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This paper aims to value defensive actions in soccer using deep learning techniques. Specifically, it develops a Defensive Action Expected Threat (DAxT) model to quantify the value of tackles and interceptions based on the threat of attacks they prevented. The model is validated on event data from the English Premier League and combined with other features to rate defensive player ability. Overall, the paper finds the model can better value defenders using event data by predicting the impact of their defens

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hwykvycjs8

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