Dynamic Zoning in the Course of GIS-Based Football Game Analysis

This paper is one of a series of articles about GIS-based game analysis in association football and presents an approach to dynamic zoning of football pitches based upon the players’ movements. For this purpose, tracking data are employed, which were kindly provided by ProzoneSports. Since football is highly dynamic, spaces are constantly changing over the entire game’s period. Therefore, it is reasonable to capture these alterations in the team’s use of space and to analyse them. In order to do so a Python script which automatically trisects the pitch vertically in a defenders’, midfielders’, and forwards’ zone was developed. It can be executed as a custom tool in ArcGIS and determines the zones’ height, width and area. Furthermore, its functionality can be considered the basis for manifold analysis opportunities. To provide an example, in the paper’s second part another custom tool for ArcGIS is presented, which applies the conception of dynamic zoning for analysing the teams’ offensive qualities based upon the defenders’ zone’s vertical height. This paper’s overall objective is to highlight the benefits of dynamic zoning in the course of football game analyses. Moreover, the demonstration of the tools’ functionality is intended to foster the discussion about the presented conception’s methodological principles as well as its potential application areas. In addition to this, an expert survey was conducted interviewing professional game analysts from Austria and Germany. The results provide evidence that the conception of dynamic zoning is worth to refine, as it provides a novel approach of analysing the game.

[1]  Di Salvo Valter,et al.  Validation of Prozone ®: A new video-based performance analysis system , 2006 .

[2]  A. Mitchell The ESRI guide to GIS analysis , 1999 .

[3]  Robert Weibel,et al.  Towards a taxonomy of movement patterns , 2008, Inf. Vis..

[4]  Jochen Gläser,et al.  Experteninterviews und qualitative Inhaltsanalyse als Instrumente rekonstruierender Untersuchungen. , 2010 .

[5]  M. Roderick,et al.  Fédération Internationale de Football Association , 2012 .

[6]  Michael Zeiler Modeling Our World: The ESRI Guide to Geodatabase Concepts , 2010 .

[7]  Gemma Robinson,et al.  Validation of the ProZone3® player tracking system: a preliminary report , 2009 .

[8]  Paul S. Bradley,et al.  Evaluation of Research Using Computerised Tracking Systems (Amisco® and Prozone®) to Analyse Physical Performance in Elite Soccer: A Systematic Review , 2014, Sports Medicine.

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

[10]  Richard Pollard,et al.  Measuring the effectiveness of playing strategies at soccer , 1997 .

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

[12]  B Drust,et al.  Analysis of High Intensity Activity in Premier League Soccer , 2009, International journal of sports medicine.

[13]  V. Di Salvo,et al.  Performance Characteristics According to Playing Position in Elite Soccer , 2006, International journal of sports medicine.

[14]  Emmy Nurhayati,et al.  PENDEKATAN LEAN SIX SIGMA DAN TAGUCHI UNTUK MENGATASI MASALAH PENGEMASAN DAN PEMASARAN PRODUK WEDANG UWUH INSTAN SRUPUT , 2017 .

[15]  Peter Carr,et al.  Characterizing Multi-Agent Team Behavior from Partial Team Tracings: Evidence from the English Premier League , 2012, AAAI.