Miss it like Messi: Extracting value from off-target shots in soccer.

Area of Science:

• Sports Analytics
• Football Analytics
• Performance Measurement

Background:

• Measuring soccer shooting skill is difficult due to limited scoring data and contextual factors.
• Existing advanced metrics like expected goals added and post-shot expected goals improve upon conversion rates but ignore off-target shots.
• Off-target shots, comprising nearly two-thirds of all attempts, are currently assigned zero value in all developed metrics.

Purpose of the Study:

• To propose novel soccer shooting skill metrics that incorporate data from off-target shots.
• To develop a player-specific generative model for shot trajectories to quantify skill in off-target attempts.
• To demonstrate the improved stability and predictive power of these new metrics compared to existing state-of-the-art methods.

Main Methods:

• Developed a player-specific generative model for soccer shot trajectories using a mixture of truncated bivariate Gaussian distributions.
• Utilized the generative model to compute new shooting skill metrics that assign non-zero value to off-target shots.
• Evaluated the proposed metrics against current state-of-the-art metrics for stability and predictive accuracy.

Main Results:

• The proposed metrics successfully incorporate the skill signal present in off-target shot trajectories.
• New metrics demonstrate greater stability compared to existing advanced soccer analytics metrics.
• The enhanced metrics exhibit increased predictive power in evaluating shooting performance.

Conclusions:

• Off-target shot trajectories contain valuable information for assessing soccer shooting skill.
• The developed generative model and associated metrics offer a more comprehensive approach to measuring shooting performance.
• These novel metrics provide a more accurate and stable evaluation of player shooting ability in soccer analytics.