A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game. We investigate the problem of producing an automated system to make the same evaluation of passes. We present a model that constructs numerical predictor variables from spatiotemporal match data using feature functions based on methods from computational geometry, and then learns a classification function from labelled examples of the predictor variables. Furthermore, the learned classifiers are analysed to determine if there is a relationship between the complexity of the algorithm that computed the predictor variable and the importance of the variable to the classifier. Experimental results show that we are able to produce a classifier with 85.8% accuracy on classifying passes as Good, OK or Bad, and that the predictor variables computed using complex methods from computational geometry are of moderate importance to the learned classifiers. Finally, we show that the inter-rater agreement on pass classification between the machine classifier and a human observer is of similar magnitude to the agreement between two observers.

本文研究了如何基于足球比赛的时空数据使用计算几何的方法构造预测变量并从带标签的样本中学习分类函数，进而生产一个自动化系统来评估球员之间的传球质量。实验结果表明，我们可以使用计算几何的方式获得相对较高的分类准确率，并且这些变量对于学习到的分类器是有一定的重要性的。最后，我们还验证了机器分类器与人类观察者之间的分类一致性程度相近。

基于时空数据的足球比赛传球分类