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arXiv · 1806.06696

SMOGS: Social Network Metrics of Game Success

Fan Bu·Sonia Xu·Katherine Heller·Alexander Volfovsky
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Abstract

This paper develops metrics from a social network perspective that are directly translatable to the outcome of a basketball game. We extend a state-of-the-art multi-resolution stochastic process approach to modeling basketball by modeling passes between teammates as directed dynamic relational links on a network and introduce multiplicative latent factors 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 then apply the model to the first high-resolution optical tracking dataset collected in college basketball games. The learned latent factors demonstrate significant differences between players' passing and receiving tendencies in a loss than those in a win. The model is applicable to team sports other than basketball, as well as other time-varying network observations.

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Fan Bu, Sonia Xu, Katherine Heller, Alexander Volfovsky. 2018-06-18. SMOGS: Social Network Metrics of Game Success. https://arxiv.org/abs/1806.06696

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