Physiological, physical and on-ice performance criteria for selection of elite ice hockey teams

R Roczniok, A Stanula, A Maszczyk, A Mostowik, M Kowalczyk, O Fidos-Czuba, A Zając, R Roczniok, A Stanula, A Maszczyk, A Mostowik, M Kowalczyk, O Fidos-Czuba, A Zając

Abstract

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.

Keywords: Biometric model; Logistic regression models; On ice special tests; Performances prediction on ice.

Figures

FIG. 1
FIG. 1
Goodness-of-fit test for the logistic regression model

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Source: PubMed

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