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
Nathan D. Dicks, Nicholas A. Jamnick, Steven R. Murray, and Robert W. Pettitt
To investigate a new power-to-body-mass (BM) ratio 3-min all-out cycling test (3MT%BM) for determining critical power (CP) and finite work capacity above CP (W ′).
The gas-exchange threshold (GET), maximal oxygen uptake (VO2max), and power output evoking VO2max (W peak) and GET (W GET) for cycle ergometry were determined in 12 participants. CP and W′ were determined using the original “linear factor” 3MT (3MTrpm^2) and compared with CP and W′ derived from a procedure, the 3MT%BM, using the subject’s body mass and self-reported physical activity rating (PA-R), with values derived from linear regression of the work–time model and power–inverse-time model (1/time) data from 3 separate exhaustive squarewave bouts.
The VO2max, VO2GET, W peak, and W GET values estimated from PA-R and a non-exercise-regression equation did not differ (P > .05) from actual measurements. Estimates of CP derived from the 3MT%BM (235 ± 56 W), 3MTrpm^2 (234 ± 62 W), work–time (231 ± 57 W), and 1/time models (230 ± 57 W) did not differ (F = 0.46, P = .72). Similarly, estimates of W′ between all methods did not differ (F = 3.58, P = .07). There were strong comparisons of the 3MT%BM to 1/time and work–time models with the average correlation, standard error of the measurement, and CV% for critical power being .96, 8.74 W, and 4.64%, respectively.
The 3MT%BM is a valid, single-visit protocol for determining CP and W′.
William A. Sands, Cindy Slater, Jeni R. McNeal, Steven Ross Murray, and Michael H. Stone
The lay press, scientists, and physicians appear to believe that gymnasts are continually getting smaller and that their “smallness” is a health risk.
To assess the historical changes in the size and age of the US women’s Olympic gymnastics teams from 1956 to 2008.
The official records from the US Olympic Committee and USA Gymnastics of Olympic team members were assessed at 2 levels: individual height, mass, age, and body-mass index (BMI) and the team performance scores and rankings. Fourteen Olympic teams with a total of 106 team members, including the alternates, were included. Trend analyses were conducted using linear and polynomial models.
Simple linear correlations indicated that since 1956, height, mass, age, BMI, and team Olympic rank have been declining. However, second-order polynomial curve fits indicated that in the last 4 Olympic Games the members of the US women’s gymnastics teams have been getting larger.
Women Olympic gymnasts were getting smaller through approximately the 1980s and early 1990s. Since then the size of these gymnasts has increased. The minimum-age rule modifications may have played a role in athlete size changes along with a shift from the near dominance of the former communist Eastern Bloc.