Relationship between pre-season measures of fitness and power to in game measures for a Division 1 collegiate ice hockey team
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Miami University / OhioLINK
2016
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=miami1468855444 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-miami14688554442021-08-03T06:37:35Z Relationship between pre-season measures of fitness and power to in game measures for a Division 1 collegiate ice hockey team groezinger, erich yager Sports Medicine Statistics Anatomy and Physiology ice hockey heart rate SAS physiology collegiate Introduction: In recent years, technology for ice hockey coaches has both rapidly advanced and become readily available. With coaches desperately seeking an edge over the competition and the introduction of new technology used in team sports settings (Polar Team Pro heart rate system), the need for assessment of the data generated is needed. The purpose of this study was to see if predictive relationships existed between the basic physiological (aerobic, anaerobic, and strength) profiles of the athletes and the in game performance values.Methods: The entire Miami University hockey team, composed of 24 players, participated in some manner in this study. However, the analysis was done on the data from the 18 forwards and defensemen that played in the November 6-7, 2015 home series against Western Michigan. All athletes were put through a series of on and off ice measures over the course of a month prior to the start of the season. Off ice measures included the following: VO2 maximal graded exercise test, Wingate anaerobic power test, vertical jump height test, long jump test, trap bar dead lift (TBDL), FMS, and body composition (Inbody Bioelectric Impedance Analyzer BIA). The on ice measures included the following: 89 ft sprint test, and an on ice RAST. Linear regression models were created using the SAS software in order to evaluate the potential predictive capability of testing measures for game performance values (training load, speed, and ratio of time spent above 80% to time spent aove 90% of max heart rate).Results: Three significant models were returned when the testing measures were run against the in game performance values. The first model used average yds/min (speed) per time on ice for each player as the game performance variable with the equation being Yds/min = 1.57(LJump) + 5.03(VJump) + 6.67(FMST) + 1.08(VO2) + 115.07(Sprint) + 0.85(Fatigue) – 826.38 (p<0.05). The second model examined in game training load, a value of stress calculated by the heart rate system, and returned the equation TL = 52.1(FMST) – 8.23(Body Fat%) + 30(Wingate) – 1.18(Towel Hang) – 1257.36 (p<0.05). Finally, the third model examined the game performance proxy, the ratio of time spent above 80% of max heart rate to time spent above 90% of max and returned the following: Ratio = 0.24(VJump) – 0.04(LJump) -0.11(VO2) + 4.33(Sprint) + 0.02(Fatigue) – 0.01(TBDL) – 8.47 (p<0.05).Conclusion: In addition to the anaerobic demands of a hockey shift, aerobic fitness allows a player to recover quickly on the bench, which is beneficial for consistent performance during a 60 minute regulation game. This is shown in the appearance of the VO2 max in each of the significant models. Strength and conditioning coaches should be sure to incorporate a proper mixture of aerobic, anaerobic and strength training in order to maximize performance potential for their athletes. 2016-07-19 English text Miami University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=miami1468855444 http://rave.ohiolink.edu/etdc/view?acc_num=miami1468855444 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
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NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Sports Medicine Statistics Anatomy and Physiology ice hockey heart rate SAS physiology collegiate |
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Sports Medicine Statistics Anatomy and Physiology ice hockey heart rate SAS physiology collegiate groezinger, erich yager Relationship between pre-season measures of fitness and power to in game measures for a Division 1 collegiate ice hockey team |
| author |
groezinger, erich yager |
| author_facet |
groezinger, erich yager |
| author_sort |
groezinger, erich yager |
| title |
Relationship between pre-season measures of fitness and power to in game measures for a Division 1 collegiate ice hockey team |
| title_short |
Relationship between pre-season measures of fitness and power to in game measures for a Division 1 collegiate ice hockey team |
| title_full |
Relationship between pre-season measures of fitness and power to in game measures for a Division 1 collegiate ice hockey team |
| title_fullStr |
Relationship between pre-season measures of fitness and power to in game measures for a Division 1 collegiate ice hockey team |
| title_full_unstemmed |
Relationship between pre-season measures of fitness and power to in game measures for a Division 1 collegiate ice hockey team |
| title_sort |
relationship between pre-season measures of fitness and power to in game measures for a division 1 collegiate ice hockey team |
| publisher |
Miami University / OhioLINK |
| publishDate |
2016 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=miami1468855444 |
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AT groezingererichyager relationshipbetweenpreseasonmeasuresoffitnessandpowertoingamemeasuresforadivision1collegiateicehockeyteam |
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