A Big Data Approach to Forecast Injuries in Professional Sports Using Support Vector Machine

Injuries are a big concern in professional sports. It is recognized as one of the significant factors in athletes’ careers and team performance. Early detection of injuries in sports can assist teams in taking preventive measures and enhance player’s performance. This paper explores the use of machine learning algorithm namely Support Vector Machines (SVMs) to predict injuries in professional sports and use Big Data Analytics (BDA) techniques to provide useful insights regarding players. SVMs are capable of handling complex and non-linear relationships among data and classifying it accurately while BDA aids in player health management and resource allocation The study commences by collecting large amounts of data from various sources related to athletes and storing it in Cassandra. These sources include athlete performance records, medical histories and wearable technology data. The data is then cleaned and transformed into a uniform format for processing. The Recursive Feature Elimination (RFE) technique is used to pick the most relevant data points. These tools are pivotal in handling the volume, velocity and variety of the data. Secondly, an SVM model is formulated which includes input features, kernel functions and a decision function. The model works by mapping input data into a high-dimensional space using the kernel function. It then finds the optimal hyperplane that maximizes the margin between the two classes which are injured and not injured. The data points closest to the hyperplane are represented in the form of support vectors and are used to predict new data points and classify the vector as injury or non-injury. Finally, the proposed SVM model is trained on a subset of the data. It uses grid search and cross-validation techniques to optimize the model’s performance. The results show that the proposed SVM model achieved an accuracy of 92.3% and a prediction rate of 87.5%, which highlights the effectiveness of our approach.