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Purpose: This study investigated (1) the transition rate of elite world-class throwers, (2) the age of peak performance in either elite junior and/or elite senior athletes, and (3) if relative age effect (RAE) influences the chance of being considered elite in junior and/or senior category. Methods: The career performance trajectories of 5108 throwers (49.9% females) were extracted from the World Athletics database. The authors identified throwers who had reached the elite level (operationally defined as the World all-time top 50 ranked for each age category) in either junior and/or senior category and calculated the junior-to-senior transition rate. The age of peak performance and the RAE were also investigated. Results: The transition rate at 16 and 18 years of age was 6% and 12% in males and 16% and 24% in females, respectively. Furthermore, elite senior throwers reached their personal best later in life than elite junior throwers. The athletes of both genders considered elite in the junior category showed a large RAE. Interestingly, male athletes who reached the elite level in senior category also showed appreciable RAE. Conclusions: Only a few of the athletes who reach the top 50 in the world at 16 or 18 years of age manage to become elite senior athletes, underlining that success at the beginning of an athletic career does not predict success in the athlete’s senior career. Moreover, data suggest that being relatively older may confer a benefit across the whole career of male throwers.

© 2017 Human Kinetics, Inc. Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on "Monitoring Athlete Training Loads-The Hows and the Whys" was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and-medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.

Purpose:An extensive battery of physical tests is typically employed to evaluate athletic status and/or development, often resulting in a multitude of output variables. The authors aimed to identify independent physical predictors of elite skeleton start performance to overcome the general problem of practitioners employing multiple tests with little knowledge of their predictive utility.Methods:Multiple 2-d testing sessions were undertaken by 13 high-level skeleton athletes across a 24-wk training season and consisted of flexibility, dry-land push-track, sprint, countermovement-jump, and leg-press tests. To reduce the large number of output variables to independent factors, principal-component analysis (PCA) was conducted. The variable most strongly correlated to each component was entered into a stepwise multiple-regression analysis, and K-fold validation assessed model stability.Results:PCA revealed 3 components underlying the physical variables: sprint ability, lower-limb power, and strength–power characteristics. Three variables that represented these components (unresisted 15-m sprint time, 0-kg jump height, and leg-press force at peak power, respectively) significantly contributed (P < .01) to the prediction (R2 = .86, 1.52% standard error of estimate) of start performance (15-m sled velocity). Finally, the K-fold validation revealed the model to be stable (predicted vs actual R2 = .77; 1.97% standard error of estimate).Conclusions:Only 3 physical-test scores were needed to obtain a valid and stable prediction of skeleton start ability. This method of isolating independent physical variables underlying performance could improve the validity and efficiency of athlete monitoring, potentially benefitting sport scientists, coaches, and athletes alike.

This study evaluated the effect of game venue and starting status on precompetitive psychophysiological measures in elite rugby union. Saliva samples were taken from players (starting XV, n = 15, and nonstarters, n = 9) on a control day and 90 min before 4 games played consecutively at home and away venues against local rivals and league leaders. Precompetition psychological states were assessed using the Competitive State Anxiety Inventory−2. The squad recorded 2 wins (home) and 2 losses (away) over the study period. Calculated effect sizes (ESs) showed higher pregame cortisol- (C) and testosterone- (T) difference values before all games than on a baseline control day (ES 0.7−1.5). Similar findings were observed for cognitive and somatic anxiety. Small between-venues C differences were observed in starting XV players (ES 0.2−0.25). Conversely, lower home T- (ES 0.95) and higher away C- (ES 0.6) difference values were observed in nonstarters. Lower T-difference values were apparent in nonstarters (vs starting XV) before home games, providing evidence of a between-groups effect (ES 0.92). Findings show an anticipatory rise in psychophysiological variables before competition. Knowledge of starting status appears a moderating factor in the magnitude of player endocrine response between home and away games.

Purpose:Running performance is influenced by the interaction of biomechanical and physiological factors. Miniaturized accelerometers worn by athletes can be used to quantify mechanical aspects of running and as a noninvasive tool to assess training status and progression. The aim of this study was to define and validate a method to assess running regularity and allow the estimation of an individual’s oxygen uptake (V̇O2) and/or blood lactate—[La]b—based on data collected with accelerometers and heart rate.Methods:Male adolescent endurance athletes completed an incremental submaximal aerobic stage test where V̇O2 and [La]b were measured. The test was terminated when [La]b concentration at the end of the stage exceeded 4 mmol/L. Two wireless triaxial accelerometers were placed on participants’ right shank and lower back throughout the test. The root mean square (RMS) and sample entropy (SampEn) were calculated for the vertical, mediolateral, and anteroposterior components of acceleration.Results:There were significant positive correlations of acceleration and entropy variables with [La]b and V̇O2, with moderate to high coefficients (r = .43–.87). RMS of the shank acceleration was the most highly related with both physiological variables. When the accelerometer was attached on the trunk, SampEn of the vertical acceleration had the strongest relationship with V̇O2 (r = .76, P < .01).Conclusions:The described method analyzing running complexity may allow an assessment of gait variability, which noninvasively tracks V̇O2 and/or [La]b, allowing monitoring of fatigue or training readiness for trained adolescent individuals.

Purpose:To examine the training and concomitant changes in laboratory- and field-test performance of highly trained endurance runners.Methods:Fourteen highly trained male endurance runners (mean ± SD maximal oxygen uptake [VO2max] 69.8 ± 6.3 mL · kg−1 · min−1) completed this 1-y training study commencing in April. During the study the runners undertook 5 laboratory tests of VO2max, lactate threshold (LT), and running economy and 9 field tests to determine critical speed (CS) and the modeled maximum distance performed above CS (D′). The data for different periods of the year were compared using repeated-measures ANOVA. The influence of training on laboratory- and field-test changes was analyzed by multiple regression.Results:Total training distance varied during the year and was lower in May–July (333 ± 206 km, P = .01) and July–August (339 ± 206 km, P = .02) than in the subsequent January–February period (474 ± 188 km). VO2max increased from the April baseline (4.7 ± 0.4 L/min) in October and January periods (5.0 ± 0.4 L/min, P ≤ .01). Other laboratory measures did not change. Runners’ CS was lowest in August (4.90 ± 0.32 m/s) and highest in February (4.99 ± 0.30 m/s, P = .02). Total training distance and the percentage of training time spent above LT velocity explained 33% of the variation in CS.Conclusion:Highly trained endurance runners achieve small but significant changes in VO2max and CS in a year. Increases in training distance and time above LT velocity were related to increases in CS.

The need to quantify aspects of training to improve training prescription has been the holy grail of sport scientists and coaches for many years. Recently, there has been an increase in scientific interest, possibly due to technological advancements and better equipment to quantify training activities. Over the last few years there has been an increase in the number of studies assessing training load in various athletic cohorts with a bias toward subjective reports and/or quantifications of external load. There is an evident lack of extensive longitudinal studies employing objective internal-load measurements, possibly due to the cost-effectiveness and invasiveness of measures necessary to quantify objective internal loads. Advances in technology might help in developing better wearable tools able to ease the difficulties and costs associated with conducting longitudinal observational studies in athletic cohorts and possibly provide better information on the biological implications of specific external-load patterns. Considering the recent technological developments for monitoring training load and the extensive use of various tools for research and applied work, the aim of this work was to review applications, challenges, and opportunities of various wearable technologies.