Expected by Whom? A Skill-Adjusted Expected Goals Model for NHL Shooters and Goaltenders
J.T.P. Noel

TL;DR
This paper presents a skill-adjusted expected goals model for NHL shooters and goaltenders using gradient boosting, which improves prediction accuracy by accounting for multiple skill aspects and outperforms previous models.
Contribution
The study introduces a novel, multi-aspect skill-adjusted expected goals model for NHL, utilizing spatiotemporal data and feature engineering to enhance prediction performance.
Findings
Skill-adjusted model outperforms baseline in log loss, Brier scores, and ROC AUC.
Performance improvements up to 5% over previous models.
Highlighting the importance of modeling both shooter and goaltender skills.
Abstract
This study outlines a light gradient boosted model aimed at predicting shot outcomes in the NHL. The model uses the NHL's spatiotemporal data to account for both the skill of shooters and goaltenders. This approach involves isolating and engineering features for different aspects of shooter and goaltender skill. These aspects include the overall skill, the locational skill, which is engineered using a shot binning technique previously outlined by Shuckers and Curro, and the situational skill, which is engineered using Gower distance. Three separate datasets were created based on the skill of the shooter and goaltender. For each, a baseline model was created in order to compare and contrast its performance with the skill-adjusted model. The results seen in this study show performance increases for the skill-adjusted model over the baseline model in log loss, brier scores, and area under…
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Taxonomy
TopicsSports Analytics and Performance · Sports Performance and Training · Sport Psychology and Performance
