Interclass correlation coefficient (ICC).

ObjectivesThis study aimed to develop and evaluate a real-time sensor-based monitoring and feedback system for enhancing four core football performance metrics, passing accuracy, sprint speed, agility, and shot power, each defined and quantified using validated wearable sensors and baseline‐referenced improvement thresholds.MethodsA randomized controlled trial (RCT) was conducted over eight weeks with 30 university-level male football players (aged 21.70 ± 1.28 years) from Zhengzhou University. Participants were randomly assigned to an experimental group (n = 15), which trained using the real-time monitoring system, or a control group (n = 15), which followed traditional training methods. The wearable system integrated accelerometers, gyroscopes, and magnetometers to provide real-time, skill-specific feedback during drills. Performance data were collected weekly and analyzed using repeated measures ANOVA with effect sizes calculated via partial eta squared (η²p).ResultsThe results demonstrated statistically and practically significant improvements in the experimental group across all measured parameters. Notably, the effect sizes ranged from large to very large (η²p = .59 to.89), indicating that the improvements were not only statistically reliable but also substantial enough to have meaningful impact on the players’ performance. Passing accuracy increased by 10.21% (F(1,27) = 210.02, p ConclusionBy delivering instantaneous, sensor-validated feedback on precisely defined performance metrics, the system accelerated improvements in both technical and physical skills. These findings support the integration of wearable sensor technology into football training to achieve data-driven, individualized skill development. Future work should explore AI-driven personalization and long-term retention of gains.