The development of optimized training regimens requires a comprehensive understanding of traininginduced adaptations using a combination of laboratory-based and field-based research methods. Fieldbased research often necessitates the use of data-reduction methods, which frequently require sports scientists to make discretization choices. In the present paper, we show how Shannon entropy can be used to reduce the inherent subjectivity of these binning choices when exposure variation analysis is used to quantify variation in power output in training data from competitive cyclists.
- exposure variation analysis
- Shannon entropy