How do teams use data analytics in sports?

Teams use data analytics in sports to gain a competitive edge by systematically collecting, analyzing, and applying a wide range of statistical information to improve performance, strategy, and player management. This process helps coaches, managers, and players make more informed decisions on and off the field. Professional teams analyze various types of stats depending on the sport, but common categories include player performance metrics (such as shooting accuracy, speed, distance covered, or defensive actions), team-based statistics (like possession percentages, scoring efficiency, or turnovers), and situational data (performance under pressure, success rates in specific game scenarios). For example, in basketball, teams track shooting percentages from different court areas, player movement patterns, and assist-to-turnover ratios. In soccer, data on passing accuracy, expected goals (xG), and player positioning are key. Beyond performance, teams also use analytics for injury prevention by monitoring workload and physiological data, and for scouting by evaluating potential recruits statistically. Advanced techniques such as machine learning and video analysis further enhance insights, enabling teams to predict opponent strategies, optimize lineups, and tailor training programs. Overall, data analytics has become an integral part of modern sports, transforming raw statistics into actionable intelligence that provides a strategic advantage.