Extended Energy-Expenditure Model in Soccer: Evaluating Player Performance in the Context of the Game

Abstract

Every soccer game influences each player’s performance differently. Many studies have tried to explain the influence of different parameters on the game; however, none went deeper into the core and examined it minute-by-minute. The goal of this study is to use data derived from GPS wearable devices to present a new framework for performance analysis. A player’s energy expenditure is analyzed using data analytics and K-means clustering of low-, middle-, and high-intensity periods distributed in 1 min segments. Our framework exhibits a higher explanatory power compared to usual game metrics (e.g., high-speed running and sprinting), explaining 45.91% of the coefficient of variation vs. 21.32% for high-, 30.66% vs. 16.82% for middle-, and 24.41% vs. 19.12% for low-intensity periods. The proposed methods enable deeper game analysis, which can help strength and conditioning coaches and managers in gaining better insights into the players’ responses to various game situations.

Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review

“…Similarly, additional researchers conducted a study of clustering and performance profiling utilising GPS data and 38 male football players, respectively, in order to identify unique performance profiles. The incorporation of machine learning techniques in this study enhances comprehension of the diverse performance attributes exhibited by players, hence facilitating the development of customised training and optimization approaches ( 48 ). In an effort to forecast individual acceleration-velocity profiles in real-world scenarios utilising GNSS readings, some researchers sought to further the individualization of acceleration-velocity profiling in the context of professional male footballers.…”