The aim of this research was to analyze the player’s pass style with enhanced accuracy using the deep learning technique. We proposed Pass2vec, a passing style descriptor that can characterize each player’s passing style by combining detailed information on passes. Pass data was extracted from the ball event data from five European football leagues in the 2017–2018 season, which was divided into training and test set. The information on location, length, and direction of passes was combined using Convolutional Autoencoder. As a result, pass vectors were generated for each player. We verified the method with the player retrieval task, which successfully retrieved 76.5% of all players in the top-20 with the descriptor and the result outperformed previous methods. Also, player similarity analysis confirmed the resemblance of players passes on three representative cases, showing the actual application and practical use of the method. The results prove that this novel method for characterizing player’s styles with improved accuracy will enable us to understand passing better for player training and recruitment.

本研究的目的是使用深度学习技术更准确地分析球员的传球风格。我们提出了 Pass2vec，这是一种传球风格描述符，可以通过结合传球的详细信息来表征每个球员的传球风格。传球数据是从2017-2018赛季欧洲5个足球联赛的球事件数据中提取的，分为训练集和测试集。使用卷积自动编码器将有关位置、长度和传递方向的信息结合起来。结果，为每个球员生成了传球向量。我们使用玩家检索任务验证了该方法，该任务成功检索了前 20 名所有玩家中的 76.5% 的描述符，并且结果优于以前的方法。还，球员相似度分析在三个代表性案例中证实了球员传球的相似性，显示了该方法的实际应用和实际使用。结果证明，这种以更高的准确度表征球员风格的新方法将使我们能够更好地理解传球以进行球员训练和招募。