PERFORMANCE MONITORING OF SOCCER PLAYERS USING PHYSIOLOGICAL SIGNALS AND PREDICTIVE ANALYTICS
January 1, 2022
With the rapid advancement in wearable sensor technologies and predictive analytics, college and professional sports teams are facing new opportunities of leveraging such technologies and at the same time challenges of maintaining their competency. In professional sports today, the margin between winning and losing is narrow although its impact can be massive. Effective use of predictive analytics along with the implementation of advanced sensors can bring about a deeper understanding of players’ physical condition and performance potential throughout individual games or the entire season, and help sports medicine personnel get the most from available resources while keeping players healthy and minimizing their risks of injury. This paper presents a predictive analytics framework for analyzing and predicting soccer players’ performance data. The data consists of GPS and physiological measurements, collected from female soccer players during both practices and games using Zephyr Bioharness device. The proposed framework consists of data cleaning, filtering, visualizations and analytics modules to provide deeper insights into the data. The preprocessing modules automatically remove outliers using intelligent tools and determine first half, second half and potential overtime RISKS based on data patterns. Furthermore, comparison-based metrics have been developed to analyze the performance of players from different aspects including their activity level, fitness and consistency. For instance, Kolmogorov-Smirnov (KS) test was utilized to extract performance metrics based on players’ Heart Rate and Speed, or a Neural Network-based approach was utilized to analyze the Heart Rate recovery rate of the players and quantify their recovery rate, which is important for effective play. At the end, different visualization tools were used to combine players’
