Player Pressure Map - A Novel Representation of Pressure in Soccer for Evaluating Player Performance in Different Game Contexts

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Chaoyi Gu, Jaming Na, Yisheng Pei, Varuna De Silva


This paper presents a novel machine learning method to measure the pressure in soccer games. We first propose a technique to quantify the pressure on individual player with 3D body motion parameters considered. Compared to the vanilla approach, our 3D quantification method provides more accurate results. Based on the individual assessment of each player, we propose player pressure map (PPM) to represent a given game scene, which lowers the dimension of raw data and still contains rich contextual information. We then train a possession outcome prediction model on PPMs and use the predicted probability to quantify the pressure on the whole team. This quantification enables contextualized performance analysis with team pressure taken into consideration. Overall, our model provides coaches and analysts with an efficient tool to quantify the pressure at both individual and team levels, which can help them evaluate the team performance more precisely.