Discovering patterns of play in netball with network motifs and association rules

Open access


In netball, analysis of the movement of players and the ball across different court locations can provide information about trends otherwise hidden. This study aimed to develop a method to discover latent passing patterns in women’s netball. Data for both pass location and playing position were collected from centre passes during selected games in the 2016 Trans-Tasman Netball Championship season and 2017 Australian National Netball League. A motif analysis was used to characterise passing-sequence observations. This revealed that the most frequent, sequential passing style from a centre pass was the “ABCD” motif in an alphabetical system, or in a positional system “Centre–Goal Attack–Wing Attack–Goal Shooter” and rarely was the ball passed back to the player it was received from. An association rule mining was used to identify frequent ball movement sequences from a centre pass play. The most confident rule flowed down the right-hand side of the court, however seven of the ten most confident rules demonstrated a preference for ball movement down the left-hand side of the court. These results can offer objective insight into passing sequences, and potentially inform team strategy and tactics. This method can also be generalised to other invasion sports.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Agrawal R. & Srikant R. (1994). Fast algorithms for mining association rules. Paper presented at the Proc. 20th int. conf. very large data bases VLDB.

  • Beckkers J. & Dabadghao S. (2017). Flow Motifs in Soccer: What can passing behaviour tell us? Paper presented at the In Proceedings of the 11th MIT sloan sports analytics conference Boston Massachusetts.

  • Bruce L. Brooks E. R. & Woods C. T. (2018). Team and seasonal performance indicator evolution in the ANZ Championship netball league. Journal of sports sciences 36:24 2771-7777. doi:10.1080/02640414.2018.1473099

  • Bruce L. Farrow D. Raynor A. & May E. (2009). Notation analysis of skill expertise differences in netball. International Journal of Performance Analysis in Sport 9(2) 245-254.

  • Clemente F. M. Martins F. M. L. Couceiro M. S. Mendes R. S. & Figueiredo A. J. (2014). A network approach to characterize the teammates’ interactions on football: A single match analysis. Cuadernos de Psicología del Deporte 14(3) 141-148.

  • Croft H. Willcox B. & Lamb P. (2017). Using performance data to identify styles of play in netball: an alternative to performance indicators. International Journal of Performance Analysis in Sport 17(6) 1034-1043.

  • Davidson A. & Trewartha G. (2008). Understanding the physiological demands of netball: A time-motion investigation. International Journal of Performance Analysis in Sport 8(3) 1-17.

  • Dudek D. (2010). Measures for Comparing Association Rule Sets. Paper presented at the International Conference on Artificial Intelligence and Soft Computing Berlin Heidelberg.

  • Fewell J. H. Armbruster D. Ingraham J. Petersen A. & Waters J. S. (2012). Basketball teams as strategic networks. PloS one 7(11) e47445.

  • Fonseca S. Milho J. Travassos B. & Araújo D. (2012). Spatial dynamics of team sports exposed by Voronoi diagrams. Human Movement Science 31(6) 1652-1659.

  • Gentleman R. & Carey V. (2008). Unsupervised machine learning Bioconductor Case Studies (pp. 137-157): Springer.

  • Gudmundsson J. & Wolle T. (2014). Football analysis using spatio-temporal tools. Computers Environment and Urban Systems 47 16-27.

  • Gyarmati L. & Anguera X. (2015). Automatic Extraction of the Passing Strategies of Soccer Teams. arXiv preprint arXiv:1508.02171.

  • Gyarmati L. Kwak H. & Rodriguez P. (2014). Searching for a unique style in soccer. Paper presented at the 2014 KDD Workshop on Large-Scale Sports Analytics New York City.

  • Hughes M. D. & Bartlett R. M. (2002). The use of performance indicators in performance analysis. Journal of sports sciences 20(10) 739-754.

  • International Federation of Netball. (2018). Rules of Netball. Retrieved from

  • López Peña J. & Sánchez Navarro R. (2015). Who can replace Xavi? A passing motif analysis of football players. arXiv preprint arXiv:1506.07768.

  • Lusher D. Robins G. & Kremer P. (2010). The Application of Social Network Analysis to Team Sports. Measurement in Physical Education and Exercise Science 14(4) 211-224. doi:10.1080/1091367x.2010.495559

  • Milo R. Shen-Orr S. Itzkovitz S. Kashtan N. Chklovskii D. & Alon U. (2002). Network motifs: simple building blocks of complex networks. Science 298(5594) 824-827. doi:10.1126/science.298.5594.824

  • Morgan S. (2011). Detecting Spatial Trends in Hockey Using Frequent Item Sets. Paper presented at the Proceedings of the 8th International Symposium on Computer Science in Sport.

  • Passos P. Davids K. Araújo D. Paz N. Minguéns J. & Mendes J. (2011). Networks as a novel tool for studying team ball sports as complex social systems. Journal of Science and Medicine in Sport 14(2) 170-176.

  • Rocha L. E. & Blondel V. D. (2013). Flow motifs reveal limitations of the static framework to represent human interactions. Physical Review E 87(4).

  • Spencer B. Morgan S. Zeleznikow J. & Robertson S. (2016). Clustering team profiles in the Australian Football League using performance indicators. Paper presented at the Proceedings of the 13th Australasian Conference on Mathematics and Computers in Sport Melbourne 11-13 July 2016.

  • Steele J. R. & Chad K. E. (1991). An analysis of the movement patterns of netball players during match play: implications for designing training programs. Journal of human movement studies 20 249-278.

  • Stöckl M. & Morgan S. (2013). Visualization and analysis of spatial characteristics of attacks in field hockey. International Journal of Performance Analysis in Sport 13(1) 160-178.

  • Sweeting A. (2017). Discovering the movement sequences of elite and junior elite netball athletes. (Doctorate of Philosophy) Victoria University.

  • Sweeting A. Morgan S. Cormack S. & Aughey R. (2014). A movement sequencing analysis of team-sport athlete match activity profile. Paper presented at the Proceedings of the 10th Australasian Conference on Mathematics and Computers in Sport Darwin Australia.

Journal information
Impact Factor

CiteScore 2018: 0.71

SCImago Journal Rank (SJR) 2018: 0.355
Source Normalized Impact per Paper (SNIP) 2018: 0.462

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 378 378 370
PDF Downloads 105 105 103