Biomechanics and Medicine in Swimming XI

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Table 2. The results of tests of between-subjects effects. Dependent Variable Type III Sum of Squares df Mean Square F Sig. m15crawl .043 2 .022 .078 .925 m25crawl .363 2 .181 .209 .812 m50crawl 1.434 2 .717 .253 .777 m200crawl 61.343 2 30.671 .641 .530 m25crawlHUP .626 2 .313 .390 .678 m25crawlWB .068 2 .034 .027 .974 Beside the fact that statistically significant differences were not found (Table 2) between these observed groups (P,CD,C) for the variables (Table 2); 15m crawl (F=0.078, p=0.925), 25m crawl (F=0.209, p=0.812), 50m crawl (F=0.253, p=0.777), 200m crawl (F=0.641, p=0.530), 25m crawlHUP (F=0.390, p=0.678), 25m crawlWB (F=0.027, p=0.974) three different multidimensional models for prediction of basic horizontal swimming abilities for 16 yrs old water polo players were created, as Horizontal Swim Score (HSS). The ANOVA regression analysis results show that the separated set of 6 predicting variables statistically significantly describes the HSS (P) , HSS (CD) and HSS (C) at the level p=0.000. The model explains 100% of all three playing positions, variability with values of standard estimation error of ±0.00307%, ±0.00213% and ±0.00255% of horizontal swimming abilitiy, respectively. It can be considered that the calculated model is useful and reliable since the standard estimation error is lower than the standard error (SD) HSS (P) =17.48%, HSS (CD) =16.28% and HSS (C) =15.81%. Table 3. Function of equations of mathematical models of predicting HSS. Model predicting equations Peripherals Horizontal Swim SCORE; HSS (P) = 349.882 - (m15crawl*5.681) - (m25crawl*3.555) - (m50crawl*1.893) - (m200crawl*0.408) - (m25crawlHUP*3.608) - (m25crawlWB*2.844) Center Defenders Horizontal Swim SCORE; HSS (CD) = 349.872 - (m15crawl*5.682) - (m25crawl*3.559) - (m50crawl*1.893) - (m200crawl*0.408) - (m25crawlHUP*3.603) - (m25crawlWB*2.845) Center Horizontal Swim SCORE; HSS (C) = 349.938 - (m15crawl*5.679) - (m25crawl*3.557) - (m50crawl*1.893) - (m200crawl*0.408) - (m25crawl- HUP*3.607) - (m25crawlWB*2.845) Table 3 shows the results of multiple regression analysis for variables which by regression model method, defined the model of the mathematical regression equation. From this model the set of 6 variables which make up the complex of horizontal swimming abilities, (which integrally and on the statistically significant level, describes HSS of three playing positions) were extracted. dIscussIon Competitive water polo can be characterized as a physiologically demanding intermittent activity, mainly relying on players’ aerobic power and lactate threshold (Hohmann and Frase, 1992; Platanou and Geladas, 2006) and a sport that requires a variety of technical skills and athletic abilities that depend on each player’s positional role (Smith, 1998). However, Hohmann and Frase (1992) reported no significant differences in the swimming intensity or the distance covered among various field positions among senior players. Our findings point out no swimming ability differences between players who play three different positions. Based on the analysis made, no statistical differences were found between peripherals, defenders and centers in HSS. Although there were mathematical differences in HSS between players playing three different positions, HSS figures were still chaPter4.trainingandPerformance very close to each other. As expected the best scores in HSS belonged to peripherals followed by centers and then center defenders. Prior to our analysis, it was estimated that peripherals would perform much better when compared to the other two positions but our findings did not confirm this. Peripherals usually stand out with their ability play a faster game, better reflexes, better swimming performance, more drives in the pool. Taking into account all of this, it was expected that their scores would be much better than centers and center defenders. The latter two are accoustomed to do more one-on-one battle due to their position in the game and this builds up power and strength for the players in these positions. However the findings did not entirely follow the hypotheses and theory. Especially when looking at their alactic swimming skills, there were no major differences between these three position. In fact, the center defenders showed better performance on the alactic swimming ability than the peripherals. Based on all of the findings above, it was found that players do not exhibit specific swimming charcateristics associated with their position at the youth national team level. On the other hand Bratusa and Dopsaj (2006) reported that there definitely are differences in swimming features of the players in different positions of junior Slovenian players. A greater number of swimming efforts (Dopsaj and Matkovic, 1994; Sarmento, 1994; Smith, 1998) in the wing positions and a greater number of duels particularly in center and back positions caused the greatest differences exactly between these positions (Bratusa et al., 2003; Sarmento, 1994; Smith, 1998). Different positions in water polo have their specificities that should be responded to by the players who play in those positions (Bratusa and Dopsaj, 2006). conclusIon In this study analyzing horizontal swimming abilities of players in different positions, the results show no differences between these observed groups. It seems that the process of specialization is not yet strong among 16 years old water polo players from these four countries. The defined models of mathematical regression equations, the models of HSS prediction of the three positions (Table 3), can be used as a model, as a testing procedure for checking the horizontal swimming abilities of the young water polo players and the present study provided effective information on the horizontal swimming performance of elite young water polo players according to playing position. Defined equations can be used by coaches as an effective and easy to apply method for testing the actual level of horizontal swimming ability of junior water polo players. reFerences Bratuša, Z. (2000). Speed abilities development of young school age boys under influence of specific water polo trainings. Unpublished master thesis, Belgrade, Faculty of Physical Education. Bratuša, Z., Matkovic, I. & Dopsaj, M. (2003). Model characteristics of water polo players’ activities in vertical position during a match. In: Chatard J.C. (ed.), Biomechanics and Medicine in Swimming IX. Saint-Etienne: Publications de L’Universite de Saint-Etienne, 481-486. Bratuša, Z. & Dopsaj, M. (2006). Difference between general and specific swimming abilities of junior top water polo players based on their position within the team. Rev Port Cien Desp, 6, Supl.2, 285-324. Dopsaj M. & Matkovic I. (1994). Motor activities during the game. Physical Culture, 48, 4, 339 – 347. Falk B., Lidor R., Lander Y. & Land B. (2004). Talent identification and early development of elite water polo players: a 2-year follow-up study. Journal of Sports Sciences, 4, 347-355. Hohmann A. & Frase R. (1992). Analysis of swimming speed and energy metabolism in competition water polo games. In: MacLaren D., 277
BiomechanicsandmedicineinswimmingXi Reilly T., Lees A. (eds.), Swimming science VI: Biomechanics and medicine in swimming (pp. 313 – 319). London: E & F N Spon. Lozovina, V. (1983). Influence of morphological characteristics and motorical abilities in swimming on success of water polo players. Unpublished doctoral dissertation, Zagreb: Faculty of Physical Education. Lozovina, M., Durovic, N. & Katic, R. (2009). Position specific morphological characteristics of elite water polo players. Coll Antropol, 33, 3, 781–789. Matković, I., Gavrilović, P., Jovović D. & Thanopoulos V. (1999). Specific abilities test of top Yugoslav water polo players and its validation. In: Keskinen K.L., Komi P.V., Pitkanen P.L. (eds.), Biomechanics and Swimming VIII (pp. 259-264). Juvaskyla: University of Jyvaskyla. Platanou, T. & Geladas, N. (2006). The influence of game duration and playing position on intensity of exercise during match-play in elite water polo players. Journal of Sports Sciences, 24, 11, 1173 – 1181. Platanou, T. (2009). Cardiovascular and metabolic requirements of water polo. Serb. J. Sports. Sci, 3, 4, 85-97. Sarmento, P. (1994). Physiological and morphological task related profiles of Portuguese water polo players. II. International Symposium on Biomechanics and Medicine in Swimming, Atlanta. Smith, H. (1998). Applied physiology of water polo. Sports Medicine, 26, 317 – 334. Tsekouras, Y.E., Kavouras, S.A., Campagna, A., Kotsis, S.A., Syntosi S.S., Papazoglou, K. & Sidossis, L. (2005). The anthropometrical and physiological characteristics of water polo players. European Journal of Applied Physiology, 95, 35-41. 278 A Markov Chain Model of Elite Water Polo Competition Pfeiffer, M., hohmann, A., siegel, A., Böhnlein, s. Institute of Sport Science, University of Bayreuth, Bayreuth, Germany When it comes to water polo, performance diagnostics offers many different methods to analyze a game. In this context, problems mostly occur in adequately modelling the game, in part because the various approaches lack scope for uniform performance criteria. In this study the concept of match-play analysis through mathematical simulation by means of the Markov-chain model was applied to water polo and used to analyse the performance relevance of tactical behaviour patterns at the World League Final 2007 (Berlin) with focus on the German and Serbian teams. The results suggest that in international-level water polo, the performance relevance of “Turn over to counter attack” and “Turn over to One-center-attack” were the most worthwhile tactical behaviour patterns. No significant differences between Germany and Serbia could be found. Key words: game analysis, water polo, tactical behaviour, simulation, Markov chain IntroductIon From a performance diagnostic point of view, the key task of notational analysis is to analyze the game structure and find determining factors, in this case tactics, of performance. A look at the literature shows that a large number of methods have been developed – based on rapid technological developments in the area of computers, video etc. – to analyse the structure of game sports by game observation, in order to identify performance indicators (Hughes & Bartlett, 2002). A comprehensive review of the evolution of notational analysis in sports games is given by Hughes and Franks (2004), Hughes (2008) and Hughes and Bartlett (2008). Most of these methods use structure-oriented observation models, which enable the researcher to register isolated elementary actions in a match, but do not allow for the obtaining of data on the interaction process itself. Considering a sports game as a dynamic system or an interactionprocess, different stochastic models have been developed. The models of the finite Markov chain were successfully applied to describe and predict playing profiles or patterns (McGarry & Franks, 1994; McGarry & Perl, 2004), and to determine the performance relevance of tactical behaviour (Hirotsu, Miyaji, Ezake, Shigenaga & Taguchi, 2004; Hohmann, Zhang & Koth, 2004; Lames, 1991; Zhang, 2003). In the game analysis approach of Lames (Lames, 1991; Lames & McGarry, 2007), comparable quantitative values for the performance relevance of selected tactical patterns are determined by the method of simulation. The concept of performance analysis through mathematical simulation by means of the Markov chain model was first established in the area of return games and successfully transferred to invasion games (e.g. handball and soccer; Pfeiffer, 2004; Pfeiffer, 2005; Pfeiffer & Hohmann, 2008). In this study, the same approach was applied to water polo to analyse the relevance of specific tactical behaviour to performance in general and with focus on the German and Serbian Water Polo teams at the World League Final 2007 in Berlin. Methods Observation model: The water polo match is described as a system that gradually moves through different states. The starting point of our observation model was the possession of the ball (state “Turn over”). The offensive play was modelled on the basis of all offensive attempts, which were characterized by 16 different states of the game. These states represent different player positions in even-man offense (6-6; position at-
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Pahlen Norge AS Pahlen Norge AS Cha
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