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.
William A. Sands, Ashley A. Kavanaugh, Steven R. Murray, Jeni R. McNeal, and Monèm Jemni
Caleb D. Bazyler, Satoshi Mizuguchi, Ashley A. Kavanaugh, John J. McMahon, Paul Comfort, and Michael H. Stone
Purpose: To determine if jumping-performance changes during a peaking phase differed among returners and new players on a female collegiate volleyball team and to determine which variables best explained the variation in performance changes. Methods: Fourteen volleyball players were divided into 2 groups—returners (n = 7) and new players (n = 7)—who completed a 5-wk peaking phase prior to conference championships. Players were tested at baseline before the preseason on measures of the vastus lateralis cross-sectional area using ultrasonography, estimated back-squat 1-repetition maximum, countermovement jump height (JH), and relative peak power on a force platform. Jumping performance, rating of perceived exertion training load, and sets played were recorded weekly during the peaking phase. Results: There were moderate to very large (P < .01, Glass Δ = 1.74) and trivial to very large (P = .07, Δ = 1.09) differences in JH and relative peak power changes in favor of returners over new players, respectively, during the peaking phase. Irrespective of group, 7 of 14 players achieved peak JH 2 wk after the initial overreach. The number of sets played (r = .78, P < .01) and the athlete’s preseason relative 1-repetition maximum (r = .54, P = .05) were the strongest correlates of JH changes during the peaking phase. Conclusions: Returners achieved greater improvements in jumping performance during the peaking phase compared with new players, which may be explained by the returners’ greater relative maximal strength, time spent competing, and training experience. Thus, volleyball and strength coaches should consider these factors when prescribing training during a peaking phase to ensure their players are prepared for important competitions.