## 1 Introduction  
There are numerous factors influencing a team sport athlete's workload in competition, including competitive experience, systems of play, opposition, and physical preparedness [1, 2]. [PERSON] et al. (2021) identified higher workloads in an elite men's soccer team following losses to top-ranked teams, and [PERSON] et al. (2015) have demonstrated that more experienced AFL players have higher training workloads than less experienced players for the same sessions [3,4]. These workload differences impact player preparation and performance, making workload monitoring valuable for assessing an athlete's physical preparedness to meet, or exceed, the competitive demands of the sport [5,6]. Common examples of workload measurement in team sports include the use of session rating of perceived exertion (sRPE) and mechanical work [7,8]. sRPE is the product of an athlete's subjective self-report rating of perceived exertion (RPE) and an objective measurement of the duration of an activity, normally obtained from an athlete-worn sensor [7]. This metric is considered a workload measure, as it includes both an indicator of intensity (RPE) and duration (time), and it has relationships to other relevant measures, such as total and high-speed distance and training load including training impulse (TRIMP) [7]. Mechanical work is the product of force and distance produced by athletes in their sport performance [7, 8, 9, 10]. This can be further calculated by using an athlete's mass and acceleration to calculate force and the same athlete's velocity and activity time to calculate distance. Mechanical work has been demonstrated to be associated with sRPE in athletes [11]. Statistical and machine learning workload models have also been developed using training and competition data [12,13]. All models offer unique value in monitoring athletes in training and competition environments, yet they may result in different outcomes. Rugby sevens, like other team field sports, includes many unique factors that could contribute to an experience of workload, such as elements of high-speed running, changes in speed through accelerations and decelerations, and physical contact [14]. Both high-speed running and distances covered are known drivers of sRPE [14]. Since sRPE includes time, more time spent in a match can lead to greater acceleration and deceleration opportunities at various speeds to meet the demands of play. Further, accelerations (and decelerations) have been shown to be associated with metabolic power in men's rugby league [8,15]. Therefore, the use of high-speed accelerations and decelerations as specific objective components may enhance the understanding of drivers of load, enabling coaches and staff to appropriately manage player exposure in a tournament. Additionally, aspects of physical contact, especially in sports like rugby, can potentially impact athlete load. [PERSON] et al. (2024) found that even within sRPE, contact load was observed to be accounted for [13]. This suggests that there is value in including multiple objective factors in load monitoring models. As an alternative workload model, [PERSON] et al. (2024) used factors of acceleration/deceleration and contact as a proxy to predict sRPE with a speed-deceleration-contact (SDC) model [13,16], identifying that distances covered at low deceleration (between \(-2.0\) and \(0.0\) m/s\({}^{2}\)) while at low and high speeds, as well as mass and counts of contact, explained almost half of the global variance in sRPE (R\({}^{2}\)adjusted = \(0.487\)) [16]. This model demonstrated strong agreement with sRPE using objective external measures and presented a potential new model for workload measurement in women's rugby sevens. While this model shows promise, it has not been compared to sRPE or mechanical load using applied, match-specific comparisons. In rugby sevens, workload monitoring preferences may vary based on data collection methods and emphasis on subjective or objective factors.