Anthropometric charachteristics, physical fitness and the prediction of throwing velocity in handball men young players

The objectives of this study were: (i) to analyse anthropometric parameters, physical fitness, and throwing velocity of handball male elite youth players of different ages; and (ii) to develop a multivariate model that explains throwing velocity. Fifty-three handball men players (17.99±1.68 years old), members of the Icelandic National Teams, participated in the study. The participants were classified into the U21 National Team (n=12), U19 National Team (n=17), and U17 National Team (n=24). All were evaluated by basic anthropometry (body height, body mass, body mass index), physical fitness tests (counter movement jump, medicine ball throw, hand dynamometry, 10 m and 30 m sprint, yo-yo IR2 test) and ball speed after various handball throws at goal (a 7-m throw, a 9-m ground shot after a three-step run-up, and a 9-m jump shot after a three-step approach). A one-way analysis of variance with a Bonferroni post-hoc test was used to establish the differences between the teams. Multiple linear regression was used to predict the speed of the ball from each of the three shots taken for each team. There were no differences between the U21 and U19 teams except for the medicine ball throw, but the U19 team scored better than the U17 team in almost all variables. Ball speed after a handball shot was predicted (between 22% and 70% of accuracy) with only one or two physical fitness variables in each model ‒ medicine ball throw (in four models), counter movement jump (in two models), and 10 m sprint (in two models), being the variables that were most selective

Are they all born to score? The relationship between throwing arm and scoring from the 7-meter line in semi-professional handball

Indications of laterall biases favouring left-handers have been found in various sports; especially interactive sports where the athletes have limited time to react to incoming objects. The aim of this study was therefore to explore whether any lateral biases exist in handball by examining 7-meter shots. A total of 6846 7-meter throws from 240 7-meter shooters across four seasons in the semi-professional Icelandic elite handball division (male and female) were analyzed. Out of the 240 7-meter shooters, of which 151 were male and 89 were female, 22% were left-handed (22% of the males and 20% of the females). The left-handed 7-meter shooters took a disproportionate number of the 7-meter shots, with left-handed shooters performing 29% of the 7-meter shots (27% in the male league and 33% in the female league). The results of a Bayesian two-level analysis indicated that left-handedness is not associated with greater success from the 7-meter line at the semi-professional level.publishedVersio

Performance Profiling in Handball Using Discriminative Variables and its Practical Applications

Performance profiles of teams performance highlight areas of weaknesses and strengths for coaches to inform their decision-making on how to spend their limited training time with athletes. This study used a stepwise discriminative analysis approach comparing one successful team’s (TEAM) performances through five consecutive seasons against a) other top four teams (TOP4) and b) teams with a final rank between 5th and eight (LOW) in a semi-professional league. The predictive model created was used to set forth a performance profile for the selected team. A total of 95 matches of the TEAM’s matches from the last five seasons are in the analysis. The objective was to create a performance profile with relevant performance indicators selected based on the discriminant analysis results of the selected TEAM and discuss its practical applicability. For matches against other TOP4 teams, the predictive model created consisted of three variables; legal stops, blocked shots and 9 m shots, classifying 72.6% correctly. The LOW ranked teams model had six variables and correctly classified 94.4% of cases (assists, blocked shots, legal stops, the goalkeeper saved shots, 2-minute exclusion, and shot efficiency). The selected variables are presented in Table 4, with medians and a 95% confidence interval of the median as a team performance profile. The profile provides the coaches with two models containing values that can serve as a reference for this team’s performance. The profile of this TEAM’s performances during the last five seasons generally aligns with the variables associated with success in other studies in female handball.publishedVersio

Women\u27s beach handball game statistics: Differences and predictive power for winning and losing teams

The objectives of the present study were: (i) to compare beach handball game-related statistics by match outcome (winning and losing teams), and (ii) to identify characteristics that discriminate performances in the match. The game-related statistics of the 72 women’s matches played in the VIII Women’s Beach Handball World Championship (2018) were analysed. The game-related statistics were taken from the official Web page. A validation of the data showed their reliability to be very good (the inter-observer mean reliability was α=0.82 and the intra-observer mean was α=0.86). For the differences between winning/losing teams a parametric (unpaired t-test) or non-parametric (Mann-Whitney U test) test was applied depending on whether the variable met or did not meet normality, respectively. A stepwise discriminant analysis was then performed to determine the variables that predicted performance (victory or defeat). Five variables showed differences between the winning and losing teams: total points (p<.001; ES=1.09), technical faults (p<.001; ES=‑0.96), the number of players with either negative (p<.001; ES=‑0.86) or positive (p<.001; ES=1.05) valuations and overall valuation (p<.001; ES=1.29). The predictive model correctly classified 80.6% of the matches using two variables (Wilks’s λ=0.618; canonical correlation index=0.618): overall valuation and GK shots

Estadísticas de juego en balonmano masculino en los Juegos Olímpicos (2004-2016): Diferencias y poder discriminatorio

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked FilesHandball can be considered a complex game. Sports performance analysis is a relevant topic for scientists and coaches. The objectives of the present study were: (i) to compare handball game-related statistics by match outcome (winning and losing teams) and (ii) to identify characteristics that discriminate the performance in elite men's handball. The game-related statistics of the 324 games played in the last four Olympic Games (Athens, Greece, 2004; Beijing, China, 2008; London, United Kingdom, 2012; and Rio de Janeiro, Brazil, 2016) were analyzed. Differences between match outcomes (winning or losing teams) were determined by using the chi-squared statistic, and by calculating the effect sizes of the differences. A discriminant analysis was then performed applying the sample-splitting method according to match outcomes. The results showed that the differences between winning and losing teams were shots, 9 m shots, assists, goalkeeper-blocked shots fast break. Also, discriminant analysis selected four variables (shots, goalkeeper-blocked shots, technical foul, and attacks) that classified correctly 82% of matches (Wilks's lambda=0.575; canonical correlation index 0.652). The selected variables included offensive and defensive predictors: Shots, goalkeeper-blocked shots, technical foul, attacks. Coaches and players can use these results as a reference against which to assess their performance and plan their team's training.Resumen. El balonmano puede considerarse un juego complejo. El análisis del rendimiento deportivo es un tópico relevante para los científicos y entrenadores. Los objetivos del presente estudio fueron: (i) comparar las estadísticas de juego en balonmano en función del contexto (equipos ganadores y perdedores) e (ii) identificar las estadísticas que discriminan el rendimiento en el balonmano masculino de élite. Se analizaron las estadísticas de juego de los 324 partidos disputados en los últimos cuatro Juegos Olímpicos (Atenas, Grecia, 2004, Beijing, China, 2008, Londres, Reino Unido, 2012 y Río de Janeiro, Brasil, 2016). Las diferencias entre los equipos ganadores y perdedores) se determinaron usando el estadístico chi-cuadrado y calculando los tamaños del efecto de las diferencias. A continuación, se realizó un análisis discriminante aplicando el método de por pasos. Los resultados mostraron que las diferencias entre los equipos vencedores y perdedores se presentaron en las variables lanzamientos de 9 m, asistencias, lanzamientos bloqueados por el portero en situación de contrataque. Además, el análisis discriminante seleccionó cuatro variables (lanzamientos, lanzamientos bloqueados por el portero, falta técnica y número de ataques) que clasificaron correctamente el 82% de los partidos (Lambda de Wilks=0,575; índice de correlación canónica=0,652). Las variables seleccionadas incluyeron predictores ofensivos y defensivos: lanzamientos, paradas del portero, faltas técnicas y ataques. Los entrenadores y los jugadores pueden utilizar estos resultados como referencia para evaluar su rendimiento y planificar el entrenamiento del equipo. Palabras clave: análisis notacional, rendimiento, partido, lanzamiento, portero

Comparison of Training Volumes in Different Elite Sportspersons According to Sex, Age, and Sport Practised

Training is a complex process that depends, among other factors, on the intensity and volume of training. Th e objective of this study was to analyse the volume of training in several sports as a function of sex and age. Th e study sample consisted of 302 sportspersons (men, n=132; women, n=170) who participated in the 16th Games of the Small States of Europe (1st to 6th June 2015) in representing nine countries. Th e subjects practised the following sports: artistic gymnastics, athletics, basketball, beach volleyball, golf, judo, shooting, swimming, table tennis, tennis, and volleyball, and were classifi ed by sex, sport, and age (younger: ≤20 years; intermediate: from 21 to 30 years; older: ≥31 years). Th ey responded to fi ve questions about their training volume and the annual number of competitions in which they participated. A one-way ANOVA with a Bonferroni post hoc test was used to establish diff erences by sex, sport, and age group. Th ree-way ANOVAs (sex [men, women] × age [3 levels: younger, intermediate, older] × sport [11 sports]) were performed to determine any relationships between the variables. Neither interactions between the groups nor diff erences depending on sex were found in the training volumes, but the older the sportsperson, the lower the training volume (days per week, and total time per week). Th e sports with the greatest training volumes were artistic gymnastics and swimming, while those with the most competitions per year were basketball and volleyball

The physical and physiological difference between soccer academy players and their non-academy teammates

Development of elite soccer athletes by means of sport academies has received a lot of interest recently from scientists looking for predicting factors of success. The aim of this study was to assess if there is a difference in physiological and/or physical attributes between players of a soccer academy and club players only, as academy players receive three extra practice sessions per week in the academy, for 12-15 weeks each semester compared to club players. Academy players (n =15) were compared to their teammates (n =33) who play the same field position respectively. Sport background questionnaire was administered and anthropometric measures performed (height, weight and body fat). Physiological performance was assessed with three fundamental performance tests; VO2 MAX (aerobic endurance), Illinois agility run [IAR] (agility) and vertical jump (anaerobic power). No significant difference was observed in the performance tests or anthropometry. Therefore it must be assumed that the soccer academy has not yet reached a significantly higher physiological standard compared to club teammates. However, the academy players outperformed club players in IAR (0.34 s) and vertical jump (3.5-7%), but not in relative VO2 MAX were club players scored higher (academy 55.6 s = 4.91 mlkg-1min-1 versus club 57 s = 5.37 mlkg-1min-1). Anthropometry was similar between the two groups and in harmony with other previously published studies on soccer academy athletes. It would be of benefit to the academy to administer an entrance exam to raise the physiological standard compared to average club values. Suggestions for further research are provided. Keywords: soccer, physiological, performance, teammates, academyaðgangur verði lokaður þangað til niðurstöður hafa verið birtar í tímarit

Ráðleggingar til drengja um val á milli knattspyrnu og handknattleiks

Hvernig á að styðja unga íþróttamenn sem æfa handknattleik og knattspyrnu samtímis? Það er viðfangefni þeirrar eigindlegu rannsóknar sem ritgerðin fjallar um. Viðtöl með hálfopnum spurningum voru tekin við reynslumikla þjálfara og unglingalandsliðsmenn. Rannsóknin fór fram á tímabilinu 20. mars til 23. apríl 2010. Markmiðið var að komast að því hvenær og hvernig íþróttamennirnir ættu að hætta í annarri greininni og sérhæfa sig í hinni. Niðurstöðurnar rannsóknar mæla með því við unga íþróttamenn og þjálfara þeirra að þeim verði gert kleift að æfa tvær greinar fram að 15 ára aldri. Eftir 15 ára aldurinn getur álagið orðið það mikið að það getur verið skaðlegt íþróttamanninum að æfa tvær íþróttir. Þjálfarar beggja greina þurfa að hafa samráð og gefa eftir æfingar til að stýra álaginu á unglingnum. Þeir ættu veita íþróttamanninum einlæga og heiðarlega ráðgjöf varðandi möguleika þeirra í hvorri grein fyrir sig. Íþróttamaðurinn þarf svo að gera upp við sig á endanum og fylgja eigin tilfinningu

Handball game-related statistics in men at Olympic Games (2004-2016): differences and discriminatory power

Handball can be considered a complex game. Sports performance analysis is a relevant topic for scientists and coaches. The objectives of the present study were: (i) to compare handball game-related statistics by match outcome (winning and losing teams) and (ii) to identify characteristics that discriminate the performance in elite men´s handball. The game-related statistics of the 324 games played in the last four Olympic Games (Athens, Greece, 2004; Beijing, China, 2008; London, United Kingdom, 2012; and Rio de Janeiro, Brazil, 2016) were analyzed. Differences between match outcomes (winning or losing teams) were determined by using the chi-squared statistic, and by calculating the effect sizes of the differences. A discriminant analysis was then performed applying the sample-splitting method according to match outcomes. The results showed that the differences between winning and losing teams were shots, 9 m shots, assists, goalkeeper-blocked shots fast break. Also, discriminant analysis selected four variables (shots, goalkeeper-blocked shots, technical foul, and attacks) that classified correctly 82% of matches (Wilks’s lambda=0.575; canonical correlation index 0.652). The selected variables included offensive and defensive predictors: Shots, goalkeeper-blocked shots, technical foul, attacks. Coaches and players can use these results as a reference against which to assess their performance and plan their team’s training.El balonmano puede considerarse un juego complejo. El análisis del rendimiento deportivo es un tópico relevante para los científicos y entrenadores. Los objetivos del presente estudio fueron: (i) comparar las estadísticas de juego en balonmano en función del contexto (equipos ganadores y perdedores) e (ii) identificar las estadísticas que discriminan el rendimiento en el balonmano masculino de élite. Se analizaron las estadísticas de juego de los 324 partidos disputados en los últimos cuatro Juegos Olímpicos (Atenas, Grecia, 2004, Beijing, China, 2008, Londres, Reino Unido, 2012 y Río de Janeiro, Brasil, 2016). Las diferencias entre los equipos ganadores y perdedores) se determinaron usando el estadístico chi-cuadrado y calculando los tamaños del efecto de las diferencias. A continuación, se realizó un análisis discriminante aplicando el método de por pasos. Los resultados mostraron que las diferencias entre los equipos vencedores y perdedores se presentaron en las variables lanzamientos de 9 m, asistencias, lanzamientos bloqueados por el portero en situación de contrataque. Además, el análisis discriminante seleccionó cuatro variables (lanzamientos, lanzamientos bloqueados por el portero, falta técnica y número de ataques) que clasificaron correctamente el 82% de los partidos (Lambda de Wilks=0,575; índice de correlación canónica=0,652). Las variables seleccionadas incluyeron predictores ofensivos y defensivos: lanzamientos, paradas del portero, faltas técnicas y ataques. Los entrenadores y los jugadores pueden utilizar estos resultados como referencia para evaluar su rendimiento y planificar el entrenamiento del equipo

Handball game-related statistics in men at Olympic Games (2004-2016): differences and discriminatory power

Handball can be considered a complex game. Sports performance analysis is a relevant topic for scientists and coaches. The objectives of the present study were: (i) to compare handball game-related statistics by match outcome (winning and losing teams) and (ii) to identify characteristics that discriminate the performance in elite men´s handball. The game-related statistics of the 324 games played in the last four Olympic Games (Athens, Greece, 2004; Beijing, China, 2008; London, United Kingdom, 2012; and Rio de Janeiro, Brazil, 2016) were analyzed. Differences between match outcomes (winning or losing teams) were determined by using the chi-squared statistic, and by calculating the effect sizes of the differences. A discriminant analysis was then performed applying the sample-splitting method according to match outcomes. The results showed that the differences between winning and losing teams were shots, 9 m shots, assists, goalkeeper-blocked shots fast break. Also, discriminant analysis selected four variables (shots, goalkeeper-blocked shots, technical foul, and attacks) that classified correctly 82% of matches (Wilks’s lambda=0.575; canonical correlation index 0.652). The selected variables included offensive and defensive predictors: Shots, goalkeeper-blocked shots, technical foul, attacks. Coaches and players can use these results as a reference against which to assess their performance and plan their team’s training.El balonmano puede considerarse un juego complejo. El análisis del rendimiento deportivo es un tópico relevante para los científicos y entrenadores. Los objetivos del presente estudio fueron: (i) comparar las estadísticas de juego en balonmano en función del contexto (equipos ganadores y perdedores) e (ii) identificar las estadísticas que discriminan el rendimiento en el balonmano masculino de élite. Se analizaron las estadísticas de juego de los 324 partidos disputados en los últimos cuatro Juegos Olímpicos (Atenas, Grecia, 2004, Beijing, China, 2008, Londres, Reino Unido, 2012 y Río de Janeiro, Brasil, 2016). Las diferencias entre los equipos ganadores y perdedores) se determinaron usando el estadístico chi-cuadrado y calculando los tamaños del efecto de las diferencias. A continuación, se realizó un análisis discriminante aplicando el método de por pasos. Los resultados mostraron que las diferencias entre los equipos vencedores y perdedores se presentaron en las variables lanzamientos de 9 m, asistencias, lanzamientos bloqueados por el portero en situación de contrataque. Además, el análisis discriminante seleccionó cuatro variables (lanzamientos, lanzamientos bloqueados por el portero, falta técnica y número de ataques) que clasificaron correctamente el 82% de los partidos (Lambda de Wilks=0,575; índice de correlación canónica=0,652). Las variables seleccionadas incluyeron predictores ofensivos y defensivos: lanzamientos, paradas del portero, faltas técnicas y ataques. Los entrenadores y los jugadores pueden utilizar estos resultados como referencia para evaluar su rendimiento y planificar el entrenamiento del equipo