Literature DB >> 29878985

Evaluation of Skating Top Speed, Acceleration, and Multiple Repeated Sprint Speed Ice Hockey Performance Tests.

Colin W Bond1,2, Tylor W Bennett2,3, Benjamin C Noonan1,2.   

Abstract

Bond, CW, Bennett, TW, and Noonan, BC. Evaluation of skating top speed, acceleration, and multiple repeated sprint speed ice hockey performance tests. J Strength Cond Res 32(8): 2273-2283, 2018-Skating speed, acceleration (ACC), and economy are important attributes related to ice hockey success and should ideally be tested on the ice in a reliable and time efficient manner that is accessible to coaches at all levels of the sport. The purpose of this study was to determine the reliability of certain on-ice tests and further, to use these results to evaluate changes in performance across a season. It was hypothesized that the tests' reliability would be excellent and that players would demonstrate improvements in performance associated with enhanced physiological conditioning. Forty male ice hockey players (16.2 ± 0.8 years, 1.76 ± 0.06 m, 73.7 ± 9.8 kg) completed top speed (TS), ACC, and multiple repeated sprint time (MRST) tests twice in the preseason (PRE 1 and 2) 1 week apart to examine reliability and once postseason (POST) to examine changes across the season. A high-speed video camera was used to time each test. The TS, ACC, and MRST demonstrated excellent within- and between-day reliability (interclass correlation coefficient [ICC] ≥ 0.83, typical error [TE] ≤ 2.6%) as well as within- and between-rater reliability (ICC ≥ 0.86, TE ≤ 0.5%). The team's TS and ACC were similar at all 3 assessments (p > 0.05), whereas MRST was faster at POST than at PRE 1 (p < 0.05). This test battery is reliable, time efficient, and inexpensive. All 3 tests may be used in team selection and identification of fatigue or overtraining. The MRST may be the most sensitive to short-term improvements related to ice hockey conditioning.

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Year:  2018        PMID: 29878985     DOI: 10.1519/JSC.0000000000002644

Source DB:  PubMed          Journal:  J Strength Cond Res        ISSN: 1064-8011            Impact factor:   3.775


  1 in total

1.  Machine Learning Outperforms Logistic Regression Analysis to Predict Next-Season NHL Player Injury: An Analysis of 2322 Players From 2007 to 2017.

Authors:  Bryan C Luu; Audrey L Wright; Heather S Haeberle; Jaret M Karnuta; Mark S Schickendantz; Eric C Makhni; Benedict U Nwachukwu; Riley J Williams; Prem N Ramkumar
Journal:  Orthop J Sports Med       Date:  2020-09-25
  1 in total

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