Using Multivariate Adaptive Regression Splines in the Construction of Simulated Soccer Team's Behavior Models

Abstract In soccer, like in other collective sports, although players try to hide their strategy, it is always possible, with a careful analysis, to detect it and to construct a model that characterizes their behavior throughout the game phases. These findings are extremely relevant for a soccer coach, in order not only to evaluate the performance of his athletes, but also for the construction of the opponent team model for the next match. During a soccer match, due to the presence of a complex set of intercorrelated variables, the detection of a small set of factors that directly influence the final result becomes almost an impossible task for a human being. In consequence of that, a huge number of software packages for analysis capable of calculating a vast set of game statistics appeared over the years. However, all of them need a soccer expert in order to interpret the produced data and select which are the most relevant variables. Having as a base a set of statistics extracted from the RoboCup 2D Sim...

[1]  G. Atkinson,et al.  Match-to-Match Variability of High-Speed Activities in Premier League Soccer , 2010, International journal of sports medicine.

[2]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[3]  Dapeng Wu,et al.  A RELIEF Based Feature Extraction Algorithm , 2008, SDM.

[4]  Manuela M. Veloso,et al.  Defining and using ideal teammate and opponent agent models: a case study in robotic soccer , 2000, Proceedings Fourth International Conference on MultiAgent Systems.

[5]  A. Nevill,et al.  Selected issues in the design and analysis of sport performance research , 2001, Journal of sports sciences.

[6]  Hiroaki Kitano,et al.  The RoboCup Synthetic Agent Challenge 97 , 1997, IJCAI.

[7]  Elizabeth Sklar,et al.  RoboCupJunior: Learning with Educational Robotics , 2003, AI Mag..

[8]  I M Franks,et al.  Training coaches to observe and remember. , 1991, Journal of sports sciences.

[9]  Stephen R. Clarke,et al.  Home ground advantage of individual clubs in English soccer , 1995 .

[10]  Robert O. Keohane,et al.  Designing Social Inquiry: Scientific Inference in Qualitative Research. , 1995 .

[11]  Thomas Reilly,et al.  Applications of Logistic Regression to Shots at Goal in Association Football , 2005 .

[12]  Nic James,et al.  Possession as a performance indicator in soccer. , 2004 .

[13]  Thomas Reilly,et al.  Handbook of Soccer Match Analysis: A Systematic Approach to Improving Performance , 2006 .

[14]  P. Krustrup,et al.  High-intensity running in English FA Premier League soccer matches , 2009, Journal of sports sciences.

[15]  M. Maher.  Modelling association football scores , 1982 .

[16]  Manuela M. Veloso,et al.  On Behavior Classification in Adversarial Environments , 2000, DARS.

[17]  Mark J. Dixon,et al.  A birth process model for association football matches , 1998 .

[18]  Jian Li,et al.  Iterative RELIEF for feature weighting , 2006, ICML.

[19]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[20]  Bruce R. Kowalski,et al.  MARS: A tutorial , 1992 .

[21]  Thomas G. Dietterich.  Machine-Learning Research Four Current Directions , 1997 .

[22]  Esa Peltola,et al.  Application of four different football match analysis systems: A comparative study , 2010, Journal of sports sciences.

[23]  Hiroaki Kitano,et al.  RoboCup Rescue: search and rescue in large-scale disasters as a domain for autonomous agents research , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[24]  Thomas Reilly,et al.  Analysis of goal-scoring patterns of European top level soccer teams , 2014 .

[25]  Tamio Arai,et al.  Distributed Autonomous Robotic Systems 3 , 1998 .

[26]  Carlos Lago,et al.  Determinants of possession of the ball in soccer , 2007, Journal of sports sciences.

[27]  J F Gréhaigne,et al.  Dynamic-system analysis of opponent relationships in collective actions in soccer. , 1997, Journal of sports sciences.

[28]  Luís Paulo Reis,et al.  Using a Datawarehouse to Extract Knowledge from Robocup Teams , 2008, ICEIS.

[29]  J. Freidman,et al.  Multivariate adaptive regression splines , 1991 .

[30]  Greg Atkinson,et al.  Statistical methods for analysing discrete and categorical data recorded in performance analysis , 2002, Journal of sports sciences.

[31]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[32]  Pedro Henriques Abreu,et al.  Real-Time Wireless Location and Tracking System with Motion Pattern Detection , 2010 .

[33]  Jerome H. Friedman.  Multivariate adaptive regression splines (with discussion) , 1991 .

[34]  Hiroaki Kitano,et al.  RoboCup: The Robot World Cup Initiative , 1997, AGENTS '97.

[35]  Luís Paulo Reis,et al.  Performance analysis in soccer: a Cartesian coordinates based approach using RoboCup data , 2011, Soft Computing.