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项具有挑战性的分析问题，因为进球事件稀缺且高度依赖情境。随着更先进的射门数据引入，基于模型的指标应运而生，能更好地应对这些挑战。具体而言，预期进球增加值、超预期进球和击球后预期进球等指标均利用先进数据，相比传统进球转化率有所改进。然而，迄今为止所有开发的指标都将射偏（占总射门近三分之二）的价值设为零，因为这些射门没有进球概率。我们认为，射偏轨迹中蕴含着不可忽视的射门技术信号，并提出两种纳入射偏信号的射门技术指标。具体而言，我们开发了一种基于截断双变量高斯混合模型的球员特定射门轨迹生成模型。利用该生成模型计算的指标，我们可以为射偏赋予非零价值。我们证明，与当前最先进的指标相比，我们所提出的指标更稳定，且预测能力更强。