In recent years, scrutiny on sport-science research has intensified from both internal and external sources. 1 , 2 Several debates have arisen concerning methodological and theoretical issues, such as magnitude-based inferences (MBI) 3 and the acute chronic workload ratio (ACWR). 4 For example
Scott McLean, Hugo A. Kerhervé, Nicholas Stevens, and Paul M. Salmon
Israel Halperin, Andrew D. Vigotsky, Carl Foster, and David B. Pyne
Over the passing years, exercise and sport sciences have developed into a large field of study consisting of several disciplines including physiology, biomechanics, psychology, nutrition, performance analysis, motor learning and control, strength and conditioning, and sports medicine. Much like
Iñigo Mujika and Ritva S. Taipale
performed on female athletes: 2 studies were conducted on synchronized swimmers (now called artistic swimmers), 1 on handball players, and 1 on soccer players. By contrast, one of us (R.S.T.) has made a career in sport science by mainly studying women and sex differences in responses and adaptations to
Shona L. Halson and David T. Martin
“gold-medal-winning factory.” In an attempt to increase international competitiveness, many countries built their own centralized elite sport centers. 2 East Germany learned from the Soviet Union, and with heavy state funding, exceptional facilities, committed coaching, and sport science support, the
Iñigo Mujika and David B. Pyne
, or dwindling motivation are all factors we recognize in sport. Many of these also apply in occupational, employment, and professional settings, including sport-science practice and research. When the drum of moving on starts to beat louder and longer it’s time for self-reflection and decision making
Tim Newans, Phillip Bellinger, Christopher Drovandi, Simon Buxton, and Clare Minahan
pre-average data before running analyses. 18 Therefore, it is reasonable to suggest that mixed models are the most appropriate statistical methodology to analyze longitudinal data sets often acquired by sports scientists. This aligns with previous guidance by Hopkins et al 19 in encouraging sport-science
nature of sport science both in the field with coaches and athletes and in academic circles. These metrics are easily generated, but the challenge is to identify and articulate the impact of sport science. For instance, these numbers were achieved while simultaneously helping individual athletes and
Sport science can mean a lot of different things. At one level, it can be the collation and transmission of scientific findings to coaches and athletes. At another, it can be the evaluation of athletes in the laboratory, intended to give the coach a venue free view of the current status and
Lieselot Decroix, Kevin De Pauw, Carl Foster, and Romain Meeusen
To review current cycling-related sport-science literature to formulate guidelines to classify female subject groups and to compare this classification system for female subject groups with the classification system for male subject groups.
A database of 82 papers that described female subject groups containing information on preexperimental maximal cycle-protocol designs, terminology, biometrical and physiological parameters, and cycling experience was analyzed. Subject groups were divided into performance levels (PLs), according to the nomenclature. Body mass, body-mass index, maximal oxygen consumption (VO2max), peak power output (PPO), and training status were compared between PLs and between female and male PLs.
Five female PLs were defined, representing untrained, active, trained, well-trained, and professional female subjects. VO2max and PPO significantly increased with PL, except for PL3 and PL4 (P < .01). For each PL, significant differences were observed in absolute and relative VO2max and PPO between male and female subject groups. Relative VO2max is the most cited parameter for female subject groups and is proposed as the principal parameter to classify the groups.
This systematic review shows the large variety in the description of female subject groups in the existing literature. The authors propose a standardized preexperimental testing protocol and guidelines to classify female subject groups into 5 PLs based on relative VO2max, relative PPO, training status, absolute VO2max, and absolute PPO.
Alan McCall, Maurizio Fanchini, and Aaron J. Coutts
In high-performance sport, science and medicine practitioners employ a variety of physical and psychological tests, training and match monitoring, and injury-screening tools for a variety of reasons, mainly to predict performance, identify talented individuals, and flag when an injury will occur. The ability to “predict” outcomes such as performance, talent, or injury is arguably sport science and medicine’s modern-day equivalent of the “Quest for the Holy Grail.” The purpose of this invited commentary is to highlight the common misinterpretation of studies investigating association to those actually analyzing prediction and to provide practitioners with simple recommendations to quickly distinguish between methods pertaining to association and those of prediction.