Vital Sense Analytics—For Maximizing the Performance of Football Players Using Wearables

Wearable devices are increasingly utilized in the realm of sports, including football, to track players’ performance during training and games. These devices incorporate various sensors such as GPS trackers, Pulse oximeters, Node MCU, and ECG sensors, to capture biometric data, including speed, distance covered, and heart rate. The collected data is then transmitted to a central system for analysis (Ash and Stults-Kolehmainen in Establishing a global standard for wearable devices in sport and fitness: perspectives from the New England chapter of the American College of Sports Medicine Members, 2020 [1]). The analyzed and processed data is fed into a machine learning algorithm, which generates output that is received by an application. The model is trained with 10,000 samples and tested with almost 300 test sets. Multiple algorithms have been used out of which, random forest classifies efficiently with an accuracy of 95%. To evaluate the player with performance metrics, the random forest algorithm has been found to be the best when compared among the other algorithms.