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.