Evaluating external load responses to cumulative playing time and position in the European Handball Federation Women's Euro 2022 through an IoT and Big Data architecture approach.

The quantification of physical demands placed upon handball players, segmented by their specific roles and duration of play, is crucial for sustaining high performance and minimizing the risk of injury. Leveraging advanced inertial measurement units, this investigation captured and analyzed the external load data of athletes participating in the EHF Women's EURO 2022. The aim of this study was to provide coaching staff with information on fatigue development during periods of high match density. The study evaluated the effects of playing position and cumulative playing time on external load metrics, using linear mixed models that treated individual players as random effects. The study employed a cutting-edge computational framework integrating sensor network technologies, Local Positioning Systems (LPS), and Big Data Analytics within a descriptive analytics methodology. From over half a billion raw records, we distilled 1,013 data entries from 47 matches for analysis. The findings reveal that the wings demonstrated the highest levels of total and high-speed running distances, though they sustained lower PlayerLoad relative to backs. Interestingly, cumulative playing time did not markedly alter load profiles, which may be attributed to strategic substitution decisions by coaches and the players' own pacing strategies. Notable discrepancies within positional demands were observed over time, such as centers displaying increased distance coverage within the 2-3 hour play interval. This study underscores the efficacy of strategic load management and tailored pacing in sustaining player performance throughout high-stakes tournaments. It elucidates the relationship between managerial tactics and player-specific characteristics in the context of external load distribution.