  We examine whether social data can be used to predict how members of Major
League Baseball (MLB) and members of the National Basketball Association (NBA)
transition between teams during their career. We find that incorporating social
data into various machine learning algorithms substantially improves the
algorithms' ability to correctly determine these transitions. In particular, we
measure how player performance, team fitness, and social data individually and
collectively contribute to predicting these transitions. Incorporating
individual performance and team fitness both improve the predictive accuracy of
our algorithms. However, this improvement is dwarfed by the improvement seen
when we include social data suggesting that social relationships have a
comparatively large effect on player transitions in both MLB and in the NBA.
