SMU Data Science Review Volume 1 | Number 2 Article 12 2018 Goalie Analytics: Statistical Evaluation of Context- Specific Goalie Performance Measures in the National Hockey League Marc Naples Southern Methodist University,
[email protected] Logan Gage Southern Methodist University,
[email protected] Amy Nussbaum
[email protected] Follow this and additional works at: https://scholar.smu.edu/datasciencereview Part of the Applied Statistics Commons, Other Statistics and Probability Commons, and the Sports Studies Commons 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 This Article is brought to you for free and open access by SMU Scholar. It has been accepted for inclusion in SMU Data Science Review by an authorized administrator of SMU Scholar. For more information, please visit http://digitalrepository.smu.edu. Naples et al.: Evaluation of Context-Specific Goalie Performance Measures Goalie Analytics: Statistical Evaluation of Context-Specific Goalie Performance Measures in the National Hockey League Marc Naples, Logan Gage, Amy Nussbaum Master of Science in Data Science Southern Methodist University 6425 Boaz Lane, Dallas, TX 75205 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 other 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.