We examines video games, a form of digital innovation, and seeks to predict a successful game based on the composition of game development team members.

More specifically, team composition is measured with observable features generated from a graph network based on development team information derived from individual team member work on previous games. Besides non-network features, such as the number of games published prior by the studio, we propose a novel framework to include additional network features such as team member closeness, success percentile, and failure percentile, to predict the chance of success for new games with an nice accuracy. Further, we investigate important features for prediction and provide model interpretability for practical implementations. We then build a decision support tool that allows video game producers, and associated stakeholders such as investors, to understand how the predictive model decides, predicts, and performs its recommendations. The findings have implications for those seeking to proactively impact digital product performance through graph network-generated features of team composition, where features are directly observable, as opposed to features that are more challenging to observe, such as personalities.