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Rugby league is a contact team sport performed at an average intensity similar to that of other team sports (~70–80% VO_2max), made up of unsystematic movements of varying type, duration, and frequency. The high number of collisions, repeated eccentric muscle contractions associated with accelerating and decelerating, and prolonged aerobic nature of rugby league matches result in the development of fatigue in the days after exercise. Monitoring the presence and magnitude of this fatigue to maximize performance and training adaptation is an important consideration for applied sports scientists. Several methods have been proposed to measure the magnitude of fatigue in athletes. Perceptual measures (eg, questionnaires) are easy to employ and are sensitive to changes in performance. However, the subjective nature of such measures should be considered. Blood biochemical markers of fatigue may provide a more objective measure of homeostatic disturbances associated with fatigue; however, the cost, level of expertise required, and high degree of variability of many of these measures often preclude them from being used in the applied setting. Accordingly, simple measure of muscle function (eg, jump height) and simulated performance offer the most practical and appropriate method of determining the extent of fatigue experienced by rugby league players. A meaningful change in each measure of fatigue for the monitoring of players can be easily determined, provided that the reliability of the measure is known. Multiplying the coefficient of variation by 0.3, 0.9, and 1.6 can be used to determine a small, moderate, and large change, respectively.

Iliac blood-flow restrictions causing painful and “powerless” legs are often attributed to overtraining and may develop for some time before being correctly diagnosed. In the current study, differences between actual performance parameters and performance parameters predicted from the Lamberts and Lambert Submaximal Cycle Test (LSCT) were studied in a world-class cyclist with bilateral kinking of the external iliac artery before and after surgery. Two performance-testing sessions, including a peak-poweroutput (PPO) test and a 40-km time trial (TT) were conducted before surgery, while 1 testing session was conducted after the surgery. Actual vs LSCT-predicted performance parameters in the world-class cyclists were compared with 82 symptom-free trained to elite male cyclists. No differences were found between actual and LSCT-predicted PPO before and after surgical intervention. However, there were differences between actual and LSCT-predicted 40-km TT time in the tests performed before the surgery (2:51and 2:55 min:s, respectively). These differences were no longer apparent in the postsurgery 40-km TT (2 s). This finding suggests that iliac blood-flow restrictions seem to mainly impair endurance performance rather than peak cycling performance. A standard PPO test without brachial ankle blood-pressure measurements might not be able to reflect iliac bloodflow restrictions. Differences between actual and LSCT-predicted 40-km TT time may assist in earlier referral to a cardiovascular specialist and result in earlier detection of iliac blood-flow restrictions.

The use of big data in sport and sport management research is increasing in popularity. Prior research generally includes one of the many characteristics of big data, such as volume or velocity. The present study presents big data in a multidimensional lens by considering the use of sentiment analysis. Specifically focusing on the phenomenon of tanking, the purposeful underperformance in sport competitions, the present study considers the impact that consumers’ sentiment regarding tanking has on game attendance in the National Basketball Association. Collecting social media posts for each National Basketball Association team, the authors create an algorithm to measure the volume and sentiment of consumer discussions related to tanking. These measures are included in a predictive model for National Basketball Association home game attendance between the 2013–2014 and 2017–2018 seasons. Our results find that the volume of discussions for the home team and sentiment toward tanking by the away team impact game attendance.

Purpose: Poor athlete buy-in and adherence to training-monitoring systems (TMS) can be problematic in elite sport. This is a significant issue, as failure to record, interpret, and respond appropriately to negative changes in athlete well-being and training status may result in undesirable consequences such as maladaptation and/or underperformance. This study examined the perceptions of elite athletes to their TMS and their primary reasons for noncompletion. Methods: Nine national-team sprint athletes participated in semistructured interviews on their perceptions of their TMS. Interview data were analyzed qualitatively, based on grounded theory, and TMS adherence information was collected. Results: Thematic analysis showed that athletes reported their main reason for poor buy-in to TMS was a lack of feedback on their monitoring data from key staff. Furthermore, training modifications made in response to meaningful changes in monitoring data were sometimes perceived to be disproportionate, resulting in dishonest reporting practices. Conclusions: Perceptions of opaque or unfair decision making on training-program modifications and insufficient feedback were the primary causes for poor athlete TMS adherence. Supporting TMS implementation with a behavioral-change model that targets problem areas could improve buy-in and enable limited resources to be appropriately directed.

There is a need for markers that would help determine when an athlete’s training load is either insufficient or excessive. In this study we examined the relationship between changes in performance and changes in physiological and psychological markers during and following a period of overload training in 10 female and 10 male elite rowers. Change in performance during a 4-wk overload was determined with a weekly 30-min time-trial on a rowing ergometer, whereas an incremental test provided change in lactate-threshold power between the beginning of the study and following a 1-wk taper after the overload. Various psychometric, steroid-hormone, muscle-damage, and inflammatory markers were assayed throughout the overload. Plots of change in performance versus the 4-wk change in each marker were examined for evidence of an inverted-U relationship that would characterize undertraining and excessive training. Linear modeling was also used to estimate the effect of changes in the marker on changes in performance. There was a suggestion of an inverted U only for performance in the incremental test versus some inflammatory markers, due to the relative underperformance of one rower. There were some clear linear relationships between changes in markers and changes in performance, but relationships were inconsistent within classes of markers. For some markers, changes considered to predict excessive training (eg, creatine kinase, several proinflammatory cytokines) had small to large positive linear relationships with performance. In conclusion, some of the markers investigated in this study may be useful for adjusting the training load in individual elite rowers.