###### Contents  
* 1 Introduction
* In contemporary sports science, the monitoring of athlete performance has been revolutionized by wearable technology, particularly through the use of inertial measurement units (IMUs). IMUs are wearable devices integrating accelerometers, gyroscopes, and magnetic sensors to measure motion-related parameters, such as angular velocity, linear acceleration, and orientation, through sensor fusion algorithms [1]. These devices provide a real-time, comprehensive view of training loads and physiological responses in ecological environments [2]. The use of IMUs, combined with other wearable sensors (e.g., Global Positioning System, GPS), is increasingly becoming a staple in athlete monitoring [3], particularly for elite team sports such as football, where maintaining peak performance and minimizing injuries is critical. These systems are highly scalable, making them effective tools for large teams and underscoring their importance in modern sports science. This capability has significantly enhanced the precision of training programs, helping coaches to better optimize performance and mitigate injury risks. These integrated devices are primarily used to quantify locomotor patterns, and the intensity achieved during training or matches. They provide numerous workload metrics, categorized into kinematic (movement at various running speeds), metabolic (estimated energy cost during activity) [4], and mechanical (load on the body during accelerations and decelerations) [5]. Central to modern training strategies is the concept of monitoring training loads to achieve an optimal balance between physical stress and recovery [6]. In this field, the acute:chronic workload ratio (ACWR), conceptualized by [PERSON] [7], is frequently utilized to evaluate athlete readiness and injury susceptibility. This ratio, calculated as a fraction between the current week's training load (acute load) and a rolling average of the preceding four weeks (chronic load), offers insights into the balance between acute training stress and chronic training adaptation. Research has shown a strong correlation between heightened ACWRs and increased injury susceptibility in athletes [8; 9; 10]. Specifically, when the ACWR exceeded a value of 1.5, indicating a workload 1.5 times higher than the athlete's preparedness level, the risk of injury increased by two-four times in the following seven days [7; 11]. Notwithstanding recent criticism, it remains a measure of interest within sports science. Some studies [12; 13; 14] acknowledge and support its application, suggesting that it may help guide training strategies. Research suggests that the ACWR can serve as a useful tool when incorporated into a multifaceted monitoring system alongside other established methods for managing load and injury risk [15]. Notably, while the ACWR shows an association with injury risk, it is not designed to function as a predictor of injury occurrence. It is important to emphasize that this association should not be mistaken for an ability to predict injuries at the individual player level [16; 17]. Others, however, have questioned its methodological foundations and predictive value, emphasizing issues such as statistical artifacts, the lack of predictive accuracy, and unjustified reclassification caused by its ratio-based approach. [PERSON] et al. [18; 19] demonstrated that the ACWR magnifies the effect of acute workload (AL) without adding meaningful predictive value. [PERSON] et al. [20] and [PERSON] [21] further criticized the use of ratios, highlighting their susceptibility to noise, spurious correlations, and inflated odds ratios. To better understand how team sports athletes respond to training loads, the integration of IMUs with GPS sensors has become common practice, as this combination provides more comprehensive and detailed performance metrics. Among these metrics, vertical stiffness (K\({}_{\text{vert}}\)) is a key biomechanical parameter reflecting the body's ability to store and release elastic energy during dynamic activities.