Predictive Analytics for Tennis Elbow in Cricketers Using Machine Learning

Tennis Elbow Risk Prediction System for Cricketers is a machine learning-based tool that predicts and classifies the risk of tennis elbow among cricket players. The system uses the XGBoost algorithm and analyzes prime factors including age, grip strength, weekly bowling workload, previous injuries, and rate of recovery to categorize players into low, medium, or high-risk groups. The system is trained on real-time data from 400 cricket players for high accuracy and reliability. The system is accessible through a web application where players and coaches can enter relevant information and obtain immediate risk assessments. The backend, developed with Python Flask, supports smooth interaction, and the frontend uses HTML, CSS, and JavaScript for ease of navigation. Specific recommendations such as workload management, grip-strength training, and rehabilitation plans are presented to reduce the risks of injuries. Through the incorporation of predictive analytics within sports medicine, this system enables coaches and players to make informed injury prevention decisions. With future advancements like time-series data analysis and wearable sensor incorporation, accuracy in prediction will also be enhanced further. This technology illustrates the potential applications of machine learning in preventing sporting injuries, maximizing player performance while minimizing injury-related setbacks. (Keywords: Tennis Elbow, Machine Learning, XGBoost, Cricket Injuries, Risk Prediction, Sports Analytics, Injury Prevention)