Hassan, A. (2014). Team Handball World Cup Championship 2013 -Analysis Study. J. Hum. Sport Exerc, 9(Proc1), pp.S409-S416. The world cup of men's handball team championships was analysed using data from all matches in order to determine tactical differences at the elite level. The first aim of this study was to conduct a technical analysis of current handball and to determine factors related to success in this sport discipline. The second aim was to compare the data of first eight teams with other next teams. An analysis of the performance of the participating teams' video tapes and CD's, in the collection of matches, was completed on all the teams taking part in the World Cup Handball Championship 2013. This technical analysis also used cumulative statistics from the International Handball Federation. There were 24 teams in this competition, these were classified participating 2013 into three groups according to the final classification of the winning teams. Variables were identified relating to results of matches (win or lose), and data collection of games was completed by selecting certain codes for each variable of the study variables. The variables included:-Total shots, Total successful shots, Breakthroughs, Breakthroughs successful, Fast break successful, Fast breaks, Red C, Yellow C, 2Min, Steal, Blocked, Assist, Tech. Faults, 6M shots, 6M successful shots, Wing shots, Wing successful shots, 9M shots, 9M successful shots, 7M shots, 7M successful shots. The technical variables used to compare the teams included: the average number of shots, the efficiency of shots, the efficiency of breakthroughs, fast break goals per team, the efficiency of fast breaks, the average number of 7M shots, the efficiency of 7M shots, the average number of 6M shots, the efficiency of 6M shots, the average number of 9M shots, the efficiency of 9M shots, the average number of wing shots, the efficiency of wing shots, offence (The average number of tech faults, the efficiency of tech faults, the average number of assist, the efficiency of assist), defense ( The average number of blocked, the efficiency of the average number of steals, the efficiency of steals), and penalties (2M, yellow card, red card). This technical analysis used cumulative statistics from the International Handball Federation. ANOVA revealed significant differences between the first eight teams in the World cup championships 2013 and their counterparts in the other two groups (group two, teams from 9-16 and group three, teams from 17-24) in terms of several technical variables. The results showed that the above various affected the ranking in favor of the world cup teams in significant international teams.
The aim of the study was to evaluate the impact of an 8-weeksplyometric training program on the sprint and jump performance. The intervention study employed a controlled experimental design with two parallel groups of male long jumpers. While the experimental group (n = 18) trained with plyometric exercises, the control group (n=10) performed classical long jump training. Both groups were examined for athletic performance (30m sprint, standing long jump, vertical jump) and biomechanical parameters of a long-jump movement (max vertical height, horizontal and vertical velocity at takeoff , flight time, takeoff duration) prior and following the intervention. The experimental group demonstrated significantly better developments than the control group in most of the physical and biomechanical parameters respectively and improved their long jump records. Combining an 8-weeksplyometricprogram with athletics training significantly develops long jump and general athletic performance as well as biomechanical parameters. Therefore, plyometric training can be recommended to athletics coaches as an additional training alternative to improve sprint and long jump abilities in athletes.
While tactical performance in competition has been analysed extensively, the assessment of training processes of tactical behaviour has rather been neglected in the literature. Therefore, the purpose of this study is to provide a methodology to assess the acquisition and implementation of offensive tactical behaviour in team handball. The use of game analysis software combined with an artificial neural network (ANN) software enabled identifying tactical target patterns from high level junior players based on their positions during offensive actions. These patterns were then trained by an amateur junior handball team (n = 14, 17 (0.5) years)). Following 6 weeks of tactical training an exhibition game was performed where the players were advised to use the target patterns as often as possible. Subsequently, the position data of the game was analysed with an ANN. The test revealed that 58% of the played patterns could be related to the trained target patterns. The similarity between executed patterns and target patterns was assessed by calculating the mean distance between key positions of the players in the game and the target pattern which was 0.49 (0.20) m. In summary, the presented method appears to be a valid instrument to assess tactical training.
The purpose of this study was to examine the changes in co-activation around the knee joint during different walking speeds in healthy females using the co-activation index. Ten healthy females (age: 21.20 ± 7.21 years, height: 164.00 ± 4.00 cm, mass: 60.60 ± 4.99 kg) participated in this study and performed three walking speeds (slow, normal, and fast). A Qualisys 11-camera motion analysis system sampling at a frequency of 200 Hz was synchronized with a Trigno EMG Wireless system operating at a 2000 Hz sampling frequency. A significant decrease in the co-activation index of thigh muscles was observed between the slow and fast, and between the normal and fast, walking speeds during all walking phases. A non-significant difference was observed between the slow and normal walking speeds during most walking phases, except the second double support phase, during which the difference was significant. A negative relationship was found between walking speed and the co-activation index of thigh muscles in all speeds during walking phases: first double support (r = −0.3386, p < 0.001), single support (r = −0.2144, p < 0.01), second double support (r = −0.4949, p < 0.001), and Swing (r = −0.1639, p < 0.05). In conclusion, the results indicated high variability of thigh muscle co-activation in healthy females during the different walking speeds, and a decrease in the co-activation of the thigh muscles with the increase of speed.
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