Search Results

Purpose: A simple and 2 different exponentially weighted moving average methods were used to investigate the relationships between internal training load and elite weightlifting performance. Methods: Training impulse data (sessional ratings of perceived exertion × training duration) were collected from 21 elite weightlifters (age = 26.0 [3.2] y, height = 162.2 [11.3] cm, body mass = 72.2 [23.8] kg, previous 12-mo personal best total 96.3% [2.7%] of world record total) during the 8 weeks prior to the 2016 Olympic Games qualifying competition. The amount of training modified or cancelled due to injury/illness was also collected. The training stress balance (TSB) and acute to chronic workload ratio (ACWR) were calculated with the 3 moving average methods. Along with the amount of modified training, TSB and ACWR across the moving average methods were then examined for their relationship to competitive performance. Results: There were no consistent associations between performance and training load on the day of competition. The volatility (SD) of the ACWR in the last 21 days preceding the competition was moderately correlated with performance across moving average methods (r = −.41 to .48, P = .03–.07). TSB and ACWR volatility in the last 21 days were also significantly lower for successful performers but only as a simple moving average (P = .03 and .03, g = 1.15 and 1.07, respectively). Conclusions: Practitioners should consider restricting change and volatility in an athlete’s TSB or ACWR in the last 21 days prior to a major competition. In addition, a simple moving average seemed to better explain elite weightlifting performance than the exponentially weighted moving averages in this investigation.

Purpose: Criticisms of the acute to chronic workload ratio (ACWR) have been that the mathematical coupling inherent in the traditional calculation of the ACWR results in a spurious correlation. The purposes of this commentary are (1) to examine how mathematical coupling causes spurious correlations and (2) to use a case study from actual monitoring data to determine how mathematical coupling affects the ACWR. Methods: Training and competition workload (TL) data were obtained from international-level open-skill (basketball) and closed-skill (weightlifting) athletes before their respective qualifying tournaments for the 2016 Olympic Games. Correlations between acute TL, chronic TL, and the ACWR as coupled/uncoupled variations were examined. These variables were also compared using both rolling averages and exponentially weighted moving averages to account for any potential benefits of one calculation method over another. Results: Although there were some significant differences between coupled and uncoupled chronic TL and ACWR data, the effect sizes of these differences were almost all trivial (g = 0.04–0.21). Correlations ranged from r = .55 to .76, .17 to .53, and .88 to .99 for acute to chronic TL, acute to uncoupled chronic TL, and ACWR to uncoupled ACWR, respectively. Conclusions: There may be low risk of mathematical coupling causing spurious correlations in the TL–injury-risk relationship. Varying levels of correlation seem to exist naturally between acute and chronic TL variables regardless of coupling. The trivial to small effect sizes and large to nearly perfect correlations between coupled and uncoupled AWCRs also imply that mathematical coupling may have little effect on either calculation method, if practitioners choose to apply the ACWR for TL monitoring purposes.

Purpose : To investigate the relationships between internal and external training load (TL) metrics with elite international women’s basketball performance. Methods : Sessional ratings of perceived exertion, PlayerLoad^™/minute, and training duration were collected from 13 elite international-level female basketball athletes (age 29.0 [3.7] y, stature 186.0 [9.8] cm, body mass 77.9 [11.6] kg) during the 18 weeks prior to the International Basketball Federation Olympic qualifying event for the 2016 Rio Olympic Games. Training stress balance, differential load, and the training efficiency index were calculated with 3 different smoothing methods. These TL metrics and their change in the last 21 days prior to competition were examined for their relationship to competition performance as coach ratings of performance. Results : For a number of TL variables, there were consistent significant small to moderate correlations with performance and significant small to large differences between successful and unsuccessful performances. However, these differences were only evident for external TL when using exponentially weighted moving averages to calculate TL. The variable that seemed most sensitive to performance was the change in training efficiency index in the last 21 days prior to competition (performance r = .47–.56, P < .001 and difference between successful and unsuccessful performance P < .001, f²  = 0.305–0.431). Conclusions : Internal and external TL variables were correlated with performance and distinguished between successful and unsuccessful performances among the same players during international women’s basketball games. Manipulating TL in the last 3 weeks prior to competition may be worthwhile for basketball players’ performance, especially in internal TL.