Injury Analysis Framework for Athletes

Sports has emerged as one of the world's major industries. Thus, athlete health is one of the most critical elements influencing the sector's profits and losses. When potential injuries or poor performance are expected, the risks can be reduced by assessing the players' physical health state and performance. Although traditional methods can only provide limited forecasts, machine learning approaches will allow us to take steps to improve athlete performance and avoid potential injury concerns. In this work, modeling was used to forecast athlete injury and poor performance risks using a rule-based expert system technique. The proposed method has been implemented into software. With the model fed on the athletes' simultaneous data, it was able to take immediate action in the event of an injury or poor performance. This reduces the existing hazards for the invested sportsmen and allows them to perform better. In the study, the fuzzy logic approach was used for modeling, and an accuracy rate of 75.2% was obtained.