Spatial and Temporal Entropies in the Spanish Football League: A Network Science Perspective

We quantified the spatial and temporal entropy related to football teams and their players by means of a pass-based interaction. First, we calculated the spatial entropy associated to the positions of all passes made by a football team during a match, obtaining a spatial entropy ranking of Spanish teams during the 2017/2018 season. Second, we investigated how the player’s average location in the field is related to the amount of entropy of his passes. Next, we constructed the temporal passing networks of each team and computed the deviation of their network parameters along the match. For each network parameter, we obtained the permutation entropy and the statistical complexity of its temporal fluctuations. Finally, we investigated how the permutation entropy (and statistical complexity) of the network parameters was related to the total number of passes made by a football team. Our results show that (i) spatial entropy changes according to the position of players in the field, and (ii) the organization of passing networks change during a match and its evolution can be captured measuring the permutation entropy and statistical complexity of the network parameters, allowing to identify what parameters evolve more randomly.

[1]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[2]  J. Werner,et al.  Football , 1892, The Hospital.

[3]  M Chavez,et al.  Detection of time reversibility in time series by ordinal patterns analysis. , 2018, Chaos.

[4]  Mason A. Porter,et al.  Multilayer networks , 2013, J. Complex Networks.

[5]  Bernard Cazelles,et al.  Symbolic dynamics for identifying similarity between rhythms of ecological time series , 2004 .

[6]  Ken Yamamoto,et al.  Statistical properties of position-dependent ball-passing networks in football games , 2014 .

[7]  V. Borooah,et al.  Measuring competitive balance in sports using generalized entropy with an application to English premier league football , 2012 .

[8]  Javier López Peña,et al.  Who can replace Xavi? A passing motif analysis of football players , 2015, ArXiv.

[9]  J. Sampaio,et al.  Measuring Tactical Behaviour in Football , 2012, International Journal of Sports Medicine.

[10]  Jaime Sampaio,et al.  Timescales for exploratory tactical behaviour in football small-sided games , 2016, Journal of sports sciences.

[11]  Jaime Sampaio,et al.  Exploring Team Passing Networks and Player Movement Dynamics in Youth Association Football , 2017, PLoS ONE.

[12]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Massimiliano Zanin,et al.  Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review , 2012, Entropy.

[14]  Luca Pappalardo,et al.  Effective injury forecasting in soccer with GPS training data and machine learning , 2017, PloS one.

[15]  Michael Hauhs,et al.  Ordinal pattern and statistical complexity analysis of daily stream flow time series , 2013 .

[16]  P. J. Clark,et al.  Distance to Nearest Neighbor as a Measure of Spatial Relationships in Populations , 1954 .

[17]  O A Rosso,et al.  Distinguishing noise from chaos. , 2007, Physical review letters.

[18]  Jordi Luque,et al.  Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game , 2018, Front. Psychol..

[19]  Keith Davids,et al.  Team Sports Performance Analysed Through the Lens of Social Network Theory: Implications for Research and Practice , 2017, Sports Medicine.

[20]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[21]  W. Ochs Basic properties of the generalized Boltzmann-Gibbs- Shannon entropy , 1976 .

[22]  Hilda A. Larrondo,et al.  Generalized Statistical Complexity Measure , 2010, Int. J. Bifurc. Chaos.

[23]  A. Shorrocks,et al.  Inequality Decompositions By Factor Components , 1982 .

[24]  Bruno Travassos,et al.  Application of entropy measures to analysis of performance in team sports , 2016 .

[25]  Luca Pappalardo,et al.  A network-based approach to evaluate the performance of football teams , 2015 .

[26]  Archit Navandar,et al.  Validation of a Video-Based Performance Analysis System (Mediacoach®) to Analyze the Physical Demands during Matches in LaLiga , 2019, Sensors.

[27]  Juan Julián Merelo Guervós,et al.  A network analysis of the 2010 FIFA world cup champion team play , 2013, J. Syst. Sci. Complex..

[28]  Pablo Rodriguez,et al.  Searching for a Unique Style in Soccer , 2014, ArXiv.

[29]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[30]  D. Garlaschelli,et al.  Ensemble approach to the analysis of weighted networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Javier M. Buldú,et al.  Successful strategies for competing networks , 2013, Nature Physics.

[32]  J. Busquets,et al.  Defining a historic football team: Using Network Science to analyze Guardiola’s F.C. Barcelona , 2019, Scientific Reports.

[33]  Keith Davids,et al.  Capturing complex, non-linear team behaviours during competitive football performance , 2013, Journal of Systems Science and Complexity.

[34]  Piet Van Mieghem,et al.  Graph Spectra for Complex Networks , 2010 .

[35]  Z. Wang,et al.  The structure and dynamics of multilayer networks , 2014, Physics Reports.

[36]  Osvaldo A. Rosso,et al.  Generalized statistical complexity measures: Geometrical and analytical properties , 2006 .

[37]  Cristina Masoller,et al.  Quantifying the statistical complexity of low-frequency fluctuations in semiconductor lasers with optical feedback , 2010 .

[38]  Duarte Araújo,et al.  Network Characteristics of Successful Performance in Association Football. A Study on the UEFA Champions League , 2017, Front. Psychol..

[39]  Joachim Gudmundsson,et al.  Spatio-Temporal Analysis of Team Sports , 2016, ACM Comput. Surv..

[40]  José António Tenreiro Machado,et al.  Dynamical Stability and Predictability of Football Players: The Study of One Match , 2014, Entropy.

[41]  Fernando Manuel Lourenço Martins,et al.  Using Network Metrics in Soccer: A Macro-Analysis , 2015, Journal of human kinetics.

[42]  J. Duch,et al.  Quantifying the Performance of Individual Players in a Team Activity , 2010, PloS one.