Reshaping the future of sports with artificial intelligence: Challenges and opportunities in performance enhancement, fan engagement, and strategic decision-making

The rapid advancement of artificial intelligence has significantly transformed the sports industry over the past decade. In sports performance, artificial intelligence-driven analytics has become essential for optimizing athlete training, injury prevention, and performance enhancement, with sophisticated algorithms analyzing player data to develop personalized training programs and identify areas for improvement. The impact of artificial intelligence on fan engagement has been profound, enabling the delivery of highly personalized content and recommendations that foster stronger connections between fans and their favorite teams or athletes. Leveraging artificial intelligence-powered algorithms, ticket pricing strategies are optimized, resulting in increased ticket sales and revenue. Artificial intelligence-based performance evaluation tools assist sports managers in making well-informed decisions regarding team composition and tactics. This study shows the use of an artificial neural network with artificial intelligence technologies, including data analysis, to improve sports performance, management, and decision-making. Data-driven insights aid in talent scouting and recruitment, allowing managers to identify promising athletes with precision. Issues about data privacy, algorithmic bias, and fair competition need to be addressed to ensure responsible and equitable utilization of artificial intelligence in augmenting the sports industry. The integration of artificial intelligence technologies has profoundly transformed both sports and sports management. From enhancing athlete performance and fan engagement to optimizing administrative tasks and strategic decision-making, the impact of artificial intelligence on the sports domain continues to expand. Results show significant enhancements in all areas, with artificial intelligence outperforming data analysis alone. Prediction errors of the artificial neural network model align well with experimental targets.