Enhancing IPL Match Outcome Prediction With Privacy-preserving Using Machine Learning Techniques

Privacy-preserving methods in IPL winning prediction represent a paradigm shift in how AI is seamlessly integrated into cricket analytics while addressing critical concerns regarding data security. These innovative techniques encompass a range of advanced methodologies, including federated learning, differential privacy, and secure multi-party computation. By leveraging these techniques, teams can collaborate securely and derive accurate predictions without compromising sensitive player statistics or confidential information. AI deployment in sports analytics. As data privacy regulations continue to evolve, the adoption of privacypreserving methods becomes increasingly crucial for IPL teams to remain compliant while utilizing AI to its fullest extent for competitive advantage and strategic decision-making. Ultimately, these developments highlight the significance of privacy and data protection in the digital world of sports analytics and open the door to a more ethical, safe, and successful era of AI-driven IPL winning predictions. Notably, the Random Forest algorithm demonstrates exceptional accuracy of 93.59%, surpassing other models and showcasing the power of ensemble learning within the PPML framework.