The aim of this study was to compare two different player load models in professional handball league matches with respect to the player role. The results confirmed our first hypothesis, that wings cover the largest distances especially in the highest speed zones (Table 3). Secondly, the equivalent distances are greater for each role by a small effect size. Finally, the discrepancy between distance covered and equivalent distance is significantly greater in wings compared to backs and pivots by moderate to large effect sizes. The comparison of time spent and respective energy spent running leads to similar results. All positions spend about 21% of their time running, however, wings spent 67% of their energy running, which is substantially more than backs (62%) and pivots (60%). Time spent over 10 and 20 W was longer for wings than pivots and backs but not different between backs and pivots. These findings confirm that wings cover more distance due to longer times on court and cover longer distances at higher speeds. The results further show that their acceleration profiles differ substantially from the other positions. Accordingly, individual player load estimates should include measures of accelerations and deceleration. Compared to the results reported by Manchado et al. (2021), total distance covered is greater for wings (3567 m vs. ~2400 m; 48 %), backs (2462 m vs. ~2000 m; 23%), and pivots (2445 m vs. 1835 m; 33%). Competition-specific differences have been observed previously and may be due to different load management strategies depending on match demands and competition density. Although slightly lower, the present results are similar to the findings by Büchel et al. (2019), who observed 16 home matches of one team in the German Handball Bundesliga. The authors found greater time on court and longer total distances covered for all players. However, similar to the present results, about 80% of time was spent using speeds of under 2 m·s-1 for all positions. The differences may be due to the different sample sizes. Further, in the present study, time spent and distances covered during game stoppages were excluded from the analysis resulting in 24.3% excluded frames. However, it seems reasonable to assume few high-intensity actions were performed during these periods, and the inclusion of game stoppages would lead to a bias and underestimation of real game demands (Mernagh et al., 2021). The comparison of distance covered and equivalent distance showed moderate to large interaction effects between wings vs. pivots and wings vs. backs, while the interaction between pivots vs. backs was trivial. Some limitations of the present study can be identified. During interruptions of the game, e.g., due to fouls, no data was recorded. Therefore, only net playing time is presented in this study. However, considering that players are mostly standing or walking during such breaks, removing them prevents underestimating player load, especially in variables normalized by time on court. The state of game (active vs. inactive) was assumed to be in line with data recordings. Consequently, time on court and all related variables were computed when position data was available. This may lead to discrepancies if non-active players are still recorded due to their spatial proximity to the pitch or missing data due to erroneous sensors being considered off the court. Additionally, only the player role (wing/back/pivot) in the offense formation was available. The influence of individual tactical decisions, like a defense specialist, is not accounted for in this study. However, with respect to the amount of data analyzed in this study, we doubt that this will have a substantial impact on the results. Although all MP parameters are computed relative to body mass, it is unclear if the extreme anthropometrics of handballers, especially pivots, may impact their true energy expenditure during certain movements. More research needs to be conducted to control for inter-individual differences in locomotion costs (di Prampero and Osgnach, 2018). Further, in high body contact sports like handball, energy is spent when blocking opponents from moving (Fuchs et al., 2021). These maximum isometric efforts are not visible in position data and are not accounted for in running distances or the MP concept. Therefore, the load of players who participate in body contact situations more frequently are underestimated even by the MP concept (Gray et al., 2018; Fuchs et al., 2021). Still, the incorporation of acceleration in MP addresses a flaw in the traditional approach and allows for more adequate monitoring of athletes in handball (Polglaze and Hoppe, 2019). Applying MP to practice requires care as the underlying formulas contain several high-degree (4th and 5th) polynomials to calculate MP and differentiate between walking and running. Minetti and Parvei’s (2018) new approach solves a major part of the problem by remodeling part of the data into a more stable exponential function. We would still encourage optimization of the model to increase robustness and easy application in practice. Further data processing methods should be reported in detail. Especially as different smoothing and differentiation methods may have substantial effects on the signal properties (Winter, 2009). Thus, there is a demand to establish a standardized approach to post-processing and smoothing of sports position data. We approach this problem by describing our data processing algorithms in detail and using open source algorithms the reader can verify and reproduce (Raabe et al., 2022). Despite these limitations, there are some strengths of this study. Previous studies analyzing player load in league play have been limited in sample size and data accuracy (Büchel et al., 2019; González-Haro et al., 2020). The present study presents a large and representative sample size of high-quality position data from elite league play. Further, we used a more robust model of calculating MP from raw position data by merging two existing models (Minetti and Pavei, 2018; di Prampero and Osgnach, 2018). The algorithms used for data processing and player load calculations are available as part of an open source software package for the readers to verify and reproduce (Raabe et al., 2022). The findings of this study are also important for practitioners. On the one hand, they show representative player load values for elite handball league play. On the other hand, they show that considering only traditional player load models will lead to an underestimation of player load of certain players because their frequent accelerations and decelerations are not included in the analysis. Therefore, we suggest combining both approaches, e.g., by comparing total distance covered and equivalent distance individually to assess the movement profile of the player in comparison to players with similar roles. As the current research suggests (Manzi et al., 2014; Fuchs et al., 2021; Brochhagen, 2022), if implemented correctly, MP has the potential of overcoming the central problems of traditional player load models in team sports. |