Rate of perceived exertion (RPE) is used to calculate athlete load. Incomplete load data, due to missing athlete-reported RPE, can increase injury risk. The current standard for missing RPE imputation is daily team mean substitution. However, RPE reflects an individual’s effort; group mean substitution may be suboptimal. This investigation assessed an ideal method for imputing RPE. A total of 987 datasets were collected from women’s rugby sevens competitions. Daily team mean substitution, k-nearest neighbours, random forest, support vector machine, neural network, linear, stepwise, lasso, ridge, and elastic net regression models were assessed at different missingness levels. Statistical equivalence of true and imputed scores by model were evaluated. An ANOVA of accuracy by model and missingness was completed. While all models were equivalent to the true RPE, differences by model existed. Daily team mean substitution was the poorest performing model, and random forest, the best. Accuracy was low in all models, affirming RPE as multifaceted and requiring quantification of potentially overlapping factors. While group mean substitution is discouraged, practitioners are recommended to scrutinize any imputation method relating to athlete load.

Purpose: To determine the minimum number of events (training or matches) for producing valid acceleration–speed (AS) profiles from global navigation satellite system (GNSS) data. Methods: Nine elite female soccer players participated in a 4-week training camp consisting of 19 events. AS profile metrics calculated from different combinations of athlete events were compared to force–velocity (FV) profile metrics from 2 × 40-m stand-alone sprint effort trials, using the same GNSS 10-Hz technology. Force–velocity profiles were calculated, from which AS profiles were obtained. AS profiles from training and matches were generated by plotting acceleration and speed points and performing a regression through the maximal points to obtain the AS metrics (theoretical maximal speed, x-intercept [in meters per second], theoretical maximal acceleration, y-intercept [in meters per second squared], and the slope per second). A linear mixed model was performed with the AS metrics as the outcome variables, the number of events as a fixed effect, and the participant identifier as a mixed effect. Dunnett post hoc multiple comparisons were used to compare the means of each number of event grouping (1–19 events) to those estimated from the dedicated sprint test. Results: Theoretical maximal speed and theoretical maximal acceleration means were no longer significantly different from the isolated sprint reference with 9 to 19 (small to trivial differences = −0.31 to −0.04 m·s⁻¹, P = .12–.99) and 6 to 19 (small differences = −0.4 to −0.28 m·s⁻², P = .06–.79) events, and the slopes were no longer different with 1 to 19 events (trivial differences = 0.06–0.03 s⁻¹, P = .35–.99). Conclusions: AS profiles can be estimated from a minimum of 9 days of tracking data. Future research should investigate methodology resulting in AS profiles estimated from fewer events.

While accurate athlete load monitoring is crucial for preventing injury and optimizing performance, the commonly used session rating of perceived exertion training load or competition load method faces limitations due to compliance issues related to missing subjective data self-reported by the athlete and the subsequent challenges in imputing the sessional rating of perceived exertion (sRPE) component, an average value for a training or competition session. This study investigated the imputation of missing RPE scores from the mechanical work and from a Speed–Deceleration–Contact (SDC) model. A total of 1002 datasets were collected from women’s rugby sevens competitions. Using either the mechanical work or SDC, linear regression and random forest imputation models were assessed at different missingness levels and their results compared to those of a common method of daily team mean substitution (DTMS) using an ANOVA of the accuracy by the model type and missingness. The statistical equivalence was evaluated for true and imputed sRPE scores by the model and strategy. Significant interactions between the model type and missingness were found, with all the imputed scores being deemed statistically equivalent. From the ANOVA, DTMS was found to be the poorest-performing model and the random forest model was the best. However, the best-performing model was not superior to previously reported imputation approaches, which confirms the difficulty in using subjective measures of the load when missing data is a prevalent issue in team sports. Practitioners are encouraged to critically evaluate any method of imputation for an athlete’s load.

Background: Para-sports such as wheelchair rugby have seen increased use of inertial measurement units (IMU) to measure wheelchair mobility. The accessibility and accuracy of IMUs have enabled the quantification of many wheelchair metrics and the ability to further advance analyses such as force-velocity (FV) profiling. However, the FV modeling approach has not been refined to include wheelchair specific parameters. Purpose: The purpose of this study was to compare wheelchair rugby sprint FV profiles, developed from a wheel-mounted IMU, using current mono-exponential modeling techniques against a dynamic resistive force model with wheelchair specific resistance coefficients. Methods: Eighteen athletes from a national wheelchair rugby program performed 2 × 45 m all-out sprints on an indoor hardwood court surface. Results: Velocity modelling displayed high agreeability, with an average RMSE of 0.235 ± 0.07 m/s⁻¹ and r² of 0.946 ± 0.02. Further, the wheelchair specific resistive force model resulted in greater force and power outcomes, better aligning with previously collected measures. Conclusions: The present study highlights the proof of concept that a wheel-mounted IMU combined with wheelchair-specific FV modelling provided estimates of force and power that better account for the resistive forces encountered by wheelchair rugby athletes.

Purpose: Ice hockey is a team invasion sport characterized by repeated high-intensity skating efforts, technical and tactical skill, physical contact, and collisions requiring considerable levels of muscular strength. The purpose of this study was to evaluate the relationships between lower-body vertical force–time metrics and skating qualities in subelite female ice hockey players. Methods: A cross-sectional cohort design was employed utilizing 14 athletes (body mass = 66.7 [1.8] kg; height = 171.6 [6.2] cm; age = 21.1 [1.7] y). The relationships between metrics of lower-body strength collected from a drop jump, squat jump, countermovement jump, loaded countermovement jump, and an isometric squat and 4 skating qualities collected from a linear sprint, repeated sprint test, and a multistage aerobic test were evaluated. Results: The regression models revealed a positive relationship between relative peak force in the isometric squat and skating multistage aerobic test performance (r ² = .388; P = .017) and a positive relationship between repeated-sprint ability and eccentric mean force during the loaded countermovement jump (r ² = .595; P = .001). No significant relationships were observed between strength metrics and skating acceleration or maximal velocity. Conclusions: These data suggest that skating ability is most affected by relative isometric strength in female ice hockey players. It is recommended that practitioners focus training on tasks that improve relative force output. It is also recommended that isometric relative peak force be used as a monitoring metric for this cohort.

Wheelchair sports have been using Inertial Measurement Units (IMU) to measure mobility metrics during training, testing and competition. Presently, the most suitable solution to calculate wheelchair speed and frame rotation is the 3IMU method as there is uncertainty about the ability of a one wheel-mounted IMU (1IMU) approach to calculate wheelchair frame rotational kinematics. A new method for calculating wheelchair frame rotational kinematics using a single wheel-mounted IMU is presented and compared to a criterion measurement using a wheelchair-frame-mounted IMU. Goodness-of-fit statistics demonstrate very strong linear relationships between wheelchair frame angular velocity calculated from the wheel-mounted IMUs and a wheelchair-frame-mounted IMU. Root mean square error (RMSE), mean absolute error (MAE) and Bland–Altman analysis show very small differences between the wheelchair frame angular velocity calculated from the wheel-mounted IMUs and the wheelchair-frame-mounted IMU. This study has demonstrated a simple and accurate approach to estimating wheelchair frame rotation using one wheel-mounted IMU during an elite wheelchair athlete agility task. Future research is needed to reexamine and compare wheelchair mobility metrics determined using the 3IMU and 1IMU solutions using this new approach.