Expected Goals Chain (xGC) extends traditional xG analysis by attributing goal probability credit across entire possession sequences rather than just the final shot. At 1X2.TV, our AI models use xG chain analysis to evaluate team and player contributions more comprehensively.

What Is xG Chain

xG Chain assigns a share of a possession sequence's total xG value to every player involved in the buildup, not just the shooter and assister. A midfielder who played a key progressive pass early in a sequence that ultimately produced a high-xG shot receives partial credit. This provides a more complete picture of creative contribution throughout the team.

Team-Level xGC Patterns

Analyzing team-level xG chain data reveals how goals are constructed. Some teams generate xG through long, intricate possession sequences involving many players, while others create chances through rapid transitions involving fewer players. Our models classify team attacking styles using xGC patterns and evaluate how these styles interact in specific matchups.

Identifying Key Contributors

xG chain analysis identifies players whose involvement in possession sequences is disproportionately associated with high-xG outcomes, even if they rarely shoot or provide the final pass. These players are often undervalued by traditional statistics but their absence significantly reduces team attacking effectiveness. Our models track xGC contributions to better assess key player importance.

Defensive xGC Analysis

We also apply chain analysis defensively, identifying at what point in opposition possession sequences defensive interventions are most effective. Teams that regularly break up opposition attacks early in sequences (before they develop into dangerous positions) concede lower xG than teams that allow extended buildups before intervening.