This research project provides novel tools and applications of techniques to better understand and utilize Construction Manager-at-Risk (CMR) project delivery in the context of public university capital projects by evaluating CMR project delivery in comparison to Design-Bid-Build (DBB) project delivery to understand and quantify the value of owner-driven scope growth in achieving critical success factors. Machine learning algorithms were used to support construction research and analysis, modeling cost and schedule growth using a sample of 37 CMR and 74 DBB projects, and accurately imputing missing data values in a sample of 67 CMR and 99 DBB projects. In-depth analysis of design and preconstruction services contracts illustrates the importance of synchronizing contractual language governing the duties, responsibilities, and obligations of collaboration and cooperation between the designer and general contractor. Analysis of project performance metrics demonstrated that traditional cost and schedule growth metrics may not accurately describe CMR projects in this market segment and a novel metric to evaluate design fee efficiency in developing scope changes was developed to assess the quality of project team integration. Results were validated by data envelopment which included a survey of public university capital projects staff, a content analysis of 44 CMR procurement documents not previously included in the research, and case studies of three database projects which appeared to have performed poorly but were considered as successful projects to their owners and end users. Finally, a method of quantifying the value for money from using CMR over DBB is proposed that accounts for collaborative behavior and improved relationships within the project team. Four best practices synthesized from the research project are proposed as techniques to allow public university owners to maximize the potential benefits of CMR project delivery.