The purpose of this study was to examine physiological and physical determinants of ice-hockey performance in order to assess their impact on the result during a selection for ice hockey. A total of 42 ice hockey players took part in the selection camp. At the end of the camp 20 best players were selected by team of expert coaches to the ice hockey team and created group G1, while the second group (G2) consisted of not selected players (non-successful group Evaluation of goodness of fit of the model to the data was based on the Hosmer Lemeshow test. Ice hockey players selected to the team were taller 181.95±4.02 cm, had lower% body fat 13.17±3.17%, a shorter time to peak power 2.47±0.35 s, higher relative peak power 21.34±2.41 W·kg−1 and higher relative total work 305.18±28.41 J·kg−1. The results of the aerobic capacity test showed significant differences only in case of two variables. Ice hockey players in the G1 had higher VO2max 4.07±0.31 l·min−1 values than players in the G2 as well as ice hockey players in G1 showed a higher level of relative VO2max 51.75±2.99 ml·min−1·kg−1 than athletes in G2. Ice hockey players selected to the team (G1) performed better in the 30 m Forwards Sprint 4.28±0.31 s; 6x9 Turns 12.19±0.75 s; 6x9 stops 12.79±0.49 s and Endurance test (6x30 m stops) 32.01±0.80 s than players in G2. The logistic regression model showed that the best predictors of success in the recruitment process of top level ice hockey players were time to peak power, relative peak power, VO2max and 30 m sprint forwards on ice. On the basis of the constructed predictive logistic regression model it will be possible to determine the probability of success of the athletes during following the selection processes to the team.
This study is a contribution to the discussion about the structure of performance of sport rock climbers. Because of the complex and multifaceted nature of this sport, multivariate statistics were applied in the study. The subjects included thirty experienced sport climbers. Forty three variables were scrutinised, namely somatic characteristics, specific physical fitness, coordination abilities, aerobic and anaerobic power, technical and tactical skills, mental characteristics, as well as 2 variables describing the climber’s performance in the OS (Max OS) and RP style (Max RP). The results show that for training effectiveness of advanced climbers to be thoroughly analysed and examined, tests assessing their physical, technical and mental characteristics are necessary. The three sets of variables used in this study explained the structure of performance similarly, but not identically (in 38, 33 and 25%, respectively). They were also complementary to around 30% of the variance. The overall performance capacity of a sport rock climber (Max OS and Max RP) was also evaluated in the study. The canonical weights of the dominant first canonical root were 0.554 and 0.512 for Max OS and Max RP, respectively. Despite the differences between the two styles of climbing, seven variables – the maximal relative strength of the fingers (canonical weight = 0.490), mental endurance (one of scales : The Formal Characteristics of Behaviour–Temperament Inventory (FCB–TI; Strelau and Zawadzki, 1995)) (−0.410), climbing technique (0.370), isometric endurance of the fingers (0.340), the number of errors in the complex reaction time test (−0.319), the ape index (−0.319) and oxygen uptake during arm work at the anaerobic threshold (0.254) were found to explain 77% of performance capacity common to the two styles.
This paper analyses the dynamics of changes between the performances of elite freestyle swimmers recorded at particular Olympic Games. It also uses a set of chronologically ordered results to predict probable times of swimmers at the 2012 Olympic Games in London. The analysis of past performances of freestyle swimmers and their prediction have revealed a number of interesting tendencies within separately examined results of men and women. Women’s results improve more dynamically compared with men’s. Moreover, the difference between women’s and men’s results is smaller, the longer the swimming distance. As both male and female athletes tend to compete more and more vigorously within their groups, the gap between the gold medallist and the last finisher in the final is constantly decreasing, which provides significant evidence that this sport discipline continues to develop.
This research problem was indirectly but closely connected with the optimization of an athlete-selection process, based on predictions viewed as determinants of future successes. The research project involved a group of 249 competitive swimmers (age 12 yr., SD = 0.5) who trained and competed for four years. Measures involving fitness (e.g., lung capacity), strength (e.g., standing long jump), swimming technique (turn, glide, distance per stroke cycle), anthropometric variables (e.g., hand and foot size), as well as specific swimming measures (speeds in particular distances), were used. The participants (n = 189) trained from May 2008 to May 2009, which involved five days of swimming workouts per week, and three additional 45-min. sessions devoted to measurements necessary for this study. In June 2009, data from two groups of 30 swimmers each (n = 60) were used to identify predictor variables. Models were then constructed from these variables to predict final swimming performance in the 50 meter and 800 meter crawl events. Nonlinear regression models and neural models were built for the dependent variable of sport results (performance at 50m and 800m). In May 2010, the swimmers' actual race times for these events were compared to the predictions created a year prior to the beginning of the experiment. Results for the nonlinear regression models and perceptron networks structured as 8-4-1 and 4-3-1 indicated that the neural models overall more accurately predicted final swimming performance from initial training, strength, fitness, and body measurements. Differences in the sum of absolute error values were 4:11.96 (n = 30 for 800m) and 20.39 (n = 30 for 50m), for models structured as 8-4-1 and 4-3-1, respectively, with the neural models being more accurate. It seems possible that such models can be used to predict future performance, as well as in the process of recruiting athletes for specific styles and distances in swimming.
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