Developing a Football Tactical Metric to Estimate the Sectorial Lines: A Case Study
The aim of this study was to propose a new tactical metric that characterises teammates’ organisation within a tactical sector. This metric was developed based on the Cartesian information of football players’ location at each second of three official matches. From the tracking procedures, 9218 moments were collected which were then organised into defensive (without possession of the ball) and attacking (with possession of the ball) instants. Significant differences were found between the two statuses of the possession of the ball for the defensive line (F (1, 9216) = 44.520; p-value = 0.001; η 2 = 0.005; Power = 1.000) and forward line (F (1, 9216) = 26.175; p-value = 0.001; η 2 = 0.000; Power = 0.108). From the specific results of this case study, it was possible to propose a new concept to help coaches observe a match with some tactical parameters that can allow a quicker identification of team properties.
KeywordsMatch Analysis Tactical Metrics Performance Football
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- 1.Gréhaigne, J.F.: L’organisation du jeu en football. Joinville-le-Pont. Éditions Actio, France (1992)Google Scholar
- 3.Okihara, K., Kan, A., Shiokawa, M., Choi, C.S., Deguchi, T., Matsumoto, M., et al.: Compactness as a strategy in a football match in relation to a change in offense and defence. J. Sport. Sci. 22, 515 (2004)Google Scholar
- 8.Lemoine, A., Jullien, H., Ahmaidi, S.: Technical and tactical analysis of one-touch playing in soccer-Study of the production of information. Int. J. Perform. Anal. Sport 5, 83–103 (2005)Google Scholar
- 9.Abdel-Aziz, Y., Karara, H.: Direct linear transformation from comparator coordinates into object space coordinates in close-range photogrammetry. In: ASP Symposium on Close-Range Photogrammetry, Falls Church, VA, pp. 1–18Google Scholar
- 11.Clemente, F.M., Couceiro, M.S., Martins, F.M.L., Mendes, R., Figueiredo, A.J.: Measuring Collective Behaviour in Football Teams: Inspecting the impact of each half of the match on ball possession. Int. J. Perform. Anal. Sport 13, 678–689 (2013)Google Scholar
- 12.Surhone, L.M., Tennoe, M.T., Henssonow, S.F.: Nearest-Neighbor Interpolation: Multivariate interpolation, dimension, interpolation. Betascript Publishing, United States (2010)Google Scholar
- 13.Hopkins, K.D., Hopkins, B.R., Glass, G.V.: Basic statistics for the behavioral sciences. Allyn and Bacon, Boston (1996)Google Scholar