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 16 th 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 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.
Handball (team handball) is a multifactorial sport. The aims of this study were (i) to analyse anthropometric variables, conditioning abilities, and handball skills in club handball players according to age and sex, and (ii) to develop multivariate models explaining club handball performance from a multidimensional perspective. Two hundred and twenty six handball players (age 16.9 ± 4.0 years, 54% males) participated in the study. The players belonged to under-14, under-16, under-19, and A teams. They were evaluated with a battery of 18 tests covering kinanthropometry, conditioning abilities, and handball skills. A one-way ANOVA with a Bonferroni post-hoc test was used to investigate differences between teams, and a t-test for differences between the sexes. For each team, a discriminant analysis was performed to determine differences between performance levels. The results showed little differences between the U19 and A teams in any of the variables studied in either men or women, and that the lowest values corresponded to the U14 team. The differences according to sex were clear in the kinanthropometric and conditioning variables, but much less so in handball skills. The eight multivariate models that were constructed classified successfully from 48.5 to 100% of the sample using at most three variables (except for the women's A team whose model selected six variables). Conditioning variables were most discriminating in men, and handball skills in women. This would seem to reflect the different performance profiles.
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.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.
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