This study introduces expected Game Value (xGV), a new analytical model for rugby union that quantifies the value of each game state. Unlike traditional methods relying on overall match data, xGV provides an intra-game perspective by weighting the impact of each action based upon its context. While comparable measures used in other sports often rely on machine learning methods, xGV employs generalised additive mixed models (GAMM), which account for the impact of teams as random effects while maintaining parameter flexibility through the use of penalised splines. The exclusion of these random effects from the final estimations enables the model to provide outputs that can be consistently compared across teams, regardless of their baseline strength while accounting for the repeated structure of the dataset. Built using spatio-temporal data derived from major competitions at elite standards, the model highlights teams with strong performances during this period, identifying them as top performers across various aspects of the game. As such, xGV provides valuable information to coaches and analysts and can help refine tactical decision-making and arguably represents a significant advance in rugby analysis by quantifying the value of the game at any given moment, enabling objective comparisons across teams and strategies.
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