Research Article
A Clustering Approach to Analyzing NHL Goaltenders’ Performance
@INPROCEEDINGS{10.1007/978-3-031-17292-2_1, author={Ruksana Khan and Patrick Schena and Kathleen Park and Eugene Pinsky}, title={A Clustering Approach to Analyzing NHL Goaltenders’ Performance}, proceedings={Computer Science and Education in Computer Science. 18th EAI International Conference, CSECS 2022, On-Site and Virtual Event, June 24-27, 2022, Proceedings}, proceedings_a={CSECS}, year={2022}, month={11}, keywords={Clustering NHL performance comparison Goaltender statistics}, doi={10.1007/978-3-031-17292-2_1} }- Ruksana Khan
Patrick Schena
Kathleen Park
Eugene Pinsky
Year: 2022
A Clustering Approach to Analyzing NHL Goaltenders’ Performance
CSECS
Springer
DOI: 10.1007/978-3-031-17292-2_1
Abstract
Ice hockey is among the top 10 sports in the world by global popularity, and the National Hockey League (NHL) is one of the major professional sports leagues in United States and Canada. In the NHL there are 32 teams, 25 in the U.S. and 7 in Canada. In ice hockey, the goaltender, also known as the goalie, is one of the most important players in the game. The result of the game greatly depends on the performance of the goaltender. One of the most important statistics of the goaltender is save percentage SV% (calculated as the number of saves divided by the total number of shots attempted on the goal). In spite of the goaltender being a key player in the game, there are shortcomings in existing methods of ranking goaltenders, as these methods do not comprehensively capture the performance of the goaltender. This paper proposes the use of clustering methods from machine learning to compare performance of NHL goaltenders by using SV% and to look for patterns in their performance.
