SMOGS: Social Network Metrics of Game Success

Fan Bu, Sonia Xu, Katherine Heller, Alexander Volfovsky
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:2406-2414, 2019.

Abstract

In this paper we propose a novel metric of basketball game success, derived from a team’s dynamic social network of game play. We combine ideas from random effects models for network links with taking a multi-resolution stochastic process approach to model passes between teammates. These passes can be viewed as directed dynamic relational links in a network. Multiplicative latent factors are introduced to study higher-order patterns in players’ interactions that distinguish a successful game from a loss. Parameters are estimated using a Markov chain Monte Carlo sampler. Results in simulation experiments suggest that the sampling scheme is effective in recovering the parameters. We also apply the model to the first high-resolution optical tracking data set collected in college basketball games. The learned latent factors demonstrate significant differences between players’ passing and receiving patterns in a loss, as opposed to a win. Our model is applicable to team sports other than basketball, as well as other time-varying network observations.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-bu19a, title = {SMOGS: Social Network Metrics of Game Success}, author = {Bu, Fan and Xu, Sonia and Heller, Katherine and Volfovsky, Alexander}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {2406--2414}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/bu19a/bu19a.pdf}, url = {https://proceedings.mlr.press/v89/bu19a.html}, abstract = {In this paper we propose a novel metric of basketball game success, derived from a team’s dynamic social network of game play. We combine ideas from random effects models for network links with taking a multi-resolution stochastic process approach to model passes between teammates. These passes can be viewed as directed dynamic relational links in a network. Multiplicative latent factors are introduced to study higher-order patterns in players’ interactions that distinguish a successful game from a loss. Parameters are estimated using a Markov chain Monte Carlo sampler. Results in simulation experiments suggest that the sampling scheme is effective in recovering the parameters. We also apply the model to the first high-resolution optical tracking data set collected in college basketball games. The learned latent factors demonstrate significant differences between players’ passing and receiving patterns in a loss, as opposed to a win. Our model is applicable to team sports other than basketball, as well as other time-varying network observations.} }
Endnote
%0 Conference Paper %T SMOGS: Social Network Metrics of Game Success %A Fan Bu %A Sonia Xu %A Katherine Heller %A Alexander Volfovsky %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-bu19a %I PMLR %P 2406--2414 %U https://proceedings.mlr.press/v89/bu19a.html %V 89 %X In this paper we propose a novel metric of basketball game success, derived from a team’s dynamic social network of game play. We combine ideas from random effects models for network links with taking a multi-resolution stochastic process approach to model passes between teammates. These passes can be viewed as directed dynamic relational links in a network. Multiplicative latent factors are introduced to study higher-order patterns in players’ interactions that distinguish a successful game from a loss. Parameters are estimated using a Markov chain Monte Carlo sampler. Results in simulation experiments suggest that the sampling scheme is effective in recovering the parameters. We also apply the model to the first high-resolution optical tracking data set collected in college basketball games. The learned latent factors demonstrate significant differences between players’ passing and receiving patterns in a loss, as opposed to a win. Our model is applicable to team sports other than basketball, as well as other time-varying network observations.
APA
Bu, F., Xu, S., Heller, K. & Volfovsky, A.. (2019). SMOGS: Social Network Metrics of Game Success. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:2406-2414 Available from https://proceedings.mlr.press/v89/bu19a.html.

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