| Summary: | Social network analysis reveals a significant set of essential information on the behaviour of the players and teams: such as passing sequences between players in the attacking third position of attacking performance. Despite their usefulness, network metrics related to expected goal values for soccer analysis are minimal. Thus, the study compared network approaches that led to Chelsea FC's goal and playing style in the English Premier League in season 21/22. Moreover, ‘expected goal values’ (xG) show the probability of the goal scored, which can be related to the goals scored passing network. The study used centrality in network analysis such as degree prestige, degree centrality, and betweenness centrality to find the significant contributor of the player's position independently during the match that led to the goal scoring and did not consider the playing style of Chelsea FC. Furthermore, the xG values of shots in every game were visualized using Tableau software. A set of adjacency metrics were computed using the highlight videos of goals for every match, and the results of network analysis found that the most received the ball from their teammates were left wing-back and right wing-back, defensive midfielders and right wing-back have the highest degree centrality and attacking midfielder and left wing-back have the highest betweenness centrality. Furthermore, the statistical data from the visualization such as cross percentage, passing percentage, shots and xG percentage can be used to enhance the team performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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