One difficulty with analyzing performance in hockey is the relatively low scoring rates compared to sports like basketball. Fenwick rating (shots plus missed shots) and Corsi rating (shots, missed shots, blocked shots) have been used to analyze players and teams because they have been shown to be better than goals as a predictor of future goals. In this paper, we use variables like faceoffs, hits, and other statistics as predictor variables in addition to goals, shots, missed shots, and blocked shots, to predict goals. Our models outperform previous models with regard to mean squared error of actual goals and predicted goals. The results can be interpreted as expected goals and can be used in adjusted plus-minus models instead of goals. We use ridge regression to estimate a player’s contribution to his team’s expected goals per 60 minutes, independent of his teammates, opponents, and the zone in which his shifts begin. We also give adjusted plus-minus estimates based on goals, shots, Fenwick rating, and Corsi rating and use these results alongside the results for expected goals to provide an additional means by which NHL analysts, decision- makers, and fans can measure how valuable a player is to his team.
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