Burgess, Kiefer Joe2023-08-292023-08-292023-08-292023-08-16http://hdl.handle.net/10012/19794This dissertation includes two essays on applications of management science methods to modelling service systems and developing novel improvements to sports team ranking systems. The first essay proposes a novel approach to modelling changes in business procedures that have neither explicitly positive nor explicitly negative effects on operational performance, but are changes to operating rules; we call these procedure changes Operational Protocol Modifications (OPMs). Our approach is to model these OPMs via distributional censoring. Using the scenario of a technical support employee at a SaaS firm, we model changes in OPMs as censoring effects on the distributions of both service quality and service time. We demonstrate the nonlinear effects OPMs can have on the optimal service contract and the employer's (principal's) expected utility in hiring the technical support employee (agent), under certain distributional assumptions. This modelling approach arms operations management analysts with a new tool to better capture the impact of OPMs and their non-linear impacts on operational performance. The second essay proposes a number of additions to both static and dynamic network ranking models for professional soccer teams. We introduce ways to incorporate relevant home/away game status and goal difference information. Further, we introduce a collection of methods to measure the competitive similarity between teams, which we then integrate into the ranking systems. We demonstrate, using a large collection of data on five of the top European professional soccer leagues, that our methods produce superior empirical performance when compared to comparable approaches. Importantly, our work is the first to integrate the competitive similarity notion directly into network ranking models, providing the first direct link between two related bodies of literature.enmechanism designranking modelsgame theorynetwork modelsoperations researchcensoringDoctoral Thesis