Adjusting expected goals (xG) and shots on target for game context in soccer

With advancements in soccer analytics, considerable attention has been devoted to developing sophisticated measures for quality scoring opportunities - such as expected goals and expected threat. Far less effort, however, has gone toward adjusting these and other performance metrics for game context, including factors like score and red card differential. It is well known that certain match scenarios prompt systematic tactical adjustments - for example, teams often defend more conservatively when leading or playing shorthanded. In this study, we employ generalized additive mixed-effects models (GAMMs) on minute-by-minute match event data from Europe’s five major leagues to quantify how such contextual variables influence offensive production metrics like shots on target and expected goals. This approach allows us to estimate global effects of contextual variables on offensive production, while also allowing for league-specific distinctions to be captured via random effects. We then use these estimates to adjust observed team statistics, projecting each team’s performance onto a standardized baseline scenario - a tied home game played at even manpower. We believe the resulting adjusted measures to better isolate underlying team quality by removing distortions arising from transient tactical responses to game state, thereby providing improved inputs for downstream analytical and predictive tasks.