SPRING 2026

AUTHOR

Shriyansh Singh, Middleton International School Tampines

ABSTRACT

We present an enhanced expected goals (xG) modeling framework with improved data pipeline, richer features, and interactive deployment. Using StatsBomb event data, we implement thorough cleaning (merging event and lineup JSON, extracting freeze-frame defense data) and engineer novel features (angular defensive pressure, goalkeeper distance, pre-shot sequence). An XGBoost model is trained and calibrated, achieving strong discrimination (AUC ≈ 0.878) and calibration (Brier ≈ 0.0686) on held-out shots. Key predictors include game-context and shot geometry (goal difference, shot angle and distance) and defensive metrics, as revealed by SHAP analysis.

We summarize recent xG studies, highlighting that our model outperforms prior work (e.g. AUC≈0.80) by incorporating these new features. An accompanying Streamlit app demonstrates real-time xG prediction (single-shot sliders, batch CSV upload) and SHAP explanations. Results indicate that the enriched feature set significantly improves predictive accuracy over baseline models, and the deployment prototype facilitates practical analytics for coaches and analysts. Our contributions include (i) a novel “angular pressure” m