Anita C Sirotic’s research while affiliated with Catapult Sports and other places

This study aimed to identify the predictive capacity of wellness questionnaires on measures of training load using machine learning methods. The distributions of, and dose–response between, wellness and other load measures were also examined, offering insights into response patterns. Data (n= 14,109) were collated from an athlete management systems platform (Catapult Sports, Melbourne, Australia) and were split across three sports (cricket, rugby league and football) with data analysis conducted in R (Version 3.4.3). Wellness (sleep quality, readiness to train, general muscular soreness, fatigue, stress, mood, recovery rating and motivation) as the dependent variable, and sRPE, sRPE-TL and markers of external load (total distance and m.min⁻¹) as independent variables were included for analysis. Classification and regression tree models showed high cross-validated error rates across all sports (i.e., > 0.89) and low model accuracy (i.e., < 5% of variance explained by each model) with similar results demonstrated using random forest models. These results suggest wellness items have limited predictive capacity in relation to internal and external load measures. This result was consistent despite varying statistical approaches (regression, classification and random forest models) and transformation of wellness scores. These findings indicate practitioners should exercise caution when interpreting and applying wellness responses.

This study seeks to evaluate the effect of periodized fluctuations in training load on wellness and psychological questionnaires, perceived exertion, performance, and neuromuscular measures in team-sport athletes. Thirteen amateur Australian rules football athletes completed 6 weeks of periodized training, consisting of 2-week normal training (NT), intensified training (IT), and taper training (TT). Training sessions were quantified using global positioning system devices, heart-rate, and session rating of perceived exertion (sRPE), with wellness (general soreness, sleep quality/quantity, readiness to train, fatigue, stress, mood, and motivation) questionnaires collected daily. Psychological (Recovery-Stress Questionnaire for Athletes) and physical performance (countermovement jump, cycle ergometer peak power, 30-m sprint, and 2-km time trial) markers were measured after each training period. Perceived (sRPE) and mechanical loading were higher for IT than NT, and IT than TT (p < 0.03; d = 0.65–25.34). Cycle ergometer peak power, 30-m sprint, 2-km time trial, and countermovement jump height showed reductions in performance after IT compared to initial testing (p < 0.02; d = 0.51–1.46), with subsequent increases in performance after TT (p < 0.04; d = 0.66–2.27). Average wellness was higher during NT compared to IT (p = 0.005; d = 1.11). Readiness to train did not significantly differ from NT to IT or TT (p < 0.55; d = <0.59); however, readiness to train did improve during TT after the IT (p = 0.01; d = 1.05). The disturbances in performance, perceptual, and mood states may indicate a state of functional overreaching. The findings suggest that an averaged wellness score may be useful in potentially identifying overreaching. However, despite the popularity of wellness in monitoring systems, these measures overall demonstrated a limited capacity to differentiate between periodized fluctuations in load.

Wellness questionnaires are common in monitoring systems, yet the sensitivity to variations in acute training intensity is unclear. This study examined the controlled dosage effects of differing exercise intensities on wellness variables and subsequent associations with neuromuscular performance. Participants (n=10) completed low-, moderate- and high-intensity conditions of a 90min simulated football match shuttle running protocol scaled relative to beep test scores. The protocols were completed in a randomised and counterbalanced fashion matched for time of day. Wellness (sleep quality, readiness to train, soreness, fatigue, stress, mood, motivation) and neuromuscular performance (maximal voluntary contraction, countermovement jump, 6s cycle-ergometer sprint) were assessed pre-, post- and 24h post-exercise. Heart rate (HR) and rating of perceived exertion (RPE) were recorded during, and session RPE (sRPE) after exercise. Generalised linear mixed models demonstrated main effects between conditions with increased HR, RPE and sRPE (P <0.03; d >0.8) responses from the low-high condition. Total and z-score wellness showed no significant differences between trials at any time-point (P >0.05; d =0.03-0.91). Fatigue was lower 24h post-exercise for the low, compared to moderate and high conditions (P =0.006-0.047; d =1.20-1.77). Ratings of fatigue and soreness increased from pre- to 24h post-trial (P <0.003; d =0.96-2.48), while total wellness and readiness to train decreased over time (P <0.04; d =0.91-1.86). Wellness showed limited capacity to differentiate training intensities. Practitioners should be aware while wellness may be highly practical, it may be limited to solely determine athlete accommodation of load considering the strength of association observed with the applied load.

Purpose: This was the first study to examine differences in physical and technical performance profiles using a large sample of match observations drawn from successful and less-successful professional rugby league teams. Methods: Match activity profiles were collected using global positioning satellite (GPS) technology from 29 rugby league players from a successful team during 24 games and 25 players from a less-successful team during 18 games throughout two separate competition seasons. Technical performance data were obtained from a commercial statistics provider. A progressive magnitude based statistical approach was used to compare differences in physical and technical performance variables between the reference teams. Results: There were no clear differences in playing time, nor absolute and relative total distances or LSR distances between successful and less-successful teams. The successful team had possibly to very likely lower higher-speed running demands and likely fewer physical collisions than the less-successful team, although they likely to most likely demonstrated more accelerations and decelerations and likely higher average metabolic power. The successful team very likely gained more territory in attack, very likely had more possession and likely committed fewer errors. In contrast, the less-successful team was likely required to attempt more tackles, most likely missed more tackles and very likely had a lower effective tackle percentage. Conclusions: In the present study, successful match performance was not contingent on higher match running outputs or more physical collisions, rather proficiency in technical performance components better differentiated between successful and less-successful teams.

The aims of this study were to examine the impact of varying between-match microcycles on training characteristics (i.e. intensity, duration and load) in professional Rugby League players and to report on match load related to these between-match microcycles. Training load data was collected during a 26-week competition period of an entire season. Training load was measured using the session rating of perceived exertion (session-RPE) method for every training session and match from 44 professional Rugby League players from the same National Rugby League team. Using the category-ratio 10 RPE scale, the training intensity was divided into three zones (low <4 AU; moderate ≥4 to ≤7 AU and high >7 AU). Three different length between-match recovery microcycles were used for analysis: a) 5-6 days, b) 7-8 days, c) and 9-10 days. A total of 3,848 individual sessions were recorded. During the shorter length between-match microcycles (5-6 days), significantly lower training load was observed. No significant differences for subsequent match load or intensity were identified between the various match recovery periods. Overall, 16% of the training sessions were completed at the low-intensity zone, 61% at the moderate-intensity zone, and 23% at the high-intensity zone. The present findings demonstrate that Rugby League players undertake higher training load as the length between-match microcycles is increased. The majority of in-season training of professional Rugby League players was at moderate-intensity, and a polarized approach to training that has been reported in elite endurance athletes does not occur in professional Rugby League.

Purpose: To describe the metabolic demands of rugby league match play for positional groups and compare match distances obtained from high-speed-running classifications with those derived from high metabolic power. Methods: Global positioning system (GPS) data were collected from 25 players from a team competing in the National Rugby League competition over 39 matches. Players were classified into positional groups (adjustables, outside backs, hit-up forwards, and wide-running forwards). The GPS devices provided instantaneous raw velocity data at 5 Hz, which were exported to a customized spreadsheet. The spreadsheet provided calculations for speed-based distances (eg, total distance; high-speed running, >14.4 km/h; and very-high-speed running, >18.1 km/h) and metabolic-power variables (eg, energy expenditure; average metabolic power; and high-power distance, >20 W/kg). Results: The data show that speed-based distances and metabolic power varied between positional groups, although this was largely related to differences in time spent on field. The distance covered at high running speed was lower than that obtained from high-power thresholds for all positional groups; however, the difference between the 2 methods was greatest for hit-up forwards and adjustables. Conclusions: Positional differences existed for all metabolic parameters, although these are at least partially related to time spent on the field. Higher-speed running may underestimate the demands of match play when compared with high-power distance-although the degree of difference between the measures varied by position. The analysis of metabolic power may complement traditional speed-based classifications and improve our understanding of the demands of rugby league match play.

Abstract This study examined the changes in external outputs, including metabolic power variables, and internal response whilst considering contextual factors on physical performance variables during rugby league match play. Physical performance (total distance, high-speed running and high-power distances, average metabolic power), heart-rate (percentage heart-rate peak and training impulse), collisions (attacking and defensive) and contextual (time in attack, time in defence, time out of play) data were collected from 18 rugby league players during 38 games throughout two National Rugby League seasons. Physical variables were highest in the first 10-min period of each half (P < 0.001). Heart-rate indices peaked in the second 10-min period and were lower during second half periods (P < 0.001). Few differences existed in collisions and contextual factors across 10-min periods. Physical variables were highest during the first 5-min period compared to the final (P < 0.001). There was no difference in heart-rate response, attacking collisions or contextual factors between these periods. Following the peak 5-min period in the match, there were reductions in physical, heart-rate, defensive collisions and contextual factors (P < 0.001). The data show temporal changes in physical performance, heart-rate response and collisions during rugby league match play, although these are affected by contextual factors.

Objectives: To assess between match variability of physical performance measures over both the total and sub sections of the match in professional rugby league competition. Design: Longitudinal observational study. Methods: Global positioning system (GPS) data were collected from 24 players from the same team competing in the National Rugby League (NRL) competition over 23 matches during 2011 season. The GPS data were categorised into total distance, high-speed running (>15kmh(-1)) and very high-speed running (>21kmh(-1)) distance for discrete reference periods (10min, 20min, 40min and 80min). The data was then log transformed to provide the coefficient of variation (CV) and the between subject standard deviation (both expressed as percentages). Results: The data show that the between match variability is greater for high-speed (CV 14.6%) and very-high speed (CV 37.0%) running compared to total distance (CV 3.6%). Within each speed category, the variability of performance tended to increase as the duration of the reference period decreased. Conclusions: The results show that while global measures of physical performance such as total distance are relatively stable, higher-speed activities exhibit a large degree of between match variability. In addition, when segmenting the match into short periods of time for analysis, all physical performance measures increased in variability. These findings have implications for determining sample size, identifying reliable performance measures and selecting appropriate time periods for future applied studies that involve observational match analysis.

Abstract This study examined the influence of match-related fatigue on physical and technical skill performance in ball playing positions at two different levels of rugby league competition. Time-motion analyses were performed using global positioning systems from 6 elite National Rugby League (NRL) and 11 junior elite National Youth Competition (NYC) players from 45 matches. A standardised 5-point technical coding criteria was used to qualitatively assess skill involvements during match-play. The distance travelled in the 0-5 and 40-45 min period were significantly higher compared to the 30-35, 35-40, 70-75 and 75-80 min periods (P < 0.001). Skill rating and involvements were higher in the 0-5 and 40-45 min compared to 70-75 and 75-80 min periods (P < 0.001 and P < 0.001, respectively).There was no significant difference in the number of physical collisions between the 5-min periods (P = 0.051). Following the peak 5-min bout of exercise intensity there were reductions in distance (P < 0.001), quality of skill involvements (P < 0.001), number of involvements (P < 0.001) and collisions (P < 0.001). Elite NRL and NYC "ball players" exhibit reductions in physical performance towards the end of matches and following brief periods of intense exercise. There also appears to be a reduction in technical performance for NRL and NYC ball players, which may be attributable to match-related fatigue.

The purpose of this study was to examine the validity of session rating of perceived exertion (sRPE) for monitoring training intensity in rugby league. Thirty-two professional rugby league players participated in this study. Training-load (TL) data were collected during an entire season and assessed via microtechnology (heart-rate [HR] monitors, global positioning systems [GPS], and accelerometers) and sRPE. Within-individual correlation analysis was used to determine relationships between sRPE and various other measures of training intensity and load. Stepwise multiple regressions were used to determine a predictive equation to estimate sRPE during rugby league training. There were significant within-individual correlations between sRPE and various other internal and external measures of intensity and load. The stepwise multiple-regression analysis also revealed that 62.4% of the adjusted variance in sRPE-TL could be explained by TL measures of distance, impacts, body load, and training impulse (y = 37.21 + 0.93 distance - 0.39 impacts + 0.18 body load + 0.03 training impulse). Furthermore, 35.2% of the adjusted variance in sRPE could be explained by exercise-intensity measures of percentage of peak HR (%HRpeak), impacts/min, m/min, and body load/min (y = -0.01 + 0.37%HRpeak + 0.10 impacts/min + 0.17 m/min + 0.09 body load/min). A combination of internal and external TL factors predicts sRPE in rugby league training better than any individual measures alone. These findings provide new evidence to support the use of sRPE as a global measure of exercise intensity in rugby league training.