Miss It Like Messi: Extracting Value from Off-Target Shots in Soccer

TL;DR

This paper introduces new soccer shooting metrics that leverage off-target shot trajectories to better evaluate player skill, addressing a gap in existing models that ignore these shots.

Contribution

It proposes a novel generative model for shot trajectories and new metrics that assign value to off-target shots, improving stability and predictive power.

Findings

Metrics are more stable than existing ones.

Proposed metrics have higher predictive accuracy.

Off-target shot analysis reveals valuable skill signals.

Abstract

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.