Impact of tapering and proactive recovery on young elite rugby union players’ repeated high intensity effort ability

To assess the effects of a taper combined with proactive recovery on the repeated high intensity effort (RHIE) of elite rugby union players, and the possible interaction of pre-taper fatigue and sleep. Eighteen players performed a 3-week intensive training block followed by a 7-day exponential taper combined with a multicomponent recovery strategy. Following the intervention, players were divided into 3 groups (Normal Training: NT, Acute Fatigue: AF or Functional Overreaching: F-OR) based on their readiness to perform prior to the taper. Total sprint time [TST], percentage decrement [%D] and the number of sprints ≥90% of the best [N90] were analyzed to assess performance during a RHIE test. Subjective sleep quality was assessed through the Pittsburg Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS). No improvement in TST was reported in either NT or F-OR after the taper, whereas AF tended to improve (-1.58 ± 1.95%; p > 0.05; g = -0.20). F-OR players reported baseline PSQI and ESS indicative of sleep disturbance (6.2 ± 2.2 and 10.6 ± 5.4, respectively). AF displayed a small impairment in PSQI during intensive training (11.5 ± 80.6%; p > 0.05; g = 0.20), which was reversed following the taper (-34.6 ± 62.1%; p > 0.05; g = -0.73). Pre-taper fatigue precluded the expected performance benefits of the combined taper and recovery intervention, likely associated with a lack of strictly controlled intensive training block. Poor sleep quality before the intensive training period appeared to predispose the players to developing functional overreaching.     

A novel application of entropy analysis for assessing changes in movement variability during cumulative tackles in young elite rugby league players

The aim of this study was to identify between-position (forwards vs. backs) differences in movement variability in cumulative tackle events training during both attacking and defensive roles. Eleven elite adolescent male rugby league players volunteered to participate in this study (mean ± SD, age; 18.5 ± 0.5 years, height; 179.5 ± 5.0 cm, body mass; 88.3 ± 13.0 kg). Participants performed a drill encompassing four blocks of six tackling (i.e. tackling an opponent) and six tackled (i.e. being tackled by an opponent while carrying a ball) events (i.e. 48 total tackles) while wearing a micro-technological inertial measurement unit (WIMU, Realtrack Systems, Spain). The acceleration data were used to calculate sample entropy (SampEn) to analyse the movement variability during tackles performance. In tackling actions SampEn showed significant between-position differences in block 1 (p = 0.0001) and block 2 (p = 0.0003). Significant between-block differences were observed in backs (block 1 vs 3, p = 0,0021; and block 1 vs 4, p = 0,0001) but not in forwards. When being tackled, SampEn showed significant between-position differences in block 1 (p = 0.0007) and block 3 (p = 0.0118). Significant between-block differences were only observed for backs in block 1 vs 4 (p = 0,0025). Movement variability shows a progressive reduction with cumulative tackle events, especially in backs and when in the defensive role (tackling). Forwards present lower movement variability values in all blocks, particularly in the first block, both in the attacking and defensive role. Entropy measures can be used by practitioners as an alternative tool to analyse the temporal structure of variability of tackle actions and quantify the load of these actions according to playing position.     

Predicting ratings of perceived exertion in youth soccer using decision tree models

The purpose of this study was to determine the effectiveness of white-box decision tree models (DTM) for predicting the rating of perceived exertion (RPE). The second aim was to examine the relationship between RPE and external measures of intensity in youth soccer training at the group and individual level. Training load data from 18 youth soccer players were collected during an in-season competition period. A total of 804 training observations were undertaken, with a total of 43 ± 17 sessions per player (range 12–76). External measures of intensity were determined using a 10 Hz GPS and included total distance (TD, m/min), high-speed running distance (HSR, m/min), PlayerLoad (PL, n/min), impacts (n/min), distance in acceleration/deceleration (TD ACC/TD DEC, m/min) and the number of accelerations/decelerations (ACC/DEC, n/min). Data were analysed with decision tree models. Global and individualized models were constructed. Aggregated importance revealed HSR as the strongest predictor of RPE with relative importance of 0.61. HSR was the most important factor in predicting RPE for half of the players. The prediction error (root mean square error [RMSE] 0.755 ± 0.014) for the individualized models was lower compared to the population model (RMSE 1.621 ± 0.001). The findings demonstrate that individual models should be used for the assessment of players’ response to external load. Furthermore, the study demonstrates that DTM provide straightforward interpretation, with the possibility of visualization. This method can be used to prescribe daily training loads on the basis of predicted, desired player responses (exertion).     

Training,psychometric status,biological markers and neuromuscular fatigue in soccer

The study examined the relationship between psychometric status, neuromuscular, and biochemical markers of fatigue in response to an intensified training (IT) period in soccer. Fifteen professional soccer players volunteered to participate in the study (mean ± SD: age: 25 ± 1 years; body height: 179 ± 7 cm, body mass: 73.7 ± 16.2 kg, experience: 13.2 ± 3 years). Training load, monotony, strain, Hooper index and total quality recovery (TQR) were determined for each training session during a 2-week of IT. Counter-movement jump (CMJ) and biochemical responses [testosterone, cortisol, testosterone-to-cortisol ratio (T/C ratio), creatine kinase, and C-reactive protein] were collected before and after IT. Results showed that IT induced significant increases in cortisol, creatine kinase and C-reactive protein and significant decreases in T/C ratio and CMJ performance from before to after IT (p < 0.01, p < 0.001, p < 0.001, p < 0.01, p < 0.05, respectively). However, testosterone did not differ from before to after IT (p > 0.05). Training loads were positively correlated with Hooper index (p < 0.05) and negatively correlated with total quality recovery (p < 0.05). Hooper index was positively correlated with cortisol (p < 0.05), T/C ratio (p < 0.01), and creatine kinase (p < 0.01), and negatively correlated with CMJ (p < 0.05). Furthermore, TQR was negatively correlated with T/C ratio (p < 0.01), creatine kinase (p < 0.001), and C-reactive protein (p < 0.05), and positively correlated with CMJ (p < 0.01). Neuromuscular fatigue, muscle damage, and change in the anabolic/catabolic state induced by the IT were related to well-being and perceived recovery state among professional soccer players.     

Training load,recovery and game performance in semiprofessional male basketball: influence of individual characteristics and contextual factors

This study examined the effects of individual characteristics and contextual factors on training load, pre-game recovery and game performance in adult male semi-professional basketball. Fourteen players were monitored, across a whole competitive season, with the session-RPE method to calculate weekly training load, and the Total Quality Recovery Scale to obtain pre-game recovery scores. Additionally, game-related statistics were gathered during official games to calculate the Performance Index Rating (PIR). Individual characteristics and contextual factors were grouped using k-means cluster analyses. Separate mixed linear models for repeated measures were performed to evaluate the single and combined (interaction) effects of individual characteristics (playing experience; playing position; playing time) and contextual factors (season phase; recovery cycle; previous game outcome; previous and upcoming opponent level) on weekly training load, pre-game recovery and PIR. Weekly load was higher in guards and medium minute-per-game (MPG) players, and lower for medium-experienced players, before facing high-level opponents, during later season phases and short recovery cycles (all p < 0.05). Pre-game recovery was lower in centers and high-experience players (p < 0.05). Game performance was better in high-MPG players (p < 0.05) and when facing low and medium-level opponents (p < 0.001). Interestingly, players performed better in games when the previous week’s training load was low (p = 0.042). This study suggests that several individual characteristics and contextual factors need to be considered when monitoring training load (playing experience, playing position, playing time, recovery cycle, upcoming opponent level), recovery (playing experience, playing position) and game performance (opponent level, weekly training load, pre-game recovery) in basketball players during the competitive season.