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Soccer Analytics Using Touch-by-Touch Match Data

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Abstract

This paper discusses several soccer analytics directions exploiting detailed ball touch data from a soccer game. The topics discussed include visualizing team formations and quantifying territorial advantage; determining the network-based structural properties of team play, and computing the importance of individual players for the team interactions. The proposed ideas are illustrated using the data from a real-life Barclays Premier League game, which was made available by StatDNA.

Keywords

Span Tree Social Network Analysis Gini Coefficient Centrality Score Soccer Game 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We would like to thank Jaeson Rosenfeld, CEO of StatDNA for providing the data used in this study.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Industrial and Systems EngineeringTexas A&M UniversityCollege StationUSA

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