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

Arian Skoki; Alessio Rossi; Paolo Cintia; Luca Pappalardo; Ivan Štajduhar

doi:10.3390/s22249842

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

Sensors (Basel). 2022 Dec 14;22(24):9842. doi: 10.3390/s22249842.

Authors

Arian Skoki¹, Alessio Rossi^{2

3}, Paolo Cintia², Luca Pappalardo^{2

3}, Ivan Štajduhar^{1

4}

Affiliations

¹ Department of Computer Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.
² Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy.
³ Institute of Information Science and Technologies (ISTI), National Research Council of Italy (CNR), Giuseppe Moruzzi 1, 56124 Pisa, Italy.
⁴ Center for Artificial Intelligence and Cybersecurity, University of Rijeka, R. Matejcic 2, 51000 Rijeka, Croatia.

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.

Keywords: clustering; fatigue; fitness tracking; game intensity; machine learning.

MeSH terms

Athletic Performance* / physiology
Football*
Health Expenditures
Running* / physiology
Soccer* / physiology

Grants and funding

This work was supported by the Horizon 2020 project EuroCC 951732 National Competence Centres in the Framework of EuroHPC and by the University of Rijeka, Croatia, grant number uniri-tehnic-18-15. Moreover, this work was also supported by the European Community’s H2020 Program under the funding scheme INFRAIA-2019-1: Research Infrastructures grant agreement 871042, www.sobigdata.eu, SoBigData. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.