Spatial roles in hockey special teams
Author:
Arsenault Jonathan1ORCID, Cunniff Margaret2, Tulsky Eric2, Forbes James Richard1
Affiliation:
1. 5620 Department of Mechanical Engineering, McGill University , Montreal , Canada 2. Carolina Hurricanes , Raleigh , USA
Abstract
Abstract
Special teams (i.e. power play and penalty kill) situations play an outsized role in determining the outcome of ice hockey games. Yet, quantitative methods for characterizing special teams tactics are limited. This work focuses on team structure and player deployment during in-zone special teams possessions. Leveraging player and puck tracking data from the National Hockey League (NHL), a framework is developed for describing player positioning during 5-on-4 power play and 4-on-5 penalty kill possessions. More specifically, player roles are defined directly from the player tracking data using non-negative matrix factorization, and every player is allocated a unique role at every frame of tracking data by solving a linear assignment problem. Team formations naturally arise through the combination of roles occupied in a frame. Roles that vary on a per-frame basis allow for a fine-grained analysis of team structure. This property of the roles-based representation is used to group together similar power play possessions using latent Dirichlet allocation, a topic modelling technique. The concept of assignments, which remain constant over an entire possession, is also introduced. Assignments provide a more stable measure of player positioning, which may be preferable when assessing deployment over longer periods of time.
Funder
Carolina Hurricanes
Publisher
Walter de Gruyter GmbH
Reference40 articles.
1. Anzer, G. and Bauer, P. (2022). Expected passes: determining the difficulty of a pass in football (soccer) using spatio-temporal data. Data Min. Knowl. Discov. 36: 295–317, https://doi.org/10.1007/s10618-021-00810-3. 2. Askari, F., Ramaprasad, R., Clark, J.J., and Levine, M.D. (2022). Interaction classification with key actor detection in multi-person sports videos. In: IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW). IEEE, New Orleans, LA, USA. https://doi.org/10.1109/CVPRW56347.2022.00402. 3. Bialkowski, A., Lucey, P., Carr, P., Yue, Y., and Matthews, I. (2014) “Win at home and draw away”: automatic formation analysis highlighting the differences in home and away team behaviors. In: MIT sloan sports analytics conference, Boston, MA, USA. 4. Bialkowski, A., Lucey, P., Carr, P., Matthews, I., Sridharan, S., and Fookes, C. (2016). Discovering team structures in soccer from spatiotemporal data. IEEE Trans. Knowl. Data Eng. 28: 2596–2605, https://doi.org/10.1109/TKDE.2016.2581158. 5. Blei, D.M., Ng, A.Y., and Jordan, M.I. (2003). Latent dirichlet allocation. J. Mach. Learn. Res. 3: 993–1022.
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