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Abstract

Allard, P, Martinez, R, Deguire, S, and Tremblay, J. In-season session training load relative to match load in professional ice hockey. J Strength Cond Res XX(X): 000-000, 2020-The aim of this study was to describe match load and intensity across player positions and match periods, the distribution of pregame training load and intensity over training days before a match, by player position, and the cumulative weekly training load over a season. Fifty professional ice hockey players, which at one time or another were part of the roster for the Laval Rocket during the 2017-18 season of the American Hockey League, participated in this study. External load was monitored in every training session and match over the season using portable inertial measurement units. Data are reported in absolute and relative values to a match. Defensemen have a lower intensity during matches than forwards (-1.70 OIL·min) while load is similar across position. The first period shows a higher relative load (+5.28% relative on-ice load [OIL]) while the third has a lower relative intensity (-2.91% OIL·min). Defensemen seem to train at a systematic higher relative intensity than wingers and centers (+8.34% relative OIL·min). Finally, the weekly training load remains relatively constant throughout the season (equivalent of 3.56 ± 1.69 matches played per week). Our results support the need for player monitoring in ice hockey using an individualized approach.

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... Match-analysis reports have revealed that activity demands might vary, or not, across playing positions, as well as at different stages of the game as consequence of fatigue development Journal of Human Kinetics -volume 84/2022 http://www.johk.pl and/or possible tactical alterations (Allard et al., 2020;Brocherie et al., 2018;Lignell et al., 2018;Vigh-Larsen et al., 2020a). Reductions in game performance in male ice hockey have been documented in various studies reporting decreases in average skating speed (Lignell et al., 2018), the amount of efforts with drastic forward lean (Brocherie et al., 2018), total number of accelerations and decelerations (Vigh-Larsen et al., 2020a), accelerations >0.3 m·s 2 (Allard et al., 2020) and the amount of distance >24 km·h -1 covered (Douglas and Kennedy, 2020) during the latter stages of the game, which are supported by a decrease in repeated sprint ability (RSA) after an experimental ice hockey game (Vigh-Larsen et al., 2020a). ...
... and/or possible tactical alterations (Allard et al., 2020;Brocherie et al., 2018;Lignell et al., 2018;Vigh-Larsen et al., 2020a). Reductions in game performance in male ice hockey have been documented in various studies reporting decreases in average skating speed (Lignell et al., 2018), the amount of efforts with drastic forward lean (Brocherie et al., 2018), total number of accelerations and decelerations (Vigh-Larsen et al., 2020a), accelerations >0.3 m·s 2 (Allard et al., 2020) and the amount of distance >24 km·h -1 covered (Douglas and Kennedy, 2020) during the latter stages of the game, which are supported by a decrease in repeated sprint ability (RSA) after an experimental ice hockey game (Vigh-Larsen et al., 2020a). In contrast, increased skating distance >17 km·h -1 covered per minute played during the 3 rd game-interval compared to the 1 st and the 2 nd period has been observed (Lignell et al., 2018) as well as an unchanged activity profile across periods for defensemen (Allard et al., 2020;Stanula et al., 2014Stanula et al., , 2016. ...
... Reductions in game performance in male ice hockey have been documented in various studies reporting decreases in average skating speed (Lignell et al., 2018), the amount of efforts with drastic forward lean (Brocherie et al., 2018), total number of accelerations and decelerations (Vigh-Larsen et al., 2020a), accelerations >0.3 m·s 2 (Allard et al., 2020) and the amount of distance >24 km·h -1 covered (Douglas and Kennedy, 2020) during the latter stages of the game, which are supported by a decrease in repeated sprint ability (RSA) after an experimental ice hockey game (Vigh-Larsen et al., 2020a). In contrast, increased skating distance >17 km·h -1 covered per minute played during the 3 rd game-interval compared to the 1 st and the 2 nd period has been observed (Lignell et al., 2018) as well as an unchanged activity profile across periods for defensemen (Allard et al., 2020;Stanula et al., 2014Stanula et al., , 2016. Previous studies investigating alterations in the work rate during an ice hockey game are limited to three studies examining one single game (Brocherie et al., 2018;Lignell et al., 2018;Vigh-Larsen et al., 2020a), and three studies considering multiple matches (4 to 76 files (Allard et al., 2020;Douglas and Kennedy, 2020;Stanula et al., 2016)). ...
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The aim of this study was to describe the game activity profile of a professional ice hockey team with special emphasis on fatigue development and playing position. Data were collected using a wearable 200-Hz accelerometric system and heart rate (HR) throughout eight official games in a professional ice hockey team (6 defensemen and 11 forwards; n = 122 files). On-ice 10- and 30-m sprint performance, repeated sprint ability and HR responses to the submaximal Yo-Yo Intermittent recovery level 1 test were assessed to determine associations with game performance. Although the 3 rd period was largely longer than the 1 st and 2 nd periods (r = 0.56–0.59), no differences were observed between periods in activity pattern, except a moderate decline in the number of decelerations <-2 m·s ⁻² per min (Dec2/min) in the 2 nd period for forwards (r = 0.06–0.60). Mean HR, time spent >85% HRmax (t85HR), as well as the total number of intense accelerations and decelerations were higher for defensemen. However, demands were similar when expressed relative to time on-ice, except that defenders performed more Dec2/min than forwards in all periods, whereas forwards spent more t85HR during the 2 nd period (r = 0.46–0.57). Time spent on ice was inversely correlated with the total number of accelerations (Acc tot ), accelerations >2 m·s ⁻² per min (Acc2/min), total decelerations per min (Dec tot /min), Dec2/min and t85HR (r = -0.63 to -0.18) and positively correlated with mean HR and peak HR (r = 0.20– 0.53). No significant correlations were found between physical fitness and game activity variables scaled by individual time on ice. Absolute acceleration and HR demands of professional ice hockey seem to differ between playing positions, but not in relation to time on ice. Further, no clear signs of fatigue were captured, possibly due to the longer duration of rest intervals in the 3 rd period.
... In ice hockey, this is usually done using wearable technology embedding accelerometer and heart rate (HR) or collecting the rating of perceived exertion (RPE) during practice sessions and official games. However, limited attempts have been done to describe TL measures and intensity in elite ice hockey [2][3][4][5]. ...
... The external TL (ETL) describes the amount of physical work completed by the athlete. The ETL has recently been described in elite male [3,2,5] and female [4] ice hockey players using wearable technology. Beyond the relevant information available from ETL, different athletes may produce the same exercise output while experiencing different physiological and/or perceptual loading. ...
... Our positional analysis revealed that although defensemen and forwards were exposed to similar training duration, defensemen were imposed slightly to moderately greater acceleration-and HR-based TL and intensity than forwards (r = 0.23-0.38). This contrasts with recent findings by Allard et al. [3] showing similar positional demands in on-ice load during practice sessions. However, our findings are similar to that observed in professional soccer, showing elite Danish Ice Hockey team according to the opponent standard is reported in Table 3. ...
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We aimed to quantify training load (TL) and intensity during practice sessions according to game-related contextual variables (game outcome, opponent standard, game location) in an elite male ice hockey team. Practice data were collected using a wearable 200-Hz accelerometer, heart rate (HR) recording, and session-rating of perceived exertion (s-RPE) throughout 23 sessions (n = 306 files). The reference team performed a greater number of accelerations, decelerations, spent longer time > 85% maximum HR (t85%HRmax) and reported greater s-RPE after losing a game compared to a win (r = 0.13–0.19). Moreover, a lower number of accelerations, decelerations, t85%HRmax and s-RPE (r = 0.15–0.45) were found before playing against a top-ranked opponent. In contrast, more accelerations, decelerations, longer 85%HRmax and greater s-RPE were observed after playing against a top-ranked team opponent (r = 0.15–0.41). The players performed more accelerations/min, spent more t85%HRmax and reported greater s-RPE before playing an away game (r = 0.13–0.22). Weekly TL seems to slightly increase after losing a game, when preparing a game against a weaker opponent, after playing against a stronger opponent, and when preparing an away game. On the other hand, training intensity seems not to be affected by game-related contextual variables. Thus, ice hockey practitioners involved with TL monitoring should consider the interplay of the numerous variables that influence the volume of prescribed training and the actual training responses in each individual player.
... Discussion of the available and most appropriate measures used to quantify external load in team sports is ongoing as technology continues to improve (Ortega et al., 2021). While timing gates, video analysis, and other wearable technology have all been used to measure external load in ice hockey (Van Iterson et al., 2017;Lignell et al., 2018;Link et al., 2019;Stetter et al., 2019;Douglas et al., 2019a;Allard et al., 2020), they do not provide the same amount of information as LPS. Based on positional information in indoor sports (similar to outdoor GPS position-tracking), distance travelled, speed, acceleration, and deceleration can all be determined. ...
... Previous research reported faster skating speeds or higher intensity external load in forwards compared to defense in both female and male players using various methods of athlete monitoring (Lignell et al., 2018;Douglas et al., 2019a;Allard et al., 2020;Douglas and Kennedy, 2020). While athlete load research is relatively new in ice hockey and more studies are needed to determine the most appropriate LPS metric to measure external load, rugby research has suggested the importance of considering relative measures of external load to prevent misrepresentation of individual workloads between positions or sexes (Clarke et al., 2015;Reardon et al., 2015;Takamori et al., 2020;Nyman and Spriet, 2021). ...
... Studies investigating male players have reported forwards skating at higher intensities during elite junior and National Hockey League (NHL) games (Lignell et al., 2018;Douglas and Kennedy, 2020). While American Hockey League (AHL) defense was shown to train at a greater relative intensity, forwards had a greater intensity of external load during AHL games, with overall load in the game similar between positions (Allard et al., 2020). Based on these results and the consistent positional differences identified, strategies should attempt to develop position-specific training loads to maximize preparation for games, especially if forwards are required to skate at faster speeds during the games as they pressure the defense of the opposing team. ...
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Purpose The purposes of this study were to quantify the external load for female and male varsity ice hockey players during regular season games using a local positioning system (LPS), compare LPS-derived external load between sexes and positions, and compare skating distances in absolute and relative speed zones. Methods Data were collected for 21 female (7 defense, 14 forwards; 20.0 ± 1.4 yrs., 69.1 ± 6.7 kg, 167.1 ± 5.4 cm) and 25 male (8 defense, 17 forwards; 21.9 ± 1.1 yrs., 85.9 ± 5.4 kg, 181.1 ± 5.2 cm) varsity ice hockey players. Measures included skating distance (total, and in absolute and relative speed zones), peak skating speed, peak acceleration and deceleration, accumulative acceleration load, and number of accelerations, decelerations, turns, skating transitions, direction changes, and impacts. Results Female and male players had a high external load during games, with average peak skating speeds >28 km/h and average skating distances >4.4 km. Most LPS-derived measures showed greater external load in males than females ( p < 0.05). Forwards skated further at higher speeds compared to defense in both sexes ( p < 0.001). Skating distances were significantly different when comparing absolute and relative speed zones ( p < 0.001), with absolute speed zones potentially overestimating skating at very slow, very fast, and sprint speeds and underestimating skating at slow and moderate speeds. Conclusion This was the first study to measure external load in female ice hockey players with a LPS. Both female and male varsity players had high external loads during games, with forwards having greater external load at higher intensities and defense having greater external load at lower intensities. Sex and positional differences outline the importance of individualized athlete monitoring.
... Further analysis of ice hockey match demands has revealed significant differences in intensity distribution between positions, periods, and odd-man situations (Douglas and Kennedy, 2019). Typically, forwards cover more distance in high-intensity zones (>17 km/h) compared with defensive players (Lignell et al., 2018;Douglas and Kennedy, 2019;Allard et al., 2020) and both total distance and intensity have been shown to decline from 1st to 3rd period (Brocherie et al., 2018;Lignell et al., 2018;Douglas and Kennedy, 2019;Douglas et al., 2019a;Allard et al., 2020). Interestingly, one study by Douglas et al. (2019b) compared the external load difference between training and matches in a group of elite female players. ...
... Further analysis of ice hockey match demands has revealed significant differences in intensity distribution between positions, periods, and odd-man situations (Douglas and Kennedy, 2019). Typically, forwards cover more distance in high-intensity zones (>17 km/h) compared with defensive players (Lignell et al., 2018;Douglas and Kennedy, 2019;Allard et al., 2020) and both total distance and intensity have been shown to decline from 1st to 3rd period (Brocherie et al., 2018;Lignell et al., 2018;Douglas and Kennedy, 2019;Douglas et al., 2019a;Allard et al., 2020). Interestingly, one study by Douglas et al. (2019b) compared the external load difference between training and matches in a group of elite female players. ...
... 90 ± 2% of HR max ). Furthermore, Allard et al. (2020) recommended more match-like intensity during training drills, after assessing intensity distribution across a whole season. ...
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Objective A limited number of studies have explored the external load experienced in indoor sports such as ice hockey, and few the link between training and match performance. As a paucity exists within this topic, this study explored whether a simulated match design (i.e., scrimmage) could be representative of official match demands and elicit similar external loads as in official matches in a group of elite youth male ice hockey players. Methods A total of 26 players were monitored during eight official and four simulation matches using a Local Positioning System. Total distance, max velocity, slow (0–10.9 km/h), moderate (11–16.9 km/h), high (17.0–23.9 km/h), and sprint (>24 km/h) speed skating distance, distance per min, PlayerLoad TM , PlayerLoad TM per min, high-intensity events (HIEs) (>2.5 m/s ⁻² ), acceleration (ACCs), decelerations (DECs), and change of directions (CODs) were extracted from the tracking devices. A two-level regression analysis was conducted to compare the difference between match types when controlling for time on ice, match day, and position. Results Between match-type results showed a credible difference in all variables except max velocity and ACCs. Distance per min was 27.3% higher during simulation matches and was explained by a 21.3, 24.1, and 14.8% higher distance in sprint-, high-, and moderate speed skating distance, while slow speed-skating distance was 49.2% lower and total distance only trivially different from official to simulation matches. Total PlayerLoad TM was 11.2% lower, while PlayerLoad TM per min was 8.5% higher during simulation matches. HIEs, CODs, and DECs were 10.0, 11.9, and 22.3% higher during simulation matches. Conclusion The simulated match design is related to official match demands with comparable match-time, playing time, number of shifts, and shift duration. However, simulation matches provoked a higher external load output compared with official matches, possibly explained by a more continuous movement design. A game-based simulation match design can therefore be utilized when match-related actions at high intensity are warranted.
... This measure has been widely used in different team sports during competition or training to quantify players' mechanical workload [10][11][12][13]. However, its application during ice hockey tasks remains specific (i.e., gliding sport) due to the movement demands associated with skating stride [14][15][16]. Previous studies [15,16] have shown that mechanical workload drops by ~8% and ~13% from the first to the second and third period, respectively. ...
... However, its application during ice hockey tasks remains specific (i.e., gliding sport) due to the movement demands associated with skating stride [14][15][16]. Previous studies [15,16] have shown that mechanical workload drops by ~8% and ~13% from the first to the second and third period, respectively. This decline has also been reported in video-based TMA studies in men's ice hockey and might be attributed to the accumulation of both progressive and transient fatigue experienced throughout the match [1,5,6]. ...
... In this context, total and peak AR and PL moderately decreased by ~8.5% and ~5.5% respectively from P1 to P3. This finding aligns with previous TMA [1,5] or LPS/IMU technologies [6,15,16]. Our results confirm this decline in highintensity output across periods (Table 2), to a similar extent as in elite male American Hockey League (-4.5% and -6.0% from P1 to P2 and P3, respectively) [15]. ...
Article
This study aimed to quantify in- and between-match characteristics and mechanical workload variations elicited by a congested schedule in high-level female ice hockey. Six players were monitored during four international pre-season exhibition matches against the same opponent. Two different methods (Player Load and Accel’Rate) were used to assess specific mechanical workload. Number of shifts and effective playing time per shift were significantly higher for period 2 (p = 0.03 for both). Mechanical workload intensity (i.e., relative and peak workload) showed a significant (p ≤ 0.05) decrease from period 1 to period 2 and period 3 (moderate-to-large Cohen’s d). All workload variables remained stable between matches (p > 0.25). Team variability showed good-to-moderate CVs (< 10%) for all variables for in- and between-match variability. Accumulated workload computed with the Player Load method was threefold higher compared to the Accel’Rate method (+ 87.8% mean difference; large Cohen’s d). These findings demonstrate that high-level female ice hockey-specific mechanical workload declines with reduced high-intensity output across periods, while it remains stable between matches against standardized opposition. This study strongly suggests that the present workload metrics could be used to determine the mechanical demand elicited by matches played against various opponents in real game conditions.
... For example, quantifying accelerations in ice hockey is important, based on the relatively short average distance of single intense efforts (∼15 m) 2 and the large proportions of on-ice time gliding or cruising across the ice. 8 The ETL has recently been described in elite male 3,9 and female 10 ice hockey players using wearable technology. Beyond the relevant information available from ETL, different athletes may maintain the same exercise output while experiencing different physiological and/or perceptual responses. ...
... Our hypothesis was that there would be greater physiological demands during competition compared with practice sessions based on previous ice hockey studies, with reduced TL in the practice sessions encountered in close proximity to games. 9,10 body mass = 81.6 [6.9] kg, body fat = 13.6% [2.2%], and HR max = 193 [7] bpm) competing at the highest Danish national level of play were regularly monitored in the context of their team practice sessions and game routines. The institutional review board of the University of Southern Denmark approved the study, and written informed consent was obtained from all participants. ...
... As such, the GD − 4 and GD − 3 data were considered outliers and removed from the study. The final sample totaled 427 individual files, of which 306 were derived from practice sessions (median [range] = 18.5 [9][10][11][12][13][14][15][16][17][18][19][20][21] per player) and 121 were derived from official games (median [range] = 7.5 [4][5][6][7][8] per player). A description of a typical week is reported in Table 1. ...
Purpose: The aim of this study was to compare training load (TL) between practice and games across in-season microcycles in elite Danish male ice hockey. Methods: Practice sessions and game data were collected using a wearable 200-Hz accelerometer, heart rate (HR) recording, and rating of perceived exertion (RPE) throughout 23 practice sessions and 8 competitive games (n = 427 files) and examined in relation to the number of days before the game (game day minus). Results: Total accelerations, accelerations >2 m·s-1 (Acc2), total decelerations, decelerations less than -2 m·s-1 (Dec2), time >85% maximum heart rate (t85HRmax), Edwards TL, modified training impulse (TRIMPMOD), session-RPE, peak HR (HRpeak), and RPE were greater during competition than during practice (r = .19-.91; P < .05), whereas total accelerations per minute and total decelerations per minute were lower (r = .27-.36; P < .001). Acc2, t85HRmax, Edwards TL and TRIMPMOD, % t85HRmax, mean HR (HRmean), and RPE progressively decreased toward game day (r = .13-.63; P < .001). Positive correlations were found between Acc2, Dec2, Acc2 per minute, and Dec2 per minute during practice and during competition (r = .66-.84; P < .001). Conclusions: Evident within-week decreases in internal TL but not external TL were observed as the game day approached. Day-to-day variations were more pronounced in HR- and RPE-based parameters than accelerations and decelerations. Finally, the amount of intense accelerations and decelerations performed during practice was associated to the amount performed during competition, whereas physiological and perceptual demands showed no such relationship.
... Forwards and defensemen both accumulated higher PL in matches compared to training, but only defensemen had a higher PL•min -1 in matches. In contrast, no significant differences were reported between forwards and defensemen in match or training On-Ice Load (OIL), a derivative of PL filtering out the lowest band of activity, when tracked across an entire season of men's professional hockey (Allard et al., 2020). Defensemen had lower match OIL•min -1 compared to both forward positions, but higher OIL•min -1 in training. ...
... All four cumulative workload measures (i.e., PL, SL, EE, and HFS) were significantly higher in matches compared to training, indicating that matches required higher volumes of both total and high-intensity work. PL·min -1 and SL·min -1 were higher in training, which is in contrast to previous reports of higher measures of work rate in competition compared to training (Allard et al., 2020;. This discrepancy may be reflective of population differences or philosophical differences related to the overall tempo of training design. ...
... In support of this, despite no differences in total distance covered between positions, forwards competing in elite international U-20 competitions covered significantly greater distances at very fast and sprint speed thresholds, whereas defensemen covered greater distances within very slow, slow, and moderate speeds (Douglas & Kennedy, 2020). Similarly, across an American Hockey League season, forwards performed at a higher relative intensity in matches compared to defensemen, but the total load was similar across positions (Allard et al., 2020). The higher intensity outputs are apparently sufficient to cause forwards to accumulate higher total training volumes and higher work rates, despite the decreased exposure time compared to defensemen. ...
Article
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Background: Despite the importance of using a thorough understanding of competition demands to optimize preparatory strategies, there is a paucity of longitudinal workload data in ice hockey. Objectives: The purpose of this study was to determine differences in workload characteristics between training and matches, and assess differences between forwards and defensemen. Methods: All players from a NCAA Division I Men’s Ice Hockey Team wore Catapult S5 units for all on-ice activities for two consecutive seasons. Seven workload variables (Player Load, Skating Load, Explosive Efforts, High Skate Load, Player Load·min-1, Skating Load·min-1, and Average Stride Force·lb-1) were used to quantify training and match workload characteristics. Results: Compared to training, matches resulted in significantly higher Player Load (p<0.001), Total Skating Load (p<0.001), Explosive Efforts (p<0.001), High Force Strides (p<0.001), and Average Stride Force·lb-1 (p=0.001), but training resulted in higher Player Load·min-1 (p<0.001) and Skating Load·min-1 (p<0.001). Compared to defensemen, forwards accumulated higher values in all seven workload measures, across all session types (p<.001). Conclusion: Matches required higher values in measures of intensity and volume, but lower work rate compared to training. Training had unique emphases based on when it occurred relative to the next match. Regardless of session type, forwards consistently produced higher workloads across all variables.
... Ice hockey comprises high-intensity actions that require players to perform at technical and tactical levels. Such demands become a challenge for coaches and strength and conditioning trainers, who must ensure that their athletes stay at the top of their game while respecting their ability to adapt to the demands of a season (Allard, 2021). Rigorous annual planning and sustained physical preparation are therefore major assets in development and performance at the elite level. ...
... Some authors also paid attention to variables such as the workload and volume over a season. Results from their work showed some fluctuations during a season (Allard, 2021;Brocherie et al., 2018;. Modern technology such as LPS-GPS systems brought the opportunity to assess players' attributes and performance with high efficiency. ...
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This study aimed to explore relationships between fitness, on-ice physical abilities and game performance among elite junior male ice hockey players. Twenty-one major junior ice hockey players (18.9 ± 1.4 years old) participated in the study. Measures including five fitness tests (anthropometric measures, pull up test, bench press test, broad jump, vertical jump) and three on-ice skating tests (multi-stage aerobic skating test, 44-m sprint test, and backward skating test) were assessed during their pre-season training camp. Game performance metrics (collected during the regular season) were collected using InStat software. Results of the (on-ice and off-ice) functional performance test protocol and on-ice tests were analyzed by evaluating correlation coefficients in multiple areas of game performance: 1) physical implication (body checks), 2) offensive contribution (expected goals for, types of zone entries), and 3) defensive actions (blocked shots, expected goals against). They revealed that performance in the broad jump test was associated with skating speed. Some significant correlations were also observed between on-ice test performance indicators such as received body checks, expected goals and blocked shots. In summary, results indicate that on-ice test protocols were associated with players’ performance in multiple aspects of the game. Partial correlation analyses revealed that some of these relationships were specific to the player's position. Forward skating was associated with forwards’ offensive play, and backward skating was specifically related with defensemen’s performance (offense and defense). The addition of on-ice physical tests appears essential for interpreting the results of ice hockey players' physical tests and integrating these results into players’ physical preparation and the in-season follow-up.
... The activity profiles of forwards and defensemen positional groups differ during an ice hockey game. Forwards average higher skating speeds and frequency of sprints, whereas defensemen perform a greater number of shifts with higher skating volumes (1,10,24). When repeated over the duration of a competitive season, professional men's ice hockey places significant physical demands on athletes because of the high frequency of games (2.7 6 0.6 per week; mean 6 SD) and length of season (7 months) (1). ...
... Residual fatigue also increased the game injury rate in contact-sport athletes during the late stages of a season similar in duration to this study (13). Reducing the negative effects of residual fatigue, which limits the risk of overreaching, has been achieved by the appropriate periodization of taper periods and regeneration training that facilitate a supercompensation in recovery (1,24). The current findings highlight the importance of developing effective between-game recovery strategies and training load periodization in professional team sports such as ice hockey. ...
Article
To quantify changes in neuromuscular function over a full professional men's ice hockey season, 27 players (n = 18 forwards and 9 defensemen) performed 3 countermovement jumps (CMJ) each week over 30 sessions separated into 4 phases: preseason, early-season, midseason, and late-season. Outcome variables represented jump performance (jump height), kinematics (mean velocity and peak velocity), and movement strategy (countermovement depth). Mixed models characterized relationships between positional group, season phase, and CMJ outcomes. Statistical significance was set at p ≤ 0.05. Concentric peak velocity (p = 0.02), jump height (p = 0.001), and countermovement depth (p < 0.001) displayed a significant reduction across the season. Peak velocity was lower during the early-season than the preseason (-0.10 ± 0.06 m·s-1, mean change ± 95% confidence limit, p = 0.05). Countermovement depth was reduced during the early-season (-0.06 ± 0.03 m, p = 0.02), midseason (-0.10 ± 0.04 m, p = 0.002), and late-season (-0.15 ± 0.04 m, p < 0.001) relative to the preseason. Reductions in CMJ variables from preseason to in-season ranged from trivial to large. Changes in countermovement depth differed for forwards and defensemen by the season phase (p = 0.04). A professional ice hockey season decreases CMJ performance, with the effects of fatigue most prominent during the late-season phase. Countermovement depth was most sensitive to fatigue and differentiated positional-group responses. Frequent CMJ testing is useful for identifying the neuromuscular status of team-sport athletes relative to season-specific phases. Fatigue monitoring should incorporate movement-strategy variables alongside traditional measures of performance and kinematics.
... Inertial measurement units (IMUs) have gained popularity in recent years due to their ability to measure gait patterns in an accessible, inexpensive, and portable manner. IMUs have been used in clinical contexts [1,2], team sports [3,4], activity recognition [5,6], and to investigate various aspects of walking and running gait [7][8][9][10]. Moreover, a growing number of studies have employed research-grade and commercial IMUs to detect fatiguerelated changes in running, using various experimental setups and sensor locations [11][12][13]. ...
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The purpose of this study was to determine if fatigue-related changes in biomechanics derived from an inertial measurement unit (IMU) placed at the center of mass (CoM) are reliable day-to-day. Sixteen runners performed two runs at maximal lactate steady state (MLSS) on a treadmill, one run 5% above MLSS speed, and one run 5% below MLSS speed while wearing a CoM-mounted IMU. Trials were performed to volitional exhaustion or a specified termination time. IMU features were derived from each axis and the resultant. Feature means were calculated for each subject during non-fatigued and fatigued states. Comparisons were performed between the two trials at MLSS and between all four trials. The only significant fatigue state × trial interaction was the 25th percentile of the results when comparing all trials. There were no main effects for trial for either comparison method. There were main effects for fatigue state for most features in both comparison methods. Reliability, measured by an intraclass coefficient (ICC), was good-to-excellent for most features. These results suggest that fatigue-related changes in biomechanics derived from a CoM-mounted IMU are reliable day-to-day when participants ran at or around MLSS and are not significantly affected by slight deviations in speed.
... Periodization refers to the logical sequencing of varying volumeintensity training workloads to achieve peak performance and minimize the deleterious effects of fatigue (12,24). Running-based team sports have shown a submaximal training workload during all training days between games via linear periodization (2,24) or a gradual decrease in training load on days closest to competitive matches via nonlinear periodization (5,6). Periodization studies in American football specifically have observed lower workloads in training sessions compared with games in combination with further decreases in loads of training sessions closest to games (22,24). ...
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Quantifying player training loads allows football coaching staff to make informed adjustments to the volume and intensity of training. Physical workload intensity in American football practices have not been extensively quantified. The current study examined physical workload intensities across positions in American collegiate football during training. Data from player tracking technology (Catapult Vector) were collected from 72 American football players (National Collegiate Athletic Association Division I) during in-season practices. Players were involved in individualized skill (indy), team playbook (team), and special team (ST) drills during practice and analyzed for their specialist offensive or defensive role (e.g., linebacker or wide receiver). Player running (i.e., high-speed running and sprint) and accelerations (i.e., high-intensity PlayerLoad and high-intensity inertial movement analysis) per minute were of interest. Drill type and practice day had significant effects on all workload intensity metrics (p < 0.01), but not position. Greater running intensities were seen in ST drills compared with other drill types. Tuesday practice sessions had greater overall intensities compared with other days. Interaction effect of position and drill type was significant (p < 0.001) for all intensity metrics, indicating that position groups exhibited unique workload responses to the drill types. Drill type and practice day interaction effect was significant for all intensity metrics (p < 0.01). The findings may be informative for coaches to tailor physical workloads of practice drills for positional roles in preparation for games and practices. Player tracking technology can add value for strength and conditioning coaches to adjust training programs based on position-specific on-field demands of players.
... The TL can be described using 2 different constructs, specifically external TL and internal TL. The external TL is the amount of work voluntarily completed by the athlete (e.g., distance covered in various speed zones and the number of accelerations/ decelerations during a game or training session) and has been recently described in elite male (4) and female (12) ice hockey players using time-motion analysis (TMA; semiautomated camera systems or wearable tracking technology). Although most monitoring studies in team sports have considered external TL metrics based on speed (27), it is important to consider that the average distance of single intense efforts during a shift in ice hockey is relatively short (;15 m) (23), underlining the importance of expressing external TL as a rate of change in speed (e.g., accelerations and decelerations in m·s 22 ). ...
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Rago, V, Vigh-Larsen, JF, Deylami, K, Muschinsky, A, and Mohr, M. Use of rating of perceived exertion-based training load in elite ice hockey training and match-play. J Strength Cond Res XX(X): 000-000, 2020-Training load (TL) based on the subjective rating of perceived exertion (RPE) may be a useful athlete monitoring alternative when wearable technology is unavailable. The aim of this study was to examine the validity of RPE-based TL monitoring in elite ice hockey. A male ice hockey team (n 5 18) was monitored using a 200-Hz accelerometer, heart rate (HR) and RPE (0-10 scale), throughout a 4-week competitive period (n 5 309 individual observations). Session-RPE (RPE 3 duration) averaged 244.8 6 135.2 and 728.6 6 150.9 arbitrary units (AU) during practice sessions and during official games, respectively. The smallest worthwhile change was 19.8 AU. Within-individual correlations between session-RPE and total accelerations .0.5 m·s 22 (Acc tot), accelerations .2 m·s 22 (Acc2), total decelerations .20.5 m·s 2 2 (Dec tot), decelerations , 22 m·s 22 (Dec2), time. 85% maximum HR, Edwards' TL, and modified training impulse were very large (r 5 0.70-0.89; p , 0.001). In addition, correlations between RPE and measures of exercise intensity (Acc tot per min, Acc2 per min, Dec tot per min, mean HR, and peak HR) were small (r 5 0.02-0.29; p , 0.05) except for Dec2 being unclear (p 5 0.686). Differences in intensity parameters between RPE range (easy to very hard, 2-7 AU) were small (r 5 0.22-0.31; p , 0.05). The session-RPE method can be used as a global indicator of TL in ice hockey. Specific ranges of time-motion and HR intensity variables can be demarcated between RPE categories (easy to very hard; 2-7 AU). Accounting for training volume (session-RPE) more accurately reflects objective methods of TL based on accelerative efforts and HR, than the RPE score (based on the perception of the intensity).
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Background: The relationship between training load and musculoskeletal injury is a rapidly advancing area of research in need of an updated systematic review. Objective: This systematic review examined the evidence for the relationship between training load and musculoskeletal injury risk in athlete, military, and first responder (i.e. law enforcement, firefighting, rescue service) populations. Methods: The CINAHL, EMBASE, MEDLINE, SportDISCUS, and SCOPUS databases were searched using a comprehensive strategy. Studies published prior to July 2017 were included if they prospectively examined the relationship between training load and injury risk. Study quality was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS) and Oxford Centre for Evidence-Based Medicine levels of evidence. A narrative synthesis of findings was conducted. Results: A total of 2047 articles were examined for potential inclusion. Forty-six met the inclusion criteria and 11 known to the authors but not found in the search were added, for a total of 57 articles. Overall, 47 studies had at least partially statistically significant results, demonstrating a relationship between training load and injury risk. Included articles were rated as poor (n = 15), fair (n = 6), and good (n = 36) based on NOS score. Articles assessed as 'good' were considered level 2b evidence on the Oxford Centre for Evidence-Based Medicine Model, and articles assessed as 'fair' or 'poor' were considered level 4 evidence. Conclusions: Our results demonstrate that the existence of a relationship between training load and injury continues to be well supported in the literature and is strongest for subjective internal training load. The directionality of this relationship appears to depend on the type and timeframe of load measured.
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Aim: The aim of the present study was to determine the validity of position, distance traveled and instantaneous speed of team sport players as measured by a commercially available local positioning system (LPS) during indoor use. In addition, the study investigated how the placement of the field of play relative to the anchor nodes and walls of the building affected the validity of the system. Method: The LPS (Catapult ClearSky T6, Catapult Sports, Australia) and the reference system [Qualisys Oqus, Qualisys AB, Sweden, (infra-red camera system)] were installed around the field of play to capture the athletes' motion. Athletes completed five tasks, all designed to imitate team-sports movements. The same protocol was completed in two sessions, one with an assumed optimal geometrical setup of the LPS (optimal condition), and once with a sub-optimal geometrical setup of the LPS (sub-optimal condition). Raw two-dimensional position data were extracted from both the LPS and the reference system for accuracy assessment. Position, distance and speed were compared. Results: The mean difference between the LPS and reference system for all position estimations was 0.21 ± 0.13 m (n = 30,166) in the optimal setup, and 1.79 ± 7.61 m (n = 22,799) in the sub-optimal setup. The average difference in distance was below 2% for all tasks in the optimal condition, while it was below 30% in the sub-optimal condition. Instantaneous speed showed the largest differences between the LPS and reference system of all variables, both in the optimal (≥35%) and sub-optimal condition (≥74%). The differences between the LPS and reference system in instantaneous speed were speed dependent, showing increased differences with increasing speed. Discussion: Measures of position, distance, and average speed from the LPS show low errors, and can be used confidently in time-motion analyses for indoor team sports. The calculation of instantaneous speed from LPS raw data is not valid. To enhance instantaneous speed calculation the application of appropriate filtering techniques to enhance the validity of such data should be investigated. For all measures, the placement of anchor nodes and the field of play relative to the walls of the building influence LPS output to a large degree.
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Physical demands of soccer competition vary between playing positions. Previous research investigated total, and high-speed distances, with limited research into acceleration demands of competition. Research investigating speed and acceleration demands have utilised arbitrary thresholds, overlooking the individual nature of athlete locomotion. The current investigation was the first utilising individual speed and acceleration thresholds, investigating the relative intensity of activities. Relationship between match outcome and physical outputs were also investigated. GPS data from 44 professional matches was collected using 10-Hz GPS and 100-Hz accelerometer devices. 343 observations were divided by playing position, and match result, with differences in GPS metrics analysed. Central midfielders produced the highest total distances, and moderate-intensity acceleration distances (p<0.01). Wide defenders and attackers produced the highest very high-speed running, sprinting, and high-intensity acceleration distances (p<0.01). Central defenders produced the lowest values for all metrics (p<0.01). No significant differences were found between GPS metrics for differing match outcomes (p>0.05). In addition to differing tactical and technical roles, soccer playing positions have specific physical demands associated. Current results allow overload of individual training intensities relative to competition. No relationships were evident between GPS metrics and match outcome, suggesting soccer success is the result of superior technical and tactical strategies.
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The aim of this study was to examine changes in time-motion patterns of elite male ice hockey players during an international game with special reference to the development of fatigue. Ten elite male ice hockey players were filmed during an official international game. Detailed time-motion patterns and behaviours (effective playing, stoppage and resting times, number of shifts, low- and high-intensity skating activities across periods as well as passing, shooting and body checking) were analysed during the three game periods. Shift duration averaged 85.72±4.89 s (44.01±5.71 s of effective playing time and 41.71±4.07 s of stoppage) and was repeated ~7.4±1.8 times per period. Mean effective playing time and effective time per shift decreased over the periods (-6.8±17.3%, P = 0.18, d = 0.71 and -8.5±12.7%, P = 0.20, d = 0.24, respectively), resulting in a shorter distance covered (-12.8±5.7%, P = 0.16, d = 0.46) from period 1 to 3. At similar time intervals, stoppage (+8.2±9.8%, P<0.05, d = 0.78) and bench resting period (+35.6±34.0%, P<0.05, d = 1.26) also increased. The number of sprints performed in period 3 was significantly lower than in period 1 (-46.7±32.1%, P<0.01, d = 1.12). This was accompanied by a lower effective time (-16.8±24.9%, P<0.05, d = 0.82) spent in high-intensity activities (fast forward skating, forward sprinting and fast backward and sprinting) – particularly in forward sprints (-54.8±20.7%, P<0.01, d = 1.07) – in period 3 vs. 1. Detailed analysis of players’ time-motion patterns of an international ice hockey game indicates that the capacity to perform intense actions is impeded towards the end of the match (period 3). Assessing performance fatigability may help practitioners to tailor ice hockey-specific training routines to help prevent in-game premature and/or excessive fatigue development.
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The increasing interest in assessing physical demands in team sports has led to the development of multiple sports related monitoring systems. Due to technical limitations, these systems primarily could be applied to outdoor sports, whereas an equivalent indoor locomotion analysis is not established yet. Technological development of inertial measurement units (IMU) broadens the possibilities for player monitoring and enables the quantification of locomotor movements in indoor environments. The aim of the current study was to validate an IMU measuring by determining average and peak human acceleration under indoor conditions in team sport specific movements. Data of a single wearable tracking device including an IMU (Optimeye S5, Catapult Sports, Melbourne, Australia) were compared to the results of a 3D motion analysis (MA) system (Vicon Motion Systems, Oxford, UK) during selected standardized movement simulations in an indoor laboratory (n = 56). A low-pass filtering method for gravity correction (LF) and two sensor fusion algorithms for orientation estimation [Complementary Filter (CF), Kalman-Filter (KF)] were implemented and compared with MA system data. Significant differences (p < 0.05) were found between LF and MA data but not between sensor fusion algorithms and MA. Higher precision and lower relative errors were found for CF (RMSE = 0.05; CV = 2.6%) and KF (RMSE = 0.15; CV = 3.8%) both compared to the LF method (RMSE = 1.14; CV = 47.6%) regarding the magnitude of the resulting vector and strongly emphasize the implementation of orientation estimation to accurately describe human acceleration. Comparing both sensor fusion algorithms, CF revealed slightly lower errors than KF and additionally provided valuable information about positive and negative acceleration values in all three movement planes with moderate to good validity (CV = 3.9-17.8%). Compared to x-and y-axis superior results were found for the z-axis. These findings demonstrate that IMU-based wearable tracking devices can Roell et al. Inertial Sensors for Player Monitoring successfully be applied for athlete monitoring in indoor team sports and provide potential to accurately quantify accelerations and decelerations in all three orthogonal axes with acceptable validity. An increase in accuracy taking magnetometers in account should be specifically pursued by future research.
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This study aimed to determine the intra- and inter-device accuracy and reliability of wearable athletic tracking devices, under controlled laboratory conditions. A total of nineteen portable accelerometers (Catapult OptimEye S5) were mounted to an aluminum bracket, bolted directly to an Unholtz Dickie 20K electrodynamic shaker table, and subjected to a series of oscillations in each of three orthogonal directions (front-back, side to side, and up-down), at four levels of peak acceleration (0.1g, 0.5g, 1.0g, and 3.0g), each repeated five times resulting in a total of 60 tests per unit, for a total of 1140 records. Data from each accelerometer was recorded at a sampling frequency of 100Hz. Peak accelerations recorded by the devices, Catapult PlayerLoad™, and calculated player load (using Catapult’s Cartesian formula) were used for the analysis. The devices demonstrated excellent intradevice reliability and mixed interdevice reliability. Differences were found between devices for mean peak accelerations and PlayerLoad™ for each direction and level of acceleration. Interdevice effect sizes ranged from a mean of 0.54 (95% CI: 0.34–0.74) (small) to 1.20 (95% CI: 1.08–1.30) (large) and ICCs ranged from 0.77 (95% CI: 0.62–0.89) (very large) to 1.0 (95% CI: 0.99–1.0) (nearly perfect) depending upon the magnitude and direction of the applied motion. When compared to the player load determined using the Cartesian formula, the Catapult reported PlayerLoad™ was consistently lower by approximately 15%. These results emphasize the need for industry wide standards in reporting validity, reliability and the magnitude of measurement errors. It is recommended that device reliability and accuracy are periodically quantified.
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Objectives: The aims of this study were (1) to quantify and compare the load of a professional football team's training days and matches and (2) to compare training of nonstarters the day after the match with regular training of starters and nonstarters. Methods: On-field training load during in-season training days (categorized as days before match day, i.e., MD minus) and 3 friendly matches were recorded using alocal positioning measurement system. Results: Mixed linear models showed lower load when training approached match day. Relative to match values (100%), training values for running (52 -20%; MD-4 -MD-1) and high-speed running (38 -15%) were lower than for total distance (67 -35%), and all considerably lower than match values. On average, medium and high accelerations and decelerations during training were more similar to match values (90 -39%). Load during nonstarters training was lower than during regular training for almost all variables on MD-4 and several high-intensity variables on MD-3 and MD-2. Conclusions: The results highlight that acceleration and deceleration measures complement more commonly used external load variables based on distance and speed. Furthermore, nonstarters are potentially under-loaded compared to starters, especially in terms of (high-speed) running.
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The use of global positioning systems (GPS) has increased dramatically over the last decade. Using signals from orbiting satellites, the GPS receiver calculates the exact position of the device and the speed at which the device is moving. Within team sports GPS devices are used to quantify the external load experienced by an athlete, allowing coaches to better manage trainings loads and potentially identify athletes who are overreaching or overtraining. This review aims to collate all studies that have tested the validity and/or the reliability of GPS devices in a team sport setting, with a particular focus on 1) measurements of distance, speed, velocities and accelerations across all sampling rates and 2) accelerometers, player/body load and impacts in accelerometer-integrated GPS devices. A comprehensive search of the online libraries identified 22 articles that fit search criteria. The literature suggests that all GPS units, regardless of sampling rate, are capable of tracking athlete's distance during team sport movements with adequate intra-unit reliability. 1Hz and 5Hz GPS units have limitations in their reporting of distance during high intensity running, velocity measures and short linear running (particularly those involving changes of direction), although these limitations seem to be overcome during measures recorded during team sport movements. 10Hz GPS devices appear the most valid and reliable to date across linear and team sport simulated running, overcoming many limitations of earlier models, while the increase to 15Hz GPS devices have had no additional benefit.
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Purpose: Load monitoring in Australian Football (AF) has been widely adopted, yet team sport periodization strategies are relatively unknown. Here we have aimed to quantify training and competition load across a season in an elite AF team, using rating of perceived exertion (RPE) and GPS. tracking. Methods: Weekly totals for RPE and GPS loads (including accelerometer data; Playerload) were obtained for 44 players across a full season for each training modality and for competition. General linear mixed models compared mean weekly load between 3x pre-season and 4x in-season blocks. Effects were assessed with inferences about magnitudes standardized with between-player SD. Results: Total RPE load was most likely greater during pre-season, where the majority of load was obtained via skills and conditioning. There was a large reduction in RPE load in the last pre-season block. During in-season, half the total load came from games and the remaining half from training, predominantly skills and upper-body weights. Total distance, high-intensity running, and Playerload showed large to very large reductions from pre-season to in-season, whereas changes in mean speed were trivial across all blocks. All these effects were clear at the 99% level. Conclusions: These data provide useful information about targeted periods of loading and unloading across different stages of a season. Our study also provides a framework for further investigation of training periodization in AF teams.
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The principle aim of the study was to assess the acute alterations in tri-axial accelerometry (PlayerLoad™; PLVM) and its individual axial-planes (anterior-posterior-PlayerLoad™ [PLAP], medial-lateral-PlayerLoad™ [PLML] and vertical-PlayerLoadTM [PLV]) during a standardised 90-min soccer match-play simulation (SAFT90). Secondary aims of the study were to assess the test-retest reliability and anatomical location of the devices. Semi-professional (n=5) and University (n=15) soccer players completed 3 trials (1 familiarisation, 2 experimental) of SAFT90. PlayerLoad™ and its individual planes were measured continuously using micromechanical-electrical systems (MEMS) positioned at the scapulae (SCAP) and near the centre of mass (COM). There were no between-half differences in PLVM, however, within-half increases were recorded at the COM, but only during the 1st half at the SCAP. Greater contributions to PLVM were provided by PLV and PLML when derived from the SCAP and COM, respectively. PLVM (COM: 1451 ± 168; SCAP: 1029 ± 113), PLAP (COM: 503 ± 99; SCAP: 345 ± 61), PLML (COM: 712 ± 124; SCAP: 348 ± 61) and PLV (COM: 797 ± 184; SCAP: 688 ± 124) were significantly greater at the COM compared to the SCAP. Moderate and high test-retest reliability was observed for PlayerLoad™ and its individual planars at both locations (ICC: 0.80-0.99). PlayerLoad™ and its individual planes are reliable measures during SAFT90 and detected within-match changes in movement strategy when the unit was placed at the COM, which may have implications for fatigue management. Inferring alterations in lower-limb movement strategies from MEMS units positioned at the SCAP should be undertaken with caution.
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Microtechnology has allowed sport scientists to understand the locomotor demands of various sports. While wearable global positioning technology has been used to quantify the locomotor demands of sporting activities, microsensors (i.e. accelerometers, gyroscopes and magnetometers) embedded within the units also have the capability to detect sport-specific movements. The objective of this study was to determine the extent to which microsensors (also referred to as inertial measurement units and microelectromechanical sensors) have been utilised in quantifying sport-specific movements. A systematic review of the use of microsensors and associated terms to evaluate sport-specific movements was conducted; permutations of the terms used included alternate names of the various technologies used, their applications and different applied environments. Studies for this review were published between 2008 and 2014 and were identified through a systematic search of six electronic databases: Academic Search Complete, CINAHL, PsycINFO, PubMed, SPORTDiscus, and Web of Science. Articles were required to have used athlete-mounted sensors to detect sport-specific movements (e.g. rugby union tackle) rather than sensors mounted to equipment and monitoring generic movement patterns. A total of 2395 studies were initially retrieved from the six databases and 737 results were removed as they were duplicates, review articles or conference abstracts. After screening titles and abstracts of the remaining papers, the full text of 47 papers was reviewed, resulting in the inclusion of 28 articles that met the set criteria around the application of microsensors for detecting sport-specific movements. Eight articles addressed the use of microsensors within individual sports, team sports provided seven results, water sports provided eight articles, and five articles addressed the use of microsensors in snow sports. All articles provided evidence of the ability of microsensors to detect sport-specific movements. Results demonstrated varying purposes for the use of microsensors, encompassing the detection of movement and movement frequency, the identification of movement errors and the assessment of forces during collisions. This systematic review has highlighted the use of microsensors to detect sport-specific movements across a wide range of individual and team sports. The ability of microsensors to capture sport-specific movements emphasises the capability of this technology to provide further detail on athlete demands and performance. However, there was mixed evidence on the ability of microsensors to quantify some movements (e.g. tackling within rugby union, rugby league and Australian rules football). Given these contrasting results, further research is required to validate the ability of wearable microsensors containing accelerometers, gyroscopes and magnetometers to detect tackles in collision sports, as well as other contact events such as the ruck, maul and scrum in rugby union.
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With the advancements in player tracking technology, the topic of fatigue and pacing in team sport has become increasingly popular in recent years. Initially based upon a pre-conceived pacing schema, a central metabolic control system is proposed to guide the movement of players during team sport matches, which can be consciously modified based on afferent signals from the various physiological systems and in response to environmental cues. On the basis of this theory, coupled with the collective findings from motion-analysis research, we sought to define the different pacing strategies employed by team sport players. Whole-match players adopt a 'slow-positive' pacing profile (gradual decline in total running intensity), which appears to be global across the different team sports. High-intensity movement also declines in a 'slow-positive' manner across most team sport matches. The duration of the exercise bout appears to be important for the selected exercise intensity, with the first introduction to a match as a substitute or interchange player resulting in a 'one bout, all out' strategy. In a limited interchange environment, a second introduction to the match results in a 'second-bout reserve' strategy; otherwise, the 'one bout, all out' strategy is likely to be adopted. These pacing profiles are proposed to reflect the presence of a central regulator that controls the movement intensity of the player to optimize performance, as well as avoiding the harmful failure of any physiological system. The presence of 'temporary fatigue' reflects this process, whereby exercise intensity is consciously modulated from within the framework of a global pacing schema.
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In team sports, tactical periodisation refers to the planned manipulation of training loads with the aim of prioritising athlete readiness for matches of greatest importance. Although monitoring of athletes’ physical condition is often used to inform this planning, the direct influence of external factors on match difficulty has not been well quantified. In this study, a ‘match difficulty index’ for use in Super Rugby was developed, based on the influence imparted by five external factors on previous match outcomes. Specifically, information relating to match location, days break between matches, time-zone change and opposition ladder position (both current and previous year) were collected for matches played during the 2011, 2012 & 2013 Super Rugby seasons. Logistic regression analyses were used to assess the importance of each of these factors with respect to match outcome (Win/Loss), with opposition ladder position and match location (home, domestic away or international) exerting the greatest influence on match difficulty. Three separate cross-validated models were constructed, with match outcome classification performance reported as 66.2%, 65.5% and 63.7% respectively. The three match difficulty index models emanating from this study can each be used to inform tactical periodisation program design both prior to and during the regular season.
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To determine the economic burden of salary costs lost due to injury in the National Hockey League (NHL). All NHL players who engaged in at least one regular season game during the 2009-2010 to 2011-2012 seasons comprised the study population. We performed a retrospective cross-sectional analysis of publically available media sources to collect injury and salary data. Outcome measurements were games missed during regular season play due to hockey-related injury and lost salary. A total of 50.9% of all NHL players missed at least one game within a season of play, and injuries represented a total salary cost of approximately US$218 million per year. Concussions alone amounted to a salary loss of US$42.8 million a year. Head/neck injuries and leg/foot injuries were the most expensive in terms of overall cost, while head/neck and shoulder injuries had the highest mean cost. NHL players commonly miss time due to injury, which creates a substantial burden in lost salary costs.
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Use of Global positioning system (GPS) technology in team sport permits measurement of player position, velocity, and movement patterns. GPS provides scope for better understanding of the specific and positional physiological demands of team sport and can be used to design training programs that adequately prepare athletes for competition with the aim of optimizing on-field performance. The objective of this study was to conduct a systematic review of the depth and scope of reported GPS and microtechnology measures used within individual sports in order to present the contemporary and emerging themes of GPS application within team sports. A systematic review of the application of GPS technology in team sports was conducted. We systematically searched electronic databases from earliest record to June 2012. Permutations of key words included GPS; male and female; age 12-50 years; able-bodied; and recreational to elite competitive team sports. The 35 manuscripts meeting the eligibility criteria included 1,276 participants (age 11.2-31.5 years; 95 % males; 53.8 % elite adult athletes). The majority of manuscripts reported on GPS use in various football codes: Australian football league (AFL; n = 8), soccer (n = 7), rugby union (n = 6), and rugby league (n = 6), with limited representation in other team sports: cricket (n = 3), hockey (n = 3), lacrosse (n = 1), and netball (n = 1). Of the included manuscripts, 34 (97 %) detailed work rate patterns such as distance, relative distance, speed, and accelerations, with only five (14.3 %) reporting on impact variables. Activity profiles characterizing positional play and competitive levels were also described. Work rate patterns were typically categoriszed into six speed zones, ranging from 0 to 36.0 km·h(-1), with descriptors ranging from walking to sprinting used to identify the type of activity mainly performed in each zone. With the exception of cricket, no standardized speed zones or definitions were observed within or between sports. Furthermore, speed zone criteria often varied widely within (e.g. zone 3 of AFL ranged from 7 to 16 km·h(-1)) and between sports (e.g. zone 3 of soccer ranged from 3.0 to <13 km·h(-1) code). Activity descriptors for a zone also varied widely between sports (e.g. zone 4 definitions ranged from jog, run, high velocity, to high-intensity run). Most manuscripts focused on the demands of higher intensity efforts (running and sprint) required by players. Body loads and impacts, also summarized into six zones, showed small variations in descriptions, with zone criteria based upon grading systems provided by GPS manufacturers. This systematic review highlights that GPS technology has been used more often across a range of football codes than across other team sports. Work rate pattern activities are most often reported, whilst impact data, which require the use of microtechnology sensors such as accelerometers, are least reported. There is a lack of consistency in the definition of speed zones and activity descriptors, both within and across team sports, thus underscoring the difficulties encountered in meaningful comparisons of the physiological demands both within and between team sports. A consensus on definitions of speed zones and activity descriptors within sports would facilitate direct comparison of the demands within the same sport. Meta-analysis from systematic review would also be supported. Standardization of speed zones between sports may not be feasible due to disparities in work rate pattern activities.
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The performance skating characteristics of the typical National Hockey League forward have not been investigated to the best of the authors' knowledge. The purpose of this study was: (1) to establish an observational protocol and perform motion analysis to identify the individual performance skating characteristics of the high and low, point scoring, National Hockey League forward (n= 12), (2) determine the percentage of execution time spent in each skating characteristic measured by time and the percentage of occurrence of each skating characteristic counted and (3) identify each variable that differentiates a high from a low point scorer. A two‐tiered slow‐motion observational protocol was used to analyze each skating characteristic adopted during a game. Fifteen timed, and 12 frequency, performance skating characteristics were identified and analyzed. The highest mean percentages of time spent by a player in a game were on a two foot glide (39.0%), a cruise stride (16.2%), medium intensity skating (10.0%), a struggle for puck or position (9.8%), and low intensity skating (7.8%). The highest percentage of total occurrences were a left crossover turn (20.2%), a gliding left turn (17.8%), a right crossover turn (17.7%) and a gliding right turn (16.4%). The primary difference between a high and a low point scorer was that a high point scorer spent more time on the ice, and had a higher mean percentage of time spent in a two foot glide with and without a puck. A low point scorer had a higher mean percentage of occurrence in transition from forward to backward skating, and a higher mean percentage of occurrence in a gliding right turn and a left cross over turn while stickhandling the puck.
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It has been suggested that assessment of high-intensity activities during a match is a valid measure of physical performance in elite soccer. Recently, sprinting activities have been analysed in more depth. The aim of this study was to develop a detailed analysis of the sprinting activities of different playing positions during European Champions League and UEFA Cup competitions. Altogether, 717 elite outfield soccer players were evaluated throughout 2002-2006 using ProZone® (Leeds, UK). Sprinting (explosive and leading) was analysed for each playing position. To compare positional differences, a Kruskal-Wallis analysis was performed. Differences were found among positions for total number of sprints and total sprint distance covered: wide midfielders > (attackers = wide defenders) > central midfielders > central defenders (P < 0.001), as well as for explosive sprints: (wide midfielders = attackers = wide defenders) > central defenders, wide midfielders > central midfielders > central defenders and attackers = wide defenders = central midfielders (P < 0.001), and leading sprints: wide midfielders > (attackers = wide defenders) > central midfielders > central defenders (P < 0.001). For each group, there were no differences in ratio of explosive to leading sprints. Wide midfielders performed a higher number of sprints in all five distance categories than all other positions. This study showed that sprinting characteristics are influenced by position. Wide midfielders have to complete additional high-intensity activities during training sessions compared with the other positions to achieve the performance level required during the match.
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To characterize the physical and physiological responses during different basketball practice drills and games. Male basketball players (n=11; 19.1+/-2.1 y, 1.91+/-0.09 m, 87.9+/-15.1 kg; mean+/-SD) completed offensive and defensive practice drills, half court 5on5 scrimmage play, and competitive games. Heart rate, VO2, and triaxial accelerometer data (physical demand) were normalized for individual participation time. Data were log-transformed and differences between drills and games standardized for interpretation of magnitudes and reported with the effect size (ES) statistic. There was no substantial difference in the physical or physiological variables between offensive and defensive drills; physical load (9.5%; 90% confidence limits+/-45); mean heart rate (-2.4%; +/-4.2); peak heart rate (-0.9%; +/-3.4); and VO2 (-5.7%; +/-9.1). Physical load was moderately greater in game play compared with a 5on5 scrimmage (85.2%; +/-40.5); with a higher mean heart rate (12.4%; +/-5.4). The oxygen demand for live play was substantially larger than 5on5 (30.6%; +/-15.6). Defensive and offensive drills during basketball practice have similar physiological responses and physical demand. Live play is substantially more demanding than a 5on5 scrimmage in both physical and physiological attributes. Accelerometers and predicted oxygen cost from heart rate monitoring systems are useful for differentiating the practice and competition demands of basketball.
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To test whether the Global Positioning System (GPS) could be potentially useful to assess the velocity of walking and running in humans. A young man was equipped with a GPS receptor while walking running and cycling at various velocity on an athletic track. The speed of displacement assessed by GPS, was compared to that directly measured by chronometry (76 tests). In walking and running conditions (from 2-20 km/h) as well as cycling conditions (from 20-40 km/h), there was a significant relationship between the speed assessed by GPS and that actually measured (r = 0.99, P < 0.0001) with little bias in the prediction of velocity. The overall error of prediction (s.d. of difference) averaged +/-0.8 km/h. The GPS technique appears very promising for speed assessment although the relative accuracy at walking speed is still insufficient for research purposes. It may be improved by using differential GPS measurement.
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To summarize 16 years of National Collegiate Athletic Association (NCAA) injury surveillance data for 15 sports and to identify potential modifiable risk factors to target for injury prevention initiatives. In 1982, the NCAA began collecting standardized injury and exposure data for collegiate sports through its Injury Surveillance System (ISS). This special issue reviews 182 000 injuries and slightly more than 1 million exposure records captured over a 16-year time period (1988-1989 through 2003-2004). Game and practice injuries that required medical attention and resulted in at least 1 day of time loss were included. An exposure was defined as 1 athlete participating in 1 practice or game and is expressed as an athlete-exposure (A-E). Combining data for all sports, injury rates were statistically significantly higher in games (13.8 injuries per 1000 A-Es) than in practices (4.0 injuries per 1000 A-Es), and preseason practice injury rates (6.6 injuries per 1000 A-Es) were significantly higher than both in-season (2.3 injuries per 1000 A-Es) and postseason (1.4 injuries per 1000 A-Es) practice rates. No significant change in game or practice injury rates was noted over the 16 years. More than 50% of all injuries were to the lower extremity. Ankle ligament sprains were the most common injury over all sports, accounting for 15% of all reported injuries. Rates of concussions and anterior cruciate ligament injuries increased significantly (average annual increases of 7.0% and 1.3%, respectively) over the sample period. These trends may reflect improvements in identification of these injuries, especially for concussion, over time. Football had the highest injury rates for both practices (9.6 injuries per 1000 A-Es) and games (35.9 injuries per 1000 A-Es), whereas men's baseball had the lowest rate in practice (1.9 injuries per 1000 A-Es) and women's softball had the lowest rate in games (4.3 injuries per 1000 A-Es). In general, participation in college athletics is safe, but these data indicate modifiable factors that, if addressed through injury prevention initiatives, may contribute to lower injury rates in collegiate sports.
Article
Purpose: The purpose of this study was to assess the reliability and sensitivity of commercially available inertial measurement units (IMU) to measure physical activity in team handball. Method: Twenty-two handball players were instrumented with two IMUs (OptimEye S5, Catapult Sports, Australia) taped together. They participated in either a laboratory assessment (n=10), consisting of seven team handball specific tasks, or field assessment (n=12) conducted in twelve training sessions. Variables, including PlayerLoad™ and inertial movement analysis (IMA) magnitude and counts, were extracted from the manufactures software. IMA count was divided into intensity bands of low (1.5-2.5m·s(-1)), medium (2.5-3.5m·s(-1)), high (>3.5m·s(-1)), medium/high (>2.5m·s(-1)), and total (>1.5m·s(-1)). Reliability between devices and sensitivity was established using coefficient of variation (CV) and smallest worthwhile difference (SWD). Results: Laboratory assessment : IMA magnitude showed a good reliability (CV: 3.1%) in well-controlled tasks. CV increased (4.4-6.7%) in more complex tasks. Field assessment : Total IMA count (CV: 1.8%, SWD: 2.5%), PlayerLoad™ (CV: 0.9 % SWD: 2.1%), and its associated variables (CV: 0.4-1.7%) showed a good reliability, well below the SWD. However, the CV of IMA increased when categorized into intensity bands (2.9-5.6%). Conclusion: The reliability of IMA count were good, when data was displayed as total, high or medium/high counts. A good reliability for PlayerLoad™ and associated variables was evident. The CV of the aforementioned variables was well below the SWD, suggesting that OptimEye IMU and its software are sensitive for use in team handball.
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An important distinction is made between situations where the existence of a missing data item can be considered a random event and those where it is informative, and the result of a nonrandom mechanism. Randomly missing data may be missing ‘completely at random’ (MCAR) or ‘at random’ (MAR) conditionally on the values of other measurements. Otherwise it will be ‘missing not at random’ (MNAR). Where missingness applies to whole records in a dataset, one approach is to use auxiliary data, along with variables in the model of interest (MOI), to set out a model that predicts the probability that a record is missing. This chapter describes a 2-level model with responses of different types at both levels such as sampling level 1 non-normal responses and sampling level 2 non-normal responses. It provides a simulation example of multiple imputations for missing data. Missing data; panel data; probability distribution
Team handball matches place diverse physical demands on players, which may result in fatigue and decreased activity levels. However, previous speed-based methods of quantifying player activity may not be sensitive for capturing short-lasting team-handball-specific movements. PURPOSE: To examine activity profiles of a women's team handball team and individual player profiles, using inertial measurement units. METHODS: Match data were obtained from 1 women's national team in 9 international matches (N = 85 individual player samples), using the Catapult OptimEye S5. PlayerLoad/min was used as a measure of intensity in 5- and 10-min periods. Team profiles were presented as relative to the player's match means, and individual profiles were presented as relative to the mean of the 5-min periods with >60% field time. RESULTS: A high initial intensity was observed for team profiles and for players with ≥2 consecutive periods of play. Substantial declines in PlayerLoad/min were observed throughout matches for the team and for players with several consecutive periods of field time. These trends were found for all positional categories. Intensity increased substantially in the final 5 min of the first half for team profiles. Activity levels were substantially lower in the 5 min after a player's most intense period and were partly restored in the subsequent 5-min period. DISCUSSION: Possible explanations for the observed declines in activity profiles for the team and individual players include fatigue, situational factors, and pacing. However, underlying mechanisms were not accounted for, and these assumptions are therefore based on previous team-sport studies.
Article
Purpose: To quantify the external training loads of positional groups within pre-season training drills. Methods: Thirty-three elite rugby league players were categorized into one of four positional groups: outside backs (n=9), adjustables (n=9), wide-running forwards (n=9) and hit-up forwards (n=6). Data for eight pre-season weeks were collected using microtechnology devices. Training drills were classified based on drill focus; speed and agility, conditioning, generic and positional skills. Results: Distance, high speed and very-high speed running demands decreased across the pre-season in speed and agility (moderate, small and small, respectively), conditioning (large, large and small, respectively) and generic skills (large, large and large, respectively). The duration of speed and generic skills also decreased (77% and 48% respectively). This was matched by a concomitant increase in distance (small), high speed running (small), very-high speed running (moderate) and two-dimensional (2D) BodyLoad(TM) (small) demands in positional skills. Within positional skills, hit-up forwards (1240±386m) completed less very-high speed running than outside backs (2570±1331m) and adjustables (2121±1163m), respectively. Hit-up forwards (674±253AU), experienced greater 2D BodyLoad(TM) demands than outside backs (432±230 AU, p=.034). Within positional drills hit-up forwards experienced greater relative 2D BodyLoad(TM) demands than outside backs (p=0.015). Conversely, outside backs experienced greater relative high (p=0.007) and very-high speed running (p<0.001) demands than hit-up forwards. Conclusion: Significant differences were observed in training loads between positional groups during positional skills but not within speed and agility, conditioning and generic skills. This work also highlights the importance of different external load parameters to adequately quantify workload across different positional groups.
Article
Wearable microsensor technology incorporating triaxial accelerometry is used to quantify an index of mechanical stress associated with sport-specific movements termed PlayerLoad™. The test-retest reliability of PlayerLoad™ in the environmental-setting of ice-hockey is unknown. The primary aim of this study was to quantify the test-retest reliability of PlayerLoad™ in ice-hockey players during performance of tasks simulating game-conditions. Division I collegiate male ice-hockey players (N=8) wore Catapult Optimeye S5 monitors during repeat performance of 9 ice-hockey tasks simulating game-conditions. Ordered ice-hockey tasks during repeated bouts included: acceleration (forward/backward), 60% top-speed, top-speed (forward/backward), repeated shift circuit, ice-coasting, slap-shot, and bench-sitting. Coefficient of variation (CV), intraclass correlation coefficient (ICC), and minimum differences (MD) were used to assess PlayerLoad™ reliability. Test-retest CVs and ICCs of PlayerLoad™ were: Forward (8.6, 0.54) or backward (13.8, 0.78) acceleration, 60% top-speed (2.2, 0.96), forward (7.5, 0.79) or backwards (2.8, 0.96) top-speed, repeated-shift test (26.6, 0.95), slap-shot (3.9, 0.68), coasting (3.7, 0.98), and bench-sitting (4.1, 0.98), respectively. Raw differences between bouts were not significant for ice-hockey tasks (P>0.05). For each task, between bout raw differences were lower versus MD: Forward (0.06 vs. 0.35) or backward (0.07 vs. 0.36) acceleration, 60% top-speed (0.00 vs. 0.06), forward (0.03 vs. 0.20) or backwards (0.02 vs. 0.09) top-speed, repeated-shift test (0.18 vs. 0.64), slap-shot (0.02 vs. 0.10), coasting (0.00 vs. 0.10), and bench-sitting (0.01 vs. 0.11), respectively. These data suggest PlayerLoad™ demonstrates moderate-to-large test-retest reliability in the environmental-setting of male Division I collegiate ice-hockey. Without previously testing reliability, these data are important as PlayerLoad™ is routinely quantified in male collegiate ice-hockey to assess on ice physical activity.
Article
Practitioners and coaches often use external training load variables such as distance run and the number of high speed running activities to quantify football training. However an important component of the external load may be overlooked when acceleration activities are not considered. The aim of this study was to describe the within-microcycle distribution of external load, including acceleration, during in-season 1-game weeks in an elite football team. Global positioning system technology (GPS) was used to collect time-motion data from twelve representative 7-day microcycles across a competitive season (48 training days, 295 data sets). Training time, total distance (TD), high-speed running distance (HSR [>5.8 m·s]), sprint running distance (SPR [>6.7 m·s]) and acceleration variables were recorded during each training session. Data were analysed for inter-day and inter-position differences using mixed linear modelling. The distribution of external load was characterised by the second training day of the microcycle (5 days pre-match) exhibiting the highest values for all variables of training load, with the fourth day (1 day pre-match) exhibiting the lowest values. Central midfield players covered ∼8%-16% greater TD than other positions excluding wide midfielders (P≤0.03, d=0.2-0.4) and covered ∼17% greater distance accelerating 1-2 m·s than central defenders (P=0.03, d=0.7). When expressed relative to training duration and TD, the magnitude of inter-day and inter-position differences were markedly reduced (P=0.03, d=0.2-0.3). When managing the distribution of training load practitioners should be aware of the intensity of training sessions and consider the density of external load within sessions.
Article
To describe the external load of Australian football matches and training using accelerometers. Nineteen elite and 21 subelite Australian footballers wore accelerometers during matches and training. Accelerometer data were expressed in 2 ways: from all 3 axes (player load; PL) and from all axes when velocity was below 2 m/s (PLSLOW). Differences were determined between 4 playing positions (midfielders, nomadics, deeps, and ruckmen), 2 playing levels (elite and subelite), and matches and training using percentage change and effect size with 90% confidence intervals. In the elite group, midfielders recorded higher PL than nomadics and deeps did (8.8%, 0.59 ± 0.24; 34.2%, 1.83 ± 0.39 respectively), and ruckmen were higher than deeps (37.2%, 1.27 ± 0.51). Elite midfielders, nomadics, and ruckmen recorded higher PLSLOW than deeps (13.5%, 0.65 ± 0.37; 11.7%, 0.55 ± 0.36; and 19.5%, 0.83 ± 0.50, respectively). Subelite midfielders were higher than nomadics, deeps, and ruckmen (14.0%, 1.08 ± 0.30; 31.7%, 2.61 ± 0.42; and 19.9%, 0.81 ± 0.55, respectively), and nomadics and ruckmen were higher than deeps for PL (20.6%, 1.45 ± 0.38; and 17.4%, 0.57 ± 0.55, respectively). Elite midfielders, nomadics, and ruckmen recorded higher PL (7.8%, 0.59 ± 0.29; 12.9%, 0.89 ± 0.25; and 18.0%, 0.67 ± 0.59, respectively) and PLSLOW (9.4%, 0.52 ± 0.30; 11.3%, 0.68 ± 0.25; and 14.1%, 0.84 ± 0.61, respectively) than subelite players. Small-sided games recorded the highest PL and PLSLOW and were the only training drill to equal or exceed the load from matches across positions and playing levels. PL differed between positions, with midfielders the highest, and between playing levels, with elite higher. Differences between matches and training were also evident, with PL from small-sided games equivalent to or higher than matches.
Article
In this study, we assessed the validity and reliability of 5 and 10 Hz global positioning systems (GPS) for measuring instantaneous velocity during acceleration, deceleration, and constant velocity while straight-line running. Three participants performed 80 running trials while wearing two GPS units each (5 Hz, V2.0 and 10 Hz, V4.0; MinimaxX, Catapult Innovations, Scoresby, VIC, Australia). The criterion measure used to assess GPS validity was instantaneous velocity recorded using a tripod-mounted laser. Validity was established using the standard error of the estimate (± 90% confidence limits). Reliability was determined using typical error (± 90% confidence limits, expressed as coefficient of variation) and Pearson's correlation. The 10 Hz GPS devices were two to three times more accurate than the 5 Hz devices when compared with a criterion value for instantaneous velocity during tasks completed at a range of velocities (coefficient of variation 3.1-11.3%). Similarly, the 10 Hz GPS units were up to six-fold more reliable for measuring instantaneous velocity than the 5 Hz units (coefficient of variation 1.9-6.0%). Newer GPS may provide an acceptable tool for the measurement of constant velocity, acceleration, and deceleration during straight-line running and have sufficient sensitivity for detecting changes in performance in team sport. However, researchers must account for the inherent match-to-match variation reported when using these devices.
Article
To examine the difference in distance measured by two global positioning system (GPS) units of the same model worn by the same player while performing movements common to team sports. Twenty elite Australian football players completed two trials of the straight line movement (10, 20, 40 m) at four speeds (walk, jog, stride, sprint), two trials of the changes of direction (COD) courses of two different frequencies (gradual and tight), and five trials of a team sport running simulation circuit. To assess inter-unit variability for total and high intensity running (HIR) distance measured in matches, data from eight field players were collected in three Australian Hockey League (AHL) matches during the 2009 season. Each subject wore two GPS devices (MinimaxX v2.5, Catapult, Australia) that collected position data at 5 Hz for each movement and match trial. The percentage difference ±90% confidence interval (CI) was used to determine differences between units. Differences (±90% CI) between the units ranged from 9.9 ± 4.7% to 11.9 ± 19.5% for straight line running movements and from 9.5 ± 7.2% to 10.7 ± 7.9% in the COD courses. Similar results were exhibited in the team sport circuit (11.1 ± 4.2%). Total distance (10.3 ± 6.2%) and HIR distance (10.3 ± 15.6) measured during the match play displayed similar variability. It is recommended that players wear the same GPS unit for each exercise session to reduce measurement error. The level of between-unit measurement error should be considered when comparing results from players wearing different GPS units.
Article
Ice hockey performance was studied during 10 contests to assess time-motion characteristics and associated physiological changes that occur for each position. Depending on the position played, the actual playing time per game for forwards and defensemen ranged between 20.7 and 28.0 min. The number of shifts ranged from 14 to 21 with an average playing time of 85.4 s/shift. Further analysis of each shift indicated that there was an average of 2.3 play stoppages which averaged 27.1 s, producing a continuous playing time of 39.7 s. Telemetered recordings of heart rate during each shift revealed sustained rates of between 170 and 174 beats/min over the three periods of the game. For both the forwards and defensemen, values for blood lactates were highest during the first and second periods (x = 78.0 and 66.1 mg/100 ml), then declined considerably during the third period (x = 44.5 mg/100 ml). Blood glucose showed a similar tendency (x = 139.3, 133.7, 114.1 mg/100 ml), while hematocrit and total protein showed little tendency to change.
Article
Ice hockey is characterised by high intensity intermittent skating, rapid changes in velocity and duration, and frequent body contact. The typical player performs for 15 to 20 minutes of a 60-minute game. Each shift lasts from 30 to 80 seconds with 4 to 5 minutes of recovery between shifts. The intensity and duration of a particular shift determines the extent of the contribution from aerobic and anaerobic energy systems. The high intensity bursts require the hockey player to develop muscle strength, power, and anaerobic endurance. The length of the game and the need to recover quickly from each shift demands a good aerobic system. Physical characteristics of elite players show that defensemen are taller and heavier than forwards probably due to positional demands. Hockey players are mesomorphic in structure. They are relatively lean since excess mass is detrimental to their skating performance. There is a large interindividual variability in V̇O2 during skating. Both the aerobic and anaerobic energy systems are important during a hockey game. Peak heart rates during a shift on the ice exceed 90% of HRmax with average on-ice values of about 85% of HRmax. Blood lactate is elevated above resting values confirming the anaerobic nature of the game. Glycogen depletion studies show a preferential utilisation of glycogen from the slow twitch fibres but also significant depletion from the fast twitch fibres. Elite hockey players display a muscle fibre composition similar to untrained individuals. Physiological profiles of elite hockey teams reveal the importance of aerobic endurance, anaerobic power and endurance, muscular strength and skating speed. Training studies have attempted to improve specific components of hockey fitness. Using traditional laboratory tests, a season of hockey play shows gains in anaerobic endurance but no change in aerobic endurance. On-ice tests of hockey fitness have been recommended as an essential part of the hockey player’s physiological profile. Existing training procedures may develop chronic muscular fatigue in hockey players. Lactic acidosis is associated with the onset and persistence of muscle fatigue. Muscle force output remains impaired throughout the hockey player’s typical cycle of practices and games. A supplementary programme of low-intensity cycling during the competitive phase of training was unsuccessful in altering V̇O2max Strength decrements during the hockey season are attributed to a lack of a specifically designed strength maintenance programmes. On-ice and off-ice training programmes should focus on the elevation of aerobic endurance, anaerobic power and endurance, muscular strength and skating speed.
Characterization of the Weekly External Load Profile of Professional Soccer Teams from Portugal and the Netherlands
  • F M Clemente
  • A Owen
  • J Serra-Olivares
  • P Theodoros
  • Cmi Nikolaidis
  • B Mendes
Clemente FM, Owen A, Serra-Olivares J, Theodoros P, Nikolaidis CMI, Mendes B. Characterization of the Weekly External Load Profile of Professional Soccer Teams from Portugal and the Netherlands. 2018. Available at: https://doi.org/10.2478/hukin-2018-0054. Accessed May 10, 2018.
The t-test for means
  • J Cohen
Cohen J. The t-test for means. In: Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: L. Erlbaum Associates, 1988. pp. 20-27.
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Montgomery DL. Physiology of ice hockey. Sports Medicine 5: 99-126, 1988.