Running performance is correlated with creatine kinase levels & muscle soreness during an Olympic Games in Hockey

Original Investigation

Gerard E. McMahon1, Lee-Ann Sharp1 and Rodney A. Kennedy1

1 Sport & Exercise Science Research Institute, Ulster University, Newtownabbey, Belfast, United Kingdom

Corresponding Author:Dr Gerard McMahonSport & Exercise Science Research InstituteSchool of SportUlster UniversityNewtownabbeyBelfastN. IrelandBT37 0QB

Email: g.mcmahon@ulster.ac.uk

Phone: +44 2890 665811

Preferred Running Head: Running demands maintained during an Olympics

Abstract Word Count: 227

Text Word Count: 3502

Figures & Tables: 4 figures, 2 tables

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Abstract:Purpose: The purpose of this study was to compare the GPS and accelerometry-derived running demands, creatine kinase (CK) and self-reported wellness during an Olympic Games in international hockey. Methods: Data was collected across 5 games during the 2016 Rio Olympic Games. GPS units (10Hz) were used to assess the running demands, accelerations & decelerations of outfield players in a men’s hockey squad with matches 2-5 compared to match 1. CK was used as a marker of muscle damage and self-reported psychometric questionnaires were used to assess wellness, with each of the 5 matches compared to pre-competition assessments. Results: There were significant increases (p<0.05) in either, or both, absolute and relative total distance, player load, high speed running distance, sprint distance, accelerations & decelerations, compared to baseline. There was a significant decrease (p<0.05) in maximal velocity by match 5. CK significantly increased from match 1-5, and displayed significant correlations with total distance (TD, r=0.55) and player load (PL, r=0.41). Muscle soreness (MS) correlated with TD & PL, with other wellness markers unchanged compared to baseline. Conclusions: International hockey athletes may maintain or increase running activities over the course of an Olympic tournament, however this may be impacted by situational (match score/ outcome) and environmental (ambient temperature) factors. Despite CK and MS displaying relationships with running variables, further work is needed to establish their individual value in monitoring international hockey athletes.

Key Words: monitoring, muscle damage, performance, recovery, tournament

Introduction:To prepare for repeated field sport performances within a relatively short timeframe, monitoring of competition related physical variables in conjunction with markers and perceptions of recovery are of interest to sport scientists. The analysis and quantification of changes in such variables may be of particular relevance during periods of high fixture congestion, such as international tournaments. Previous research in elite men’s 1-3 and women's4 international hockey has demonstrated that over the time-course of competitive international tournaments, there may be no, little or substantial changes in the game-to-game output of players in physical activity variables. However limitations in time-motion methods2, GPS unit sampling frequency1,3, and data collection pre-2015 international hockey rule changes (moved from 2 x 35-min halves to 4 x 15-min quarters)1,2,5 provides an opportunity for more improved data acquisition. We have recently demonstrated that the running demands of an Olympic Games is significantly greater than those placed upon athletes competing in an Olympic qualifying tournament or competitive season tournament6. Currently no data describing the within-tournament game changes in running demands, and its relationship to markers of recovery or wellness at an Olympic Games exists. In addition, sports science practitioners also seek information on the biochemical and perceptual responses to training and competition demands. The most commonly used biomarker to indirectly assess potential muscle damage in team sports is creatine kinase (CK)7-9. Recent systematic reviews and meta analyses in both soccer9 and contact codes of football8 have shown that CK levels are significantly elevated anywhere from immediately up to 72 hours post-match in athletes. Russell et al.10 investigated the relationships between GPS derived running performance variables and pre-post changes in CK and CMJ peak power (CMJ PP) over 5 consecutive games in professional footballers. The authors found that the pre-post change in both CK and CMJ PP at 24 hours post-match was correlated with high intensity distance, high intensity distance per minute, high speed running distance and

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number of sprints completed during the match. Furthermore, in Australian Rules Football (AFL), Hunkin et al.7 assessed pre match (24-36 hours) CK levels over the course of a season. Results showed that pre-match CK levels were negatively associated with 6-minute running performance and the coach’s subjective performance ratings. Chesher et al.11 showed that in international hockey, an average of 102±20 decelerations per player (≤ -3 m/s-2), per game took place in the 2016 Olympic Games, with decelerations as high as 13.6 m/s-2 suggesting large lower limb eccentric forces are required during match-play. These sport specific decelerations arising from running are likely to result in exercise-induced muscle damage and significant increases in CK levels12. Based off these lines of evidence, CK may be a particularly important monitoring variable during an international hockey tournament scenario, with multiple games played in just a few days. This may permit coaches and/ or sport science support staff to make informed decisions around modification of training loads or in-competition load management to optimize performance or adaptation. However, it must also be acknowledged that this requires more longitudinal, robust, individual data sets including baseline data to have an applied impact. CK levels have, to the authors’ knowledge, not been reported in international hockey to date. Another important aspect in providing a holistic view of monitoring training, performance and recovery are the now common use of athlete self-report measures (such as completion of psychometric questionnaires)13, which have shown to be sensitive and also significantly correlated with changes in training load in AFL and English premiership players14. In hockey, pre-match perceptions of wellness were highly or almost perfectly associated with subsequent match running variables normalized to RPE and time components3. Therefore, the aims of the current study are 1) to describe the running demands of male international hockey during an Olympic Games, 2) outline changes in CK levels and perceptions of wellness during an Olympic Games, and 3) probe the potential relationships between changes in running, biomarker and wellness variables.

Methods:

Participants

Elite international male hockey players (n=15, Age 26±3 years, mass 80.4±3.2 kg, 102±24 International caps, 30:15 Intermittent test score range 21.0 – 24.0) participated in the study. Each participant gave written and informed consent, with ethics approved by the ethics committee at Ulster University and follows the principles of the Declaration of Helsinki. Data collected was part of the routine squad performance profiling and monitoring.

Design: The current study was an observational study design.

Methodology

Match Data

Match activity profiles were analysed from 5 matches of the 2016 Rio Olympic Games of all 15 outfield players. Profiles of all outfield players were analysed in every match. When a player had played less than 30% of the total match time, the data was not included in the analysis as to not add undue variation in more complete activity profiles6. Due to an injury suffered by one player, 71 out of a possible 75 individual profiles were analysed due to playing time of <30%. Opposition teams were ranked 2nd, 3rd, 5th, 7th and 15th in the world at the time of play. A schematic overview of the tournament is shown in Figure 1.

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(INSERT FIGURE 1)

GPS equipment & Analyses

The process of GPS data analysis used in the current study has been reported previously 6 using Catapult Sports OptimEye S5 10Hz GPS system (Catapult Innovations, Melbourne, Australia), which was used for all match data collection. GPS equipment use and data analysis used in the current study has been reported previously 6 using Catapult Sprint version 5.1.7 software. The mean Horizontal Dilution of Precision (HDOP) for the games analysed were 0.71±0.02 and mean satellite number for the games analysed were 15±1. Therefore, GPS quality was deemed as excellent according to manufacturer’s guidelines. Velocity bands were standardised and are as used previously in male international hockey6,15. These bands were further collapsed into low speed running (LSR) defined as player activity at velocities between ≤ 14.99kph, moderately high speed running (HSR) with velocities 15-22.99kph, sprint velocity was defined as speeds ≥ 23kph, and total high speed running defined as all velocities >15kph. Number of high speed efforts were efforts >15kph and sprint efforts were all efforts >23 kph. All relative data was calculated by dividing the individual player’s pitch playing time by the absolute distances or efforts performed.

Player Load, High Intensity Accelerations & Decelerations

Player load (PL) was calculated within the aforementioned software using the formula outlined previously16. The accelerometer contained within the GPS unit sampled at 100Hz. Accelerations and decelerations were extracted also using Catapult Sprint version 5.1.7 software using IMA version 2. Very high intensity accelerations and decelerations were defined as any change in velocity movement > +/- 3.5m.s.-2 17.

Environmental Conditions

Environmental ambient temperatures were recorded live at each game using a handheld environmental meter (Kestrel 5200, Nielson-Kellerman, USA) by the same observer as GPS. Temperature readings were taken at the beginning, half-time and end of each match, with the average of the 3 readings used as the match temperature. Mean ambient match temperature was 27±4 ºC (range 24 – 33oC).

Creatine Kinase (CK)

CK levels were assessed 48hours prior to the first match of the 2016 Olympic Games, and in the morning within 12-19 hours after each competitive match, as this has been shown to be the time period associated with peak blood creatine kinase levels following team sport competition8,9 (M1 +19hrs, M2 +12hrs, M3 & 4 +15hrs and M5 +12hrs). CK levels were 464±349 IU 48 hours prior to match 1 and were likely reflective of the final training sessions performed before the competition began and therefore not a realistic measure of baseline CK. In order to provide a more ‘true’ baseline for CK comparison, the mean of three CK samples taken following a complete 48-hour recovery period after similar training sessions in the 6 weeks prior to the Olympic Games were used as a surrogate baseline comparison. Fingertip

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blood was collected in a 32μL heparinised Reflotron capillary tube (Selzer Labortechnik, Germany) using standard sampling techniques. The blood sample was then applied to the CK test strip (Roche, Germany) and immediately analysed for CK via a Reflotron Plus system (Roche, Germany). The reliability of this system has been demonstrated previously18. The between sample reliability of CK in this cohort of athletes had a Coefficient of Variation (CV) of 4.2%.

Psychometric Self-reported wellness Data

Psychometric data was collected at ~9am each morning and was part of the athletes’ normal, routine monitoring process. The athletes had been reporting wellness using the current system for ~8 months at this time. Players completed a psychometric questionnaire ranking 1) muscle soreness, 2) sleep quality, and 3) stress on a continuous likert-scale of 1-10 with 1 being ‘the worst’ and 10 being ‘the best’. As such this data was treated as interval data19. Muscle soreness and stress scores were converted to this scale to match sleep quality, as their original 10-point scales were inverted - 1 corresponding to ‘the best’ and 10 ‘the worst’. Therefore, a decrease in in value towards 1 in any of the variables indicates a more negative outcome. Psychometric data was not provided by athletes post match 5, therefore data pertains to baseline and matches 1-4.

Statistical Analyses

Repeated measures ANOVA were carried out to assess between match 1 and other match differences in running metrics, and between baseline and match differences in CK and wellness variables. Data was assessed for normality using a Shapiro-Wilk Test and Sphericity assessed via Mauchly’s Test of Sphericity. If data was not normally distributed, the Friedman’s test was carried out. Statistical significance was set a priori at p < 0.05. To reduce type I error rates, Fisher’s least squared differences (LSD) contrasts were involved in each comparison to match 1 (or baseline) for parametric data and Wilcoxin-Signed Rank Tests used for non-parametric data. The effect size for the main effects and interactions was estimated by calculating partial eta squared values (ηp

2), with 0.01, 0.06 and 0.14 used to denote small, moderate and large effects sizes, respectively for parametric data. The effect size (ES) for post-hoc pairwise comparisons were estimated by calculating Cohen’s D (parametric) with 0.2, 0.5 and 0.8 used to denote small, moderate and large effects sizes, respectively. Pearson’s r correlations were used to assess the relationships between running metrics and CK levels, and wellness variables with bivariate distributions all normal (Q-Q plots). All statistical calculations were performed using SPSS statistical package (Version 25, IBM, USA).

Results

Following tests of normality, running metrics and CK levels were found to be normally distributed (p>0.05), however wellness data were not normally distributed (p=0.021).

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Match Activity Data

Running, acceleration/ deceleration and Player Load match variables are displayed in Figure 2, Table 1 and Table 2. There were significant main effects of time for TD (p=0.02, ηp

2 = 0.04), HSR (p=0.02, ηp

2 = 0.65), sprint distance (p=0.03, ηp2 = 0.18) Player Load (PL, p=0.05,

ηp2 = 0.16), and no. of high-speed efforts (p=0.04, ηp

2 = 0.17). Compared to match 1, there were significant increases in absolute total distance (TD, p=0.037, Match 2), total high speed running (p=0.002, 0.001,0.018, matches 2, 4 and 5 respectively) and sprint distance (p=0.021, Match 2) as shown in Figure 2. Additionally, there were significant increases in PL (p=0.008, Match 2), LSR (p=0.002, 0.049, Match 2 & 4), HSR (p=0.002, 0.008, 0.003, Matches 2, 4 & 5 respectively) and no. of high-speed efforts (p=0.005, 0.032, Match 2 & 5) compared to match 1 (Table 1). Maximum velocity was the only variable to show a significant decrease (p=0.008) in match 5 compared to match 1. Regarding relative measures, there were significant main effects of time for PL (p=0.001, ηp

2 = 0.48), HSR (p=0.001, ηp2 = 0.83),

distance (p=0.001, ηp2 = 0.50) LSR (p=0.001, ηp

2 = 0.89), no. of high-speed efforts (p=0.02, ηp

2 = 0.64) and a strong trend for sprint distance (p=0.06, ηp2 = 0.52). There were significant

increases in PL (all matches, all p=0.001), distance (all matches, all p=0.001), LSR (all matches, all p=0.001), HSR (all matches, all p=0.001) and no. of high speed efforts (p=0.001,p=0.007, p=0.002, p=0.001, Matches 2-5 respectively) compared to match 1.

(INSERT FIGURE 2)

(INSERT TABLE 1)

(INSERT TABLE 2)

Creatine Kinase

There was a significant main effect of time (p=0.004, ηp2 = 0.81) on CK (Figure 3) with

increases in CK post all matches (p=0.007, 0.001,0.002, 0.001, 0,001 respectively) compared to baseline (d = 1.40, 0.84, 0.64, 0.21, 0.44 respectively). There were significant relationships between the following variables; change (Δ) in CK and absolute player load (p=0.021, r = 0.607), absolute CK and total distance (p=0.041, r = 0.551) and PL (p=0.042, r = 0.549). There was a trend for CK to be correlated with no. of high intensity efforts (p=0.061, r = 0.51) and no. of high intensity decelerations (p=0.058, r = 0.62).

(INSERT FIGURE 3)

Self-reported Wellness

Self-reported wellness variables are shown in Figure 4. There were no significant differences between baseline sleep quality and stress and subsequent ratings throughout the tournament. Muscle soreness (MS) scores (main effect p=0.001) were significantly lower following

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matches all matches (p=0.004, 0.004, 0.006, 0.004 respectively) compared to baseline. MS perceptions were significantly correlated with TD (p=0.031, r = 0.649) and PL (p=0.038, r = 0.630). MS also tended to moderately correlate with total high intensity distance (p=0.061) and CK (p=0.058).

(INSERT FIGURE 4)

Discussion

The current study is the first study to assess the running demands, CK and wellness responses of a hockey team during an Olympic Games. Our results demonstrate that running demands were maintained or increased across multiple variables as the tournament progressed. Furthermore, CK was elevated at all time points compared to baseline throughout the tournament, whilst MS increased as the tournament progressed. There were significant moderate relationships between running variables, CK and MS.

Changes in running demands 1-3 and deceleration characteristics11 have been reported in men’s international hockey previously. With regards to running demands, most recently Ihsan and colleagues3 compared 6 matches in 9 days during World League 2 in 2015, where matches were compared with the preceding match only. Results showed that game-to-game variations in running were accompanied by small or negligible effect sizes. Jennings et al. 1 also compared 6 matches in 9 days during the 2009 Champions Trophy using match 1 as a baseline. Consistent with Ihsan at al.3 and the current study, the authors found that the athletes were able to maintain physical outputs over the course of the tournament. The only running-related variable that displayed a significant reduction in the current study was maximal velocity in match 5. It’s reasonable to surmise why this particular variable may be the most sensitive to detect fatigue. Central and peripheral neuromuscular factors affect repeat sprint performance21, with recovery of neuromuscular function being impaired following match play, and the magnitude of impairment is linked to the physical demands of the preceding match22. Impairment in neuromuscular function following tournament match play in Rugby Sevens has been shown to persist for up to 5 days23. Therefore, it is likely that the frequency of games encountered at the Olympic Games does not permit total neuromuscular recovery, with accumulation of neuromuscular fatigue resulting in an inability to maintain previous levels of maximal velocity.

Currently, we report for the first time during an Olympic Games, that many of the matches compared to match 1 had significantly higher running demands (such as distance, high speed running and sprint distances/ efforts). There are many study design and contextual factors that may account for some of these differences. Firstly, the current GPS related activity was sampled at 10Hz, whereas the two aforementioned studies1,3 used 5Hz devices. The validity and reliability of 10Hz devices have been shown to be greater than 5Hz devices when assessing team-sport related movement patterns using GPS technology. The study of Jennings et al. 1 was carried out in 2009, which was before major rule changes introduced in 2015, that we have previously demonstrated affects the running demands in elite international hockey5. The current data set was collected during the 2016 Rio Olympic Games, with the study of Jennings et al.1 and Ihsan et al.3 performed during Champions Trophy and World League 2

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tournaments respectively. Therefore, the data of the current study introduces a novel, situational aspect to a within-tournament comparison. Importantly, it is also unclear to how the changes in running demands actually influenced hockey performance without additional tactical or technical data, with opposition physical performance also likely a co-factor in driving running performance24. As performance as a whole is the ultimate goal for the scientist and coaching team, future research may want to integrate other forms and sources of performance data to gain a better insight into improving performances in a tournament.

Important contextual factors to consider when interpreting the pattern of change in physical data are ambient environmental conditions, and also the progressive situational factor of match outcomes. Matches took place in a range of environmental temperatures and conditions. Match 1 was the hottest at 33oC (afternoon match), with matches 2 and 5 the coolest at 24oC (evening matches). The authors collected no thermoregulatory data during the tournament. Despite arriving 7 days prior to match 1 in Rio, it may take 10-14 days to observe the induction of the majority of key thermoregulatory adaptations 25. Previous research in soccer has shown that physical output in terms of running demands and power are reduced in hot conditions26,27. Therefore, the authors cannot exclude the possibility that because all comparisons reported are done so in relation to match 1, that the baseline match used was lowered by greater thermoregulatory strain, thus permitting perceived improved performances compared to baseline in future matches. However, to partially refute this theory, the authors noted significant differences in running demands between matches with similar ambient temperatures in the pairwise comparisons e.g. greater distance covered in game 4 vs 3 (data not reported) which would suggest as the tournament progressed, physical output can be maintained or even improved upon compared to immediately earlier matches. In addition, the running performance of the opposition, score or match outcome can also alter running demands in field sports24. Having lost match 1, points were needed where possible in the remaining four games to progress beyond the group stage. Therefore, the situational factor of necessity for victory to avoid exiting the competition may have fostered individual motivation for greater physical output in the remaining matches compared to baseline.

Currently, there are no existing reports of CK levels in international hockey. Our results demonstrate that CK levels were higher post-match in all matches compared to baseline. With 5 matches in 7 days this is somewhat unsurprising as it may take up more than 72 hours post-match for CK levels to return to baseline following a single team sport performance8. Running demands of an Olympic Games are higher than other tournaments6, therefore compounding the likelihood CK substantially reducing between matches in this tournament. There were correlations between ΔCK and PL, CK and TD, and CK and PL. It has been suggested that the elevation in the pre-post changes in CK may mirror pre-post match decrements in neuromuscular function. Russell et al.10 investigated the relationships between GPS derived running performance variables and pre-post changes in CK and CMJ peak power (CMJ PP) over 5 consecutive games in professional footballers. The pre-post change in both CK and CMJ PP at 24 hours post-match was correlated with high intensity distance, high intensity distance per minute, high speed running distance and number of sprints completed during the match. The current results suggest there is a relationship between CK levels and physical performance in hockey, which is consistent with a recent systematic review and meta-analysis between CK, neuromuscular function and team sport performance8. Data from the same Olympic Games regarding deceleration characteristics of male hockey

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players11 demonstrate the magnitude of deceleration can be as high as -13.6m/s-1 and a mean number of 102±20 decelerations per player per game. In the current study, there was a strong trend for CK to correlate with the number of high intensity decelerations (p=0.058), which suggests the large and rapid eccentric action performed during decelerations from running may also contribute to the observed CK levels12. It is also worth considering that in the current study, the absolute mean number of very high decelerations were ~50% lower (range 50-55) than those reported in Chesher et al.11 in the same tournament. This could be related to a number of different factors such as between athlete conditioning, tactical style of play etc. However, another factor is also the subtle difference in measurement of decelerations, with decelerations measured via an in-built accelerometer currently, whereas the study of Chesher et al.11 used GPS-derived decelerations.

Self-reported wellness measures in sleep and stress levels showed little change over the course of the tournament. Perceptions of MS did however decrease (the lower value anchored to a more negative outcome on the scale). Despite significant reductions with small to moderate effects on MS, when inspecting the scale from which the mean score moved from baseline to post match 4 (8.1±1.0 AU to 6.8±0.7 AU), this likely means relatively little in practice across the team. The data suggest that the mean rating of muscle soreness was almost 7/10 by post match 4, which would seem quite acceptable to a practitioner in terms of perceptions of recovery having played 5 matches in 7 days. It may be surprising that there was a small reduction in MS considering 5 matches were played in 7 days, therefore consideration should also be given to the potential issue of social desirability. Specifically, the tendency for player participants to present a favourable image of themselves (to staff) on the muscle soreness questionnaire. Social desirability suggests that people to a greater or lesser degree hold back socially undesirable responses and answer according to what is assumed to be socially desirable. Whilst reporting MS prior to and during the Olympic competition, athletes may have adjusted their responses because there are perceived team and professional expectations that they may wish to conform to28. Despite what the authors would describe as a fairly minimal shift in MS over the course of the matches, on an individual level, MS correlated with TD and PL, and almost with high intensity distance and CK. The current monitoring data is reflective of contemporary thought in sport science monitoring literature, suggesting that it may be more prudent to treat an individual athlete based off their own data rather than a blanket approach across the team. The results of the current study are contrary to a recent systematic review29, where currently, the objective measures appear to be more sensitive than subjective measures when monitoring athletes’ responses to competition.

Practical applications:

No reductions in running related variables during an Olympic Games in international hockey were observed compared to Match 1, with only maximal velocity displaying a reduction in the final match compared to the first match. Therefore, this variable may be of use for future longitudinal investigations regarding its potential sensitivity to monitoring fatigue in hockey athletes in a tournament scenario. Despite restricted time for recovery, athletes can maintain or improve running related physical output with tournament progression compared to match 1. GPS derived metrics may be used to optimize recovery or substitution strategies in

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subsequent games in hockey, with further work needed to ascertain the potential use of CK monitoring in competition scenarios. Wellness questionnaires in combination with other tools (GPS, CK) may be useful on an individual player level, but not across teams, for informing recovery or other types of intervention to aid in performance.

Conclusions

The current study has shown for the first time in an Olympic Games that international hockey athletes can maintain or increase physical output such as running activities and accelerations/ decelerations throughout a tournament. CK levels were significantly elevated compared to baseline following all matches, with relationships between CK, total distance and player load identified. Self-reported measures of wellness showed little to no practical changes throughout the duration of the tournament despite changes in physical performance.

Acknowledgements

The authors would like to thank the assistance of the players and coaches in the current study. The results of the current study do not constitute endorsement of the product by the authors or the journal.

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Figure Captions:

Figure 1: Schematic diagram of competition events including day of match, opposition country, world ranking of opposition at the time, match outcome (L = lost, W = win) and environmental mean ambient temperature during the match.

Figure 2: Running demands across five matches in A) sprint distance, B) total high speed running distance, and C) total distance. † Significantly different (p<0.05) compared to match 1.

Figure 3: Creatine kinase levels from baseline across each of the matches. † Significantly different (p<0.05) compared to baseline.

Figure 4: Changes in wellness variables over the course of five matches compared to baseline. † Significantly different (p<0.05) compared to baseline.

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Table 1. Absolute Running Metrics

Match 1

mean ± SD

Match 2

mean ± SD

Match 3

mean ± SD

Match 4

mean ± SD

Match 5

mean ± SD

Mean Playing Time (mins) 52.7 ± 8.8 50.8 ± 9.4 47.9 ± 9.7 49.8 ± 11.6 48.3 ± 9.5*

0.16

Player Load (AU) 537 ± 79 602 ± 82*

0.80565 ± 82 571 ± 95 564 ± 95

Low speed running (m) 2816 ± 538 3238 ± 520*

0.793033 ± 499 3063 ± 567*

0.373052 ± 641

Moderate high speed running (m)

1392 ± 361 1629 ± 383*0.64

1506 ± 283 1605 ± 401*0.26

1590 ± 377*0.04

Max Velocity (kph)

28.3 ± 2.0 28.6 ± 2.1 27.5 ± 2.2 28.7 ± 1.5 27.5 ± 1.5*0.66

No. High Speed Efforts

79 ± 18 90 ± 17*0.64

86 ± 15 86 ± 18 86 ± 19*0.04

No. Sprint Efforts

9 ± 2 11 ± 4 9 ± 4 10 ± 5 9 ± 4

No. Very H.I. Accels 27 ± 11 27 ± 9 29 ± 11 27 ± 10 30 ± 12

No. Very H.I. Decels 50 ± 13 54 ± 15 55 ± 16 50 ± 14 54 ± 19

Data are Mean ± S.D. with Effect Size (d). H.I.; High Intensity, Accels; Accelerations, Decels; Decelerations, No.; number. * Significantly different to Match 1 (p <0.05 – refer to text for exact p value).

526

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Table 2. Relative Running Metrics

Match 1

mean ± SD

Match 2

mean ± SD

Match 3

mean ± SD

Match 4

mean ± SD

Match 5

mean ± SD

Player Load.min (AU) 10.3 ± 1.0 12.0 ± 1.3*

1.4911.9 ± 1.3*

0.0311.7 ± 1.3*

0.2011.8 ± 1.4*

0.10Relative Distance (m.min)

109 ± 8 122 ± 10*0.20

126 ± 10*0.06

121 ± 12*0.04

123 ± 12*0.03

Low speed running (m.min)

54 ± 8 65 ± 10*1.25

64 ± 9*0.04

63 ± 11*0.12

64 ± 9*0.04

Moderate high speed running (m.min)

26 ± 7 32 ± 9*0.75

31 ± 8*0.10

33 ± 9*0.06

33 ± 7*0.05

Sprint Distance (m.min)

3 ± 1 4 ± 2 3 ± 1 3 ± 2 3 ± 1

Total High Speed Running (m.min)

24 ± 6 31 ± 8 28 ± 7 31 ± 8 30 ± 7

No. High Speed Efforts.min

1.52 ± 0.35 1.81 ± 0.39*0.12

1.85 ± 0.39*0.02

1.77 ± 0.37*0.12

1.80 ± 0.32*0.12

No. Sprint Efforts.min

0.18 ± 0.04 0.23 ± 0.10 0.19 ± 0.09 0.21 ± 0.09 0.18 ± 0.08

No. Very H.I. Accels.min 0.5 ± 0.2 0.5 ± 0.2 0.6 ± 0.2 0.5 ± 0.2 0.6 ± 0.2

No. Very H.I. Decels.min

0.95 ± 0.23 1.10 ± 0.32 1.14 ± 0.23 1.03 ± 0.33 1.14 ± 0.43

Data are Mean ± S.D. with Effect Size (d). H.I.; High Intensity, Accels; Accelerations, Decels; Decelerations, No.; number. * Significantly

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different to Match 1 (p <0.05) refer to text for p values.532533