Building an Expected Goals (xG) Model: From Shot Data to Match Predictions

What xG Measures

Expected Goals quantifies the quality of a scoring opportunity by estimating the probability that an average player would score from that specific shot. Factors include shot location (distance and angle to goal), body part used (foot, head, other), assist type (through ball, cross, set piece), game state (open play, counter-attack, set piece), and defensive pressure.

Training the xG Model

The model achieves strong calibration — shots assigned an xG of 0.15 result in goals approximately 15% of the time across large samples.

From Shot-Level to Match-Level Predictions

Individual shot xG values aggregate to team-level expected goals for each match. A team creating chances with a total xG of 2.1 in a match is performing well offensively regardless of the actual scoreline.

xG Difference as a Predictive Feature

Teams with positive xGD but poor actual goal difference are strong candidates for improvement, while teams outperforming their xG are likely to regress.