Extended energy-expenditure model in soccer: evaluating player performance in the context of the game

Skoki A., Rossi A., Cintia P., Pappalardo L., Stajduhar I.

and Optics  Fatigue  Instrumentation  Fitness tracking  Biochemistry  Clustering  machine learning  fitness tracking  Atomic and Molecular Physics  Game intensity  fatigue  Electrical and Electronic Engineering  Analytical Chemistry  game intensity  Machine learning  clustering 

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.

Source: Sensors (Basel) 22 (2022). doi:10.3390/s22249842

Publisher: Molecular Diversity Preservation International (MDPI),, Basel

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