Assessing the Offensive Productivity of NHL Players Using In-game Win Probabilities

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Abstract: Hockey journalists and statisticians currently lack many of the empirical tools available in other sports. In this paper I introduce a win probability metric for the NHL and use it to develop a new statistic, Added Goal Value, which evaluates player offensive productivity. The metric is the first of its kind to incorporate power play information and is the only NHL in-game win probability metric currently available. I show how win probabilities can enhance the narrative around an individual game and can also be used to evaluate playoff series win probabilities. I then introduce Added Goal Value, which improves upon traditional offensive player statistics by accounting for game context. A player’s AGV has a strong positive correlation between seasons, making it a useful statistic for predicting future offensive productivity. By accounting for the context in which goals are scored, AGV also allows for comparisons to be made between players who have identical goal-scoring rates. The work in this paper provides several advances in hockey analytics and also provides a framework for unifying current and future work on Corsi, Fenwick and other NHL analytics.

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