Team sports have varying physiological demands. Soccer, for example, tends to require its athletes to have a high aerobic capacity, whereas sports such as football place an emphasis on strength and power (33). Teams with athletes who are better suited to meet their sports demands often outperform their competition through superior sport-specific athleticism. To that end, the ability to evaluate and recruit players in possession of these physical qualities becomes imperative for a team's success. Anthropometric and physiological characteristics have been used by coaches in many team sports to identify and recruit elite players, classify starters vs. nonstarters, and stratify athletes by level of play (16,19,21).
To be effective, decision makers (coaches, general managers, and scouts) need a quantifiable list of characteristics that have been shown to be associated with aspects of performance in their respective sport. The creation of a sport-specific physiological profile would aid coaches in selecting athletes from a much larger pool who have the physiological attributes to compete at their respective level, identify athlete strengths and weaknesses, and allow for the creation of athlete-specific training programs that address their limitations.
Ice-hockey requires a unique set of physiological qualities to be successful; speed, power, endurance, and skill (7,24,28). Previous literature has found on- and off-ice tests that correlate with performance, including anthropometry, aerobic capacity, power, and skating ability (1,3,4,23). However, this research has focused solely on evaluating the National Hockey League (26,29,34). Little to no research has investigated differences that may exist at the collegiate and Elite Junior levels. This has lead to a void in the literature that prevents coaches at the collegiate level from making informed scientifically based decisions regarding the recruiting training methods used with their teams.
This study aims to address the current limitations specific to ice-hockey by (a) evaluating common physiological performance characteristics of collegiate hockey players and (b) stratifying these results by division of play to look for differences that may exist between the Division I, Elite Junior, and Division III levels. The results of this study could have important implications for the training and recruiting practices used by coaches at different levels of collegiate hockey. It is hypothesized that Division I hockey players will have higher on- and off-ice performance scores compared with their peers playing at the Elite Junior or Division III level.
Experimental Approach to the Problem
Subjects were signed up for 3 testing sessions: (a) anthropometric and off-ice performance, (b) aerobic capacity, and (c) on-ice performance testing. Each session took approximately 1 hour and was held on the University of Minnesota campus. All subjects were tested in early June at the start of their summer training schedules. All 3 sessions were completed within a 10-day period, ensuring at least 2-days rest between sessions. Subjects were told before each session to refrain from heavy exercise for 24 hours before their testing sessions. All subjects were told to eat a light meal 2 hours before testing and to refrain from caffeine, tobacco, and alcohol 12 hours before testing. Aside from these guidelines, subjects were asked to maintain a normal diet and exercise regimen during testing. Exclusion criteria for the study included absence from on-ice skating over the previous 30 days because of prior or current injury and players self-reporting their position as goaltender.
Forty-five male hockey players, aged 18–24 years and playing Division I, Division III, or Elite Junior level hockey in the Minneapolis, MN, volunteered for this study. All testing procedures were approved by the Institutional Review Board at the University of Minnesota before participant recruitment and data collection. Written informed consent was obtained from all participants before the start of the study.
Session 1—Anthropometric and Off-Ice Performance Testing
Session 1 consisted of anthropometric, vertical jump, Wingate, and grip strength testing. Standing height was recorded using the Frankfort Plane criterion (15) and weight was recorded using a Detecto Mechanical Doctor's scale (model 439). Body composition was assessed through hydrostatic weighing using Exertech Body Densitometry Systems software (Dresbach, MN, USA). The method is considered to be a valid and reliable method for measurement of body composition (15). The participant's weight was recorded 8 times to account for both a learning effect and to ensure consistency of the measure and accuracy of the reported weight (15). The heaviest duplicated weight was recorded as the participant's underwater weight. If the participants were unable to duplicate their first or second heaviest weight, their third heaviest weight was recorded as the official underwater weight. Percent body fat was calculated using the Brozek equation (15). Residual lung volume was also estimated (22).
Vertical jump was tested using a Vertec Vertical Jump Trainer (Huntington Beach, CA, USA). Each participant performed a 5-minute treadmill warm-up, starting at 5 miles per hour (mph) and increasing 0.5 mph per minute to a maximum of 7 mph, before performing the vertical jump protocol to standardize the measure. The vertical jump protocol consisted of 2 submaximal practice jumps to gain familiarization, followed by 3–5 maximal jumps, allowing a minimum of 30 seconds of rest between jumps. For each jump, the participant was instructed to squat to a self-selected depth (approximately 90°) and hold that position for 3 seconds. The participant was also instructed to place 1 arm on their hip with the other above their head in a reaching position. After a 3-second count, the instructor would give an audible “jump” command, and the participant would jump, touching the highest vane possible on the Vertec Vertical Jump Trainer. These instructions ensured a standardized jump protocol for each subject and help reduce confounding of arm swing during jumping (10). Jump height was assessed by counting the highest vane contacted by the participant. The test was terminated when the participant failed to increase jump height in 2 consecutive trials, or after the fifth trial.
The Wingate Anaerobic Test (WAnT), performed on a Monarch cycle ergometer (Langley, WA, USA), was used to assess lower-body power production. The testing protocol for the WAnT has been standardized in earlier literature (22). After the seat and bar height were properly adjusted for the participant, they were told warm-up by lightly pedaling for 30 seconds. During the last 5 seconds of the warm-up time, the participant was told to increase their pedal velocity to its maximum and maintain it for the duration of the test. When the warm-up time expired, a flywheel resistance load equaling 0.075 kg per kilogram body weight was applied, with the participant continuing to pedal maximally for 30 seconds. Participants' peak (PP) and mean power (MP) were calculated as the highest average power attained during the first 5 seconds and full 30 seconds of the test (22). Power values were recorded in watts and normalized for body weight. The reliability of these measures, PP and MP, has been established as robust in previous literature (r = 0.92) when using the 0.075 kg per kilogram loading parameter (25).
Grip strength was assessed using a Jamar Dynamometer. Grip strength in the participant's dominant hand was measured in kilograms using the standard testing protocol outlined by the American Society of Hand Therapists (17). The participant sat on the edge of a chair, holding the dynamometer with the grip diameter set at 3.8 cm, with his elbow at his side in 90 degrees of flexion. The participant was given 1 submaximal attempt to account for a learning affect, followed by a 1-minute rest period. The participant was then instructed to maximally grip the dynamometer for 3 seconds. This protocol was repeated 2 additional times, with the scores from all 3 attempts being averaged into a composite score used for data analysis.
Session 2—Determination of Aerobic Capacity
Aerobic capacity was assessed on both a Frappier (Acceleration, Minneapolis, MN, USA; n = 30) and the Blade (Woodway, Waukesha, WI, USA; n = 15) skating treadmills to ascertain the
of each participant. Breath-by-breath analysis was performed by an Ultima CPX (Medgraphics, St. Paul, MN, USA). The skating treadmill protocol used has been previously validated as a reliable means for participants to reach volitional exhaustion and accurately measure
(20). The protocol began with participants skating at a speed of 6.5 mph and a 2% grade. Every minute, the speed of the treadmill was increased 0.5 mph until a maximal speed of 10 mph was reached; this occurred 8 minutes into the test. Once the participants had reached maximal speed, the grade was increased by 1% every minute until they reached volitional exhaustion. Criteria for reaching maximal aerobic capacity were determined by achieving 2 of the 3 following criteria: (a) maximal heart rate (220-age ± 10), (b) RER value >1.10, and (c) rate of perceived exertion >18 (2).
Session 3—On-Ice Performance Testing
Session 3 took place at the hockey arena and consisted of an acceleration, top-speed, and on-ice repeated shift test (RST). All on-ice testing was performed in full gear with skates sharpened to game specifications. Acceleration was assessed by having the participant sprint, from a stationary start, blue line to blue line (distance = 15.24 m). Similar protocols have been implemented in earlier research, and the method is considered a valid way to measure acceleration (12). The participant started by standing with his front skate directly behind the blue line (starting line), stick in hand. When the participant felt he was ready, he would accelerate as fast as possible through the second blue line (finish line). Time was recorded by a TC Speed Trap-II wireless timing system (E38720; Gill Athletics, Champaign, IL, USA). The photocells of both timing gates were placed at waist level of the participant to ensure that the laser timed his body crossing the line, not his stick. In addition, participants were told to keep their sticks on the ice to ensure they did not prematurely trip the laser timer. Once the participant crossed the finish line, they coasted back to the starting line and were given 2 minutes to recover. The recovery time started when the participant returned to the starting line. Similar tests have been reported to have test-retest values of r = 0.8 (4). Each participant performed 2 trials, with the fastest time being used for data analysis.
Top speed was assessed after completion of the acceleration test and a 2-minute recovery period. Top speed was measured by the time it took the participant to cover the distance between blue lines (15.24 m) with a skating start (4,12). The participant was instructed to take a lap around the rink, starting at the blue line (start line), increasing their speed as they re-approach the start line. When the participant reached the start line, they were instructed to be moving as fast as possible and to maintain that speed through the finish line. Times were again recorded by a TC Speed Trap-II wireless timing system (E38720; Gill Athletics) with the photocells placed at waist level. Once the participant crossed the finish line, they coasted back to the starting line and were given 2 minutes to recover. The recovery time started when the participant returned to the starting line. This test has been reported to have test-retest values of r = 0.84 (4). Each participant performed 2 trials, with the fastest time being used for data analysis.
Fatigue resistance was assessed after the top-speed test; again giving the participant a 2-minute recovery period. Fatigue was measured as a percent decrement score during an on-ice RST (27). Participants were not allowed to drink any sports drinks or mixes during testing; however, water was provided without restriction to the participants.
Statistical analysis of the data was performed using SPSS software (version 21.0; IBM, Armonk, NY, USA). Mean and SD values were calculated for all variables. 1-way multivariate analysis (analysis of variance) was performed to examine the relationship between level of play and off-ice, on-ice, and anthropometric characteristics of the participants. To quantify the strength and direction of the multivariate relationships, the Tukey method was applied to find mean values that were significantly different from each other. Standard error and alpha values were calculated for each variable. All participants met criteria for normality in each tested variable. The on-ice RST had 1 subject with a problematic point that was significantly deviated from the mean for all 3 timing gates. Because this subject's data point was not a statistical outlier, this point was winsorized to 1 unit above the next highest data point to meet criteria for normal distribution. For all statistical tests, an alpha level of p ≤ 0.05 was operationally defined as statistical significance.
Anthropometric characteristics of the 45 male hockey players (Division I = 24, Junior = 10, Division III = 11) are shown in Table 1. Division I players were significantly taller, heavier, and had a lower percent body fat than Division III players (p = 0.01, 0.04, 0.004, respectively). Body fat percentage was also significantly different between Division I and Elite Junior players (p = 0.04), as well as Elite Junior and Division III players (p = 0.001).
Division I and Elite Junior players had significantly higher values for most variables compared with Division III players (Table 2). Division I players scored significantly better in the vertical jump (p = 0.001), Wingate PP (p = 0.04), grip strength (p = 0.008), and maximum heart rate (p = 0.04). Junior level players scored significantly better than Division III players in vertical jump (p = 0.003) and Wingate fatigue index (p = 0.03).
Division I and Junior players were also significantly better in on-ice performance variables compared with Division III players (Table 3). Both groups scored significantly better in top speed (p = 0.001), fastest course time (p = 0.001), slowest course time (p = 0.001), and average course time (p = 0.001). Junior players additionally scored significantly better than Division III players in gate 2 and total course fatigue (p = 0.04 and 0.04, respectively).
The primary aim of this study was to discern differences that exist in anthropometric and physiological variables previously shown in the literature to correlate to the performance of collegiate hockey players, stratified by level of play. This study had 2 findings. First, there were significant differences in anaerobic power between Division I and Division III players. Division I players had greater vertical jump heights, Wingate PP, grip strength, top speed, and greater fastest RST course time. Although these differences were assumed to exist, this study is the first to objectively test and quantify the differences. Second, there was no significant difference in aerobic capacity between levels of play: Wingate,
, Wingate fatigue index, RST gate 1 fatigue, RST gate 2 fatigue, and RST total course fatigue.
Research has shown that an athlete's muscle fiber distribution can have a dramatic impact on their ability to produce force (35). Slow twitch muscle fibers have a high level of aerobic endurance, meaning they are efficient at producing ATP from oxidative metabolism but poorly suited to generate power (35). Fast twitch fibers rely on anaerobic mechanisms, mainly PCr metabolism, which allow them to produce high levels of force quickly (6). Several studies have shown that this discrepancy exists in the muscle fiber distribution within athletes in the same sport (8,11,31). For example, although hockey is generally played by a homogenous group of athletes, performance discrepancies exist between players of different positions (23). A study by Green (14) found that defensemen skate longer shifts, and at a lower average velocity, reporting a 61.6% decrease compared with forward. To deal with the increased skating time and decreased speed, defensemen may have adapted a higher percentage of slow switch fibers compared with forward (23).
Discrepancies in fiber distribution would have drastic effects on the rate of force production of a hockey player. Demant and Rhodes (9) reported that PCr content at rest in slow twitch and fast twitch fibers was significantly different (p ≤ 0.05), at 73.1 ± 9.5 and 82.7 ± 11.2 mmol[BULLET OPERATOR]kg−1 per dry matter, respectively. In addition, fast twitch fibers seem to rely more heavily on PCr for energy supply during maximal exercise (13). Another study, by Karatzaferi et al. (18), analyzed muscle biopsies of individuals after 10 seconds of maximal dynamic exercise. The results found that PCr levels in type I, IIa, IIAx, and IIXa fibers had been depleted to 46, 53, 62, and 59% of their resting levels, respectively. These findings were supported by Gray et al. (13), which found that fast twitch fibers had a greater decline in PCr content after a 6-second maximal bike sprint than slow twitch fibers. This discrepancy could be further exacerbated between players at the Division I and Division III level, resulting in the significant difference seen in the anaerobic measures of this study.
There was no significant difference between aerobic capacity or rate of fatigue between the levels of play. This lack of variation could be explained by the homogeneity of the participants, in terms of anthropometric characteristics and training history (29). In addition, aerobic capacity has been found to be closely tied to genetics, with heritability accounting upward of 70% of a person's
(5). With such a small portion of the variance in
coming from training effect, it is possible that hockey players, as a population, do not have enough variance in the measure to find significant differences.
The lack of a significant difference in aerobic capacity between levels of play begs the question, “what differentiates players at these levels?” There are 2 possible factors to help answer this question. First, research has demonstrated that team-sport performance is more strongly correlated with O2 kinetics than
. Rampinini et al. (30) found a significant correlation between both
and O2 kinetics to high-intensity repeated exercise; however, O2 kinetics explained more of the variability in the model (r = 0.65 vs. −0.45). Although both
and O2 kinetics seem to have an effect on performance, it would seem that O2 kinetics is a better predictor of performance. Second, skating mechanics (stride efficiency) are an important aspect of on-ice performance (26). A player with great stride efficiency has the potential to outperform a player with poor stride efficiency but the same aerobic capacity. This is because of the fact that an efficient skater does not require the same amount of energy output to meet a specific workload. Similar phenomena have been seen in endurance athletes and the effect of running economy on marathon times (32). Although this study did not directly measure O2 kinetics or stride efficiency, future research should look more closely at these relationships and their differences between levels of play in collegiate hockey players.
The results of this study indicate that Division I hockey players, when compared with Division III, have a significantly higher rate of force production on tests measuring anaerobic performance. Significant differences did not exist between any level of play, however, when comparing scores of aerobic performance. In regard to the first finding, we hypothesize that this is likely because of muscle fiber distribution differences that exist between players at these levels. In reference to the second finding, O2 kinetics and stride efficiency likely contribute to performance discrepancies between levels of play despite players possessing similar aerobic capacities. This evidence suggests that it may be prudent for hockey players to emphasize training methods that focus on the rate of force production and skating efficiency. These findings could be beneficial to coaches by providing normative data of performance markers for different levels of collegiate hockey. Coaches can use these data to evaluate future players during recruiting and target the ones that posses the anthropometric and physiological traits that best fit their system. In addition, coaches can use these markers to implement training practices with their current players to maximize performance.
The authors thank the subjects who volunteered for this study, without their participation and motivation these data would not be available. The authors also specifically thank Craig Flor, Nolan Anderson, Holly Lewis, Greg Rhodes, and Aaron Gorsche for their assistance with subject logistics and data collection. Without their help and dedication, this study would not have been possible. The authors have no conflicts of interest or external funding sources to disclose. The results of this study do not constitute endorsement by the authors or the National Strength and Conditioning Association.
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