Multiscale fractal dimension applied to tactical analysis in football: A novel approach to evaluate the shapes of team organization on the pitch

Abstract

The aim of this study was to evaluate different shape descriptors applied to images of polygons that represent the organization of football teams on the pitch. The effectiveness of different shape descriptors (area/perimeter, fractalarea, circularity, maximum fractal, rectangularity, multiscale fractal curve—MFC), and the concatenation of all shape descriptors (except MFC), denominated Alldescriptors (AllD)) was evaluated and applied to polygons corresponding to the shapes represented by the convex hull obtained from players’ 2D coordinates. A content-based image retrieval system (CBIR) was applied for 25 users (mean age of 31.9 ± 8.4 years) to evaluate the relevant images. Measures of effectiveness were used to evaluate the shape descriptors (P @ n and R @ n). The MFD (P @ 5, 0.46±0.37 and P @ 10, 0.40±0.31, p < 0.001; R @ 5, 0.14±0.13 and R @ 10, 0.24±0.19, p < 0.001) and AllD (P @ 5 = 0.43±0.36 and P @ 10 = 0.39±0.32, p < 0.001; R @ 5 = 0.13±0.11 and R @ 10 = 0.24±0.20, p < 0.001) descriptors presented higher values of effectiveness. As a practical demonstration, the best evaluated shape descriptor (MFC) was applied for tactical analysis of an official match. K-means clustering technique was applied, and different shapes of organization could be identified throughout the match. The MFC was the most effective shape descriptorin relation to all others, making it possible to apply this descriptor in the analysis of professional football matches