Pass Evaluation in Women's Olympic Ice Hockey

What is it about?

Much of modern sports analytics is based on player and ball tracking data. Such data are mostly collected using wearable devices or an array of carefully located cameras and detectors. Many teams do not have such a luxury, especially in undervalued sports such a women's ice hockey; of those that do, the data are not typically publicly available. Recent developments in computer vision have allowed for the collection of tracking data directly from widely available broadcast video. Using event and tracking data collected directly from broadcast video during the elimination round games of the 2022 Winter Olympics, we create a framework for evaluating passing in women's ice hockey. We begin with physics-based motion models for both players and the puck, which we use to develop a model for probabilistic passing. Next, 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 the various aspects of any single pass. By looking at the entire corpus of plays, we create several summary metrics describing players' risk-reward tendencies, and overall passing ability. All of our metrics can be presented graphically, allowing for easy adaptation by coaches, players, scouts, and other front-office personnel.

Featured Image

Photo by Chris Liverani on Unsplash

Photo by Chris Liverani on Unsplash

Why is it important?

Research using publicly available tracking data in hockey is extremely limited. Even less so is data that tackles women's sports. We use various technics from a variety of sports to help advance the women's hockey game with advanced passing analysis.