For professional clubs, collecting and analyzing performance data is essential for improving results and preventing injuries. WeakRisk Sport Solutions already integrates algorithms based on scientific sources to analyze internal and external workloads and to manage athletes optimally.
With intuitive apps, players can independently enter data such as RPE BORG CR10, GQR, TIA, and sleep parameters. These tools reduce the workload of athletic trainers and improve precision in data collection. The next step is the integration of Artificial Intelligence with the I.A. project to estimate individual injury probability and elevate prevention to a higher level.
Here is a direct confrontation between human intuition and artificial logic: a dialogue with Sbotta, the AI that always speaks its mind, is born.
Mathematical models analyze training loads (volume, intensity, frequency) and individual physiological data, considering recovery capacity, stress, and predispositions.
Acute:Chronic Workload Ratio (ACWR): essential for assessing injury risk from workload imbalances. AI & Machine Learning: analyze ACWR variations with clinical history, individual traits, environmental conditions, and GPS data.
The use of advanced mathematical models and real-time analysis allows for an innovative approach to injury prevention, tailoring training and recovery to the needs of each athlete.
Our goal is to offer a scientifically validated tool that helps technical and medical staff plan training and manage athletes’ health, reducing injuries and optimizing performance.