# Rugby Sevens sRPE Workload Imputation Using Objective Models of Measurement
## 4 Discussion
Objective measures can also provide a great opportunity to interpret changes in the physical workload connected to discrete factors for which subjective measures may not allow such a determination, such as in the case of rugby where the use of heart rate monitors in competition are not permitted [2]. The SDC model, especially using random forest classification, was shown to be the best model for the imputation of missing sRPE data. This model was shown to provide reasonable predictions of the sRPE-CL and includes components such as contact which have been shown to be related to the sRPE in this cohort [20; 45]. The combination of both physical and tactical components within the SDC model, may more broadly represent the athletic experience as it relates to the workload and support the use of this model as a useful strategy for the imputation of missing sRPE values as well as a standalone workload metric [20]. While the SDC model was developed using a women's rugby sevens population, it may be possible to generalize its applicability across rugby codes and Australian football, where physical contact and decelerations feature heavily [5; 10]. Further, the inclusion of a tactical variable, like physical contact, could be replaced with another high-value tactical variable with appropriate investigation. For example, deceleration is a considerable factor in football and so perhaps a variable such as the number of ball touches could be included [16]. The mechanical work was chosen as another load metric for the imputation of the sRPE in this study because it is a purely objective representation of the cumulative athlete kinematic and kinetics collected using ATDs. It has been previously suggested that the mechanical work does not necessarily account for psychological factors relating to competition, leading to different workload values than the sRPE, as demonstrated by [PERSON] et al. (2024) [20]. Therefore, it is understandable that it did not perform as well as the SDC model, which combines both physical and tactical information about the athlete [20]. Further, in cases with few missing values, the limited variability in the sRPE values may not necessarily be reflected in the mechanical work; that is to say, athletes may still demonstrate variable physical outputs, making it difficult to appropriately generalize the mechanical work output to the sRPE. However, this particular strategy may be effective in cases where ATD data is readily available relative to tactical data which may not always be available from film or broadcast footage for particular sports. It has been demonstrated here and previously that the imputation of the sRPE is difficult and that the true, collected value is vastly superior to the imputed value [14]. This strongly supports the need to develop strategies to minimize missing data and maximize the athlete reporting adherence [46]. In their development and evaluation of a training monitoring system for rugby union athletes, [PERSON] et al. (2020) found that there were many reasons for data missingness, and perhaps the most common was the athletes' perceived usefulness of the load measure [46]. The use of objective measures may provide a meaningful strategy to monitor athletes' physical output without the burden of reporting. GNSS units provide an \"invisible monitoring\" strategy that enable practitioners to gather information about the physical demands of competition as well as ensure that athletes are appropriately meeting or exceeding these demands in training to ensure a safe and lengthy sporting career [1]. Further, achieving initial and continued compliance is paramount to sustained data collection. [PERSON] et al. (2020) suggest that developing appropriate educational resources for athletes as well as an effective feedback loop between the coach and/or sport scientist is critical to increase adherence to and athletes buying into the use of training load measures [46; 47]. Nevertheless, ensuring a complete dataset in an efficient and reliable manner is key for athlete load management [11].