Sabrina Skorski and Anne Hecksteden
Sabrina Skorski, Stefan Skorski, Oliver Faude, Daniel Hammes, and Tim Meyer
To investigate whether anthropometric profiles and fitness measures vary according to birth-date distribution in the German national youth soccer teams and to analyze whether there is a difference in the chance of becoming a professional soccer player depending on birth quarter (BQ).
First, 554 players were divided into 6 age groups (U16–U21), each subdivided into 4 BQs. Every player performed at least one 30-m sprint, a countermovement jump, and an incremental test to determine individual anaerobic threshold. For players performing more than 1 test within a team, the best 1 was included. Since some players were part of several different teams, a total of 832 data sets from 495 individual soccer players, all born from 1987 to 1995, divided into 6 age categories (U16–U21) were included.
Overall, more players were born in BQ1 than in all other BQs (P < .05). No significant difference between BQs could be observed in any anthropometric or performance characteristics (P > .18). Players born in BQ4 were more likely to become professional than those born in BQ1 (odds ratio 3.04, confidence limits 1.53–6.06).
A relative age effect exists in elite German youth soccer, but it is not explained by an advantage in anthropometric or performance-related parameters. Younger players selected into national teams have a greater chance to become professionals later in their career.
Sabrina Skorski, Naroa Etxebarria, and Kevin G. Thompson
To investigate if swimming performance is better in a relay race than in the corresponding individual race.
The authors analyzed 166 elite male swimmers from 15 nations in the same competition (downloaded from www.swimrankings.net). Of 778 observed races, 144 were Olympic Games performances (2000, 2004, 2012), with the remaining 634 performed in national or international competitions. The races were 100-m (n = 436) and 200-m (n = 342) freestyle events. Relay performance times for the 2nd–4th swimmers were adjusted (+ 0.73 s) to allow for the “flying start.”
Without any adjustment, mean individual relay performances were significantly faster for the first 50 m and overall time in the 100-m events. Furthermore, the first 100 m of the 200-m relay was significantly faster (P > .001). During relays, swimmers competing in 1st position did not show any difference compared with their corresponding individual performance (P > .16). However, swimmers competing in 2nd–4th relay-team positions demonstrated significantly faster times in the 100-m (P < .001) and first half of the 200-m relays than in their individual events (P < .001, ES: 0.28–1.77). However, when finishing times for 2nd–4th relay team positions were adjusted for the flying start no differences were detected between relay and individual race performance for any event or split time (P > .17).
Highly trained swimmers do not swim (or turn) faster in relay events than in their individual races. Relay exchange times account for the difference observed in individual vs relay performance.
Sabrina Skorski, Oliver Faude, Seraina Caviezel, and Tim Meyer
To analyze the reproducibility of pacing in elite swimmers during competitions and to compare heats and finals within 1 event.
Finals and heats of 158 male swimmers (age 22.8 ± 2.9 y) from 29 nations were analyzed in 2 competitions (downloaded from swimrankings.net). Of these, 134 were listed in the world’s top 50 in 2010; the remaining 24 were finalists of the Pan Pacific Games or European Championships. The level of both competitions for the analysis had to be at least national championships (7.7 ± 5.4 wk apart). Standard error of measurement expressed as percentage of the subject’s mean score (CV) with 90% confidence limits (CL) for each 50-m split time and for total times were calculated. In addition, mixed general modeling was used to determine standard deviations between and within swimmers.
CV for total time in finals ranged between 0.8% and 1.3% (CL 0.6–2.2%). Regarding split times, 200-m freestyle showed a consistent pacing over all split times (CV 0.9–1.6%). During butterfly, backstroke, and 400-m freestyle, CVs were low in the first 3 and 7 sections, respectively (CV 0.9–1.7%), with greater variability in the last section (1.9–2.2%). In breaststroke, values were higher in all sections (CV 1.2–2.3%). Within-subject SDs for changes between laps were between 0.9% and 2.6% in all finals. Split-time variability for finals and heats ranged between 0.9% and 2.5% (CL 0.3–4.9%).
Pacing profiles are consistent between different competitions. Variability of pacing seems to be a result of the within-subject variation rather than a result of different competitions
Sabrina Skorski, Iñigo Mujika, Laurent Bosquet, Romain Meeusen, Aaron J. Coutts, and Tim Meyer
Physiological and psychological demands during training and competition generate fatigue and reduce an athlete’s sport-specific performance capacity. The magnitude of this decrement depends on several characteristics of the exercise stimulus (eg, type, duration, and intensity), as well as on individual characteristics (eg, fitness, profile, and fatigue resistance). As such, the time required to fully recover is proportional to the level of fatigue, and the consequences of exercise-induced fatigue are manifold. Whatever the purpose of the ensuing exercise session (ie, training or competition), it is crucial to understand the importance of optimizing the period between exercise bouts in order to speed up the regenerative processes and facilitate recovery or set the next stimulus at the optimal time point. This implies having a fairly precise understanding of the fatigue mechanisms that contribute to the performance decrement. Failing to respect an athlete’s recovery needs may lead to an excessive accumulation of fatigue and potentially “nonfunctional overreaching” or to maladaptive training. Although research in this area recently increased, considerations regarding the specific time frames for different physiological mechanisms in relation to exercise-induced fatigue are still missing. Furthermore, recommendations on the timing and dosing of recovery based on these time frames are limited. Therefore, the aim of this article is to describe time courses of recovery in relation to the exercise type and on different physiological levels. This summary supports coaches, athletes, and scientists in their decision-making process by considering the relationship of exercise type, physiology, and recovery.
Sabrina Skorski, Jan Schimpchen, Mark Pfeiffer, Alexander Ferrauti, Michael Kellmann, and Tim Meyer
Purpose: Despite indications of positive effects of sauna (SAU) interventions, effects on performance recovery are unknown. The aim of the current study was to investigate acute effects of SAU bathing after an intensive training session on recovery of swim performance. Methods: In total, 20 competitive swimmers and triathletes (3 female and 17 male) with a minimum of 2 y of competition experience (national level or higher) participated in the study. Athletes completed an intensive training session followed by either a SAU bathing intervention or a placebo (PLAC) condition in a randomized order. SAU consisted of 3 × 8 min of SAU bathing at 80–85°C, whereas during PLAC, athletes applied a deidentified, pH-balanced massage oil while passively resting in a seated position. Prior to training, swimmers conducted a 4 × 50-m all-out swim test that was repeated on the following morning. Furthermore, subjective ratings of fatigue and recovery were measured. Results: Swimmers performed significantly worse after SAU (4 × 50-m pre–post difference: +1.69 s) than after PLAC (−0.66 s; P = .02), with the most pronounced decrease in the first 50 m (P = .04; +2.7%). Overall performance of 15 athletes deteriorated (+2.6 s). The subjective feeling of stress was significantly higher after SAU than after PLAC (P = .03). Conclusion: Based on published findings, the smallest substantial change in swimming performance is an increase in time of more than 1.2 s; thus, the observed reductions appear relevant for competitive swimmers. According to the current results, coaches and athletes should be careful with postexercise SAU if high-intensity training and/or competitions are scheduled on the following day.
Chris R. Abbiss, Kevin G. Thompson, Marcin Lipski, Tim Meyer, and Sabrina Skorski
The purpose of this study was to compare the pacing profiles between distance- and duration-based trials of short and long duration. Thirteen trained cyclists completed 2 time-based (6 and 30 min) and 2 distance-based (4 and 20 km) self-paced cycling time trials. Participants were instructed to complete each trial with the highest average power output. Ratings of perceived exertion (RPEs) were measured throughout the trials. Average power output was not different between the 4-km and 6-min trials (324 ± 46 vs 325 ± 45 W; P = .96) or between the 20-km and 30-min trials (271 ± 44 vs 267 ± 38 W; P = .24). Power output was greater on commencement of the distance-based trials when short and long trials were analyzed together. Furthermore, the rate of decline in power output over the 1st 40% of the trial was greater in the 20-km trial than in the 30-min trial (P = .01) but not different between the 4-km and the 6-min trials (P = .13). RPE was greater in the 4-km trial than in the 6-min trial but not different between the 20-km and 30-min trials. These findings indicate that athletes commenced distance-based time trials at relatively higher power outputs than a similar time-based trial. Such findings may result from discrete differences in our ability to judge or predict an exercise endpoint when performing time- and distance-based trials.
Hugh H.K. Fullagar, Rob Duffield, Sabrina Skorski, Aaron J. Coutts, Ross Julian, and Tim Meyer
While the effects of sleep loss on performance have previously been reviewed, the effects of disturbed sleep on recovery after exercise are less reported. Specifically, the interaction between sleep and physiological and psychological recovery in team-sport athletes is not well understood. Accordingly, the aim of the current review was to examine the current evidence on the potential role sleep may play in postexercise recovery, with a tailored focus on professional team-sport athletes. Recent studies show that team-sport athletes are at high risk of poor sleep during and after competition. Although limited published data are available, these athletes also appear particularly susceptible to reductions in both sleep quality and sleep duration after night competition and periods of heavy training. However, studies examining the relationship between sleep and recovery in such situations are lacking. Indeed, further observational sleep studies in team-sport athletes are required to confirm these concerns. Naps, sleep extension, and sleep-hygiene practices appear advantageous to performance; however, future proof-of-concept studies are now required to determine the efficacy of these interventions on postexercise recovery. Moreover, more research is required to understand how sleep interacts with numerous recovery responses in team-sport environments. This is pertinent given the regularity with which these teams encounter challenging scenarios during the course of a season. Therefore, this review examines the factors that compromise sleep during a season and after competition and discusses strategies that may help improve sleep in team-sport athletes.
Daniel Hammes, Sabrina Skorski, Sascha Schwindling, Alexander Ferrauti, Mark Pfeiffer, Michael Kellmann, and Tim Meyer
The Lamberts and Lambert Submaximal Cycle Test (LSCT) is a novel test designed to monitor performance and fatigue/recovery in cyclists. Studies have shown the ability to predict performance; however, there is a lack of studies concerning monitoring of fatigue/recovery. In this study, 23 trained male cyclists (age 29 ± 8 y, VO2max 59.4 ± 7.4 mL · min−1 · kg−1) completed a training camp. The LSCT was conducted on days 1, 8, and 11. After day 1, an intensive 6-day training period was performed. Between days 8 and 11, a recovery period was realized. The LSCT consists of 3 stages with fixed heart rates of 6 min at 60% and 80% and 3 min at 90% of maximum heart rate. During the stages, power output and rating of perceived exertion (RPE) were determined. Heart-rate recovery was measured after stage 3. Power output almost certainly (standardized mean difference: 1.0) and RPE very likely (1.7) increased from day 1 to day 8 at stage 2. Power output likely (0.4) and RPE almost certainly (2.6) increased at stage 3. From day 8 to day 11, power output possibly (–0.4) and RPE likely (–1.5) decreased at stage 2 and possibly (–0.1) and almost certainly (–1.9) at stage 3. Heart-rate recovery was likely (0.7) accelerated from day 1 to day 8. Changes from day 8 to day 11 were unclear (–0.1). The LSCT can be used for monitoring fatigue and recovery, since parameters were responsive to a fatiguing training and a following recovery period. However, consideration of multiple LSCT variables is required to interpret the results correctly.
Hugh H.K. Fullagar, Rob Duffield, Sabrina Skorski, David White, Jonathan Bloomfield, Sarah Kölling, and Tim Meyer
The current study examined the sleep, travel, and recovery responses of elite footballers during and after long-haul international air travel, with a further description of these responses over the ensuing competitive tour (including 2 matches).
In an observational design, 15 elite male football players undertook 18 h of predominantly westward international air travel from the United Kingdom to South America (–4-h time-zone shift) for a 10-d tour. Objective sleep parameters, external and internal training loads, subjective player match performance, technical match data, and perceptual jet-lag and recovery measures were collected.
Significant differences were evident between outbound travel and recovery night 1 (night of arrival; P < .001) for sleep duration. Sleep efficiency was also significantly reduced during outbound travel compared with recovery nights 1 (P = .001) and 2 (P = .004). Furthermore, both match nights (5 and 10), showed significantly less sleep than nonmatch nights 2 to 4 and 7 to 9 (all P < .001). No significant differences were evident between baseline and any time point for all perceptual measures of jet-lag and recovery (P > .05), although large effects were evident for jet-lag on d 2 (2 d after arrival).
Sleep duration is truncated during long-haul international travel with a 4-h time-zone delay and after night matches in elite footballers. However, this lost sleep appeared to have a limited effect on perceptual recovery, which may be explained by a westbound flight and a relatively small change in time zones, in addition to the significant increase in sleep duration on the night of arrival after the long-haul flight.