Evaluating NHL Goalies, Skaters, and Teams Using Weighted Shots

TL;DR

This paper introduces a logistic regression model to evaluate NHL players and teams using weighted shots, assessing their performance and future potential with advanced statistics based on shot quality.

Contribution

It develops a novel weighted shot metric using probabilistic modeling and compares its effectiveness to traditional stats for player and team evaluation.

Findings

Weighted shots provide valuable insights but do not outperform traditional statistics.

Advanced weighted shot metrics can be used alongside traditional stats for comprehensive analysis.

The model estimates individual player contributions to team performance in various game situations.

Abstract

In this paper, we develop a logistic regression model to estimate the probability that a particular shot in an NHL game will result in a goal, and use the results to evaluate the performance of NHL skaters, goalies, and teams. We weight each shot based on the estimated probabilities obtained from our model, call this statistic "weighted shots", and use advanced statistics based on weighted shots as the basis of our evaluation. We also analyze whether advanced statistics based on weighted shots outperform traditional statistics as an indicator of future performance of skaters, goalies, and teams. In general, statistics based on weighted shots perform well, but not better than traditional statistics. We conclude that weighted shots should not be viewed as a replacement for those statistics, but can be used in conjunction with those statistics. Finally, we use weighted shots as the dependent variable in an adjusted plus-minus model. The results are estimates of each player's offensive and defensive contribution to his team's weighted shots during even strength, power play, and short handed situations, independent of the strength of his teammates, the strength of his opponents, and the zone in which his shifts begin.