SMU Data Science Review
Abstract
In this paper, we attempt to improve upon the classic formulation of save percentage in the NHL by controlling the context of the shots and use alternative measures than save percentage. In particular, we find save percentage to be both a weakly repeatable skill and predictor of future performance, and we seek other goalie performance calculations that are more robust. To do so, we use three primary tests to test intra-season consistency, intra-season predictability, and inter-season consistency, and extend the analysis to disentangle team effects on goalie statistics. We find that there are multiple ways to improve upon classic save percentage, including controlling for shot type, measuring performance against an “expected goals” metric, and perhaps most importantly, calculating a save percentage that includes shot attempts that go wide. Despite these avenues for improvement, many questions remain due to the questionable robustness of all measures, and the clear presence of team effects.
Recommended Citation
Naples, Marc; Gage, Logan; and Nussbaum, Amy
(2018)
"Goalie Analytics: Statistical Evaluation of Context-Specific Goalie Performance Measures in the National Hockey League,"
SMU Data Science Review: Vol. 1:
No.
2, Article 12.
Available at:
https://scholar.smu.edu/datasciencereview/vol1/iss2/12
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Applied Statistics Commons, Other Statistics and Probability Commons, Sports Studies Commons
