On an Entropy-based Performance Analysis in Sports

This paper discusses the major assumptions of influential ecological approaches on the human movement variability in sports and how it can be analyzed by benefiting from well-known measures of entropy. These measures are exploited so as to further understand the performance of athletes from a dynamical and chaotic perspective. Based on the presented evidences, entropy-based techniques will be considered to measure, analyze and evaluate the human performance variability under three different case studies: i) golf; ii) tennis; and iii) soccer. At a first stage, the athletes' performance will be analyzed at the individual level by considering the golf putting (pendulum movement) and the tennis serve (ballistic movement). Under these gestures, the approximate entropy is considered to extract the variability inherent to the process variables. Afterwards, the athletes' performance will be analyzed at the collective level by considering the soccer case (team sport). To that end,

[1]  J. Foley The co-ordination and regulation of movements , 1968 .

[2]  Micael S. Couceiro,et al.  A fractional calculus approach for the evaluation of the golf lip-out , 2012, Signal Image Video Process..

[3]  Jürgen Kurths,et al.  Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System , 2013, Entropy.

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

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

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

[7]  L. Liebovitch,et al.  "Fractal dynamics of human gait: stability of long-range correlations in stride interval fluctuations". , 1996, Journal of applied physiology.

[8]  Mert R. Sabuncu,et al.  Entropy-based Image Registration , 2006 .

[9]  P. Grassberger,et al.  Estimation of the Kolmogorov entropy from a chaotic signal , 1983 .

[10]  Thomas Reilly,et al.  The Role of Motion Analysis in Elite Soccer , 2008, Sports medicine.

[11]  F. Clemente,et al.  The variability of the serve toss in tennis under the influence of artificial crosswind. , 2013, Journal of sports science & medicine.

[12]  Dave Pelz,et al.  Dave Pelz's Putting Bible: The Complete Guide to Mastering the Green , 2000 .

[13]  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.

[14]  K. Newell,et al.  Dimensional change in motor learning. , 2001, Human movement science.

[15]  R. Schmidt A schema theory of discrete motor skill learning. , 1975 .

[16]  Micael S. Couceiro,et al.  Measuring Tactical Behaviour Using Technological Metrics: Case Study of a Football Game , 2013 .

[17]  Jonathan Bloomfield,et al.  Physical Demands of Different Positions in FA Premier League Soccer. , 2007, Journal of sports science & medicine.

[18]  S. Bennett,et al.  Emergence of sport skills under constraints , 2004 .

[19]  Nicholas Stergiou,et al.  A Perspective on Human Movement Variability With Applications in Infancy Motor Development , 2013 .

[20]  Micael S. Couceiro,et al.  A non-linear understanding of golf putting , 2013 .

[21]  Keith Davids,et al.  Approximate Entropy Normalized Measures for Analyzing Social Neurobiological Systems , 2012, Journal of motor behavior.

[22]  Fernando Manuel Lourenço Martins,et al.  Interpersonal Dynamics: 1v1 Sub-Phase at Sub-18 Football Players , 2013, Journal of human kinetics.

[23]  A. Tozeren,et al.  Human Body Dynamics: Classical Mechanics and Human Movement , 1999 .

[24]  R. Menayo Análisis de la relación entre la consistencia en la ejecución del patrón motor del servicio en tenis, la precisión y su aprendizaje en condiciones de variabilidad , 2010 .

[25]  W. Bruzek,et al.  Variability of postural “reflexes” in humans , 2004, Experimental Brain Research.

[26]  Gonçalo Dias,et al.  Distance and slope constraints: adaptation and variability in golf putting. , 2014, Motor control.

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

[28]  J. Adams,et al.  A closed-loop theory of motor learning. , 1971, Journal of motor behavior.

[29]  J. Vielliard,et al.  Using Shannon entropy on measuring the individual variability in the Rufous-bellied thrush Turdus rufiventris vocal communication. , 2000, Journal of theoretical biology.

[30]  Rui Sousa Mendes,et al.  The effect of artificial side wind on the serve of competitive tennis players , 2012 .

[31]  J Bangsbo,et al.  The physiology of soccer--with special reference to intense intermittent exercise. , 2003, Acta physiologica Scandinavica. Supplementum.

[32]  Andreas Daffertshofer,et al.  Principal components in three-ball cascade juggling , 2000, Biological Cybernetics.

[33]  Tatsuyuki Ohtsuki,et al.  Adaptive Variability in Skilled Human Movements , 2008 .

[34]  Gonçalo Dias,et al.  Accuracy of Pattern Detection Methods in the Performance of Golf Putting , 2013, Journal of motor behavior.

[35]  Karl M. Newell,et al.  The amount and structure of human movement variability , 2008 .

[36]  M. McAleer,et al.  An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series , 2016 .

[37]  J. Kurths,et al.  Quantitative analysis of heart rate variability. , 1995, Chaos.

[38]  J. Sullivan,et al.  Priors for the Ball Position in Football Match using Contextual Information , 2010 .