Biomechanics and Medicine in Swimming XI

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ent outcome at the adult age of the participants. To define a criterion variable, all athletes were categorized into three different talent groups according to their personal best adult competition performance up to the year 2006. So, the best adult swimmers taking part in international championships were assigned to the category of extremely high talented athletes, the national calibre athletes were assigned to the category of highly talented swimmers, and the regional starters were labelled as normal talents. In a third step, the six juvenile talent criteria described above (of the earlier time period 1997-2001) were used to predict the three final adult talent groups (of the year 2006). For the prediction of the final adult talent group two methods were used: firstly, a discriminate analysis (DA), and secondly, the neural network of a Self-organizing Kohonen Feature Map (SOFM; DataEngine, MIT Inc., Aachen, Germany). To obtain a “true” prognosis, the talent forecasts on the basis of the discriminate analyses and on the basis of the neural network method have to follow a cross-validation procedure. In the case of the discriminate analyses, 50 percent of the total number of cases was used to compute the discriminate functions that were then used to determine the talent outcome of the remaining 50 percent of cases. In the two specific SOFM models for the male and the female swimmers, the talent forecasts were validated by the “leave-one-out”procedure. Therefore, one less than the total number of the data sets of the talents (n-1) were used to train the network (consisting of a 5x5 neuron layer) with 5 000 training steps. The remaining data set of the one single athlete was then presented to the neural network to calculate a prognosis of his personal adult peak performance category. After that, the predicted future talent category was compared with the real, already known adult performance category. This procedure was repeated for each single data set, so that the total number of correctly predicted cases represents the quality of the talent prediction models for the male and the female swimmers. results For talent identification purposes it is essential to know the early performance prerequisites that form the structure of the current, and also the future adult peak performance (e.g. for the female swimmers see Fig. 2). Juvenile performance prerequisites in 16-20 years old female swimmers (1997-2001) R = .74; R2 = .55; R2adj = .41; F (22;72) = 4.02; p = 0.001 Psychological V. Technique & Coordination Condition Anthropometric variables Extrinsic motivation Action orientation (after failure) Psychological stress stability Arm movement frequency Sprinting power Stroking power Shoulder flexibility Hand size .77 .001 .42 Sprinting speed (71 %) .071 .74 .001 .014 .40 .37 .099 .77 .021 .61 .049 .51 .045 .33 .055 Swimming coordination (64 %) .79 .071 .60 .039 .44 Adult Competition Performance (2006) 50-m-Crawl (41 %) .005 .43 .089 Figure 2. Path analysis on the prognostic relevance of different performance prerequisites of young female swimmers for the future competition performance in 50 m sprint swimming (Hohmann, 2009) The comparison of the real adult performance groups with the predicted future groups of the talented swimmers at adult age led to far better predictions, when the neural network method was applied. The percentages of correctly predicted cases by discriminate analysis (females: 69.0 percent; males: 50.0 percent) are much lower than those delivered by SOFM (females: 87.9 percent; males: 68.3 percent; Fig. 3). Extreme Talents 100.0 % High Talents 77.3 % Normal Talents 88.9 % chaPter4.trainingandPerformance Extreme Talents 100.0 % High Talents 74.2 % Normal Talents 33.3 % Figure 3. Results of the talent prognosis in female (left) and male (right) swimmers on the basis of the nonlinear model of the Self-organizing Kohonen Feature Map (single and double arrows symbolize light resp. severe classification errors). Extreme talents: Participants in Olympic Games, World Championships, European Championships, and German Championship finals. High talents: European Youth Championships. Normal talents: German Youth Championships dIscussIon This paper aimed at finding out whether the talent development outcome can be better modeled by means of the nonlinear mathematical method of artificial neural networks or by linear methods such as discriminate analyses. The percentages of correctly modeled performances in the linear discriminate analysis were comparably lower than in the non-linear neural network procedure. Thus, the results support the assumption of Philippaerts et al. (2008) that neural networks are excellent tools to model and to predict future competitive performance categories on the basis of juvenile talent make-up data. Since there is no guarantee that such modeling and talent prediction will lead to similar results in other groups, the validation procedure has to be applied to data sets of other swimmers. Based on the results of this procedure it must be decided whether the neural network is a good or poor model of talent development in swimming. To obtain a good model, it may require changes in some of the training parameters. conclusIon The better results of the neural network analysis compared with the poorer results of the discriminate analysis support the interpretation that the adaptive behaviour of the athlete is a non-linear complex problem. This supports a dynamic systems approach to talent development in which the young athlete unfolds a performance development process in a self-organized way, which is influenced by various personal and contextual moderator variables (Gagnè, 1985; Heller & Hany, 1986; Heller, et al., 2005; Cote, et al., 2003). As neural networks are able to recognize global patterns of different talent make-ups, they are a worthwhile tool in the detection of talents under the condition of the non-linear talent development. Hence, from a dynamical systems point of view, a successful neural network modeling may be interpreted as a representation of deviations of the different states of the system from equi-probability, in our case the identification of different patterns of juvenile athletic performance. This is a very interesting aspect of the modeling of competitive performances, because the non-linear dynamic systems perspective is rapidly emerging as one of the dominant meta-theories in the natural sciences, and there is also reason to believe that in the future it will eventually provide a more general integrative understanding in training science, as well. reFerences Abbot, A. & Collins, D (2002). A theoretical and empirical analysis of a `State of the Art` talent identification model. High Ability Studies, 13(2), 157-78. Abbot, A. & Collins, D (2004) Eliminating the dichotomy between theory and practice in talent identification and development: considering the role of psychology. Journal of Sport Sciences, 22(5), 395-408. Cotè, J., Baker, J. & Abernethy, B. (2003). From Play to Practice. A Developmental Framework for the Acquisition of Expertise in Team 263
BiomechanicsandmedicineinswimmingXi Sport. In Starkes, JL, Ericsson, KA (eds.). Expert Performance in Sports. Champaign, IL: Human Kinetics, 89-110. Gagnè, F. (1985). Giftedness and talent: Reexamining a reexamination of the definitions. Gifted Child Quarterly, 19, 103-112. Hohmann, A. (2009). Entwicklung sportlicher Talente an sportbetonten Schulen, Schwimmen – Leichtathletik – Handball. [Development of talents at elite schools, swimming – track and field - handball.] Petersberg: Imhof. Hohmann, A. & Seidel, I. (2003). Scientific Aspects of Talent Development. International Journal of Physical Education, 40(1), 9-20. Kupper, K. (1980). Zur Vervollkommnung der Eignungsbeurteilung im Nachwuchsleistungssport der DDR. [On the perfection of the suitability assessment in young elite athletes in the GDR.] Theorie und Praxis des Leistungssports, 18(6), 3-34. Philippaerts, R.M., Coutts, A. & Vaeyens, R (2008). Physiological Perspectives on the Identification and Development of Talented Performers in Sport. In Fisher, R, Bailey, R (eds.). Talent Identification and Development. The Search for Sporting Excellence. Berlin: IC- SSPE, 49-67. Regnier, G., Salmela, J., Russell, S.J. (1993). Talent detection and development in sport. In Singer, RN, Murphy, M, Tennant, LK (eds.). Handbook of Research in Sport Psychology. New York: Wiley, 290-313. Schneider, W., Bös, K. & Rieder, H. (1993). Leistungsprognose bei jugendlichen Spitzensportlern. [Performance prediction in young elite athletes]. In Beckmann, J, Strang, H, Hahn, E (eds.). Aufmerksamkeit und Energetisierung. Facetten von Konzentration und Leistung. Göttingen: Hogrefe, 277-99. Vaeyens, R., Malina, R.M., Janssens, M., van Renterghem, B., Burgois, J. & Vrijens, J., et al. (2006). A multidisciplinary selection model for youth soccer : the Ghent Youth Soccer Project. British Journal of Sports Medicine, 40(11), 928-934. Willimczik, K. (1982). Determinanten der sportmotorischen Leistungsfähigkeit im Kindesalter. Konzeption und Zwischenergebnisse eines Forschungsprojektes. [Determinants of sport-motor performance in childhood. Design and intermediate results of a research project.] In Howald, H, Hahn, E (eds.). Kinder im Leistungssport. Stuttgart: Birkhäuser, 141-153. AcKnoWledGeMents This study was supported by the Federal Institute of Sports Science (BISp), Bonn, Germany 264 Determination of Lactate Threshold with Four Different Analysis Techniques for Pool Testing in Swimmers Keskinen, K.L. 1,2 , Keskinen, O.P. 2 , Pöyhönen, T. 3 1Finnish Society of Sport Sciences, Helsinki, Finland, 2Department of Biology of Physical Activity, University of Jyväskylä, Jyväskylä, Finland, 3Kymenlaakso Central Hospital, Kotka, Finland Pool testing using the blood lactate versus swimming velocity relationship is a standard in performance diagnostics of swimmers. The reliability of four techniques designed to determine the lactate threshold (LT) were examined. The subjects were 15 female and 15 male swimmers. Mean ± SD for age was 16.7 ± 3.3 years, stature 1.70 ± 0.08 m and body mass 61.8 ± 6.6 kg. They performed a set of ten 100 meter swims twice in three days. LT was analyzed from the blood lactate - velocity association by two experienced analysts. Linear regressions (R 2 ), intraclass correlation coefficients (ICC) with two-way ANOVA, and t-tests showed that both test-retest reliability and comparability between mathematical curve fitting and visual linear estimation methods were very high (R 2 = 0.85-0.96; ICC = 0.96-0.99; t-tests = N.S.). Key words: pool testing, analysis techniques, reliability IntroductIon Blood lactate concentration (BLa) is one of the most commonly measured parameters in both clinical exercise testing and during performance testing of athletes. In healthy subjects a normal physiological response to physical strain is an elevated BLa. In response to progressive incremental exercise BLa-testing offers a convenient tool to observe its systematic behavior dependent on the intensity of exercise. Under low work rates BLa either remains at its initial lowest concentration or slowly increases. As the exercise becomes more intense, the BLa eventually increases exponentially. This is recognized as the lactate threshold (LT) (Stegmann et al. 1981). As a single work rate, illustrating the curvilinear BLa versus exercise intensity response, determination of LT is a standard procedure in both predicting athletic performances as well as in diseased conditions (Goodwin et al., 2007). Even though the status of the LT as a powerful predictor of performance is well accepted the velocity at which LT appears depends on the pool test protocol applied. Keskinen et al. (1987) found that LT was different between 2·400 (Mader et al., 1978), n·100 (Gullstrand et Holmer, 1980) and n·300 m (Simon et al., 1982) swimming tests with different BLa values in each case. Evidently there is also a different reference point for LT when the definition is made using a frequently used n·200-m protocol (Pyne et al., 1992). In addition to different testing protocols, different analysis techniques have also been used. Mader et al. (1978) defined LT as a swimming velocity corresponding to fixed 4 mmol·l -1 in BLa (V 4 ) from a two-speed test. Due to its feasability this test has since served as the reference for LT in several instances (Svedahl & MacIntosh, 2003). In a practical sense, the BLa-velocity association has traditionally been plotted and the LT visually determined as the origin of the exponential increment. The concept of an individual LT was presented by Stegmann et al. (1981) with an incremental field protocol. Cheng et al. (1992) proposed a mathematical procedure to determine ventilatory threshold and LT as an objective and reliable method for threshold determination, which can be applied to various ventilatory or metabolic variables. There is only limited information available in the scientific literature about the comparability and repeatability of the methods used
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Pahlen Norge AS Pahlen Norge AS Cha
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