Network Motifs in Football

Abstract

Complex networks often display network motifs and these can be described as subgraphs. Methods for analyzing complex networks promise to be of great benefit to almost all scientific disciplines including sports. In football if we want to disrupt the opponent's game format, we must first be aware of the pass motifs that the team often uses. Determining how to break these motifs will make an important contribution to the success of a team. In this study, 3-nodes and 4-nodes pass motifs of the teams were examined within the frame of a data set of ten games and the most frequent repetitions of these motifs were determined. In addition, we suggest that in a match, the balance can be measured by the correlation between the frequencies of the motif types and there may be an inverse relationship between this correlation and the difference in the goals of the match.

Keywords

• Network Science,Network Motifs,Football

References

1. Bekkers J., Dabadghao S. Flow Motifs in Soccer: What can passing behavior tell us? MIT Sloan Sports Analytics Conference, 3-4 March 2017. Hynes Convention Center, from http://www.sloansportsconference.com/wp-content/uploads/2017/02/1563.pdf
2. Buldu JM., Busquets J., Martinez JH., Herrera-Diestra JL., Echegoyen I., Galeano J., Luque J. Using network science to analyse football passing networks: dynamics, space, time and the multilayer nature of the game. Frontiers in Psychology. 2018; 9: 1900.
3. Cintia P., Rinzivillo S., Pappalardo L. A network-based approach to evaluate the performance of football teams. Machine Learning and Data Mining for Sports Analytics Workshop (MLSA’15): ECML/PKDD Conference 2015; September.
4. Csermely P., Korcsmáros T., Kiss HJM., London, G., Nussinov, R. Structure and dynamics of molecular networks: A novel paradigm of drug discovery a comprehensive review. Pharmacology & Therapeutics. 2013; 138(3): 333-408.
5. Itzkovitz S., Alon U. Subgraphs and network motifs in geometric networks. Physical Review. 2005; E 71(2 Pt 2): 026117.
6. Liu YY., Slotine JJ., Barabasi AL. Controllability of complex networks. Nature. 2011; 473: 167-173.
7. Milo R., Shen-Orr S., Itzkovitz S., Kashtan N., Chklovskii D., Alon U. Network Motifs: Simple Building Blocks of Complex Networks. Science. 2002; 298(5564): 824-827.
8. Schmidt C., Weiss T., Komusiewicz C., Witte H., Leistritz L. An Analytical Approach to Network Motif Detection in Samples of Networks with Pairwise Different Vertex Labels. Computational and Mathematical Methods in Medicine. 2012; Article ID 910380. Sporns O., Kotter R. Motifs in Brain Networks, Plos Biol. 2004; 2(11): e369.

9. Takes FW., Kosters WA., Witte B., Heemskerk EM. Multiplex network motifs as building blocks of corporate Networks. Applied Network Science. 2018; 3(39): 1-22.
10. Trafton A. (2011, May 12). How to control complex networks: New algorithm offers ability to influence systems such as living cells or social networks. Retrieved September 20, 2018, from http://news.mit.edu/2011/network-control-0512. Wei Y., Liao X., Yan C., He Y., Xia M. Identifying topological motif patterns of human brain functional Networks. Hum Brain Mapp. 2017; 38(5): 2734-2750.
11. Yaveroğlu ÖN. Malod-Dognin N., Davis D., Levnajic Z., Janjic V., Karapandza R., Stojmirovic A., Przulj N. Revealing the Hidden Language of Complex Networks. Scientific Reports. 2014; 4: 4547.
12. Yeang CH., Huang LC., Liu WC. Recurrent structural motifs reflect characteristics of distinct Networks, Proceedings, The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Istanbul, Turkey, from http://www.stat.sinica.edu.tw/chyeang/Yeang_network_motifs_final.pdf
13. Zhang LL., Thomas W., Ashiabar A. Comparing predictive powers of Network Motif Distribution and structure of Overlapping Communities. Class Projects: 2014; CS224W: Social and Information Network Analysis. Stanford University, from http://snap.stanford.edu/class/cs224w-2014/projects2014/cs224w-69-final.pdf