A study of the geometric shapes between players on the passing success of youth Football players
Main Article Content
Abstract
This study aims to analyze and identify the geometric shapes between players that influence the success of passing among youth football players, utilizing Pearson’s Correlation Coefficient and Linear Regression Analysis. The research employed player tracking technology and video processing with Python to analyze the movements and playing patterns of the athletes. The findings revealed that triangular shapes have a significant positive correlation with passing success (r = 0.67, p = 0.003), with the highest success rate of 72.5%. Additionally, Linear Regression Analysis indicated that each unit increase in the frequency of triangles leads to a 0.45% increase in passing success rate. Quadrilateral and pentagon shapes showed decreasing success rates, with quadrilaterals having a moderate correlation (r = 0.45, p = 0.012) and pentagons
a lower correlation (r = 0.30, p = 0.080). The analysis suggests that positioning players in triangular formations is the most effective strategy for enhancing passing success in youth football.
Article Details
References
Bialkowski, A., Lucey, P., Carr, P., Yue, Y., Sridharan, S., and Matthews, I. 2014. Large-scale analysis of soccer matches using spatiotemporal tracking data. IEEE Transactions on Knowledge and Data Engineering. 28(5): 1507-1518. https://doi.org/10.1109/ICDM.2014.133
Gudmundsson, J., and Horton, M. 2017. Spatio-temporal analysis of team sports. ACM Computing Surveys (CSUR). 50(2): 22. https://doi.org/10.1145/3054132
Hinkle, D. E., Wiersma, W., and Jurs, S. G. 2003. Applied Statistics for the Behavioral Sciences. 5th Edition. Houghton Mifflin.
Hughes, M., & Franks, I. (2005). Analysis of passing sequences, shots and goals in soccer. Journal
of Sports Sciences, 23(5), 509-514. https://doi.org/10.1080/02640410410001716779
Lees, A., and Nolan, L. 2018. Biomechanics applied to soccer skill development. Science and Soccer. 128-138. Routledge.
Memmert, D., and Raabe, D. 2018. Data analytics in football: Positional data collection, modelling and analysis. Journal of Sports Science. 36(1): 28-35.
https://doi.org/10.4324/9781351210164
Montgomery, D. C., Peck, E. A., and Vining, G. G. 2012. Introduction to Linear Regression Analysis. 5th Edition. Wiley.
Rein, R., and Memmert, D. 2016. Big data and tactical analysis in elite soccer: Future challenges and opportunities for sports science. SpringerPlus. 5(1): 1410. https://doi.org/10.1186/s40064-016-3108-2