Efficient Sports Training Periodization through Cloud-Based Support Vector Machines

A unique technique to optimize sports training periodization tactics is proposed in this paper, which utilizes cloud-based support vector machines (SVMs). Static planning, which is at the heart of many traditional periodization systems, could not be flexible enough to meet the demands of modern athletes or maintain their changing performance indicators. Athletes' biometrics, past performances, environmental variables, and training procedures are just a few examples of the many data inputs that may be dynamically analyzed with the help of SVMs hosted in the cloud. Training plans are created for each athlete based on their unique traits and objectives using machine learning algorithms and real-time processing. Through enhanced flexibility and training regimen efficiency, our trials show that this technique leads to better performance results than standard periodization strategies. Athletes and coaches alike may benefit from cloud-based systems' scalability and accessibility, which allows for easy incorporation into preexisting training frameworks. It highlights the incredible power of modern machine learning approaches to transform sports training methods. It opens exciting possibilities for developing more effective and personalized tactics for athlete development across many athletic disciplines.