.627, p < 0.001; correct classification of 91.5%), for the fitness profiling, (c) Offensive power (G2 = 12.312, p < 0.001, correct classification of 82.1%), in what concerns the handball-specific skills profiling; (d) ego motivational orientation (G2 = 62.937, p < 0.001; correct classification of 80.5%), for the psychological profiling, (e) SES and the energy spent in handball activities during the week (G2 = 106.806, p < 0.001; correct classification of 79.7%), for the “biosocial” profiling. These findings are presented in Table 2.
Our results confirmed our major hypothesis that morphologic, fitness, handball-specific skills and psychological and “biosocial” characteristics are important to be successful in handball.
Moreover, it seems that to succeed in a specific sport, it is important to have specific bodily attributes. In fact, body mass can influence an athlete's speed, endurance, and power, whereas body composition can affect an athlete's strength and agility. A greater muscle mass is often an advantageous characteristic in sports, as in team handball, where speed is so much of the essence. Gorostiaga et al. (10) found that elite team-handball players were heavier and had a higher fat-free mass than the amateur team-handball players did and concluded that this seems to be advantageous in team handball. As regards the upper limb lengths (i.e., radiale-dactylion length), it seems that these measures are important for a better handball shot execution (the larger the radius of action the greater the power of the technical gesture) and for some defensive actions (e.g., blocking).
Vertical jump performance is also considered important in a large number of sports. In fact, it seems that explosive strength is an important feature of team-handball because high-intensity activities are frequently performed (i.e., fast direction changes, jumps, throws, and dribbles). According to the literature, the leg stiffness is decreased with jump height (23). Our results related to 30-m sprint time and CMJ height suggest that the leg power is an essential component for top elite in team handball.
Moreover, the major importance of abdominal strength to be successful handball player (odds ratio = 1.128) leads us to believe that it will become increasingly important to study the inclusion of abdominal muscles in team handball–specific exercises, using electromyography and kinematic analysis, in future research.
Also, the ability to repeatedly perform intense intermittent exercise seems to be an important fitness component of team-handball players (Table 2). In fact, our results showed that the YYIE2 test seems to possess some ecological validity in team handball.
We know that the metabolic pathway response in team-handball players during this maximal and incremental test should be investigated. Nevertheless, 2 studies in junior team-handball athletes exhibit (a) a positive correlation (r = 0.919, p < 0.05) between the maximum performance in the YYIE2 test (distance = 1,206.7 ± 365.2 m) and the aerobic capacity assessed through a maximum incremental treadmill running test (V[Combining Dot Above]O2max = 53.9 ± 4.2 ml·kg−1·min−1) in the Hong Kong Team (n = 7, age = 15.3 ± 1.5 years; HP Lam, unpublished observation), and (b) in the preseason, a strong positive correlation (r = 0.862) between the YYIE2 performance (distance = 844 ± 339.41 m) and the V[Combining Dot Above]O2max estimated from a Cooper test (estimated V[Combining Dot Above]O2max = 46.93 ± 7.49 ml·kg−1·min−1) in Portuguese National champions (n = 11, age = 18.1 ± 1.1 years ). In addition, in well-trained youth soccer players (n = 18, age = 17.4 ± 0.5 years), Valamatos et al. (47) also observed a positive correlation between V[Combining Dot Above]O2max (as measured with the progressive continuous running treadmill test) and the performance in the YYIE2 (p < 0.01). These findings and our results allow us to suggest that the YYIE2 can be considered an aerobic anaerobic–specific field test for team-handball players (and can be regarded as a good indicator of aerobic capacity). However, anaerobic metabolism in handball players and the recovery capacity of players after training or after competitions need further investigation.
Moreover, the fundamental issue for coaching, with high relative importance to success, is the need for the evaluation of team handball–specific skills. Our results in this measure seem to be promising, but we reinforce the need to develop a more complete instrument for the purpose of evaluation of team handball–specific skills.
Why was the TEOSQ selected as the key psychological variable for the study? Is goal orientation important for adult athletes? It is evident that in team sports, and particularly in handball, athletes (top elite or nontop elite) have a high-task orientation (i.e., they feel competent when showing the improvement of their skills and task mastery). This is related to a host of adaptive outcomes among the participants including greater positive affect, intrinsic motivation, perceived effort, and task persistence (40,45). As expected, our team-handball players exhibited a high-task/low-ego orientation profile, that is, they have greater enjoyment, importance and utility than do low-task/high-ego and low-task/low-ego athletes (44). However, it is also evident that top elite players were, on average, less ego oriented (but not significantly so) than were nontop elite players. In continuation, the psychological model showed that this goal orientation was a determinant of success, and it thus seems logical because major ego-oriented athletes tend to (a) evaluate their performance by comparing it with other athletes' results, rather than by focusing upon skill development (15), (b) tend to give up when facing situations of greater complexity (5), and (c) show some anxiety and poor concentration during competition (5). We conclude that goal orientation remains important in adult athletes and can contribute to predict the probability of individual success in team handball.
Finally, we acknowledge that the term biosocial implies an interaction between biological and social or cultural factors. However, the selected variables, although largely demographic (that is why we use commas—“biosocial”) have a significant discriminatory validity (29). Moreover, we found both variables, that is, the energy spent (MET per week) in organized handball activities and SES, contributed to explain success in team handball. It seems evident that the energy spent depends on the amount of time used in regular practice (Methods), and that expert performers accumulate more minutes of sport-specific practice than do nonexpert performers (1,18). This may be associated with (a) the time “needed” to learn and to improve technical and tactical skills, and (b) the morphologic profile of the athlete. Our finding of an influence of SES on team-handball player's success seems to be relevant. Nevertheless, it should be interpreted with caution (because the top elite athletes are presumably paid more than nontop elite are).
To our knowledge, this is the first study to adjust 5 different models (i.e., morphologic-, fitness-, specific skills and psychological-based and “biosocial”-based attributes) to predict the probability of an athlete be a top elite player with the same adult male team-handball players' sample.
What is the importance of knowing what differentiates the top elite from the nontop elite players at the senior level?
This research reinforced the effect and significance of 5 morphologic characteristics (body mass, waist girth, radiale-dactylion length, midstylion-dactylion length, and absolute muscle mass), 5 fitness results (30-m sprint time, CMJ height and average power, abdominal strength and the class of performance in the YYIE2), offensive power, motivational orientation (Ego) and 2 “biosocial” (energy spent in handball activities and SES) variables to identify top elite team-handball players (i.e., to identify successful players).
In other words, top elite athlete are (a) more robust (higher, heavier with larger bones and more muscle mass), (b) have bigger upper limbs lengths, (c) are quicker, agile, powerful, and strong, (d) have better technical and tactical game's skills, (e) are more resilient and team oriented, and (f) had a higher socioeconomic level. Such empirical and practical information is essential to select the most successful team-handball players.
Our fitness-based model allowed a superior percentage (i.e., 7.3–11.8%) of correct classification (91.5%) than all the other models did. This result suggests that players with a better fitness level have a greater chance to be selected to play in top elite team handball and provide the rational to validate and to reduce the battery of fitness tests to 4 simple and inexpensive field tests (i.e., 30-m sprint, CMJ, Sit-ups, and YYIE2) that can be used by trainers and scouts for evaluation purposes. However, the results also suggest that taking into account the multiple facets (profiles) that influence sport success other factors than fitness level is also important to select top elite athletes. So, although more studies can be conducted to examine the effectiveness of these 5 models, such information is already very important to begin work on building a multidisciplinary model (performed with the 5 models, i.e., morphologic, fitness, specific skills, psychological and social models, as predictors) that can achieve good results along the complex process of talent selection in team handball.
The authors wish to thank the coaches and athletes who participated in this study. The first author also gratefully acknowledges the support of his colleague at the Faculty of Human Kinetics, Technical University of Lisbon: Veronica Vleck, for all the valuable advice. No funding was received for this work.
1. Baker J, Côté J, Abernathy B. Sport-specific practice and the development of expert decision-making in team ball sports. J Appl Sport Psychol 15: 12–25, 2003.
2. Bangsbo J. Yo-Yo Tests. Copenhagen, Denmark: HO & Strom, 1996.
3. Bezerra ES, Simão R. Anthropometric characteristics of handball adult athletes. Fitness
Perform J 5: 318–324, 2006.
4. Bosco C, Luhtanen P, Koni P. A simple method for measurement of mechanical power in jumping. Eur J Appl Physiol 50: 273–282, 1983.
5. Duda JL, Hall HK. Achievement goal theory in sport: Recent extensions and future directions. In: Handbook of Sport Psychology. Singer R.N., Hausenblas H.A., Janelle C.M., eds. New York, NY: Wiley, 2001. pp. 417–443.
6. Durnin JV, Womersley J. Body fat assessment from total body density and its estimation from skinfold thickness: Measurements on 481 men and women aged from 16 to 72 years. Br J Nutr 32: 77–97, 1974.
7. Elferink G, Visscher C, Lemmink K, Mulder T. Relation between multidimensional performance characteristics and level of performance in talented youth field hockey players. J Sports Sci 22: 1053–1063, 2004.
8. Fox EL, Mathews DK. The Interval Training: Conditioning for Sports and General Fitness
. Philadelphia, PA: W.B. Saunders, 1974. pp. 257–258.
9. Gordon-Larsen P, Griffiths P, Bentley ME, Ward SD, Kelsev K, Shields K, Ammerman A. Barriers to physical activity: Qualitative data on caregiver-daughter perceptions and practices. Am J Prev Med 27: 218–223, 2004.
10. Gorostiaga EM, Granados C, Ibáñez J, Izquierdo M. Differences in physical fitness
and throwing velocity among elite and amateur male handball players. Int J Sports Med 26: 225–232, 2005.
11. Gorrostiaga EM, Granados C, Ibáñez J, González-Badillo JJ, Izquierdo M. Effects of an entire season on physical fitness
changes in elite male handball players. Med Sci Sports Exerc 38: 357–366, 2006.
12. Gould D, Eklund RC, Jackson S. Coping strategies used by more versus less successful Olympic wrestlers. Res Q Exerc Sport 64: 83–93, 1992.
13. Grosser M, Starischka S. Fitness
tests. Madrid, Spain: Ediciones Matínez Roca, S.A. ISBN 84-270-1253-5, 1988.
14. Gruić I, Vuleta D, Milanović D. Performance indicators of teams at the 2003 men's world handball championship in Portugal. Kinesiology 28: 164–175, 2006.
15. Hall HK, Kerr AW, Kozub SA, Finnie SB. Motivational antecedents of obligatory exercise: The influence of achievement goals and multidimensional perfectionism. Psychol Sport Exerc 8: 297–316, 2007.
16. Hasan AA, Rahaman JA, Cable NT, Reilly T. Anthropometric profile of elite male handball players in Asia. Biol Sport 24: 3–12, 2007.
17. Hasan AA, Reilly T, Cable NT, Ramadan J. Anthropometric profiles of elite Asian female handball players. J Sports Med Phys Fitness
47: 197–202, 2007.
18. Helsen WF, Hodges NJ, Winckel JV, Starkes JL. The roles of talent, physical precocity and practice in the development of soccer expertise. J Sports Sci 18: 727–736, 2000.
19. Heymsfield SB, McManus C, Smith J, Stevens V, Nixon DW. Anthropometric measurement of muscle mass: Revised equations for calculating bone-free arm muscle area. Am J Clin Nutr 36: 680–690, 1982.
20. Hodge K, Allen JB, Smellie L. Motivation in masters sport: Achievement and social goals. Psychol Sport Exerc 9: 157–176, 2008.
21. Jackson AS, Pollock ML. Generalized equations for predicting body density of men. Br J Nutr 40: 497–504, 1978.
22. Jadach A. Technical and tactical factors determining the effectiveness of female handball. Phys Educ Sport 49: 43–46, 2005.
23. Laffaye G, Bardy BG, Durey A. Leg stiffness and expertise in men jumping. Med Sci Sports Exerc 37: 536–543, 2005.
24. Laguna PL, Ravizza K. Collegiate athlete's mental skill use and perceptions of success: An exploration of the practice and competition settings. J Appl Sports Psychol 15: 115–128, 2003.
25. Lee RC, Wang Z, Heo M, Ross R, Janssen I, Heymsfield SB. Total-body skeletal muscle mass: Development and cross-validation of anthropometric prediction models. Am J Clin Nutr 72: 796–803, 2000.
26. Maldonato T, Seco J. Introduction to databases for monitoring handball players. Andalucía, Spain: Dirección General de Deportes, 1989.
27. Marfell-Jones M, Olds T, Stewart A, Carter JEL. International Standards for Anthropometric Assessment. Underdale, SA: International Society for the Advanced of Kinanthropometry, 2006. ISBN 0-620-36207-3.
28. Massuça L, Fragoso I. Junior-Senior transition in handball: A morfo-functional approach. Gymnasium 3: 73–98, 2011.
29. Massuça L, Fragoso I. Study of Portuguese handball players of different playing status. A morphological and biosocial
perspective. Biol Sport 28: 37–44, 2011.
30. Massuça L, Fragoso I. Do anthropometric characteristics and body composition vary according to playing status in Portuguese male handball players? Coll Antropol (In Press).
31. Massuça L, Fragoso I, Rosado A. Testing for validity of the Task and Ego Orientation in Sport Questionnaire (QOMD-TEOSQ) in handball players. Laboratório de Psicologia 9: 125–133, 2011.
32. Massuça L, Fragoso I, Alves F, Alvarez N, Florêncio J. Individual’s aerobic capacity in junior handball players. Motricidade 5: 83, 2009.
33. Mensink GB, Ziese T, Kok FJ. Benefits of leisure-time physical activity on the cardiovascular risk profile at older age. Int J Epidemiol 28: 659–666, 1999.
34. Nicholls JG. The Competitive Ethos and Democratic Education. Cambridge, MA: Harvard University Press, 1989.
35. Ohnjec K, Vuleta D, Milanović D, Gruić I. Performance indicators of teams at the 2003 world handball championship for women in Croatia. Kinesiology 40: 69–79, 2008.
36. Póvoas SCA, Seabra AFT, Ascensão AAMR, Magalhães J, Soares JMC, Rebelo AMC. Physical and physiological demands of elite team handball. J Strength Cond Res 26: 3365–3375, 2012.
37. Rannou F, Prioux J, Zouhal H, Gratas-Delamarche A, Delamarche P. Physiological profile of handball players. J Sport Med Phys Fitness
41: 349–353, 2001.
38. Reilly T, Maughan RJ, Hardy L. Body fat consensus statement of the steering groups of the British Olympic Association. Sports Exerc Inj 2: 46–49, 1996.
39. Reilly T, Williams AM, Nevill A, Franks A. A multidisciplinary approach to talent identification in soccer. J Sports Sci 18: 695–702, 2000.
40. Roberts GC, Treasure DC, Conroy DE. Understanding the dynamics of motivation in sport and physical activity: An achievement goal interpretation. In: Handbook of Sport and Exercise Psychology. Tenenbaum G., Eklund R.C., eds. New York, NY: Wiley, 2007. pp. 3–30.
41. Semenick D. Testing protocols and procedures. In: Essentials of Strength Training and Conditioning. Baechle T.R., ed. Champaign, IL: Human Kinetics, 1994. pp. 258–273.
42. Siri WE. Body composition from fluid spaces and density. In: Techniques for Measuring Body Composition: Analysis of Methods. Brozek J., Henschel A., eds. Washington, DC: National Academy of Science, 1961. pp. 223–244.
43. Souhail H, Castagna C, Mohamed HY, Younes H, Chamari K. Direct validity of the yo-yo intermittent recovery test in young team handball players. J Strength Cond Res 24: 465–470, 2010.
44. Stephens DE. The relationship of goal orientation and perceived ability to enjoyment and value in youth sport. Pediatr Exerc Sci 10: 236–247, 1998.
45. Stuntz CP, Weiss MR. Achievement goal orientations and motivational outcomes in youth sport: The role of social orientations. Psychol Sport Exerc 10: 255–262, 2009.
46. Trininić S, Dizdar D. System of the performance evaluation criteria weighted per positions in the basketball game. Coll Antropol 24: 217–234, 2000.
47. Valamatos MJ, Charrua C, Gomes-Pereira J, Mil-Homens PS. Aerobic fitness
in young soccer players: The yo-yo intermittent endurance test as indicator of aerobic power and anaerobic threshold. In: Proceedings of the 12th Annual Congress of the ECSS. Jyväskylä, Finland: ECSS, 2007.
48. Ziv G, Lidor R. Physical characteristics, physiological attributes, and on-court performances of handball players: A review. Eur J Sport Sci 9: 375–386, 2009.