  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.
