The Influence of Data Analytics on Sports Performance - Arya Amit Sadvilkar; Ritik Saha; Rohini Bagul (Recent Trends in Sports, 2026)
Data analytics has drastically changed how we evaluate, improve, and maintain athletic performance. Coaches used to use subjective observations as well as only limited numbers of statistics to consider player performance; however, tracking technology is now advancing at a fast pace. There are now very large amounts of real-time data available on athletes in regards to speed, movement patterns, fatigue, efficiency, etc. This enables all teams to more accurately make objective and data-driven decisions about an athlete’s performance. This study provides examples of how data analytics impacts athletic performance across several different sports disciplines, such as football, cricket, hockey, and basketball. It highlights both the theoretical aspects of data analytics and the practical side using the various tools and machine learning techniques available in Python. This research provides results on how successfully processed, visualized, and analyzed data can provide insights that can support the improvement of athletic performance, therefore, helping teams develop better performance processes. By applying various analytical models to identify patterns, predict future results, and optimize athlete training/game play strategies. Moreover, the research emphasizes how valuable data analytics can assist with injury prevention and rehabilitation management. By tracking workload and physical stress indicators on a regular basis, teams can lower the chance of sustaining an injury while helping to provide athletes with superior rehabilitation plans. With the use of data-driven approaches, it is also easier to create training programs that are tailored to each player’s unique requirements due to the incorporation of additional analytic data.
Keywords: Athlete monitoring; Machine Learning; Predictive modeling; Sports Performance; data analytics; injury prevention