Athlete preparation and performance continue to increase in complexity and costs. Modern coaches are shifting from reliance on personal memory, experience, and opinion to evidence from collected training-load data. Training-load monitoring may hold vital information for developing systems of monitoring that follow the training process with such precision that both performance prediction and day-to-day management of training become adjuncts to preparation and performance. Time-series data collection and analyses in sport are still in their infancy, with considerable efforts being applied in “big data” analytics, models of the appropriate variables to monitor, and methods for doing so. Training monitoring has already garnered important applications but lacks a theoretical framework from which to develop further. As such, we propose a framework involving the following: analyses of individuals, trend analyses, rules-based analysis, and statistical process control.
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Sands is with the US Ski and Snowboard Association, Park City, UT. Kavanaugh is with the Dept of Exercise and Sport Science, East Tennessee State University, Johnson City, TN. Murray is with the Dept of Kinesiology, Colorado Mesa University, Grand Junction, CO. McNeal the Dept of Physical Education, Health and Recreation, Eastern Washington University, Cheney, WA. Jemni is with the Dept of Sport and Exercise Science, Qatar University, Doha, Qatar.