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Tactical interaction of offensive and defensive teams in team handball analysed by artificial neural networks

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  • Norbert Schrapf
  • Shaimaa Alsaied
  • Markus Tilp

Abstract

The interaction between teams behaviour is from high relevance for success in sports games. Since the analysis of this interaction is not well established, the present study attempts to model the interaction between opposing teams in team handball. Offensive and defensive playing patterns were determined by means of artificial neural networks from position data of 723 offensive action sequences and the corresponding defensive players, respectively. The most common combinations of these patterns were then analysed statistically. Pattern efficiency was assessed by scoring rate, distance between shooting position and nearest defensive player and distance to goal. No statistically significant relation between pattern combinations and efficiency was found. However, results revealed tendencies to higher efficiency of some tactical patterns. Furthermore, odds ratio analysis revealed advantageous defensive tactics against specific offensive behaviour. Summarizing, results indicate that artificial neural networks are appropriate to model the interaction between teams based on players’ positions.

Suggested Citation

  • Norbert Schrapf & Shaimaa Alsaied & Markus Tilp, 2017. "Tactical interaction of offensive and defensive teams in team handball analysed by artificial neural networks," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 23(4), pages 363-371, July.
    • Handle: RePEc:taf:nmcmxx:v:23:y:2017:i:4:p:363-371
    DOI: 10.1080/13873954.2017.1336733
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    References listed on IDEAS

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    1. Shortridge Ashton & Goldsberry Kirk & Adams Matthew, 2014. "Creating space to shoot: quantifying spatial relative field goal efficiency in basketball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(3), pages 303-313, September.
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    Cited by:

    1. Song, Honglin & Li, Yutao & Fu, Chenyi & Xue, Feng & Zhao, Qiyue & Zheng, Xingyu & Jiang, Kunkun & Liu, Tianbiao, 2024. "Using complex networks and multiple artificial intelligence algorithms for table tennis match action recognition and technical-tactical analysis," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).

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