Measuring soccer shooting skill is a challenging analytics problem due to the scarcity and highly contextual nature of scoring events. The introduction of more advanced data surrounding soccer shots has given rise to model-based metrics which better cope with these challenges. Specifically, metrics such as expected goals added, goals above expectation, and post-shot expected goals all use advanced data to offer an improvement over the classical conversion rate. However, all metrics developed to date assign a value of zero to off-target shots, which account for almost two-thirds of all shots, since these shots have no probability of scoring. We posit that there is non-negligible shooting skill signal contained in the trajectories of off-target shots and propose two shooting skill metrics that incorporate the signal contained in off-target shots. Specifically, we develop a player-specific generative model for shot trajectories based on a mixture of truncated bivariate Gaussian distributions. We use this generative model to compute metrics that allow us to attach non-zero value to off-target shots. We demonstrate that our proposed metrics are more stable than current state-of-the-art metrics and have increased predictive power.

翻译：衡量足球射门技术是一个具有挑战性的分析问题，其难点在于得分事件的稀缺性和高度情境依赖性。随着射门相关更精细数据的引入，基于模型的指标得以发展，它们能更好地应对这些挑战。具体而言，预期进球贡献、超预期进球和射后预期进球等指标均利用了精细数据，相比传统的进球转化率有所改进。然而，迄今为止开发的所有指标都将射偏（约占所有射门的三分之二）的价值设定为零，因为这些射门没有得分概率。我们认为，射偏轨迹中蕴含着不可忽视的射门技术信号，并提出了两种融入射偏信号的射门技术水平指标。具体而言，我们基于截断双变量高斯分布的混合模型，构建了球员专属的射门轨迹生成模型。借助该生成模型，我们计算出的指标能够为射偏赋予非零价值。实验表明，我们提出的指标比当前最先进的指标更稳定，并具有更强的预测能力。