Discovering methods of scoring in soccer using tracking data
Tharindu Fernando, Xinyu Wei, Clinton Fookes, Sridha Sridharan, Patrick J. Lucey · QUT ePrints (Queensland University of Technology) · 2015
In soccer, when analyzing the performance of a team one of the key events to analyze is that of shots and goal-scoring. With the availability of fine-grained player and ball tracking data, it is now possible to find the common patterns a team uses via clustering multi-agent trajectories. The effectiveness of these methods can be then quantified by using a "expected goal value" (EGV) model which was recently proposed. Using an entire season of player and ball tracking data from Prozone, we show a method of both "discovering" and "quantifying" goal scoring methods of a team, which we also use to compare the "goal-scoring styles" of teams.