Search Results

Purpose: Tackling is a fundamental skill in collision sports such as rugby league. Given the complexity of tackling and multitude of strength and power variables available for analysis, this study aimed to predict tackle outcomes in professional rugby league based on strength and power principal components (PCs). Methods: Twenty-eight rugby league players participated in this study. Maximal strength was assessed via 1 repetition maximum on the back squat, bench press, and bench pull. Lower-body vertical and horizontal power were evaluated using a countermovement jump and standing broad jump. A postmatch analysis of 5 National Rugby League matches was conducted to examine tackling outcomes. PC analysis was performed on the strength and power assessments. The first PCs were retained in each analysis, and a series of Spearman rank-order correlations were conducted between the tackle outcomes and the retained PCs. The PCs significantly related to tackle outcomes were included in the multiple regression analyses to estimate their effect on tackle outcomes. Results: Strength PC was a significant predictor of play-the-ball speed in attack, accounting for 54% of the variance. Countermovement jump PC was a significant predictor of postcontact meters, explaining 19% of the variance. Conclusions: These findings demonstrate that a range of tackle outcomes may be predicted from strength and power components. The coaching staff may choose to develop programs and testing designed to focus on these components, which may further develop players’ tackle outcomes during competition.

Purpose: Periods away from training and competition are necessary for physical and mental restoration in sport. There is limited research investigating changes to physical qualities in rugby league following prolonged breaks. This study aimed to evaluate the effects of the off-season on muscular power in rugby league. In addition, this study aimed to determine whether the type and volume of training players chose to complete had any relationship to feelings of restoration and/or readiness to return to training. Methods: Twenty professional rugby league players participated in this study. Lower-body muscular power was evaluated using a countermovement jump prior to the off-season and at the recommencement of training. Players completed a questionnaire to identify training and activities completed during the break and to assess feelings of restoration and readiness to commence training. Linear regression was used to estimate the effects of the number of days off on muscular power. One-way repeated-measures analysis of variance was conducted to examine differences in lower-body power throughout the study. Spearman rank order correlation was calculated to determine the relationship between off-season activities and feelings of restoration and readiness. Results: Peak velocity and peak force significantly increased following the off-season break. There were significant relationships between mental restoration and upper-body resistance training, as well as physical restoration and full-body resistance training. Conclusions: The off-season has a positive effect on a player’s ability to generate muscular power. Coaches may want to encourage players to complete resistance training sessions with the aim simply to maintain training load and potentially help players to feel rested.

Purpose: This study examined the relationships between training workloads, game workloads, and match performance in an elite netball team. Methods: Ten elite female netball athletes were monitored over a complete season. Training and game external workloads were determined through inertial movement units and expressed as absolute PlayerLoad (PL) and change of direction (COD). Monthly workload and training efficiency index were also calculated, which used internal workloads (session rating of perceived exertion and summated heart-rate zones). Game performance was assessed through a performance analysis statistic algorithm called NetPoints. To account for the influence of team game workloads on each other, the average workload for midcourt positions (avgMC) was calculated for each game. Data for each athlete were transformed into z scores, and linear mixed modeling was used to build models to examine the relationships between workloads and game performance. Results: Monthly PL, training efficiency index PL, and avgMC PL were statistically significant (P < .05) and positively related to game PL (z = 0.20–0.35, P < .001–.02). For game COD, statistically significant positive relationships were found between monthly COD (z = 0.29 [0.11], P = .01) and avgMC COD (z = 0.21 [0.09], P = .03). The models for NetPoints found significant negative relationships with monthly PL (z = 0.46 [0.12], P < .001) and COD (z = −0.36 [0.11], P = .01). Conclusions: Higher monthly workloads are related to higher game workload; however, they are also related to decreases in match performance. Therefore, netball practitioners should consider that increases to training workload in a 4-week period prior to a game can influence game workloads and performance.

Purpose: Mental fatigue is emerging as an important consideration for elite sporting performance, yet it is rarely monitored. The present study assessed changes in mental fatigue in professional team-sport athletes across 2 seasons and examined the relationship between mental fatigue and other athlete self-report measures of well-being. Methods: Elite netballers contracted to all teams competing in Australia’s premier professional netball competition during the 2018 and 2019 seasons (N = 154) participated. Using 5-point Likert scales, mental fatigue, fatigue (physical), tiredness, sleep quality, stress, mood, and motivation were assessed daily across 2 seasons composed of 14 round and finals series. Results: The ratings of mental fatigue significantly changed during both seasons. In 2018, lower ratings of mental fatigue were reported in round 1 versus 3, 4, 6, 8, and 14; round 7 versus 6; and round 6 versus 10 (P < .05). In 2019, lower ratings of mental fatigue were identified for round 1 versus 3, 9, 10 to 14, and semifinal; round 2 versus 10 to 13; and 5 versus 10 to 12 (P < .05). Ordinal regression revealed significant differences between mental fatigue and physical fatigue (P < .001), tiredness (P < .001), stress (P < .001), mood (P < .001), and motivation (P < .05). Conclusions: The present study found mental fatigue to significantly fluctuate across a season in elite netballers. Moreover, perceived mental fatigue differed from physical fatigue, tiredness, stress, mood, and motivation. The data impress the need for mental fatigue to be included as an independent measure of athlete well-being. Monitoring of mental fatigue can allow practitioners to implement strategies to manage its influence on performance.