Rugby Sevens sRPE Workload Imputation Using Objective Models of Measurement
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".