Application of entropy measures to analysis of performance in team sports

Over the last years, several researchers have been claiming that team ball sports may be viewed as dynamical systems and, thus, they should be thoroughly investigated using congruent concepts and tools. The study of variability in the sport performance domain has shown potential to contribute with valuable information about tactical behaviours related with space and time management within ever changing task constraints featuring team sports contests. Here we detail how different entropy measures have been applied to the study of performance variability to uncover the interactions underlying players and teams’ performances. With that purpose, urging issues related with information entropy, approximate entropy and sample entropy applications are discussed as a mean to enrich the state of the art in team sport performance. In sum, measurements of entropy in team sports have shown great potential to assess the uncertainty of players’ spatial distributions and dominant regions areas and of several collective team behaviours (e.g., team synchrony and team dispersion) throughout the course of a match. Entropy can also be used as a potential tool to identify expert performances and differentiate skilled from novice athletes. Future holds many other applications of this statistic in the context of performance analysis in sports, and the inclusion of new and more sophisticated entropy algorithms.

[1]  Craig Wright,et al.  Comment on ‘Performance analysis in football: A critical review and implications for future research’ , 2014, Journal of sports sciences.

[2]  Dirk Helbing,et al.  Coherent moving states in highway traffic , 1998, Nature.

[3]  Jonas Poderys,et al.  Measuring the Complexity of a Physiological Time Series: a Review , 2018, Baltic Journal of Sport and Health Sciences.

[4]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[5]  Keith Davids,et al.  Field dimension and skill level constrain team tactical behaviours in small-sided and conditioned games in football , 2014, Journal of sports sciences.

[6]  Craig Wright,et al.  The wider context of performance analysis and it application in the football coaching process , 2014 .

[7]  T. McGarry.  Applied and theoretical perspectives of performance analysis in sport: Scientific issues and challenges , 2009 .

[8]  Kevin Shockley,et al.  Interpersonal Synergies , 2010, Front. Psychology.

[9]  A L Goldberger,et al.  Physiological time-series analysis: what does regularity quantify? , 1994, The American journal of physiology.

[10]  Selin Kesebir,et al.  The Superorganism Account of Human Sociality , 2012, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[11]  Keith Davids,et al.  The Role of Ecological Dynamics in Analysing Performance in Team Sports , 2012, Sports Medicine.

[12]  Thomas M. Cover,et al.  Elements of Information Theory: Cover/Elements of Information Theory, Second Edition , 2005 .

[13]  R Alcaraz,et al.  Study of Sample Entropy ideal computational parameters in the estimation of atrial fibrillation organization from the ECG , 2010, 2010 Computing in Cardiology.

[14]  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.

[15]  Jaime Sampaio,et al.  Effect of player position on movement behaviour, physical and physiological performances during an 11-a-side football game , 2014, Journal of sports sciences.

[16]  R. Emmerik,et al.  A dynamical systems approach to lower extremity running injuries. , 1999, Clinical biomechanics.

[17]  Nathaniel H. Hunt,et al.  The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets , 2012, Annals of Biomedical Engineering.

[18]  T. Reilly,et al.  Soccer as a Dynamical System: Some Theoretical Considerations , 2005 .

[19]  L. Glass.  Synchronization and rhythmic processes in physiology , 2001, Nature.

[20]  Keith Davids.  Genes, training and other constraints on individual performance: A role for dynamical systems theory? , 2001 .

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

[22]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[23]  Nicholas Stergiou,et al.  Movement Variability and the Use of Nonlinear Tools: Principles to Guide Physical Therapist Practice , 2009, Physical Therapy.

[24]  T. Lincoln.  The Information: A History, A Theory, A Flood , 2011 .

[25]  J. Alpert,et al.  Recent changes in attack and survival rates of acute myocardial infarction (1975 through 1981). The Worcester Heart Attack Study. , 1986, JAMA.

[26]  D. Sumpter.  The principles of collective animal behaviour , 2006, Philosophical Transactions of the Royal Society B: Biological Sciences.

[27]  N. Stergiou,et al.  Optimal Movement Variability: A New Theoretical Perspective for Neurologic Physical Therapy , 2006, Journal of neurologic physical therapy : JNPT.

[28]  Keith Davids,et al.  Effects of Pitch Size and Skill Level on Tactical Behaviours of Association Football Players during Small-Sided and Conditioned Games , 2014 .

[29]  D. Cuesta-Frau,et al.  Measuring body temperature time series regularity using approximate entropy and sample entropy , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  A. Barabasi,et al.  Physics of the rhythmic applause. , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[31]  Ferdinand J. Venditti,et al.  Reduced Heart Rate Variability and Mortalit Risk in an Elderly Cohort: The Framingham Heart Study , 1994, Circulation.

[32]  K. Newell,et al.  Noise, information transmission, and force variability. , 1999, Journal of experimental psychology. Human perception and performance.

[33]  Paul S Glazier,et al.  Game, Set and Match? Substantive Issues and Future Directions in Performance Analysis , 2010, Sports medicine.

[34]  C. Handford,et al.  Serving Up Variability and Stability , 2006 .

[35]  William J. McDermott,et al.  Issues in Quantifying Variability From a Dynamical Systems Perspective , 2000 .

[36]  M. P. Griffin,et al.  Sample entropy analysis of neonatal heart rate variability. , 2002, American journal of physiology. Regulatory, integrative and comparative physiology.

[37]  Breanna E. Studenka,et al.  Noise and Complexity in Human Postural Control: Interpreting the Different Estimations of Entropy , 2011, PloS one.

[38]  Dingchang Zheng,et al.  Analysis of heart rate variability using fuzzy measure entropy , 2013, Comput. Biol. Medicine.

[39]  Karl M. Newell,et al.  Variability and Motor Control , 1993 .

[40]  S. Bennett,et al.  Extended Book Review: Dynamics of Skill Acquisition: A Constraints-Led Approach , 2007 .

[41]  Benoît G. Bardy,et al.  Variability in Postural Coordination Dynamics , 2006 .

[42]  Andreas Holzinger,et al.  Selection of entropy-measure parameters for knowledge discovery in heart rate variability data , 2014, BMC Bioinformatics.

[43]  Abbey C. Thomas,et al.  Alterations in stride-to-stride variability during walking in individuals with chronic ankle instability. , 2015, Human movement science.

[44]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[45]  Steven M. Pincus,et al.  A regularity statistic for medical data analysis , 1991, Journal of Clinical Monitoring.

[46]  Keith Davids,et al.  Science of winning soccer: Emergent pattern-forming dynamics in association football , 2013, Journal of Systems Science and Complexity.

[47]  Keith Davids,et al.  Performance analysis in team sports: Advances from an Ecological Dynamics approach , 2013 .

[48]  Renato Rodano,et al.  Motor variability in sports: A non-linear analysis of race walking , 2010, Journal of sports sciences.

[49]  N. Stergiou.  Innovative Analyses of Human Movement , 2003 .