## 2 Materials and Methods
### Procedures  
A total of 103 international elite women's sevens matches were analyzed retrospectively for collected data, including GNSS-coded measures, athlete mass and self-reported RPE data, and match characteristics. Distance, in meters, speed, in meters per second, and playing time, in minutes, were collected from GNSS monitors worn by the athletes between the shoulder blades in custom vests sampling at 10 Hz (Apex v2.50, StatSports, Newry, UK). Acceleration, in meters per second squared, was calculated based on GNSS speeds using a low, dual-pass second-order Butterworth filter to smooth the data before calculating the rate of change in the smoothed speeds over time (Python, v3.9.8, [[https://www.python.org](https://www.python.org)]([https://www.python.org](https://www.python.org)), accessed 19 February 2025). Athlete mass, in kilograms, was collected using a portable weight scale before each match (ES-310, Anyload, Burnaby, BC, Canada). One RPE value was self-reported by each athlete per match, using a 0-10 scale, in arbitrary units, familiar to the participants from regular use in training and competition, which was collected within thirty minutes following each match as players returned to the team's designated area following post-match cool-down and any media obligations. Session RPE, in arbitrary units, values were calculated as the product of the RPE value and playing time (Python, v3.9.8, [[https://www.python.org](https://www.python.org)]([https://www.python.org](https://www.python.org)), accessed 19 February 2025). Mechanical work, in joules, was calculated as the product of mass, acceleration, velocity, and time increments of 0.1 s. The overall absolute mechanical work, in joules, (W) was obtained by summing the instantaneous mechanical work across the entire game, using 0.1 s intervals (Equation (1)). In this context, instantaneous mechanical work was derived from the product of instantaneous absolute power (\(P\)) and time (\(t\)), where instantaneous absolute power was defined as the product of the athlete's mass, acceleration, and velocity. \[W=\sum_{i=1}^{n}(P_{i}\cdot\Delta t_{i}) \tag{1}\]  
The SDC model, first proposed by [PERSON] et al. (2024), was used to provide predicted workload values, in arbitrary units ([PERSON] et al., 2024). This model was previously found to have reasonable explanatory power of sRPE and therefore provides an alternative means of evaluating athlete load. The SDC model is a regression that uses similar metrics as those necessary for the calculation of mechanical work but uses thresholds to define twelve speed and acceleration zones that are used to organize the distanced travelled by the athlete as well as the mass and count of physical contacts experienced by the athlete, as represented in Equation (2), where \(u_{athlete}\) represents the random error of each athlete. \[ \begin{array}{ll}sRPE=-0.852+&53.87(Total\ High\ Deceleration\ Distance)+0.159(Contact\ Count)\\ &-53.46(High\ Speed\times High\ Deceleration\ Distance)\\ &-26.59(Low\ Speed\times High\ Deceleration\ Distance)+u_{athlete}\pm 10.989\end{array}  \tag{2}\]  
The distance travelled, in meters, was categorized into three speed zones: low speeds (walking--0-1.5 m/s), moderate speeds (running--1.5 m/s to each athlete's entry to sprinting threshold), and high speeds (sprinting--entry to sprinting and above) ([PERSON] et al., 2024). The use of athlete-specific entry to sprint thresholds enabled the individual efforts to be appropriately scaled to reflect individual efforts ([PERSON] et al., 2024; [PERSON] et al., 2024). After being categorized into speed zones, an acceleration threshold of 2 m/s2 was applied to further organize the distances into four zones of low (<2.0 m/s2) and high (>2.0 m/s2) accelerations and low (<-2.0 m/s2) and high (>-2.0 m/s2) decelerations ([PERSON] et al., 2024; [PERSON] et al., 2024). The significant zones for the regression were high speed/high deceleration and low speed/high deceleration (Equation (2)).