Pass Evaluation in Women's Olympic Hockey

TL;DR

This paper introduces a probabilistic passing model for women's Olympic hockey, analyzing high-stakes power play passes to evaluate player behaviors and assist coaching decisions using detailed tracking data.

Contribution

It develops a novel probabilistic passing framework that incorporates player and puck motion, rink control, and scoring probabilities to assess pass quality and player risk-reward tendencies.

Findings

Model accurately predicts pass success and scoring chances.

Identifies players' risk-reward behaviors during power plays.

Provides metrics for optimal passing decisions in high-pressure situations.

Abstract

Passing during power plays in hockey is a crucial component to move one's team closer to scoring a goal. With the use of women's ice hockey event and tracking data from the elimination round games during the 2022 Winter Olympics, we evaluate passing and assess players' risk-reward behaviours in these high intensity moments. We develop a model for probabilistic passing that accounts for the order of arrival to a desired location and potential interceptions along the way. This model is based on a player-specific motion model and a puck motion model that determines how far each player can reach in the time it takes the puck to get to a target. In addition, we model the rink control for each team and the scoring probability of the offensive team. These models are then combined into novel metrics for quantifying where a pass should be made such that it would result in a high scoring opportunity or result in a high chance of maintaining possession. Finally, we use various metrics to evaluate passes made throughout the available power plays and compare them to the optimal options at that time. This can be used to identify players' risk-reward tendencies and can be used by coaches when selecting which players are best suited for a power play given the circumstances of the game.