In agile software development, the sprint performances and dynamics of teams often imply tendencies for the success of a project. Post mortem strategies, e.g., retrospectives help the team to report and share individually gained experiences (positives and negatives) from previous sprints, and enable them to use these experiences for future sprint planning. The interpretation of effects on sprint performance is often subjective, especially with concern to social-driven factors in teams. Involving strategies from predictive analytics in sprint retrospectives could reduce potential interpretation gaps of dynamics, and enhance the pre-knowledge, also awareness situation when preparing for the next sprint. In a case study involving 15 software projects with a total of 130 involved undergraduate students, we investigated the post-effects on team performances and behavioral-driven factors when providing predictive analytics in retrospectives. Besides measures for productivity, we consider human factors, e.g., team structures, communication, meetings and mood affects in teams as well as project success metrics. We developed a unique JIRA plugin called ProDynamics that collects performance information from projects and derives trend-insights for next sprints. The ProDynamics plugin enables the use of a times series and neural network model within a JIRA system to interpret factorial dependencies and behavioral pattern, thus to show the next sprint course of a team.